Search results for: Random Kernel Density

Authors: Medina O. Kadiri, John E. Gabriel

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Heavy metals are metallic elements with a relatively high density, at least five times greater compared to water. The sources of heavy metal pollution in the environment include industrial, medical, agricultural, pharmaceutical, domestic effluents, and atmospheric sources, mining, foundries, smelting, and any heavy metal-based operation. Although some heavy metals in trace quantities are required for biological metabolism, their higher concentrations elicit toxicities. Others are distinctly toxic and are of no biological functions. Microalgae are the primary producers of aquatic ecosystems and, therefore, the foundation of the aquatic food chain. A study investigating the effects of copper and zinc on the two chlorophytes-Chlorella vulgaris and Dictyosphaerium pulchellum was done in the laboratory, under different concentrations of 0mg/l, 2mg/l, 4mg/l, 6mg/l, 8mg/l, 10mg/l, and 20mg/l. The growth of the test microalgae was determined every two days for 14 days. The results showed that the effects of the test heavy metals were concentration-dependent. From the two microalgae species tested, Chlorella vulgaris showed appreciable growth up to 8mg/l concentration of zinc. Dictyoshphaerium pulchellum had only minimal growth at different copper concentrations except for 2mg/l, which seemed to have relatively higher growth. The growth of the control was remarkably higher than in other concentrations. Generally, the growth of both test algae was consistently inhibited by heavy metals. Comparatively, copper generally inhibited the growth of both algae than zinc. Chlorella vulgaris can be used for bioremediation of high concentrations of zinc. The potential of many microalgae in heavy metal bioremediation can be explored.

Keywords: heavy metals, green algae, microalgae, pollution

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Authors: Anjaly Joseph, Elezebeth Mathews

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Transportation and urban planning is the least recognised area for physical activity promotion in India, unlike developed regions. Identifying the preferred transportation modalities and factors associated with it is essential to address these lacunae. The objective of the study was to assess the prevalence of modes of transportation to work, and its correlates among working adults in Ernakulam District, Kerala. A cross sectional study was conducted among 350 working individuals in the age group of 18-60 years, selected through multi-staged stratified random sampling in Ernakulam district of Kerala. The inclusion criteria were working individuals 18-60 years, workplace at a distance of more than 1 km from the home and who worked five or more days a week. Pregnant women/women on maternity leave and drivers (taxi drivers, autorickshaw drivers, and lorry drivers) were excluded. An interview schedule was used to capture the modes of transportation namely, public, private and active transportation, socio demographic details, travel behaviour, anthropometric measurements and health status. Nearly two-thirds (64 percent) of them used private transportation to work, while active commuters were only 6.6 percent. The correlates identified for active commuting compared to other modes were low socio-economic status (OR=0.22, CI=0.5-0.85) and presence of a driving license (OR=4.95, CI= 1.59-15.45). The correlates identified for public transportation compared to private transportation were female gender (OR= 17.79, CI= 6.26-50.31), low income (OR=0.33, CI= 0.11-0.93), being unmarried (OR=5.19, CI=1.46-8.37), presence of no or only one private vehicle in the house (OR=4.23, CI=1.24-20.54) and presence of convenient public transportation facility to workplace (OR=3.97, CI= 1.66-9.47). The association between body mass index (BMI) and public transportation were explored and found that public transport users had lesser BMI than private commuters (OR=2.30, CI=1.23-4.29). Policies that encourage active and public transportation needs to be introduced such as discouraging private vehicle through taxes, introduction of convenient and safe public transportation facility, walking/cycling paths, and paid parking facility.

Keywords: active transportation, correlates, India, public transportation, transportation modes

Procedia PDF Downloads 164

Authors: Samson A. Oyeyinka, Taiwo Tijani, Adewumi T. Oyeyinka, Mutiat A. Balogun, Fausat L. Kolawole, John K. Joseph

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Bambara groundnut is an underutilised leguminous crop that has a similar composition to cowpea. Hence, it could be used in making traditional snack usually produced from cowpea paste. In this study, akara egbe, a traditional snack was prepared from Bambara groundnut flour or paste. Cowpea was included as the reference sample. The proximate composition and functional properties of the flours were studies as well as the proximate composition and sensory properties of the resulting akara egbe. Protein and carbohydrate were the main components of Bambara groundnut and cowpea grains. Ash, fat and fiber contents were low. Bambara groundnut flour had higher protein content (23.71%) than cowpea (19.47%). In terms of functional properties, the oil absorption capacity (0.75 g oil/g flour) of Bambara groundnut flour was significantly (p ≤ 0.05) lower than that of the cowpea (0.92 g oil/g flour), whereas, Cowpea flour absorbed more water (1.59 g water/g flour) than Bambara groundnut flour (1.12 g/g). The packed bulk density (0.92 g/mL) of Bambara groundnut was significantly (p ≤ 0.05) higher than cowpea flour (0.82 g/mL). Akara egbe prepared from Bambara groundnut flour showed significantly (p ≤ 0.05) higher protein content (23.41%) than the sample made from Bambara groundnut paste (19.35%). Akara egbe prepared from cowpea paste had higher ratings in aroma, colour, taste, crunchiness and overall acceptability than those made from cowpea flour or Bambara groundnut paste or flour. Bambara groundnut can produce akara egbe with comparable nutritional and sensory properties to that made from cowpea.

Keywords: Bambara groundnut, Cowpea, Snack, Sensory properties

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Authors: Muhammad Shahid, Muhammad Mansoor

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Increasing demands of contemporary applications for high strength and lightweight materials prompted the development of metal-matrix composites (MMCs). After the discovery of carbon nanotubes (CNTs) in 1991 (revealing an excellent set of mechanical properties) became one of the most promising strengthening materials for MMC applications. Additionally, the relatively low density of the nanotubes imparted high specific strengths, making them perfect strengthening material to reinforce MMCs. In the present study, aluminum-multiwalled carbon nanotubes (Al-MWCNTs) composite was prepared in an air induction furnace. The dispersion of the nanotubes in molten aluminum was assisted by inherent string action of induction heating at 790°C. During the fabrication process, multifunctional fluxes were used to avoid oxidation of the nanotubes and molten aluminum. Subsequently, the melt was cast in to a copper mold and cold rolled to 0.5 mm thickness. During metallographic examination using a scanning electron microscope, it was observed that the nanotubes were effectively dispersed in the matrix. The mechanical properties of the composite were significantly increased as compared to pure aluminum specimen i.e. the yield strength from 65 to 115 MPa, the tensile strength from 82 to 125 MPa and hardness from 27 to 30 HV for pure aluminum and Al-CNTs composite, respectively. To recognize the associated strengthening mechanisms in the nanocomposites, three foremost strengthening models i.e. shear lag model, Orowan looping and Hall-Petch have been critically analyzed; experimental data were found to be closely satisfying the shear lag model.

Keywords: carbon nanotubes, induction melting, strengthening mechanism, nanocomposite

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Authors: Zayed Freah Zeadat

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The wellbeing of urban dwellers is strongly associated with the quality and quantity of green infrastructure. Nevertheless, urban green infrastructure is still lagging in many Arab cities, and Jordan is no exception. The capital city of Jordan, Amman, is becoming more urban dense with limited green spaces. The unplanned urban growth in Amman has caused several environmental problems such as urban heat islands, air pollution, and lack of green spaces. This study aims to investigate the most suitable drivers to leverage the implementation of urban green infrastructure in Jordan through qualitative and quantitative analysis. The qualitative research includes an extensive literature review to discuss the most common drivers used internationally to promote urban green infrastructure implementation in the literature. The quantitative study employs a questionnaire survey to rank the suitability of each driver. Consultants, contractors, and policymakers were invited to fill the research questionnaire according to their judgments and opinions. Relative Importance Index has been used to calculate the weighted average of all drivers and the Kruskal-Wallis test to check the degree of agreement among groups. This study finds that research participants agreed that indirect financial incentives (i.e., tax reductions, reduction in stormwater utility fee, reduction of interest rate, density bonus, etc.) are the most effective incentive policy whilst granting sustainability certificate policy is the least effective driver to ensure widespread of UGI is elements in Jordan.

Keywords: urban green infrastructure, relative importance index, sustainable urban development, urban Jordan

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Authors: Gururaj Bhajantri

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Financial sector is the backbone of any economy and it plays a crucial role in the mobilisation and allocation of resources. One of the main objectives of financial sector is inclusive growth. The constituents of the financial sector are banks, and financial Institutions, which mobilise the resources from the surplus sector and channelize the same to the different needful sectors in the economy. Micro Small and the Medium Enterprises sector in India cover a wide range of economic activities. These enterprises are divided on the basis of investment on equipment. The micro enterprises are divided into manufacturing and services sector. Micro Service enterprises have investment limit up to ten lakhs on equipment. Hairdresser is one who not only cuts and shaves but also provides different types of hair cut, hairstyles, trimming, hair-dye, massage, manicure, pedicure, nail services, colouring, facial, makeup application, waxing, tanning and other beauty treatments etc., hairdressing salons provide these services with the help of equipment. They need investment on equipment not more than ten lakhs. Hence, they can be considered as Micro service enterprises. Hairdressing salons require more than Rs 2.50,000 to start a moderate salon. Moreover, hairdressers are unable to access the organised finance. Still these individuals access finance from money lenders with high rate of interest to lead life. The socio economic conditions of hairdressers are not known properly. Hence, the present study brings a light on the role of financial institutions in promoting hairdressing salons. The study also focuses the socio-economic background of individuals in hairdressings salons, problems faced by them. The present study is based on primary and secondary data. Primary data collected among hairdressing salons in Davangere city. Samples selected with the help of simple random sampling techniques. Collected data analysed and interpreted with the help of simple statistical tools.

Keywords: micro service enterprises, financial institutions, hairdressing salons, financial sector

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Authors: James Ladzekpo

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Background: The urgent need to identify new pharmacological targets for diabetes treatment and prevention has been amplified by the disease's extensive impact on individuals and healthcare systems. A deeper insight into the biological underpinnings of diabetes is crucial for the creation of therapeutic strategies aimed at these biological processes. Current predictive models based on genetic variations fall short of accurately forecasting diabetes. Objectives: Our study aims to pinpoint key epigenetic factors that predispose individuals to diabetes. These factors will inform the development of an advanced predictive model that estimates diabetes risk from genetic profiles, utilizing state-of-the-art statistical and data mining methods. Methodology: We have implemented a recursive feature elimination with cross-validation using the support vector machine (SVM) approach for refined feature selection. Building on this, we developed six machine learning models, including logistic regression, k-Nearest Neighbors (k-NN), Naive Bayes, Random Forest, Gradient Boosting, and Multilayer Perceptron Neural Network, to evaluate their performance. Findings: The Gradient Boosting Classifier excelled, achieving a median recall of 92.17% and outstanding metrics such as area under the receiver operating characteristics curve (AUC) with a median of 68%, alongside median accuracy and precision scores of 76%. Through our machine learning analysis, we identified 31 genes significantly associated with diabetes traits, highlighting their potential as biomarkers and targets for diabetes management strategies. Conclusion: Particularly noteworthy were the Gradient Boosting Classifier and Multilayer Perceptron Neural Network, which demonstrated potential in diabetes outcome prediction. We recommend future investigations to incorporate larger cohorts and a wider array of predictive variables to enhance the models' predictive capabilities.

Keywords: diabetes, machine learning, prediction, biomarkers

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Authors: Sezen Gurdag, Ayse Ebru Akin

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There is a growing concern all over the world on the health effect of the formaldehyde emission coming from the adhesive used in the MDF production. In this research, we investigated the effect of nanoparticle addition such as nanoclay and halloysite into urea-formadehyde resin on the total emitted formaldehyde from MDF plates produced using the resin modified as such. First, the curing behavior of the resin was studied by monitoring the pH, curing time, solid content, density and viscosity of the modified resin in comparison to the reference resin with no added nanoparticle. The dosing of the nanoparticle in the dry resin was kept at 1wt%, 3wt% or 5wt%. Consecutively, the resin was used in the production of 50X50 cm MDF samples using laboratory scale press line with full automation system. Modulus of elasticity, bending strength, internal bonding strength, water absorption were also measured in addition to the main interested parameter formaldehyde emission levels which is determined via spectrometric technique following an extraction procedure. Threshold values for nanoparticle dosing levels were determined to be 5wt% for both nanoparticles. However, the reinforcing behavior was observed to be occurring at different levels in comparison to the reference plates with each nanoparticle such that the level of reinforcement with nanoclay was shown to be more favorable than the addition of halloysite due to higher surface area available with the former. In relation, formaldehyde emission levels were observed to be following a similar trend where addition of 5wt% nanoclay into the urea-formaldehyde adhesive helped decrease the formaldehyde emission up to 40% whereas addition of halloysite at its threshold level demonstrated as the same level, i.e., 5wt%, produced an improvement of 18% only.

Keywords: halloysite, nanoclay, fiberboard, urea-formaldehyde adhesive

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Authors: Sokkhey Phauk, Takeo Okazaki

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The challenging task in educational institutions is to maximize the high performance of students and minimize the failure rate of poor-performing students. An effective method to leverage this task is to know student learning patterns with highly influencing factors and get an early prediction of student learning outcomes at the timely stage for setting up policies for improvement. Educational data mining (EDM) is an emerging disciplinary field of data mining, statistics, and machine learning concerned with extracting useful knowledge and information for the sake of improvement and development in the education environment. The study is of this work is to propose techniques in EDM and integrate it into a web-based system for predicting poor-performing students. A comparative study of prediction models is conducted. Subsequently, high performing models are developed to get higher performance. The hybrid random forest (Hybrid RF) produces the most successful classification. For the context of intervention and improving the learning outcomes, a feature selection method MICHI, which is the combination of mutual information (MI) and chi-square (CHI) algorithms based on the ranked feature scores, is introduced to select a dominant feature set that improves the performance of prediction and uses the obtained dominant set as information for intervention. By using the proposed techniques of EDM, an academic performance prediction system (APPS) is subsequently developed for educational stockholders to get an early prediction of student learning outcomes for timely intervention. Experimental outcomes and evaluation surveys report the effectiveness and usefulness of the developed system. The system is used to help educational stakeholders and related individuals for intervening and improving student performance.

Keywords: academic performance prediction system, educational data mining, dominant factors, feature selection method, prediction model, student performance

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Authors: Alemayehu Ambaw, Matia Mukama, Umezuruike Linus Opara

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This study introduces a comprehensive method for designing ventilated corrugated fiberboard carton for fresh fruit packaging utilising virtual prototyping. The technique efficiently assesses and analyses the mechanical and thermal capabilities of fresh fruit packing boxes prior to making production investments. Comprehensive structural, aerodynamic, and thermodynamic data from designs were collected and evaluated in comparison to real-world packaging needs. Physical prototypes of potential designs were created and evaluated afterward. The virtual prototype is created with computer-aided graphics, computational structural dynamics, and computational fluid dynamics technologies. The virtual prototyping quickly generated data on carton compression strength, airflow resistance, produce cooling rate, spatiotemporal temperature, and product quality map in the cold chain within a few hours. Six distinct designs were analysed. All the various carton designs showed similar effectiveness in preserving the quality of the goods. The innovative packaging box design is more compact, resulting in a higher freight density of 1720 kg more fruit per reefer compared to the commercial counterpart. The precooling process was improved, resulting in a 17% increase in throughput and a 30% reduction in power usage.

Keywords: postharvest, container logistics, space/volume usage, computational method, packaging technology

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Authors: Lobna Mnif Fakhfakh, Mohamed Chaieb

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Most ecological studies in North Africa reveal a process of continuous degradation of pastoral ecosystems as a result of overgrazing. This degradation appears across the depletion of perennial grass species. Indeed, the majority of steppic ecosystems are characterized by a low density of perennial grasses. This phenomenon reveals a drop in food value of rangelands, which is now estimated at less than 100 UF.ha -1. -1 Year in all North African steppes. However, for ecological restoration initiatives, some species such the genus of Stipagrostis and Stipa can be considered a good candidates species for effective pastoral improvement under arid bioclimate. The present work concerns Stipagrostis ciliata (Desf.) De Winter, perennial grasses, abundant in ecosystems characterized by the high content of gypsum (CaSO4)2H2O in the southern Tunisia. This tufted species with C4 biochemical photosynthesis type is able to grow and develop under high temperature and low annual rainfall, where the minimum water potential (ψmd), can reach -4 MPa during the summer season with a phenological growth maintained throughout the season unfavorable. At this point in the early autumn rains, S. ciliata begins its growth, especially with a heading which occurs 2-3 weeks after the first autumn rains. From the foregoing, it can be concluded that Stipagrostis ciliata is an excellent promising pastoral species for the ecological restoration, and enhancement of ecosystems biological productivity in arid bioclimate of North Africa.

Keywords: Stipagrostis ciliata, pastoral species, ecological restoration, arid bioclimate

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Authors: Ia Kurashvili, Giorgi Darsavelidze, Giorgi Chubinidze, Marina Kadaria

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Boron-doped Czochralski (CZ) silicon of p-type, widely used in the photovoltaic industry is suffering from the light-induced-degradation (LID) of bulk electrophysical characteristics. This is caused by specific metastable B-O defects, which are characterized by strong recombination activity. In this regard, it is actual to suppress B-O defects in CZ silicon. One of the methods is doping of silicon by different isovalent elements (Ge, C, Sn). The present work deals with the investigations of the influence of germanium doping on the internal friction and shear modulus amplitude dependences in the temperature interval of 600-800⁰C and 0.5-5 Hz frequency range in boron-containing monocrystalline silicon. Experimental specimens were grown by Czochralski method (CZ) in [111] direction. Four different specimens were investigated: Si+0,5at%Ge:B (5.1015cm-3), Si+0,5at%Ge:B (1.1019cm-3), Si+2at%Ge:B (5.1015cm-3) and Si+2at%Ge:B (1.1019cm-3). Increasing tendency of dislocation density and inhomogeneous distribution in silicon crystals with high content of boron and germanium were revealed by metallographic studies on the optical microscope of NMM-80RF/TRF. Weak increase of current carriers-holes concentration and slight decrease of their mobility were observed by Van der Pauw method on Ecopia HMS-3000 device. Non-monotonous changes of dislocation origin defects mobility and microplastic deformation characteristics influenced by measuring temperatures and boron and germanium concentrations were revealed. Possible mechanisms of changes of mechanical characteristics in Si-Ge experimental specimens were discussed.

Keywords: dislocation, internal friction, microplastic deformation, shear modulus

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Authors: Merih Şimşek, Recep Keşli, Özgül Çetinkaya, Cengiz Demir, Adem Aslan

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Bacteremia is a clinical entity with high morbidity and mortality rates when immediate diagnose, or treatment cannot be achieved. Microorganisms which can cause sepsis or bacteremia are easily isolated from blood cultures. Fifty-five positive blood cultures were included in this study. Microorganisms in 55 blood cultures were isolated by conventional microbiological methods; afterwards, microorganisms were defined in terms of the phenotypic aspects by the Vitek-2 system. The same microorganisms in all blood culture samples were defined in terms of genotypic aspects again by Multiplex-PCR DNA Low-Density Microarray System. At the end of the identification process, the DNA microarray system’s success in identification was evaluated based on the Vitek-2 system. The Vitek-2 system and DNA Microarray system were able to identify the same microorganisms in 53 samples; on the other hand, different microorganisms were identified in the 2 blood cultures by DNA Microarray system. The microorganisms identified by Vitek-2 system were found to be identical to 96.4 % of microorganisms identified by DNA Microarrays system. In addition to bacteria identified by Vitek-2, the presence of a second bacterium has been detected in 5 blood cultures by the DNA Microarray system. It was identified 18 of 55 positive blood culture as E.coli strains with both Vitek 2 and DNA microarray systems. The same identification numbers were found 6 and 8 for Acinetobacter baumanii, 10 and 10 for K.pneumoniae, 5 and 5 for S.aureus, 7 and 11 for Enterococcus spp, 5 and 5 for P.aeruginosa, 2 and 2 for C.albicans respectively. According to these results, DNA Microarray system requires both a technical device and experienced staff support; besides, it requires more expensive kits than Vitek-2. However, this method should be used in conjunction with conventional microbiological methods. Thus, large microbiology laboratories will produce faster, more sensitive and more successful results in the identification of cultured microorganisms.

Keywords: microarray, Vitek-2, blood culture, bacteremia

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Authors: Abdul Manan

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The necessity for efficient and cost-effective energy storage devices to intelligently store the inconsistent energy output from modern renewable energy sources is peaked today. The scientific community is struggling to identify the appropriate material system for energy storage applications. Countless contributions by researchers worldwide have now helped us identify the possible snags and limitations associated with each material/method. Energy storage has attracted great attention for its use in portable electronic devices military field. Different devices, such as dielectric capacitors, supercapacitors, and batteries, are used for energy storage. Of these, dielectric capacitors have high energy output, a long life cycle, fast charging and discharging capabilities, work at high temperatures, and excellent fatigue resistance. The energy storage characteristics have been studied to be highly affected by various factors, such as grain size, optimized compositions, grain orientation, energy band gap, processing techniques, defect engineering, core-shell formation, interface engineering, electronegativity difference, the addition of additives, density, secondary phases, the difference of Pmax-Pr, sample thickness, area of the electrode, testing frequency, and AC/DC conditions. The data regarding these parameters/factors are scattered in the literature, and the aim of this study is to gather the data into a single paper that will be beneficial for new researchers in the field of interest. Furthermore, control over and optimizing these parameters will lead to enhancing the energy storage properties.

Keywords: strategies, ceramics, energy storage, capacitors

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Authors: Fulufhelo Amanda Doboro, Kgomotso Sebeko, Stephen Njiro, Moritz Van Vuuren

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Ovine herpesvirus 2 (OvHV-2) genome obtained from the lymphopblastoid cell line of a BJ1035 cow was recently sequenced in the United States of America (USA). Information on the sequences of OvHV-2 genes obtained from South African strains from bovine or other African countries and molecular characterization of OvHV-2 is not documented. Present investigation provides information on the nucleotide and derived amino acid sequences and genetic diversity of Ov 7, Ov 8 ex2, ORF 27 and ORF 73 genes, of these genes from OvHV-2 strains circulating in South Africa. Gene-specific primers were designed and used for PCR of DNA extracted from 42 bovine blood samples that previously tested positive for OvHV-2. The expected PCR products of 495 bp, 253 bp, 890 bp and 1632 bp respectively for Ov 7, Ov 8 ex2, ORF 27 and ORF 73 genes were sequenced and multiple sequence analysis done on the selected regions of the sequenced PCR products. Two genotypes for ORF 27 and ORF 73 gene sequences, and three genotypes for Ov 7 and Ov 8 ex2 gene sequences were identified, and similar groupings for the derived amino acid sequences were obtained for each gene. Nucleotide and amino acid sequence variations that led to the identification of the different genotypes included SNPs, deletions and insertions. Sequence analysis of Ov 7 and ORF 27 genes revealed variations that distinguished between sequences from SA and reference OvHV-2 strains. The implication of geographic origin among SA sequences was difficult to evaluate because of random distribution of genotypes in the different provinces, for each gene. However, socio-economic factors such as migration of people with animals, or transportation of animals for agricultural or business use from one province to another are most likely to be responsible for this observation. The sequence variations observed in this study have no impact on the antibody binding activities of glycoproteins encoded by Ov 7, Ov 8 ex2 and ORF 27 genes, as determined by prediction of the presence of B cell epitopes using BepiPred 1.0. The findings of this study will be used for selection of gene candidates for the development of diagnostic assays and vaccine development as well.

Keywords: amino acid, genetic diversity, genes, nucleotide

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Authors: Chongtham Jiten, Radhapiyari Laishram, K. Chandramani Singh

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Alkaline niobate (Na_0.5K_0.5)NbO₃ ceramic system has attracted major attention in view of its potential for replacing the highly toxic but superior lead zirconate titanate (PZT) system for piezoelectric applications. Recently, a more detailed study of this system reveals that the ferroelectric and piezoelectric properties are optimized in the Li- and V-modified system having the composition (K_0.485Na_0.5Li_0.015)(Nb_0.98V_0.02)O₃. In the present work, we further study the pyroelectric behaviour of this composition along with another doped with Mn⁴⁺. So, (K_0.485Na_0.5Li_0.015)(Nb_0.98V_0.02)O₃ + x MnO₂ (x = 0, and 0.01 wt. %) ceramic compositions were synthesized by conventional ceramic processing route. X-ray diffraction study reveals that both the undoped and Mn⁴⁺-doped ceramic samples prepared crystallize into a perovskite structure having orthorhombic symmetry. Dielectric study indicates that Mn⁴⁺ doping has little effect on both the Curie temperature (T_c) and tetragonal-orthorhombic phase transition temperature (T_ot). The bulk density, room-temperature dielectric constant (ε_RT), and room-c The room-temperature coercive field (E_c) is observed to be lower in Mn⁴⁺ doped sample. The detailed analysis of the P-E hysteresis loops over the range of temperature from about room temperature to T_ot points out that enhanced ferroelectric properties exist in this temperature range with better thermal stability for the Mn⁴⁺ doped ceramic. The study reveals that small traces of Mn⁴⁺ can modify (K_0.485Na_0.5Li_0.015)(Nb_0.98V_0.02)O₃ system so as to improve its ferroelectric properties with good thermal stability over a wide range of temperature.

Keywords: ceramics, dielectric properties, ferroelectric properties, lead-free, sintering, thermal stability

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Authors: Ebraheem Al-Jouri, Youssef Abu-Ahmad, Ramasamy Srinivasan

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The parasitoid, Diadegma semiclausum (Hymenoptera: Ichneumonidae) is one of the most effective exotic parasitoids of diamondback moth (DBM), Plutella xylostella in the lowland areas of Homs, Syria. Molecular evolution studies are useful tools to shed light on the molecular bases of insect geographical spread and adaptation to new hosts and environment and for designing better control strategies. In this study, molecular evolution analysis was performed based on the 42 nuclear internal transcribed spacer-2 (ITS2) sequences representing the D. semiclausum and eight other Diadegma spp. from Syria and worldwide. Possible recombination events were identified by RDP4 program. Four potential recombinants of the American D. insulare and D. fenestrale (Jeju) were detected. After detecting and removing recombinant sequences, the ratio of non-synonymous (dN) to synonymous (dS) substitutions per site (dN/dS=ɷ) has been used to identify codon positions involved in adaptive processes. Bayesian techniques were applied to detect selective pressures at a codon level by using five different approaches including: fixed effects likelihood (FEL), internal fixed effects likelihood (IFEL), random effects method (REL), mixed effects model of evolution (MEME) and Program analysis of maximum liklehood (PAML). Among the 40 positively selected amino acids (aa) that differed significantly between clades of Diadegma species, three aa under positive selection were only identified in D. semiclausum. Additionally, all D. semiclausum branches tree were highly found under episodic diversifying selection (EDS) at p≤0.05. Our study provide evidence that both recombination and positive selection have contributed to the molecular diversity of Diadegma spp. and highlights the significant contribution of D. semiclausum in adaptive evolution and influence the fitness in the DBM parasitoid.

Keywords: diadegma sp, DBM, ITS2, phylogeny, recombination, dN/dS, evolution, positive selection

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Authors: Richard Ren

Abstract:

Air pollution is a serious danger to international well-being and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.

Keywords: air quality prediction, air pollution, artificial intelligence, machine learning algorithms

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Authors: Van Truong Pham

Abstract:

Background: Self-management was described as a potential strategy for blood pressure control in patients with hypertension. However, the effects of self-management interventions on blood pressure, self-efficacy, medication adherence, and body mass index (BMI) in older adults with hypertension have not been systematically evaluated. We evaluated the effects of self-management interventions on systolic blood pressure (SBP) and diastolic blood pressure (DBP), self-efficacy, medication adherence, and BMI in hypertensive older adults. Methods: We followed the recommended guidelines of preferred reporting items for systematic reviews and meta-analyses. Searches in electronic databases including CINAHL, Cochrane Library, Embase, Ovid-Medline, PubMed, Scopus, Web of Science, and other sources were performed to include all relevant studies up to April 2019. Studies selection, data extraction, and quality assessment were performed by two reviewers independently. We summarized intervention effects as Hedges' g values and 95% confidence intervals (CI) using a random-effects model. Data were analyzed using Comprehensive Meta-Analysis software 2.0. Results: Twelve randomized controlled trials met our inclusion criteria. The results revealed that self-management interventions significantly improved blood pressure control, self-efficacy, medication adherence, whereas the effect of self-management on BMI was not significant in older adult patients with hypertension. The following Hedges' g (effect size) values were obtained: SBP, -0.34 (95% CI, -0.51 to -0.17, p < 0.001); DBP, -0.18 (95% CI, -0.30 to -0.05, p < 0.001); self-efficacy, 0.93 (95%CI, 0.50 to 1.36, p < 0.001); medication adherence, 1.72 (95%CI, 0.44 to 3.00, p=0.008); and BMI, -0.57 (95%CI, -1.62 to 0.48, p = 0.286). Conclusions: Self-management interventions significantly improved blood pressure control, self-efficacy, and medication adherence. However, the effects of self-management on obesity control were not supported by the evidence. Healthcare providers should implement self-management interventions to strengthen patients' role in managing their health care.

Keywords: self-management, meta-analysis, blood pressure control, self-efficacy, medication adherence, body mass index

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Authors: Adedayo Racheal Agbonna

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The rising level of graduate unemployment in Nigeria has brought about the introduction of entrepreneurship education as a career option for self–reliance and self-employment. Sequel to this, it is important to have an understanding of the determining factors of entrepreneurial intention. Therefore this research empirically investigated the influence of entrepreneurship education on entrepreneurial intention of undergraduate students of selected universities in Ogun State, Nigeria. The study is significant to researchers, university policy makers, and the government. Survey research design was adopted in the study. The population consisted of 17,659 final year undergraduate students universities in Ogun State. The study adopted stratified and random sampling technique. The table of sample size determination was used to determine the sample size for this study at 95% confidence level and 5% margin error to arrive at a sample size of 1877 respondents. The elements of population were 400 level students of the selected universities. A structured questionnaire titled 'Entrepreneurship Education and students’ Entrepreneurial intention' was administered. The result of the reliability test had the following values 0.716, 0.907 and 0.949 for infrastructure, perceived university support, and entrepreneurial intention respectively. In the same vein, from the construct validity test, the following values were obtained 0.711, 0.663 and 0.759 for infrastructure, perceived university support and entrepreneurial intention respectively. Findings of this study revealed that each of the entrepreneurship education variables significantly affected intention University infrastructure B= -1.200, R²=0.679, F (₁,₁₈₇₅) = 3958.345, P < 0.05) Perceived University Support B= -1.027, R²=0.502, F(₁,₁₈₇₅) = 1924.612, P < 0.05). The perception of respondents in public university and private university on entrepreneurship education have a statistically significant difference [F(₁,₁₈₇₅) = 134.614, p < 0.05) α F(₁,₁₈₇₅) = 363.439]. The study concluded that entrepreneurship education positively influenced entrepreneurial intention of undergraduate students in Ogun State, Nigeria. Also, university infrastructure and perceived university support have negative and significant effect on entrepreneurial intention. The study recommended that to promote entrepreneurial intention of university undergraduate students, infrastructures and the university support that can arouse entrepreneurial intention of students should be put in place.

Keywords: entrepreneurship education, entrepreneurial intention, perceived university support, university infrastructure

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Authors: S. Archsana, Vajira Balasuriya

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In Trincomalee of Sri Lanka, a post-conflict area, resettlement projects and policy initiatives are taking place to improve the wealth of the rural communities through promoting economic activities by way of encouraging the rural women to opt to commence and operate Micro and Small Scale (MSS) businesses. This study attempts to identify the manner in which the institutional environment could facilitate these MSS businesses owned and operated by women in the rural environment. The respondents of this study are the beneficiaries of the Divi Neguma Development Training Program (DNDTP); a project designed to aid women owned MSS businesses, in Trincomalee district. 96 women business operators, who had obtained financing facilities from the DNDTP, are taken as the sample based on fixed interval random sampling method. The study reveals that primary challenges encountered by 82% of the women business operators are lack of initial capital followed by 71% initial market finding and 35% access to technology. The low level of education and language barriers are the constraints in accessing support agencies/service providers. Institutional support; specifically management and marketing services, have a significant relationship with wealth augmentation. Institutional support at the setting-up stage of businesses are thin whereas terms and conditions of the finance facilities are perceived as ‘too challenging’. Although diversification enhances wealth of the rural women business operators, assistance from the institutional framework to prepare financial reports that are required for business expansion is skinny. The study further reveals that institutional support is very much weak in terms of providing access to new technology and identifying new market networks. A mechanism that could facilitate the institutional framework to support the rural women business operators to access new technology and untapped market segments, and assistance in preparation of legal and financial documentation is recommended.

Keywords: business facilitation, institutional support, rural women business operators, wealth augmentation

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Authors: Lobna Mnif Fakhfakh, Mohamed Chaieb

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Most ecological studies in North Africa arid bioclimate reveal a process of continuous degradation of pastoral ecosystems as a result of overgrazing during a long time. This degradation appears across the depletion of perennial grass species. Indeed, the majority of steppe ecosystems are characterized by a low density of perennial grasses. The objective of the present work is to examine the phenology and the above ground growth of several Stipagrostis ciliata accessions, growing under different arid bioclimate of North Africa (case of Tunisia). The results of the ANOVA test, next to the mean values of all measurements show significant differences in all morphological parameters of S. ciliata accessions. Plant diameter, biovolume, root biomass with protective sleeve and spike number show very significant. Differences between S. ciliata accessions. Significance tests for the differences of means indicate high distinctiveness of accessions. Pearson’s correlation analysis of the morphological traits suggests that these traits are significantly and positively correlated. Cluster analysis indicates overall differences among accessions and exhibits the presence of three clusters. The Principal component analysis (PCA) is applied on a table with four observations and 12 variables. Dispersion of Stipagrostis ciliata accessions on the first two axes of principal component analysis confirms the presence of three groups of plants. The characterization of Stipagrostis ciliata plants has shown that significant differences exist in terms of morphological and phenological parameters.

Keywords: accession, morphology, phenology, Stipagrostis ciliata

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Authors: Neha Gupta

Abstract:

Landslide vulnerability is considered as the crucial parameter for the assessment of landslide risk. The term vulnerability defined as the damage or degree of elements at risk of different dimensions, i.e., physical, social, economic, and environmental dimensions. Himalaya region is very prone to multi-hazard such as floods, forest fires, earthquakes, and landslides. With the increases in fatalities rates, loss of infrastructure, and economy due to landslide in the Himalaya region, leads to the assessment of vulnerability. In this study, a methodology to measure the combination of vulnerability dimension, i.e., social vulnerability, physical vulnerability, and environmental vulnerability in one framework. A combined result of these vulnerabilities has rarely been carried out. But no such approach was applied in the Indian Scenario. The methodology was applied in an area of east Sikkim Himalaya, India. The physical vulnerability comprises of building footprint layer extracted from remote sensing data and Google Earth imaginary. The social vulnerability was assessed by using population density based on land use. The land use map was derived from a high-resolution satellite image, and for environment vulnerability assessment NDVI, forest, agriculture land, distance from the river were assessed from remote sensing and DEM. The classes of social vulnerability, physical vulnerability, and environment vulnerability were normalized at the scale of 0 (no loss) to 1 (loss) to get the homogenous dataset. Then the Multi-Criteria Analysis (MCA) was used to assign individual weights to each dimension and then integrate it into one frame. The final vulnerability was further classified into four classes from very low to very high.

Keywords: landslide, multi-criteria analysis, MCA, physical vulnerability, social vulnerability

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Authors: Ruquan You, Haiwang Li, Zhi Tao

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A time-resolved particle image velocimetry (PIV) system is developed to investigate the boundary layer flow with the effect of rotating Coriolis and buoyancy force. This time-resolved PIV system consists of a 10 Watts continuous laser diode and a high-speed camera. The laser diode is able to provide a less than 1mm thickness sheet light, and the high-speed camera can capture the 6400 frames per second with 1024×1024 pixels. The whole laser and the camera are fixed on the rotating facility with 1 radius meters and up to 500 revolutions per minute, which can measure the boundary flow velocity in the rotating channel with and without ribs directly at rotating conditions. To investigate the effect of buoyancy force, transparent heater glasses are used to provide the constant thermal heat flux, and then the density differences are generated near the channel wall, and the buoyancy force can be simulated when the channel is rotating. Due to the high temporal and spatial resolution of the system, the proper orthogonal decomposition (POD) can be developed to analyze the characteristic of the turbulent boundary layer flow at rotating conditions. With this rotating facility and PIV system, the velocity profile, Reynolds shear stress, spatial and temporal correlation, and the POD modes of the turbulent boundary layer flow can be discussed.

Keywords: rotating facility, PIV, boundary layer flow, spatial and temporal resolution

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Authors: Christian C. Vaso, Rinlee Butch M. Cervera

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Solid oxide electrolysis cell (SOEC) is a promising technology for hydrogen production that will contribute to the sustainable energy of the future. An important component of this SOEC is the anode material and one of the promising anode material for such application is the Sr-doped LaMnO3 (LSM) and Yttrium-stabilized ZrO2 (YSZ) composite material. In this study, LSM/YSZ with different weight percent compositions of LSM and YSZ were synthesized using solid-state reaction method. The obtained samples, 60LSM/40YSZ, 50LSM/50YSZ, and 40LSM/60YSZ, were fully characterized for its microstructure using X-ray diffraction, FTIR, and SEM/EDS. EDS analysis confirmed the elemental composition and distribution of the synthesized samples. Surface morphology of the sample using SEM exhibited a well sintered and densified samples and revealed a beveled cube-like LSM morphology while the YSZ phase appeared to have a sphere-like microstructure. Density measurements using Archimedes principle showed relative densities greater than 90%. In addition, AC impedance measurement of the synthesized samples have been investigated at intermediate temperature range (400-700 °C) in an inert and oxygen gas flow environment. At pure states, LSM exhibited a high electronic conductivity while YSZ demonstrated an ionic conductivity of 3.25 x 10-4 S/cm at 700 °C under Oxygen gas environment with calculated activation energy of 0.85eV. The composite samples were also studied and revealed that as the YSZ content of the composite electrode increases, the total conductivity decreases.

Keywords: ceramic composites, fuel cells, strontium lanthanum manganite, yttria partially-stabilized zirconia

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Authors: Sheikh Omar Sillah

Abstract:

Introduction: Monitoring is an important means of ensuring the smooth implementation and quality of clinical trials. For many years, traditional site monitoring approaches have been critical in detecting data errors but not optimal in identifying fabricated and implanted data as well as non-random data distributions that may significantly invalidate study results. The objective of this paper was to provide recommendations based on best statistical monitoring practices for detecting data-integrity issues suggestive of fabrication and implantation early in the study conduct to allow implementation of meaningful corrective and preventive actions. Methodology: Electronic bibliographic databases (Medline, Embase, PubMed, Scopus, and Web of Science) were used for the literature search, and both qualitative and quantitative studies were sought. Search results were uploaded into Eppi-Reviewer Software, and only publications written in the English language from 2012 were included in the review. Gray literature not considered to present reproducible methods was excluded. Results: A total of 18 peer-reviewed publications were included in the review. The publications demonstrated that traditional site monitoring techniques are not efficient in detecting data anomalies. By specifying project-specific parameters such as laboratory reference range values, visit schedules, etc., with appropriate interactive data monitoring, statistical monitoring can offer early signals of data anomalies to study teams. The review further revealed that statistical monitoring is useful to identify unusual data patterns that might be revealing issues that could impact data integrity or may potentially impact study participants' safety. However, subjective measures may not be good candidates for statistical monitoring. Conclusion: The statistical monitoring approach requires a combination of education, training, and experience sufficient to implement its principles in detecting data anomalies for the statistical aspects of a clinical trial.

Keywords: statistical monitoring, data anomalies, clinical trials, traditional monitoring

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Authors: Zeeshan Ahmed, Ajinkya Sarode, Pratik Basarkar, Atul Bhargav, Debjyoti Banerjee

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The use of CO₂ in oil recovery and in CO₂ capture and storage is gaining traction in recent years. These applications involve heat transfer between CO₂ and the base fluid, and hence, there arises a need to improve the thermal conductivity of CO₂ to increase the process efficiency and reduce cost. One way to improve the thermal conductivity is through nanoparticle addition in the base fluid. The nanofluid model in this study consisted of copper (Cu) nanoparticles in varying concentrations with CO₂ as a base fluid. No experimental data are available on thermal conductivity of CO₂ based nanofluid. Molecular dynamics (MD) simulations are an increasingly adopted tool to perform preliminary assessments of nanoparticle (NP) fluid interactions. In this study, the effect of the formation of a nanolayer (or molecular layering) at the gas-solid interface on thermal conductivity is investigated using equilibrium MD simulations by varying NP diameter and keeping the volume fraction (1.413%) of nanofluid constant to check the diameter effect of NP on the nanolayer and thermal conductivity. A dense semi-solid fluid layer was seen to be formed at the NP-gas interface, and the thickness increases with increase in particle diameter, which also moves with the NP Brownian motion. Density distribution has been done to see the effect of nanolayer, and its thickness around the NP. These findings are extremely beneficial, especially to industries employed in oil recovery as increased thermal conductivity of CO₂ will lead to enhanced oil recovery and thermal energy storage.

Keywords: copper-CO2 nanofluid, molecular dynamics simulation, molecular interfacial layer, thermal conductivity

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Authors: Giovana Perini-Loureiro

Abstract:

The purpose of this research was to identify whether the nominalizations terminating in -TION in the academic discourse of native English speakers contain the arguments required by their input verbs. In the perspective of functional linguistics, ideational metaphors, with nominalization as their most pervasive realization, are lexically dense, and therefore frequent in formal texts. Ideational metaphors allow the academic genre to instantiate objectification, de-personalization, and the ability to construct a chain of arguments. The valence of those nouns present in nominalizations tends to maintain the same elements of the valence from its original verbs, but these arguments are not always expressed. The initial hypothesis was that these arguments would also be present alongside the nominalizations, through anaphora or cataphora. In this study, a qualitative analysis of the occurrences of the five more frequent nominalized terminations in -TION in academic texts was accomplished, and thus a verification of the occurrences of the arguments required by the original verbs. The assembling of the concordance lines was done through COCA (Corpus of Contemporary American English). After identifying the five most frequent nominalizations (attention, action, participation, instruction, intervention), the concordance lines were selected at random to be analyzed, assuring the representativeness and reliability of the sample. It was possible to verify, in all the analyzed instances, the presence of arguments. In most instances, the arguments were not expressed, but recoverable, either in the context or in the shared knowledge among the interactants. It was concluded that the realizations of the arguments which were not expressed alongside the nominalizations are part of a continuum, starting from the immediate context with anaphora and cataphora; up to a knowledge shared outside the text, such as specific area knowledge. The study also has implications for the teaching of academic writing, especially with regards to the impact of nominalizations on the thematic and informational flow of the text. Grammatical metaphors are essential to academic writing, hence acknowledging the occurrence of its arguments is paramount to achieve linguistic awareness and the writing prestige required by the academy.

Keywords: corpus, functional linguistics, grammatical metaphors, nominalizations, academic English

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Authors: Rejane Maria P. da Silva, Mariana X. Milagre, João Victor de S. Araujo, Leandro A. de Oliveira, Renato A. Antunes, Isolda Costa

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The Al-Cu-Li alloys are advanced materials for aerospace application because of their interesting mechanical properties and low density when compared with conventional Al-alloys. However, Al-Cu-Li alloys are susceptible to localized corrosion. The near-surface deformed layer (NSDL) induced by the rolling process during the production of the alloy and its removal by polishing can influence on the corrosion susceptibility of these alloys. In this work, the influence of surface preparation effects on the electrochemical activity of AA2098-T351 (Al–Cu–Li alloy) was investigated using a correlation between surface chemistry, microstructure, and electrochemical activity. Two conditions were investigated, polished and as-received surfaces of the alloy. The morphology of the two types of surfaces was investigated using confocal laser scanning microscopy (CLSM) and optical microscopy. The surface chemistry was analyzed by X-ray Photoelectron Spectroscopy (XPS) and energy dispersive X-ray spectroscopy (EDS). Global electrochemical techniques (potentiodynamic polarization and EIS technique) and a local electrochemical technique (Localized Electrochemical Impedance Spectroscopy-LEIS) were used to examine the electrochemical activity of the surfaces. The results obtained in this study showed that in the as-received surface, the near-surface deformed layer (NSDL), which is composed of Mg-rich bands, influenced the electrochemical behavior of the alloy. The results showed higher electrochemical activity to the polished surface condition compared to the as-received one.

Keywords: Al-Cu-Li alloys, surface preparation effects, electrochemical techniques, localized corrosion

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Authors: Michelangelo Liban, Debbie Liquete

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A 70-year-old right-handed Filipina was seen by the Neurology service due to a new onset headache, bi-occipital in location, dull squeezing in character with a pain score of 8/10 with associated nausea and one episode of non-projectile, which provided no relief. Due to the alarming features of the headache despite the absence of risk factors and an essentially normal neurologic examination, a cranial CTA+CTV was done, which revealed a small left frontal and small right pontine hyper density with minimal perilesional edema. Findings also revealed filling defects in the straight and right transverse sinus and a consideration of hypoplastic left transverse sinus with no definite evidence of aneurysm nor A-V malformation. She had normal levels of D-Dimer, Protein C, ANA and Anti-DS DNA but had a low Protein S of 56% (N.V is 70-120%). Antithrombin, homocysteine and Factor V Leiden were not done due to unavailability of the tests. She was then treated as a case of Cerebral Venous Thrombosis with multiple hemorrhage from venous infraction and was given anticoagulants which provided relief of the headache. She did not manifest with any further cortical, bulbar or sensorimotor deficits hence was discharged improved after 15 hospital days. To our knowledge, there are no case reports of patients with CVT from Protein S deficiency and venous anomaly that presented with multiple hemorrhage from venous infarction, more so affecting the brainstem. In this paper, a rare location of CVT in a newly diagnosed Protein S deficient patient is presented together with an uneventful course and favorable outcome.

Keywords: protein S deficiency, cerebral venous thrombosis, pontine hemorrhage from venous infarction, elderly

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