An efficient eco advanced oxidation process for phenol mineralization using a 2D/3D nanocomposite photocatalyst and visible light irradiations

Nanocomposites (CNTi) with different mass ratios of carbon nitride (C3N4) and TiO2 nanoparticles were prepared hydrothermally. Different characterization techniques were used including X-ray diffraction (XRD), UV-Vis diffuse reflectance spectroscopy (DRS), X-ray photoelectron spectroscopy (XPS), transmission electron spectroscopy (TEM) and Brunauer-Emmett-Teller (BET). UV-Vis DRS demonstrated that the CNTi nanocomposites exhibited absorption in the visible light range. A sun light-simulated photoexcitation source was used to study the kinetics of phenol degradation and its intermediates in presence of the as-prepared nanocomposite photocatalysts. These results were compared with studies when TiO2 nanoparticles were used in the presence and absence of H2O2 and/or O3. The photodegradation of phenol was evaluated spectrophotometrically and using the total organic carbon (TOC) measurements. It was observed that the photocatalytic activity of the CNTi nanocomposites was significantly higher than that of TiO2 nanoparticles. Additionally, spectrophotometry and TOC analyses confirmed that degraded phenol was completely mineralized to CO2 and H2O with the use of CNTi nanocomposites, which was not the case for TiO2 where several intermediates were formed. Furthermore, when H2O2 and O3 were simultaneously present, the 0.1% g-C3N4/TiO2 nanocomposite showed the highest phenol degradation rate and the degradation percentage was greater than 91.4% within 30 min. 1 2017 The Author(s).Scopu

QuantiMus: A Machine Learning-Based Approach for High Precision Analysis of Skeletal Muscle Morphology.

Skeletal muscle injury provokes a regenerative response, characterized by the de novo generation of myofibers that are distinguished by central nucleation and re-expression of developmentally restricted genes. In addition to these characteristics, myofiber cross-sectional area (CSA) is widely used to evaluate muscle hypertrophic and regenerative responses. Here, we introduce QuantiMus, a free software program that uses machine learning algorithms to quantify muscle morphology and molecular features with high precision and quick processing-time. The ability of QuantiMus to define and measure myofibers was compared to manual measurement or other automated software programs. QuantiMus rapidly and accurately defined total myofibers and measured CSA with comparable performance but quantified the CSA of centrally-nucleated fibers (CNFs) with greater precision compared to other software. It additionally quantified the fluorescence intensity of individual myofibers of human and mouse muscle, which was used to assess the distribution of myofiber type, based on the myosin heavy chain isoform that was expressed. Furthermore, analysis of entire quadriceps cross-sections of healthy and mdx mice showed that dystrophic muscle had an increased frequency of Evans blue dye+ injured myofibers. QuantiMus also revealed that the proportion of centrally nucleated, regenerating myofibers that express embryonic myosin heavy chain (eMyHC) or neural cell adhesion molecule (NCAM) were increased in dystrophic mice. Our findings reveal that QuantiMus has several advantages over existing software. The unique self-learning capacity of the machine learning algorithms provides superior accuracy and the ability to rapidly interrogate the complete muscle section. These qualities increase rigor and reproducibility by avoiding methods that rely on the sampling of representative areas of a section. This is of particular importance for the analysis of dystrophic muscle given the "patchy" distribution of muscle pathology. QuantiMus is an open source tool, allowing customization to meet investigator-specific needs and provides novel analytical approaches for quantifying muscle morphology

NUMERICAL STUDY OF DEPTH FACTORS FOR UNDRAINED LIMIT LOAD OF STRIP FOOTINGS

Current studies of bearing capacity for shallow foundations tend to rely on the hypothesis of an isolated footing lying on the ground surface. In practice a footing never lies on the ground surface; it is mostly embedded at a depth D below the ground surface. This paper focuses on a numerical study using the finite-difference code Fast Lagrangian Analysis of Continua(FLAC),to evaluate the bearing capacity of embedded strip footings. The effect of the embedment is estimated though a depth factor, defined as a ratio of the bearing capacity of a strip footing at a depth Dto that of a strip footing at the ground surface. The results presented in this paper show that the size and shape of the shear zone and displacement field defining the undrained capacity of shallow foundations under centred vertical loading are dependent on embedment rati

Association of lipoprotein lipase gene with coronary heart disease in Sudanese population

AbstractCardiovascular disease is stabilizing in high-income countries and has continued to rise in low-to-middle-income countries. Association of lipid profile with lipoprotein lipase gene was studied in case and control subject. The family history, hypertension, diabetes mellitus, smoking and alcohol consumption were the most risk factors for early-onset of coronary heart disease (CHD). Sudanese patients had significantly (P<0.05) lower TC and LDL-C levels compared to controls. Allele frequency of LPL D9N, N291S and S447X carrier genotype was 4.2%, 30.7% and 7.1%, respectively. We conclude that lipoprotein lipase polymorphism was not associated with the incidence of CHD in Sudan

Posttraumatic stress disorder predicts poor health-related quality of life in cardiac patients in Palestine

BACKGROUND: The longitudinal association of posttraumatic stress disorder (PTSD) with health-related quality of life (HRQL) in cardiac patients' remains poorly studied, particularly in conflict-affected settings. MATERIALS AND METHODS: For this cohort study, we used baseline and one-year follow-up data collected from patients 30 to 80 years old consecutively admitted with a cardiac diagnosis to four major hospitals in Nablus, Palestine. All subjects were screened for PTSD and HRQL using the PTSD Checklist Specific and the HeartQoL questionnaire. We used a generalized structural equation model (GSEM) to examine the independent predictive association of PTSD at baseline with HRQL at follow-up. We also examined the mediating roles of depression, anxiety, and stress at baseline. RESULTS: The prevalence of moderate-to-high PTSD symptoms among 1022 patients at baseline was 27∙0%. Patients with PTSD symptoms reported an approximate 20∙0% lower HRQL at follow-up. The PTSD and HRQL relationship was largely mediated by depressive and anxiety symptoms. It was not materially altered by adjustment for socio-demographic, clinical, and lifestyle factors. DISCUSSION: Our findings suggest that individuals with a combination of PTSD and depression, or anxiety are potentially faced with poor HRQL as a longer-term outcome of their cardiac disease. In Palestine, psychological disorders are often stigmatized; however, integration of mental health care with cardiac care may offer an entry door for addressing psychological problems in the population. Further studies need to assess the effective mental health interventions for improving quality of life in cardiac patients

Gamma ray production cross sections in proton induced reactions on natural Mg, Si and Fe targets over the proton energy range 30 up to 66 MeV

Gamma-ray excitation functions have been measured for 30, 42, 54 and 66 MeV proton beams accelerated onto C + O (Mylar), Mg, Si, and Fe targets of astrophysical interest at the separate-sector cyclotron of iThemba LABS in Somerset West (Cape Town, South Africa). A large solid angle, high energy resolution detection system of the Eurogam type was used to record Gamma-ray energy spectra. Derived preliminary results of Gamma-ray line production cross sections for the Mg, Si and Fe target nuclei are reported and discussed. The current cross section data for known, intense Gamma-ray lines from these nuclei consistently extend to higher proton energies previous experimental data measured up to Ep ~ 25 MeV at the Orsay and Washington tandem accelerators. Data for new Gamma-ray lines observed for the first time in this work are also reported.Comment: 11 pages, 6 figures. IOP Institute of Physics Conference Nuclear Physics in Astrophysics VII, 28th EPF Nuclear Physics Divisional Conference, May 18-22 2015, York, U

QuantiMus: A Machine Learning-Based Approach for High Precision Analysis of Skeletal Muscle Morphology

Skeletal muscle injury provokes a regenerative response, characterized by the de novo generation of myofibers that are distinguished by central nucleation and re-expression of developmentally restricted genes. In addition to these characteristics, myofiber crosssectional area (CSA) is widely used to evaluate muscle hypertrophic and regenerative responses. Here, we introduce QuantiMus, a free software program that uses machine learning algorithms to quantify muscle morphology and molecular features with high precision and quick processing-time. The ability of QuantiMus to define and measure myofibers was compared to manual measurement or other automated software programs. QuantiMus rapidly and accurately defined total myofibers and measured CSA with comparable performance but quantified the CSA of centrally-nucleated fibers (CNFs) with greater precision compared to other software. It additionally quantified the fluorescence intensity of individual myofibers of human and mouse muscle, which was used to assess the distribution of myofiber type, based on the myosin heavy chain isoform that was expressed. Furthermore, analysis of entire quadriceps cross-sections of healthy and mdx mice showed that dystrophic muscle had an increased frequency of Evans blue dye+ injured myofibers. QuantiMus also revealed that the proportion of centrally nucleated, regenerating myofibers that express embryonic myosin heavy chain (eMyHC) or neural cell adhesion molecule (NCAM) were increased in dystrophic mice. Our findings reveal that QuantiMus has several advantages over existing software. The unique self-learning capacity of the machine learning algorithms provides superior accuracy and the ability to rapidly interrogate the complete muscle section. These qualities increase rigor and reproducibility by avoiding methods that rely on the sampling of representative areas of a section. This is of particular importance for the analysis of dystrophic muscle given the “patchy” distribution of muscle pathology. QuantiMus is an open source tool, allowing customization to meet investigatorspecific needs and provides novel analytical approaches for quantifying muscle morphology

Development of transportation models based on students’ interest in a parking charging system at Universiti Malaysia Sabah (UMS)

Transportation management and sustainable transportation planning were critical. A well-planned transportation system is extremely beneficial in terms of efficiency and environmental friendliness. To that end, parking charging was one of the transportation management topics covered in this study. A parking charging system is one in which a user can leave their vehicle at a particular place and pay a price based on the amount of time it was left unattended. Given the rising use of private vehicles, which has resulted in an increase in congestion and air pollution, it is believed that a parking fee system can be implemented to alleviate the situation. The primary purpose of this research is to develop a transportation model based on the parking price factor in Ringgit Malaysia (RM). At the completion of the study, a transportation model based on parking rates will be developed, and it is projected that once implemented, the percentage of private vehicles that use public transportation will increase. This model is deemed necessary in order to mitigate the harmful effect of an excessive number of private vehicles at UMS. The State Preference Survey (SPS) method was used. A questionnaire form was developed and distributed online to 300 respondents among the students of the Faculty of Engineering at UMS, in order to collect the required data. The data collected was then analyzed using linear regression to develop several transportation logistic models. The transportation models that have been developed in the form of a logistic model that can reflect the willingness of UMS students to shift from private vehicles to public transport. These models predict that when the parking price increases, the percentage shift of private vehicles to public transport will increase linearly. It is also found that 100% of drivers are willing to shift from private vehicles to public transport if the parking price per hour is RM 4.00. Shifting private vehicle users to public transportation may assist lower the number of private vehicles on the road and thus indirectly help mitigate the negative consequences of an excess of private automobiles