Finite element modelling of damage fracture and fretting fatigue

This paper summarises the research carried out to develop Finite Element (FE) modelling and predictive techniques for damage, fracture, fatigue and fretting fatigue problems. A damage model is developed based on Continuum Damage Mechanics and integrated within FE code. It is then used to predict the number of cycles to crack initiation in adhesively bonded joints. Furthermore, crack propagation algorithm is programmed within FE code using the principles of Fracture Mechanics and Paris law. The effect of mode mixity on crack propagation is taken into account using a Double Cantilever Beam (DCB) test specimen. Moreover, FE model of fretting fatigue aluminium test specimen is carried out in order to study the stress distribution and predict the crack propagation fatigue lifetime. Fretting fatigue problems involve two types of analyses; namely contact mechanics analysis and damage/fracture mechanics analysis. Both analyses are performed in FE code and the stress distribution along the contact surface between the two bodies is obtained and analysed. Furthermore, crack propagation analysis under fretting fatigue condition is presented. In most cases, the numerical results are compared to experimental ones

EFFECT OF THE STACKING SEQUENCE ON THE IMPACT RESPONSE OF CARBON-GLASS/EPOXY HYBRID COMPOSITES

This paper investigates low-velocity impact response of Quasi Isotropic (QI) hybrid carbon/glass fiber reinforced polymer composites with alternate stacking sequences. Cross-ply woven carbon and glass fibers were used as reinforcing materials to fabricate sandwiched and interlayer hybrid composites. For comparison, the laminates containing only-carbon and only-glass fibers were also studied. Drop weight test was used to impact the samples. The images captured by a normal camera demonstrated that localized damages (delamination) existed within plies. The hybrid laminates had smaller load drops, smaller maximum deflection, and higher maximum load compared to the single fiber laminates. In addition, carbon outside interlayer hybrid laminate showed the highest maximum load and energy absorption, showing the significant dependence of the impact performance on hybridization and stacking sequence. It was concluded that a hybrid composite would help improve impact performance of laminated composites compared to non-hybrid composites if they are properly designed

A Cell-Based Examination of Modulators of Pre-Membrane Processing As a Target against Dengue Virus

Dengue Virus (DenV) is an arbovirus that represents a budding risk in the world. Every year, up to 100 million DenV infections manifest into Dengue Fever or in extreme cases Dengue Hemorrhagic Fever and Dengue Shock Syndrome. DenV research in vaccine development has proven to be a difficult feat due to the phenomenon of antibody dependent enhancement. Furthermore, there are currently no available antivirals to fight infection, viral protein processing or viral production. DenV lifecycle begins with its genomic release in the cytoplasm, where it is then translated as a single polypeptide embedded in the Endoplasmic Reticulum (ER) membrane. DenV, like so many other viruses, exploits a range of host enzymes in the Classical Secretory Pathway (CSP) for modifications. Among these important host enzymes are proteases such as the family of Proprotein Convertases (PCs), including furin. The modulation of the pre- membrane (pr-M) protein, most likely by PCs, is a critical step in the DenV lifecycle as an absence results in noninfectious progeny. Interestingly, the maturation of pr-M by the host enzymes during infection has been characterized as incomplete, thus rendering some viral particles noninfectious. Thus, the inhibition of pr-M cleavage presents an attractive target for potential antivirals. This assay is based on a fusion that contains an ER targeting signal sequence, the substrate of significance with flanking FLAG and HA epitopes, and a transmembrane (TM) domain. The assay in the context of the pr-M boundary has shown robust transportation with a wild phenotype in both transient and stable cell expression using retroviral technology. The original pr-M substrate included only 20 aa of the substrate boundary. Here, I have designed different substrate boundaries of pr-M to monitor important motifs in enzyme recognition and secretion, hypothesizing that by adapting to larger segments, we will have a powerful platform for the discovery of competitive inhibitors rather than inhibitors of the enzyme

WORK RELATED INJURIES IN SMALL SCALE METAL PRESS INDUSTRIES OF SHAHDRAH TOWN, LAHORE, PAKISTAN

The work place injuries have to pay both direct and indirect cost of the accidents. With a population of 169 million, Pakistan has no reported estimate of the national impact of workplace injuries. This study presented a profile of workplace injuries associated with small medium enterprises of metal press cottage industries in Shahdra Town, Lahore (Pakistan) and determined the impact on the country’s economy besides to recommend strategies for delineating these important problems. The in-house accident investigation technique was used to collect the data from randomly selected small scale metal press cottage industries of study area for all types of injuries principally from minor to major ones. It was observed that role of human error in occupational injuries is momentous and keeping in view the necessity of proper safety training of the metal workers, thre is a dire need to institute an information system to evaluate the true impact of injuries and develop national safety standards

A deep learning approach for context-aware citation recommendation using rhetorical zone classification and similarity to overcome cold-start problem

In the recent decade, the citation recommendation has emerged as an important research topic due to its need for the huge size of published scientific work. Among other citation recommendation techniques, the widely used content-based filtering (CBF) exploits research articles’ textual content to produce recommendations. However, CBF techniques are prone to the well-known cold-start problem. On the other hand, deep learning has shown its effectiveness in understanding the semantics of the text. The present paper proposes a citation recommendation system using deep learning models to classify rhetorical zones of the research articles and compute similarity using rhetorical zone embeddings that overcome the cold-start problem. Rhetorical zones are the predefined linguistic categories having some common characteristics about the text. A deep learning model is trained using ART and CORE datasets with an accuracy of 76 per cent. The final ranked lists of the recommendations have an average of 0.704 normalized discounted cumulative gain (nDCG) score involving ten domain experts. The proposed system is applicable for both local and global context-aware recommendations

ECOTERRA Journal of Environmental Research and Protection Comparative assessment of different heavy metals in urban soil and vegetables irrigated with sewage/industrial waste water

Abstract. This study was conducted to investigate heavy metals content of sewage water and its impact on soil and their uptake by vegetables irrigated by the sewage/industrial effluent. Twenty five samples each of water, soil, and vegetable leaves and edible vegetable portions were collected from different sites, in Lahore city of Pakistan. Parameters like pH, and electrical conductivity (EC) were also determined The results indicated that soil irrigated by sewage water having tolerable DTPA-extractable metals contents, The concentration of heavy metals in upper layer of soil (0 -15 cm) is higher than the lower layer (15-30 cm). The reason behind is that the upper layer was receiving sewage water permanently while the penetration of sewage water below 15 cm was less. The heavy metal content was above the toxicity level in leafy vegetables grown in the area of Lahore. This study showed that among the different tested plant species, the amount of heavy metals was higher in leaves than fruits. Plants whose fruits grow below the soil showed higher concentration of heavy metals while other showed less concentration whose edible portion was above the ground level. While leafy vegetables (Spinach, Cabbage, Coriander etc) showed higher concentration in leaves than in fruits, indicating that these vegetables should be consumed carefully if produced using the polluted water

Knowledge, attitude and practices of self-medication including antibiotics among health care professionals during the COVID-19 pandemic in Pakistan; findings and implications

Since the emergence of COVID-19, several different medicines including antimicrobials have been administered to patients to treat COVID-19. This is despite limited evidence of the effectiveness of many of these, fueled by misinformation. These utilization patterns have resulted in concerns with patients’ safety and a rise in antimicrobial resistance (AMR). Health care workers (HCWs) were required to serve in high-risk areas throughout the pandemic. Consequently, they may be inclined towards self-medication. However, they have a responsibility to ensure any medicines recommended or prescribed for the management of patients with COVID-19 are evidence based. This though is not always the case. A descriptive cross-sectional study was conducted among HCWs in six districts of the Punjab to assess their knowledge, attitude and practices of self-medication during the ongoing pandemic. This included HCWs working a a range of public sector hospitals in Punjab Province. A total of 1173 HCWs were included in the final analysis. The majority of HCWs possessed good knowledge regarding self-medication and good attitudes. However, 60% were practicing self-medication amid the COVID-19 pandemic. The most frequent medicines consumed by the HCWs under self-medication were antipyretics (100%), antibiotics (80.4%) and vitamins (59.9%). Azithromycin was the most commonly purchase antibiotic (35.1%). In conclusion, HCWs possess good knowledge of, and attitude, regarding medicines they pur-chased. However, there are concerns that high rates of purchasing antibiotics, especially ‘Watch’ antibiotics, for self-medication may enhance AMR. This needs addressing

Effect of ply thickness on damage mechanisms of composite laminates under repeated loading

Barely visible impact damage (BVID) occurs in composite laminates subjected to low-velocity impact. They can then exhibit significant effect on mechanical performance of laminates. Previously, it is shown, analytically and experimentally, that BVID occurs at a critical energy level and below this energy level there is no induced damage. However, repeated impact may cause BVID even below the critical energy level. This paper is a novel investigation that deals with the cyclic behaviour of quasi-isotropic glass/epoxy laminated composites under indentation, which is a quasistatic version of low-velocity impact. In particular, this study aims to investigate the ply thickness effect on matrix crack-induced delamination damage in the case of laminated composites under cyclic quasi static indentation loadings. The effect of different parameters, such as load level and ply thickness, on the damage evolution were here investigated. Tests were performed according to the ASTM 7136 standard. Since the glass layer was translucent, it was also possible to visually inspect the matrix delamination during the tests. The laminates were subjected to load levels lower than the critical load level, while there was no evidence of damages when samples were indented just once. However, by increasing the number of cycles, matrix crack-induced delamination appeared in the samples. In brief, it was observed that the ply thickness and energy level have significant effects on the intensity of the induced damage

Static and fatigue behaviors of short glass fiber–reinforced polypropylene composites aged in a wet environment

In this paper, a new experimental study of the bending static and fatigue behaviors of a composite material reinforced with 40% by mass of short glass fibers (type E) and polypropylene matrix is presented. The composite material is obtained in the form of plates by an injection process, which inevitably affects the distribution of the fibers and therefore the behavior of the material studied. To do this, several techniques are implemented on specimens by cutting them in transverse and longitudinal directions. The effect of aging in distilled water at 40℃ on the mechanical characteristics is studied under static and fatigue loading conditions. The static tests, three-point flexure up to failure, allow us to choose the levels of stress for the fatigue tests. The endurance curves as a function of the number of cycles are plotted by adapting the end-of-test criteria N5, N10, and N20, which represent a rigidity drop of 5%, 10%, and 20%, respectively. An interpretation of the Wöhler curve equations defined for the end-of-test criteria allows defining the kinetics of material damage. The results highlighted the influence of distilled water on the mechanical behavior and the lifetime of the material. We also perform macroscopic observations of fracture and microscopic facies in order to identify the damage mechanisms of the composite material. </jats:p

A deep learning approach for context-aware citation recommendation using rhetorical zone classification and similarity to overcome cold-start problem

In the recent decade, the citation recommendation has emerged as an important research topic due to its need for the huge size of published scientific work. Among other citation recommendation techniques, the widely used content-based filtering (CBF) exploits research articles’ textual content to produce recommendations. However, CBF techniques are prone to the well-known cold-start problem. On the other hand, deep learning has shown its effectiveness in understanding the semantics of the text. The present paper proposes a citation recommendation system using deep learning models to classify rhetorical zones of the research articles and compute similarity using rhetorical zone embeddings that overcome the cold-start problem. Rhetorical zones are the predefined linguistic categories having some common characteristics about the text. A deep learning model is trained using ART and CORE datasets with an accuracy of 76 per cent. The final ranked lists of the recommendations have an average of 0.704 normalized discounted cumulative gain (nDCG) score involving ten domain experts. The proposed system is applicable for both local and global context-aware recommendations