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

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

Electrophoretic deposition of polyvinyl alcohol, C–H NRs along with moringa on an SS substrate for orthopedic implant applications

Metals are commonly used in bone implants due to their durability and load-bearing capabilities, yet they often suffer from biofilm growth and corrosion. To overcome these challenges, implants with enhanced biocompatibility, bioactivity, and antimicrobial properties are preferred. Stainless steel (SS) implants are widely favored in orthopedics for their mechanical strength and cost-effectiveness. To address the issues related to SS implants, we developed composite coatings using synthetic biopolymer polyvinyl alcohol (PVA), calcium hydrate (C–H) nanorods for improved bioactivity and antibacterial properties, and Moringa oleifera to enhance osteogenic induction. These coatings were deposited on 316L SS through electrophoretic deposition (EPD), providing protection against body fluids and enhancing the corrosion resistance of the SS. X-ray diffraction (XRD) confirmed the presence of the desired tobermorite crystal structure, while scanning electron microscopy (SEM) revealed nanorod-like C–H structures, a film thickness of 29 microns, and a hedgehog-like morphology in the composite particles. The coated sample demonstrated a contact angle of 64°, optimal for protein attachment and cellular uptake. Additionally, the coating exhibited strong adhesion with less than 5% damage observed in cross-cut hatch testing and appropriate surface roughness for protein attachment. Differential Scanning Calorimetry (DSC) and thermogravimetric analysis (TGA) assessed the thermal response of the materials. The coating also showed antibacterial activity against both Gram-negative and Gram-positive bacteria. Furthermore, the sample exhibited rapid bioactivity by forming a hydroxyapatite (HA) layer within 24 hours, with 35.4% degradability within 24 hours and 44.5% within 48 hours. These findings confirm that the composite film enhances the biocompatibility, bioactivity, and antibacterial properties of SS orthopedic implants in a cost-effective manner