-A synthetic rubber powder was used to adsorb the residual oil in palm oil mill effluent (POME). POME is the wastewater produced by the palm oil industry. It is a colloidal suspension which is 95-96% water, 0.6-0.7% oil and 4-5% total solids including 2-4% suspended solids originating in the mixing of sterilizer condensate, separator sludge and hydrocyclone wastewater. POME contains 4,000 mg dm -3 of oil and grease, which is relatively high compared to the limit of only 50 mg dm -3 set by the Malaysian Department of Environment. A bench-scale study of the adsorption of residual oil in POME using synthetic rubber powder was conducted using a jar test apparatus. The adsorption process was studied by varying parameters affecting the process. The parameters were adsorbent dosage, mixing speed, mixing time and pH. The optimum values of the parameters were obtained. It was found that almost 88% removal of residual oil was obtained with an adsorbent dosage of 30 mg dm -3 and mixing speed of 150 rpm for 3 hr at a pH 7. Adsorption equilibrium was also studied, and it was found that the adsorption process on the synthetic rubber powder fit the Freundlich isotherm model.

Palm oil mill effluent (POME) was pretreated to remove suspended solids and residual oil. The processes used were flocculation, solvent extraction, adsorption and membrane separation. Flocculation was used to remove suspended solids, and solvent extraction and adsorption processes were used to remove residual oil. Membrane separation was subsequently applied to remove any residual suspended solids and oil remaining after the pretreatments. The solvent extraction and adsorption processes were operated on a batch basis whereas membrane separation was performed in continuous mode. The treatment efficiency of the processes was measured as percentage removal of suspended solids and oil respectively. The optimum values of the process parameters obtained in the flocculation process were an alum dosage of 4000 mg dm −3 , mixing speed of 150 rpm for 1 h and sedimentation time of 270 min, resulting in 93% suspended solids removal. In the solvent extraction process, a 95% reduction in residual oil was obtained using n-hexane as a solvent with 20 min of mixing at 200 rpm. The ratio of solvent to POME was 6:10 and carried out at pH 9. In the batch adsorption process, an 88% reduction in residual oil was obtained at a mixing speed of 100 rpm for 1 h, pH 9 and an adsorbent dosage of 300 g dm −3 . In membrane separation processes, GH and CE(GH) membranes gave 63% and 49% reductions in suspended solids and residual oil respectively at pH 9 and pressure of 1000 kPa.

Skin cancer is a type of cancer that grows in the skin tissue, which can cause damage to the surrounding tissue, disability, and even death. In Indonesia, skin cancer is the third leading for most cancer cases after cervical and breast cancer. The accuracy of diagnosis and the early proper treatment can minimize and control the harmful effects of skin cancer. Due to the similar shape of the lesion between skin cancer and benign tumor lesions, physicians consuming much more time in diagnosing these lesions. The system was developed in this study could identify skin cancer and benign tumor lesions automatically using the Convolutional Neural Network (CNN). The proposed model consists of three hidden layers with an output channel of 16,32, and 64 for each layer respectively. The proposed model uses several optimizers such as SGD, RMSprop, Adam, and Nadam with a learning rate of 0.001. Adam optimizer provides the best performance with an accuracy value of 99% in identifying the skin lesions from the ISIC dataset into 4 classes, namely dermatofibroma, nevus pigmentosus, squamous cell carcinoma, and melanoma. The results obtained outperform the performance of the existing skin cancer classification system.

<p class="0abstract"><span lang="EN-US">Covid-19 pandemic has changed the education landscape hence students’ learning experience. The conventional face-to-face classroom has transformed into a synchronous online mode with minimal digital readiness. In the immense situations during the acquisition of knowledge, their learning absorptive capacity may be disrupted. To unpick their learning proficiency and design complex interactivity, this paper considers a digital platform for structured learning activities. The study particularly examines how various factors are associated with students’ online learning experience, particularly during the pandemic. An online survey of 312 respondents who used the Blackboard online learning platform was conducted, and a PLS-SEM analysis indicated that an alternative assessment mediated the relationship of learning readiness, student engagement, and student motivation toward student learning experience simultaneously. Overall, our study reiterates the need to address the mediator role of alternative assessment to succeed in online courses.</span></p>

Background: The medical risk factor associated with hepatitis C virus (HCV) infection such as blood transfusion and surgery had been intensely studied in many countries. Up to 40% of patients infected with HCV may have non identifiable routes of viral acquisition. Dental extraction may be one of these risk factors. The purpose of the present study was to determine the existence of infection and the predominated HCV genotype among subjects with dental extraction. Material and methods: A case-control study involving pregnant women with and without history of dental extraction (n=776, n=2715, respectively). HCV antibodies (anti-HCV) were tested using subsequently third generation enzyme immunoassay (EIA-3) and immunoblot assay (Lia Tek-111). In addition 94 serum samples were subjected to molecular analysis using RT-PCR and DNA enzyme immunoassay (DEIA) method for HCV-RNA and genotypes. Results: Anti-HCV seroprevelance was significantly higher (6.3%) among cases with dental extraction (cases) than their counter control group (control) (2.63%) (p=0.00001). Dental extraction act as a risk factor for HCV infection (OR=2.73; 95% CI=1.8-3.9). HCV-RNA was found to be significantly higher (74.6%) in cases than (38.7%) control group (p=0.0016). No significant association between HCV genotypic and the history of tooth extraction but HCV-1b showed higher rate (90%) among dental extraction cases. Conclusion: Our study showed that dental extraction acts as a risk factor for acquiring HCV. Complete sterilization and cleaning of equipment is necessary.