Development of a reverse supply chain model for electronic waste incorporating transportation risk

The quantity of Electronic waste (E-waste) is considerably growing due to the rapid development of technology. To diminish the influences of E-waste to the environment and recover raw materials, the reverse supply chain (RSC) has been examined. Most research focuses on minimizing the total cost of the system, however, does not integrate risk factors related to RSC operation. Risks generally derive from transportation activity in E-waste RSC such as delays for pick up, breakdown of trucks, the uncertainty of dangerous materials which might lead to disruptions and higher cost. Therefore, this paper aims to develop a mathematical model for minimizing the total cost of E-waste RSC which integrates transportation risk. A mixed integer linear programming is utilized in the model and addressed by an optimization software. The results of the proposed model can determine the optimal locations and the amount of used products transported within the RSC network.  The numerical example also demonstrates that the movement of materials or components in the RSC network is considerably affected by considering transportation risk. The suggested model can assist decision makers about establishing RSC network in which risk elements are incorporated

Ensembling techniques in solar panel quality classification

Solar panel quality inspection is a time consuming and costly task. This study tries to develop as reliable method for evaluating the panels quality by using ensemble technique based on three machine learning models namely logistic regression, support vector machine and artificial neural network. The data in this study came from infrared camera which were captured in dark room. The panels are supplied with direct current (DC) power while the infrared camera is located perpendicular with panel surface. Dataset is divided into four classes where each class represent for a level of damage percentage. The approach is suitable for systems which has limited resources as well as number of training images which is very popular in reality. Result shows that the proposed method performs with the accuracy is higher than 90%

Effects of dominance on operation policies in a two-stage supply chain in which market demands follow the Bass diffusion model

The Bass model offers several successful applications in forecasting the diffusion process of new products. Due to its potential and flexibilities, the application of this model is not only limited now to forecasting, but also extends to other fields such as analyzing a supply chain’s responses, optimizing production plans, and so forth. This study investigates inventory and production policies in a two-stage supply chain with one manufacturer and one retailer, in which the market demand process follows the Bass diffusion model. The model assumes the market parameters and essential information are available and ready for access. This study then applies dynamic programming and heuristic algorithm to find the optimal policies for each stage under different scenarios

Optimizing a Reverse Supply Chain Network for Electronic Waste under Risk and Uncertain Factors

Minimizing the impact of electronic waste (e-waste) on the environment through designing an effective reverse supply chain (RSC) is attracting the attention of both industry and academia. To obtain this goal, this study strives to develop an e-waste RSC model where the input parameters are fuzzy and risk factors are considered. The problem is then solved through crisp transformation and decision-makers are given the right to choose solutions based on their satisfaction. The result shows that the proposed model provides a practical and satisfactory solution to compromise between the level of satisfaction of constraints and the objective value. This solution includes strategic and operational decisions such as the optimal locations of facilities (i.e., disassembly, repairing, recycling facilities) and the flow quantities in the RSC

Ultrasound-Assisted Enzymatic Extraction of Adenosine from Vietnamese Cordyceps militaris and Bioactivity Analysis of the Extract

Vietnamese Cordyceps militaris (C. militaris) has long been recognized as one of the most valuable traditional Chinese medicines. In this study, adenosine was extracted from Vietnamese C. militaris by ultrasound-assisted enzymatic extraction method (UAEE) using water as a solvent. Then, the effects of five single factors on adenosine content including pH, enzyme-to-material ratio, ultrasonic power, ultrasonic time, and ultrasonic temperature were determined. After that, three factors consisting of ultrasonic power, ultrasonic time, and ultrasonic temperature were chosen based on their effects on adenosine content. The simultaneous influence of these factors on the adenosine content was investigated by response surface method using central composite design. The adenosine content was evaluated by high-performance liquid chromatography method. Under the optimal conditions, the extract was evaluated for antioxidant and anticancer bioactivities. In addition, different extraction methods including aqueous extraction (AE), ultrasound-assisted extraction (UAE), and enzyme-assisted extraction (EAE) methods were carried out to compare with UAEE. As a result, it can be concluded that UAEE is a promising method for adenosine extraction and further studies regarding isolation and purification need to be conducted

Economic optimisation of local Australian ammonia production using plasma technologies with green/turquoise hydrogen

Growing concern about the supply of goods under the COVID pandemic due to border restrictions and community lockdown has made us aware of the limitations of the global supply chain. Fertilisers are pivotal for the growth and welfare of humankind and there is more than a century history in industrial technology. Ammonia is the key platform chemical here which can be chemically diversified to all kinds of fertilisers. This paper puts a perspective on production technologies that can enable a supply of ammonia locally and on-demand in Australia, for the farmers to produce resilient and self-sustained fertilisers. To assess the validity of such a new business model, multi-objective optimisation has to be undergone, and computing is the solution to rank the millions of possible solutions. In this lieu, an economic optimisation framework for the Australian ammonia supply chain is presented. The model seeks to address the economic potential of distributed ammonia plants across Australia. Different techniques for hydrogen and related ammonia production such as thermal plasma, non-thermal plasma, and electrolysis (all typifying technology-disruption), and mini Haber-Bosch (typifying scale-disruption) are benchmarked to the central mega plant on a world-scale using conventional technology; verifying that ‘Moore’s Law’1 of growing bigger and bigger is not the only path to sustainable agriculture. Results show that ammonia can be produced at $317/ton at a regional scale using thermal plasma hydrogen generation which could be competitive to the conventional production model, if credit in terms of lead time and carbon footprint could be taken into account

Childhood encephalitis in the Greater Mekong region (the SouthEast Asia Encephalitis Project): a multicentre prospective study

International audienceBackgroundEncephalitis is a worldwide public health issue, with a substantially high burden among children in southeast Asia. We aimed to determine the causes of encephalitis in children admitted to hospitals across the Greater Mekong region by implementing a comprehensive state-of-the-art diagnostic procedure harmonised across all centres, and identifying clinical characteristics related to patients’ conditions.MethodsIn this multicentre, observational, prospective study of childhood encephalitis, four referral hospitals in Cambodia, Vietnam, Laos, and Myanmar recruited children (aged 28 days to 16 years) who presented with altered mental status lasting more than 24 h and two of the following minor criteria: fever (within the 72 h before or after presentation), one or more generalised or partial seizures (excluding febrile seizures), a new-onset focal neurological deficit, cerebrospinal fluid (CSF) white blood cell count of 5 per mL or higher, or brain imaging (CT or MRI) suggestive of lesions of encephalitis. Comprehensive diagnostic procedures were harmonised across all centres, with first-line testing was done on samples taken at inclusion and results delivered within 24 h of inclusion for main treatable causes of disease and second-line testing was done thereafter for mostly non-treatable causes. An independent expert medical panel reviewed the charts and attribution of causes of all the included children. Using multivariate analyses, we assessed risk factors associated with unfavourable outcomes (ie, severe neurological sequelae and death) at discharge using data from baseline and day 2 after inclusion. This study is registered with ClinicalTrials.gov, NCT04089436, and is now complete.FindingsBetween July 28, 2014, and Dec 31, 2017, 664 children with encephalitis were enrolled. Median age was 4·3 years (1·8–8·8), 295 (44%) children were female, and 369 (56%) were male. A confirmed or probable cause of encephalitis was identified in 425 (64%) patients: 216 (33%) of 664 cases were due to Japanese encephalitis virus, 27 (4%) were due to dengue virus, 26 (4%) were due to influenza virus, 24 (4%) were due to herpes simplex virus 1, 18 (3%) were due to Mycobacterium tuberculosis, 17 (3%) were due to Streptococcus pneumoniae, 17 (3%) were due to enterovirus A71, 74 (9%) were due to other pathogens, and six (1%) were due to autoimmune encephalitis. Diagnosis was made within 24 h of admission to hospital for 83 (13%) of 664 children. 119 (18%) children had treatable conditions and 276 (42%) had conditions that could have been preventable by vaccination. At time of discharge, 153 (23%) of 664 children had severe neurological sequelae and 83 (13%) had died. In multivariate analyses, risk factors for unfavourable outcome were diagnosis of M tuberculosis infection upon admission (odds ratio 3·23 [95% CI 1·04–10·03]), coma on day 2 (2·90 [1·78–4·72]), supplementary oxygen requirement (1·89 [1·25–2·86]), and more than 1 week duration between symptom onset and admission to hospital (3·03 [1·68–5·48]). At 1 year after inclusion, of 432 children who were discharged alive from hospital with follow-up data, 24 (5%) had died, 129 (30%) had neurological sequelae, and 279 (65%) had completely recovered.InterpretationIn southeast Asia, most causes of childhood encephalitis are either preventable or treatable, with Japanese encephalitis virus being the most common cause. We provide crucial information that could guide public health policy to improve diagnostic, vaccination, and early therapeutic guidelines on childhood encephalitis in the Greater Mekong region