Current progress on removal of recalcitrance coloured particles from anaerobically treated effluent using coagulation–flocculation

The palm oil industry is the most important agro industries in Malaysia and most of the mills adopt anaerobic digestion as their primary treatment for palm oil mill effluent (POME). Due to the public concern, decolourisation of anaerobically treated POME (AnPOME) is becoming a great concern. Presence of recalcitrant-coloured particles hinders biological processes and coagulation–flocculation may able to remove these coloured particles. Several types of inorganic and polymers-based coagulant/flocculant aids for coagulation–flocculation of AnPOME have been reviewed. Researchers are currently interested in using natural coagulant and flocculant aids. Modification of the properties of natural coagulant and flocculant aids enhanced coagulation–flocculation performance. Modelling and optimization of the coagulation–flocculation process have also been reviewed. Chemical sludge has the potential for plant growth that can be evaluated through pot trials and phytotoxicity test

Life Cycle Management of Infrastructures

By definition, life cycle management (LCM) is a framework “of concepts, techniques, and procedures to address environmental, economic, technological, and social aspects of products and organizations in order to achieve continuous ‘sustainable’ improvement from a life cycle perspective” (Hunkeler et al.\ua02001). Thus, LCM theoretically integrates all sustainability dimensions, and strives to provide a holistic perspective. It also assists in the efficient and effective use of constrained natural and financial resources to reduce negative impacts on society (Sonnemann and Leeuw\ua02006; Adibi et al.\ua02015). The LCM of infrastructures is the adaptation of product life cycle management (PLM) as techniques to the design, construction, and management of infrastructures. Infrastructure life cycle management requires accurate and extensive information that might be generated through different kinds of intelligent and connected information workflows, such as building information modeling (BIM)

Evaluation of appendicitis risk prediction models in adults with suspected appendicitis

Background Appendicitis is the most common general surgical emergency worldwide, but its diagnosis remains challenging. The aim of this study was to determine whether existing risk prediction models can reliably identify patients presenting to hospital in the UK with acute right iliac fossa (RIF) pain who are at low risk of appendicitis. Methods A systematic search was completed to identify all existing appendicitis risk prediction models. Models were validated using UK data from an international prospective cohort study that captured consecutive patients aged 16–45 years presenting to hospital with acute RIF in March to June 2017. The main outcome was best achievable model specificity (proportion of patients who did not have appendicitis correctly classified as low risk) whilst maintaining a failure rate below 5 per cent (proportion of patients identified as low risk who actually had appendicitis). Results Some 5345 patients across 154 UK hospitals were identified, of which two‐thirds (3613 of 5345, 67·6 per cent) were women. Women were more than twice as likely to undergo surgery with removal of a histologically normal appendix (272 of 964, 28·2 per cent) than men (120 of 993, 12·1 per cent) (relative risk 2·33, 95 per cent c.i. 1·92 to 2·84; P < 0·001). Of 15 validated risk prediction models, the Adult Appendicitis Score performed best (cut‐off score 8 or less, specificity 63·1 per cent, failure rate 3·7 per cent). The Appendicitis Inflammatory Response Score performed best for men (cut‐off score 2 or less, specificity 24·7 per cent, failure rate 2·4 per cent). Conclusion Women in the UK had a disproportionate risk of admission without surgical intervention and had high rates of normal appendicectomy. Risk prediction models to support shared decision‐making by identifying adults in the UK at low risk of appendicitis were identified