Smart city-ranking of major Australian cities to achieve a smarter future

© 2020 by the authors. A Smart City is a solution to the problems caused by increasing urbanization. Australia has demonstrated a strong determination for the development of Smart Cities. However, the country has experienced uneven growth in its urban development. The purpose of this study is to compare and identify the smartness of major Australian cities to the level of development in multi-dimensions. Eventually, the research introduces the openings to make cities smarter by identifying the focused priority areas. To ensure comprehensive coverage of all aspects of the smart city's performance, 90 indicators were selected to represent 26 factors and six components. The results of the assessment endorse the impacts of recent government actions taken in different urban areas towards building smarter cities. The research has pointed out the areas of deficiencies for underperforming major cities in Australia. Following the results, appropriate recommendations for Australian cities are provided to improve the city's smartness

Identification of Major Inefficient Water Consumption Areas Considering Water Consumption, Efficiencies, and Footprints in Australia

Due to population growth, climatic change, and growing water usage, water scarcity is expected to be a more prevalent issue at the global level. The situation in Australia is even more serious because it is the driest continent and is characterized by larger water footprints in the domestic, agriculture and industrial sectors. Because the largest consumption of freshwater resources is in the agricultural sector (59%), this research undertakes a detailed investigation of the water footprints of agricultural practices in Australia. The analysis of the four highest water footprint crops in Australia revealed that the suitability of various crops is connected to the region and the irrigation efficiencies. A desirable crop in one region may be unsuitable in another. The investigation is further extended to analyze the overall virtual water trade of Australia. Australia’s annual virtual water trade balance is adversely biased towards exporting a substantial quantity of water, amounting to 35 km3, per trade data of 2014. It is evident that there is significant potential to reduce water consumption and footprints, and increase the water usage efficiencies, in all sectors. Based on the investigations conducted, it is recommended that the water footprints at each state level be considered at the strategic level. Further detailed analyses are required to reduce the export of a substantial quantity of virtual water considering local demands, export requirements, and production capabilities of regions

Cost Benefits Analysis of Anthelmintic Treatment of Cattle and Buffaloes

A study was carried out to determine the point prevalence of various helminths of cattle and buffalo population of district Toba Tek Singh, Pakistan and economic benefits of deworming with oxyclozanide. Out of 540 fecal samples examined, 205 (37.96%) were found infected with helminths. Significantly higher (OR=2.2; P<0.05) prevalence of helminths was recorded in buffaloes (40%; 112/280) as compared to cattle (35.77%; 93/260). Oesophagostomum, Cooperia, Trichostrongylus, Strongyloide, Ostertagia, Fasciola (F.) hepatica, F. gigantica and Haemonchus contortus were the helminth species identified in the study area. Oxyclozanide medicated buffaloes (E=96.66%) and cattle (E=95.64%) showed a significant decrease in fecal egg counts on day 14 post-treatment. An average daily increase of 0.89 and 0.71 liters of milk along with 0.42 and 0.37% more fat per buffalo and cattle, respectively was observed in oxyclozanide medication. The economic value of reduced production of infected animals was estimated as US$0.47 (Pak Rupees 40) and US$ 0.41 (Pak Rupees 35) per animal per day for cattle and buffaloes, respectively. It can be concluded that single dose of oxyclozanide is effective against all bovine helminths

Design and implementation of Adaptive Neuro-Fuzzy Inference system for the control of an uncertain Ball on Beam Apparatus

Controlling an uncertain mechatronic system is challenging and crucial for its automation. In this regard, several control-strategies are developed to handle such systems. However, these control-strategies are complex to design, and require in-depth knowledge of the system and its dynamics. In this study, we are testing the performance of a rather simple control-strategy (Adaptive Neuro-Fuzzy Inference System) using an uncertain Ball and Beam System. The custom-designed apparatus utilizes image processing technique to acquire the position of the ball on the beam. Then, desired position is achieved by controlling the beam angle using Adaptive Neuro-Fuzzy and PID control. We are showing that adaptive neuro-fuzzy control can effectively handle the system uncertainties, which traditional controllers (i.e., PID) cannot handle

Prediction of the amount of sediment Deposition in Tarbela Reservoir using machine learning approaches

Tarbela is the largest earth-filled dam in Pakistan, used for both irrigation and power production. Tarbela has already lost around 41.2% of its water storage capacity through 2019, and WAPDA predicts that it will continue to lose storage capacity. If this issue is ignored for an extended period of time, which is not far away, a huge disaster will occur. Sedimentation is one of the significant elements that impact the Tarbela reservoir’s storage capacity. Therefore, it is crucial to accurately predict the sedimentation inside the Tarbela reservoir. In this paper, an Artificial Neural Network (ANN) architecture and multivariate regression technique are proposed to validate and predict the amount of sediment deposition inside the Tarbela reservoir. Four input parameters on yearly basis including rainfall (Ra), water inflow (Iw), minimum water reservoir level (Lr), and storage capacity of the reservoir (Cr) are used to evaluate the proposed machine learning models. Multivariate regression analysis is performed to undertake a parametric study for various combinations of influencing parameters. It was concluded that the proposed neural network model estimated the amount of sediment deposited inside the Tarbela reservoir more accurately as compared to the multivariate regression model because the maximum error in the case of the proposed neural network model was observed to be 4.01% whereas in the case of the multivariate regression model was observed to be 60.7%. Then, the validated neural network model was used for the prediction of the amount of sediment deposition inside the Tarbela reservoir for the next 20 years based on the time series univariate forecasting model ETS forecasted values of Ra, Iw, Lr, and Cr. It was also observed that the storage capacity of the Tarbela reservoir is the most influencing parameter in predicting the amount of sediment

Development of a Hydrodynamic-Based Flood-Risk Management Tool for Assessing Redistribution of Expected Annual Damages in a Floodplain

Despite spending ample resources and procedural development in flood management, flood losses are still increasing worldwide. The losses caused by floods and costs incurred on management are two components of expected annual damages (EAD) due to floods. This study introduces a generalized approach for risk-based design where a range of probable floods are considered before and after a flood mitigation measure is implemented. The proposed approach is customized from the ISO Guide 31000 along with additional advantages of flood risk visualization. A Geographic Information System (GIS)-based design of a flood-protection dike is performed to exhibit the risk redistribution. The Chenab River is selected for the existing dike system. Detailed hazard behaviour and societal vulnerability are modelled and visualized for a range of all probable floods before and after the implementation of flood-protection dikes. EAD maps demonstrate the redistribution of induced and residual risks. It can be concluded that GIS-based EAD maps not only facilitate cost-effective solutions but also provide an accurate estimate of residual risks after the mitigation measures are applied. EAD maps also indicate the high-risk areas to facilitate designing secondary measures

Production, optimization, and physicochemical characterization of biodiesel from seed oil of indigenously grown Jatropha curcas

With the growing demand for vegetable oils, alternative non-edible feedstocks like Jatropha curcas seed oil have gained interest for biodiesel production. The study aimed to comprehensively evaluate the physicochemical properties and biodiesel production potential of locally produced J. curcas seeds in Pakistan. Two different approaches were applied: a chemical synthesis approach involving acidic pretreatment and alkaline transesterification, and a biosynthetic approach using a lipase-producing strain of the Bacillus subtilis Q5 strain. The microbial biosynthesized biodiesel was further optimized using the Plackett–Burman design. The physicochemical properties of the J. curcas methyl esters were analyzed to assess their suitability as biodiesel fuel. Initially, the raw oil had a high free fatty acid content of 13.11%, which was significantly reduced to 1.2% using sulfuric acid pretreatment, keeping the oil to methanol molar ratio to be 1:12. Afterward, alkaline transesterification of purified acid-pretreated seed oil resulted in 96% biodiesel yield at an oil to methanol molar ratio of 1:6, agitation of 600 revolutions per minute (RPM), temperature 60°C, and time 2 h. Moreover, alkaline transesterification yielded ∼98% biodiesel at the following optimized conditions: oil to methanol molar ratio 1:6, KOH 1%, time 90 min, and temperature 60°C. Similarly, the Bacillus subtilis Q5 strain yielded ∼98% biodiesel at the following optimized conditions: oil: methanol ratio of 1:9, agitation 150 RPM, inoculum size 10%, temperature 37°C, and n-hexane 10%. The fuel properties of J. curcas seed biodiesel are closely related to standard values specified by the American Society for Testing and Materials (ASTM D6751–20a), indicating its potential as a viable biodiesel fuel source

Biodeterioration kinetics and microbial community organization on surface of cementitious materials exposed to anaerobic digestion conditions

Anaerobic digestion is a process that can produce renewable energy through the fermentation of biodegradable biomass. Industrial anaerobic digestion tanks are usually made of concrete but the production of various aggressive compounds (CO2, NH4+ and volatile fatty acids) during the microbial fermentation leads to deterioration of the concrete structure. In addition, the formation of a microbial biofilm on the cementitious material surface could generate even more intense biodeterioration. The objective of this study is to gain a better understanding of the involvement of biofilm in the biodeterioration of cementitious materials during an anaerobic digestion process. More specifically, the study focuses on the heterogeneity of microbial populations within the biofilm and the reactive medium in anaerobic digestion. Laboratory scale anaerobic bioreactors mimicking the industrial anaerobic digestion medium were constructed and CEM I cement pastes were immersed in this medium for 2, 3, 4, 5, 10 and 15 weeks. The biodeterioration of the cement pastes was evaluated by determining the deteriorated thickness. The aggressive compounds in the medium were quantified. The biofilm attached to the surface of the cement pastes was analyzed using 16 s rRNA gene sequencing. To evaluate the heterogeneity of the biofilm, the growth of biofilm layers was successively caused to stall by using two distinct biofilm removal techniques. Three microbial fractions were defined: planktonic microorganisms, and the microorganisms within the biofilm that were loosely and strongly attached. The results showed that the planktonic lifestyle was more associated with microorganisms producing methane and consuming volatile fatty acids, while the biofilm was more associated with bacteria producing acids, mainly members of the Clostridium genus. A microbial community shift due to a reversible propionic acid accumulation during the first 5 weeks was also observed. In addition, no major differences were spotted between the loosely and strongly attached biomass, indicating homogeneity in the two layers of the biofilm. These results suggest that the biofilm could increase the biodeterioration of concrete since volatile fatty acids could be produced in massive quantities near the surface of the cement samples by the acidogenic microbial population more present within the biofilm

Effects of antibiotic resistance, drug target attainment, bacterial pathogenicity and virulence, and antibiotic access and affordability on outcomes in neonatal sepsis: an international microbiology and drug evaluation prospective substudy (BARNARDS)

Background Sepsis is a major contributor to neonatal mortality, particularly in low-income and middle-income countries (LMICs). WHO advocates ampicillin–gentamicin as first-line therapy for the management of neonatal sepsis. In the BARNARDS observational cohort study of neonatal sepsis and antimicrobial resistance in LMICs, common sepsis pathogens were characterised via whole genome sequencing (WGS) and antimicrobial resistance profiles. In this substudy of BARNARDS, we aimed to assess the use and efficacy of empirical antibiotic therapies commonly used in LMICs for neonatal sepsis. Methods In BARNARDS, consenting mother–neonates aged 0–60 days dyads were enrolled on delivery or neonatal presentation with suspected sepsis at 12 BARNARDS clinical sites in Bangladesh, Ethiopia, India, Pakistan, Nigeria, Rwanda, and South Africa. Stillborn babies were excluded from the study. Blood samples were collected from neonates presenting with clinical signs of sepsis, and WGS and minimum inhibitory concentrations for antibiotic treatment were determined for bacterial isolates from culture-confirmed sepsis. Neonatal outcome data were collected following enrolment until 60 days of life. Antibiotic usage and neonatal outcome data were assessed. Survival analyses were adjusted to take into account potential clinical confounding variables related to the birth and pathogen. Additionally, resistance profiles, pharmacokinetic–pharmacodynamic probability of target attainment, and frequency of resistance (ie, resistance defined by in-vitro growth of isolates when challenged by antibiotics) were assessed. Questionnaires on health structures and antibiotic costs evaluated accessibility and affordability. Findings Between Nov 12, 2015, and Feb 1, 2018, 36 285 neonates were enrolled into the main BARNARDS study, of whom 9874 had clinically diagnosed sepsis and 5749 had available antibiotic data. The four most commonly prescribed antibiotic combinations given to 4451 neonates (77·42%) of 5749 were ampicillin–gentamicin, ceftazidime–amikacin, piperacillin–tazobactam–amikacin, and amoxicillin clavulanate–amikacin. This dataset assessed 476 prescriptions for 442 neonates treated with one of these antibiotic combinations with WGS data (all BARNARDS countries were represented in this subset except India). Multiple pathogens were isolated, totalling 457 isolates. Reported mortality was lower for neonates treated with ceftazidime–amikacin than for neonates treated with ampicillin–gentamicin (hazard ratio [adjusted for clinical variables considered potential confounders to outcomes] 0·32, 95% CI 0·14–0·72; p=0·0060). Of 390 Gram-negative isolates, 379 (97·2%) were resistant to ampicillin and 274 (70·3%) were resistant to gentamicin. Susceptibility of Gram-negative isolates to at least one antibiotic in a treatment combination was noted in 111 (28·5%) to ampicillin–gentamicin; 286 (73·3%) to amoxicillin clavulanate–amikacin; 301 (77·2%) to ceftazidime–amikacin; and 312 (80·0%) to piperacillin–tazobactam–amikacin. A probability of target attainment of 80% or more was noted in 26 neonates (33·7% [SD 0·59]) of 78 with ampicillin–gentamicin; 15 (68·0% [3·84]) of 27 with amoxicillin clavulanate–amikacin; 93 (92·7% [0·24]) of 109 with ceftazidime–amikacin; and 70 (85·3% [0·47]) of 76 with piperacillin–tazobactam–amikacin. However, antibiotic and country effects could not be distinguished. Frequency of resistance was recorded most frequently with fosfomycin (in 78 isolates [68·4%] of 114), followed by colistin (55 isolates [57·3%] of 96), and gentamicin (62 isolates [53·0%] of 117). Sites in six of the seven countries (excluding South Africa) stated that the cost of antibiotics would influence treatment of neonatal sepsis