Towards understanding the dynamics of the Regional Property markets of Australia

Housing one third of the population and generating two thirds of the export economy, regional towns and rural areas are a significant part of the Australian socio-economic structure. Although there is substantial policy focus on regional development and improving the well-being of its residents at federal and state levels, much of the detailed, large scale, longitudinal studies on the built environment, real-estate and property markets are often focused on the capital city regions. To tackle this issue, this study uses the detailed, comprehensive, large scale, long-term longitudinal data on property sales made available by the Valuer General offices of New South Wales and South Australia to understand the dynamics of the property markets in regional Australia and the factors affecting them

The in- vitro inhibitory effect of Barije (Ferula gummosa Boiss) essential oil loaded in Zein electrospun nanofibres on α-glucosidase and α-amylase level

The aim of this study was to produce nanofibres from zein incorporated with varying concentrations of Barijeh essential oil (BEO) (1-4% w/w) with anti-diabetic properties by electrospinning technique. BEO loaded nanofibres were characterised for its chemical composition, morphology, chemical structure, thermal behaviour and α-amylase and α-glucosidase inhibitory. In addition, the kinetic release of BEO in simulated gastric and intestinal media are investigated. Among the various concentrations of zein studied (i.e. 15-40% w/v), 35% w/v zein solution yielded ribbon-like structures webs substantially free of defects with mean fibre diameters in the range of 425 nm-1µm.SEM and FT-IR results indicated that BEO was successfully entrapped in zein electrospun matrix, and nanofibres showed high encapsulation efficiency close to 95%. The encapsulated BEO also retained its antioxidant capacity in nanofibres, although it had been exposed to the high voltage during the process. BEO-loaded zein nanofibres showed α-glucosidase and α-amylase inhibition activity with an IC50 value ranging of 0.78±0.01 to 1.25±0.03 and 1.09±0.02 to 1.64±0.01 mg/ml, respectively. The models fitting results showed that the First order, Hixon-Crowell and Rigter-Peppas models were the best models for describing the release of BEO in simulated stomach media, intestinal conditions and the total time of release respectively. The feasibility of combining BEO within fibres provides a promising route for manufacture of novel delivery vehicle for controlling the diabetes

Sustainable supply chain management towards disruption and organizational ambidexterity:A data driven analysis

Balancing sustainability and disruption of supply chains requires organizational ambidexterity. Sustainable supply chains prioritize efficiency and economies of scale and may not have sufficient redundancy to withstand disruptive events. There is a developing body of literature that attempts to reconcile these two aspects. This study gives a data-driven literature review of sustainable supply chain management trends toward ambidexterity and disruption. The critical review reveals temporal trends and geographic distribution of literature. A hybrid of data-driven analysis approach based on content and bibliometric analyses, fuzzy Delphi method, entropy weight method, and fuzzy decision-making trial and evaluation laboratory is used on 273 keywords and 22 indicators obtained based on the experts’ evaluation. The most important indicators are identified as supply chain agility, supply chain coordination, supply chain finance, supply chain flexibility, supply chain resilience, and sustainability. The regions show different tendencies compared with others. Asia and Oceania, Latin America and the Caribbean, and Africa are the regions needs improvement, while Europe and North America show distinct apprehensions on supply chain network design. The main contribution of this review is the identification of the knowledge frontier, which then leads to a discussion of prospects for future studies and practical industry implementation

Burden of disease scenarios for 204 countries and territories, 2022–2050: a forecasting analysis for the Global Burden of Disease Study 2021

Background: Future trends in disease burden and drivers of health are of great interest to policy makers and the public at large. This information can be used for policy and long-term health investment, planning, and prioritisation. We have expanded and improved upon previous forecasts produced as part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) and provide a reference forecast (the most likely future), and alternative scenarios assessing disease burden trajectories if selected sets of risk factors were eliminated from current levels by 2050. Methods: Using forecasts of major drivers of health such as the Socio-demographic Index (SDI; a composite measure of lag-distributed income per capita, mean years of education, and total fertility under 25 years of age) and the full set of risk factor exposures captured by GBD, we provide cause-specific forecasts of mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) by age and sex from 2022 to 2050 for 204 countries and territories, 21 GBD regions, seven super-regions, and the world. All analyses were done at the cause-specific level so that only risk factors deemed causal by the GBD comparative risk assessment influenced future trajectories of mortality for each disease. Cause-specific mortality was modelled using mixed-effects models with SDI and time as the main covariates, and the combined impact of causal risk factors as an offset in the model. At the all-cause mortality level, we captured unexplained variation by modelling residuals with an autoregressive integrated moving average model with drift attenuation. These all-cause forecasts constrained the cause-specific forecasts at successively deeper levels of the GBD cause hierarchy using cascading mortality models, thus ensuring a robust estimate of cause-specific mortality. For non-fatal measures (eg, low back pain), incidence and prevalence were forecasted from mixed-effects models with SDI as the main covariate, and YLDs were computed from the resulting prevalence forecasts and average disability weights from GBD. Alternative future scenarios were constructed by replacing appropriate reference trajectories for risk factors with hypothetical trajectories of gradual elimination of risk factor exposure from current levels to 2050. The scenarios were constructed from various sets of risk factors: environmental risks (Safer Environment scenario), risks associated with communicable, maternal, neonatal, and nutritional diseases (CMNNs; Improved Childhood Nutrition and Vaccination scenario), risks associated with major non-communicable diseases (NCDs; Improved Behavioural and Metabolic Risks scenario), and the combined effects of these three scenarios. Using the Shared Socioeconomic Pathways climate scenarios SSP2-4.5 as reference and SSP1-1.9 as an optimistic alternative in the Safer Environment scenario, we accounted for climate change impact on health by using the most recent Intergovernmental Panel on Climate Change temperature forecasts and published trajectories of ambient air pollution for the same two scenarios. Life expectancy and healthy life expectancy were computed using standard methods. The forecasting framework includes computing the age-sex-specific future population for each location and separately for each scenario. 95% uncertainty intervals (UIs) for each individual future estimate were derived from the 2·5th and 97·5th percentiles of distributions generated from propagating 500 draws through the multistage computational pipeline. Findings: In the reference scenario forecast, global and super-regional life expectancy increased from 2022 to 2050, but improvement was at a slower pace than in the three decades preceding the COVID-19 pandemic (beginning in 2020). Gains in future life expectancy were forecasted to be greatest in super-regions with comparatively low life expectancies (such as sub-Saharan Africa) compared with super-regions with higher life expectancies (such as the high-income super-region), leading to a trend towards convergence in life expectancy across locations between now and 2050. At the super-region level, forecasted healthy life expectancy patterns were similar to those of life expectancies. Forecasts for the reference scenario found that health will improve in the coming decades, with all-cause age-standardised DALY rates decreasing in every GBD super-region. The total DALY burden measured in counts, however, will increase in every super-region, largely a function of population ageing and growth. We also forecasted that both DALY counts and age-standardised DALY rates will continue to shift from CMNNs to NCDs, with the most pronounced shifts occurring in sub-Saharan Africa (60·1% [95% UI 56·8–63·1] of DALYs were from CMNNs in 2022 compared with 35·8% [31·0–45·0] in 2050) and south Asia (31·7% [29·2–34·1] to 15·5% [13·7–17·5]). This shift is reflected in the leading global causes of DALYs, with the top four causes in 2050 being ischaemic heart disease, stroke, diabetes, and chronic obstructive pulmonary disease, compared with 2022, with ischaemic heart disease, neonatal disorders, stroke, and lower respiratory infections at the top. The global proportion of DALYs due to YLDs likewise increased from 33·8% (27·4–40·3) to 41·1% (33·9–48·1) from 2022 to 2050, demonstrating an important shift in overall disease burden towards morbidity and away from premature death. The largest shift of this kind was forecasted for sub-Saharan Africa, from 20·1% (15·6–25·3) of DALYs due to YLDs in 2022 to 35·6% (26·5–43·0) in 2050. In the assessment of alternative future scenarios, the combined effects of the scenarios (Safer Environment, Improved Childhood Nutrition and Vaccination, and Improved Behavioural and Metabolic Risks scenarios) demonstrated an important decrease in the global burden of DALYs in 2050 of 15·4% (13·5–17·5) compared with the reference scenario, with decreases across super-regions ranging from 10·4% (9·7–11·3) in the high-income super-region to 23·9% (20·7–27·3) in north Africa and the Middle East. The Safer Environment scenario had its largest decrease in sub-Saharan Africa (5·2% [3·5–6·8]), the Improved Behavioural and Metabolic Risks scenario in north Africa and the Middle East (23·2% [20·2–26·5]), and the Improved Nutrition and Vaccination scenario in sub-Saharan Africa (2·0% [–0·6 to 3·6]). Interpretation: Globally, life expectancy and age-standardised disease burden were forecasted to improve between 2022 and 2050, with the majority of the burden continuing to shift from CMNNs to NCDs. That said, continued progress on reducing the CMNN disease burden will be dependent on maintaining investment in and policy emphasis on CMNN disease prevention and treatment. Mostly due to growth and ageing of populations, the number of deaths and DALYs due to all causes combined will generally increase. By constructing alternative future scenarios wherein certain risk exposures are eliminated by 2050, we have shown that opportunities exist to substantially improve health outcomes in the future through concerted efforts to prevent exposure to well established risk factors and to expand access to key health interventions

Optimization of multipurpose reservoir operation using evolutionary algorithms / Mohammed Heydari

Today, the water resources are among the great human treasures. Optimal reservoir operation, due to the numerous needs, shortcomings and restrictions on the use of these resources is necessary. The main purpose of this study was presenting a model for an optimal operation of multi-purpose dams of water resources systems. In this study, a hybrid evolutionary algorithm model (HPSOGA) and linear programming (LP) has been developed for optimizing the operation of reservoirs with the objectives of maximizing hydroelectric power generation, meeting the water demand for agricultural purposes and predicting the cost and estimating amount of agriculture products. An improved particle swarm algorithm (HPSOGA) is used to solve complex problems of water resources optimization. One of the main problems of this method is premature convergence and to improve this problem, the compound of the particle swarm algorithm and genetic algorithm were evaluated. The basis of this compound is in such a way that the advantages of the Particle Swarm Optimization (PSO) algorithm and Genetic Algorithm (GA) have been applied simultaneously. Two efficient operators of Genetic Algorithm, that is, mutation and crossover are used in the obtained algorithm, the mutation causes an increase in the diversity of the population and the intersection of information between the particles of the population. To evaluate the hybrid algorithm, optimization of hydro-power energy of Karun dams were considered. Cases studied in this research were reservoirs of Karun I, Karun III and Karun IV. The three dams are located in a consecutive series of Karun River in Iran. In order to optimize, 41 years of the common statistical period were used. Then, the optimal output of the problem in the form of curves that represent the desired amount of discharge from the reservoir at a specified time interval were prepared and compared with the Lingo model. The regression analysis and artificial neural networks (ANN) were used to check the quality of the results. By using the Weibull distribution, the base year which is consistent with the percent probability of agricultural needs was determined for downstream of the Karun III dam. To achieve the best cultivation pattern, initially the arable land was categorized into 6 classes and only 2100 hectares of agricultural irrigable land that had the best agricultural conditions were studied. The amount of water allocated to the mentioned land was about 6.240 MCM. Seventeen important agricultural products of the region were used for the modelling. The optimization problem was modelled with the aim of maximizing the ultimate value of agriculture in terms of the number of acres of each crop. The described model was resolved by linear programming and evolutionary algorithms in Microsoft Excel (Solver). The results showed full compliance of these two methods. To estimate and predict the cost of the different stages of farming, and the cost of fertilizers needed for agricultural products, the obtained results of cultivation pattern per acre multiplied to cost breakdown values in tables taken from the ministry of agriculture. Comparing the results of the combination of the PSO and GA algorithms makes clear that the obtained algorithm increased flexibility and improving the ability of the PSO algorithm to create the population with high-speed convergence and it is very applicable to solve the problems of operation optimization of water resources. To compare the accuracy of the results, three criteria were used for RMSE, NRMSD and CV. In all the obtained results, i.e. optimum release, optimum storage and the produced energy, for all dams, the accuracy of HPSOGA was better than GA and GA accuracy was remarkably better than PSO. However, exceptionally, the accuracy of the GA algorithm was approximately 34% better than the HPSOGA algorithm for only the optimal storage capacity at Karun IV Dam. The overall results show that the optimal values have higher importance in the preparation of the rule curve, especially in periods of drought

Investigation of the Influence of Excess Pumping on Groundwater Salinity in the Gaza Coastal Aquifer (Palestine) Using Three Predicted Future Scenarios

The Gaza coastal aquifer (GCA) is the only source of water for about two million citizens living in Gaza Strip, Palestine. The groundwater quality in GCA has deteriorated rapidly due to many factors. The most crucial factor is the excess pumping due to the high population density. The objective of this article was to evaluate the influence of excess pumping on GCA's salinity using 10-year predicted future scenarios based on artificial neural networks (ANNs). The ANN-based model was generated to predict the GCA's salinity for three future scenarios that were designed based on different pumping rates. The results showed that when the pumping rate remains at the present conditions, salinity will increase rapidly in most GCA areas, and the availability of fresh water will decrease in disquieting rates by 2030. Only about 8% of the overall GCA's area is expected to stay within 500 mg/L of the chloride concentration. Results also indicate that salinity would be improved slightly if the pumping rate is kept at 50% of the current pumping rates while the improvement rate is much faster if the pumping is stopped completely, which is an unfeasible scenario. The results are considered as an urgent call for developing an integrated water management strategy aiming at improving GCA quality by providing other drinking water resources to secure the increasing water demand

Association of cord blood asprosin concentration with atherogenic lipid profile and anthropometric indices

BACKGROUND: Elevated lipids in umbilical cord blood affect fetal programming, leading to a higher risk of developing cardiovascular disease in later life. However, the causes of changes in the lipid profile of umbilical cord blood are not clear yet. This study aimed for the first time to determine the association of asprosin concentration with TAG, TC, HDL-C, LDL-C concentrations and TAG/HDL-C, TC/HDL-C, LDL-C/HDL-C and non-HDL-C/HDL-C ratio in umbilical cord blood as well as newborn anthropometric indices. This cross-sectional study was based on 450 mother- newborn pairs of a birth cohort study in Sabzevar, Iran. Multiple linear regression was used to estimate the association of lipid concentration and lipid ratios as well as birth weight (BW), birth length (BL), head circumference (HC) and chest circumference (CC) with asprosin in cord blood samples controlled for the relevant covariates. RESULT: In fully adjusted models, each 1 ng/mL increase in asprosin was associated with 0.19 (95% CI 0.06, 0.31, P < 0.01), 0.19 (95% CI 0.10, 0.29, P < 0.01), 0.17 (95% CI 0.09, 0.25, P < 0.01), 0.17 (95% CI 0.09, 0.25, P < 0.01), 0.01 (95% CI 0.00, 0.013, P < 0.01), 0.01 (95% CI 0.01, 0.01, P < 0.01), 0.01 (95% CI 0.01, 0.01, P < 0.01) and 0.01 (95% CI 0.01, 0.01, P < 0.01) increase in TAG, TC, LDL-C, TAG/HDL-C, TC/HDL-C, LDL-C/HDL-C and non-HDL-C/HDL-C ratio respectively. Moreover, higher asprosin levels was positively associated with newborn BW, BL, HC and CC; however, these associations were not statistically significant. CONCLUSION: Overall, our findings support the positive association between cord asprosin concentration and the development of atherogenic lipid profile in newborns. Further studies are needed to confirm the findings of this study in other populations