Explore the vast range of titles available.

Solar water splitting is one of the key steps in artificial photosynthesis for future carbon-neutral, storable and sustainable source of energy. Here we show that one of the major obstacles for achieving efficient and stable overall water splitting over the emerging nanostructured photocatalyst is directly related to the uncontrolled surface charge properties. By tuning the Fermi level on the nonpolar surfaces of gallium nitride nanowire arrays, we demonstrate that the quantum efficiency can be enhanced by more than two orders of magnitude. The internal quantum efficiency and activity on p -type gallium nitride nanowires can reach ~51% and ~4.0 mol hydrogen h −1  g −1 , respectively. The nanowires remain virtually unchanged after over 50,000 μmol gas (hydrogen and oxygen) is produced, which is more than 10,000 times the amount of photocatalyst itself (~4.6 μmol). The essential role of Fermi-level tuning in balancing redox reactions and in enhancing the efficiency and stability is also elucidated. One of the obstacles in implementing solar water splitting is the requirement for materials with high internal quantum efficiency. Here, the authors investigate the effects of magnesium doping on the Fermi levels of gallium nitride nanowires, and tune this value to maximize redox efficiency.

Solar water splitting for hydrogen generation can be a potential source of renewable energy for the future. Here we show that efficient and stable stoichiometric dissociation of water into hydrogen and oxygen can be achieved under visible light by eradicating the potential barrier on nonpolar surfaces of indium gallium nitride nanowires through controlled p -type dopant incorporation. An apparent quantum efficiency of ∼12.3% is achieved for overall neutral (pH∼7.0) water splitting under visible light illumination (400–475 nm). Moreover, using a double-band p- type gallium nitride/indium gallium nitride nanowire heterostructure, we show a solar-to-hydrogen conversion efficiency of ∼1.8% under concentrated sunlight. The dominant effect of near-surface band structure in transforming the photocatalytic performance is elucidated. The stability and efficiency of this recyclable, wafer-level nanoscale metal-nitride photocatalyst in neutral water demonstrates their potential use for large-scale solar-fuel conversion. Solar water splitting for hydrogen generation may be a future source of renewable energy. Here, the authors demonstrate that controlled p -type doping of metal-nitride nanowires can eradicate surface potential barriers and promotes stable stoichiometric dissociation of water under visible light.

For a non-negative continuous random variable , Chaudhry and Zubair (2002, p. 19) introduced a probability distribution with a completely monotonic probability density function based on the generalized gamma function, and called it the Macdonald probability function. In this paper, we establish various basic distributional properties of Chaudhry and Zubair’s Macdonald probability distribution. Since the percentage points of a given distribution are important for any statistical applications, we have also computed the percentage points for different values of the parameter involved. Based on these properties, we establish some new characterization results of Chaudhry and Zubair’s Macdonald probability distribution by the left and right truncated moments, order statistics and record values. Characterizations of certain other continuous probability distributions with completely monotonic probability density functions such as Mckay, Pareto and exponential distributions are also discussed by the proposed characterization techniques.   

The British Society of Gastroenterology (BSG) and the Bangladesh Gastroenterology Society (BGS) have collaborated on an endoscopy training programme, which has grown up over the past decade from a small scheme borne out of the ideas of consultant gastroenterologists in Swansea, South Wales (United Kingdom) to improve gastroenterology services in Bangladesh to become a formalised training programme with broad reach. In this article, we document the socioeconomic and historical problems that beset Bangladesh, the current training needs of doctors and how the BSG-BGS collaboration has made inroads into changing outcomes both for gastroenterologists in Bangladesh, but also for the populations they serve.

Blue carbon ecosystems, such as mangroves, tidal marshes, and seagrasses, are important for climate mitigation. As carbon sinks, they often exhibit higher per hectare carbon storage capacity and sequestration rates than terrestrial systems. These ecosystems provide additional benefits, including enhancing water quality, sustaining biodiversity, and maintaining coastal resilience to climate change impacts. The widespread loss of blue carbon ecosystems due to anthropogenic activities can contribute to increasing carbon emissions globally. Monetizing blue carbon through carbon credits offers an avenue to generate revenue and incentivize conservation and restoration efforts. However, limited data on project costs and carbon benefits make prioritization of blue carbon projects challenging. To address these challenges, we have developed, in collaboration with blue carbon experts, the Blue Carbon Cost Tool. This is a user-friendly interface enabling comparison of three core market project components – 1) carbon credit estimation, 2) project cost estimation, and 3) a qualitative, non-economic feasibility assessment – to assess and compare potential for blue carbon projects. Tool simulations with data available from nine countries demonstrate (a) how factors such as country, ecosystem type and project scale drive variability, (b) the need for local or project-specific data to enhance accuracy and reduce uncertainty, particularly in tidal marsh and seagrass systems, and (c) that higher price tolerance or upfront capital is needed to bridge implementation and maintenance cost gaps. The Blue Carbon Cost Tool can aid project developers and investors to better understand market opportunity and the resources needed to develop high quality blue carbon market projects.

The ecological footprint has become a popular indicator of environmental degradation, with academics increasingly using it to measure the extent of ecosystem degradation. This study examines the impact of Bangladesh's economic complexity and natural resources on its ecological footprint from 1995 to 2018. The results show that a more complex economy has a positive long-term effect on the ecological footprint, while a simplified economy has less impact. The study also shows that both positive and negative changes in natural resources contribute to environmental quality rises in Bangladesh, which negatively influences the ecological footprint. A 1% increase in natural resources reduces the ecological footprint by 0.14%, while a 1% decrease in resources reduces it by 0.59%. The findings suggest a two-way causal relationship between the size of an economy's ecological footprint and its complexity. Policymakers should focus on technological advancements and reducing operational costs by adopting innovative research and development frameworks and investing in natural resource policies that promote an adaptable ecological footprint.

Background Firefighting is a challenging and stressful job, and firefighters face many adverse conditions while performing their duties. The study aimed to assess the prevalence of insomnia among firefighting staff working in the Dhaka division of Bangladesh and identify the factors contributing to the severity of insomnia. Methods A cross-sectional study was conducted among a total of 406 employees of the Department of Fire Service & Civil Defense (FSCD) working in randomly selected nine districts of the Dhaka division using a simple random sampling (SRS) technique. Data were collected from the firefighting staff through face-to-face interviews. The severity of insomnia was assessed during the past 2 weeks using the Bangla version of the Insomnia Severity Index (ISI). Multivariable ordinal logistic regression (OLR) was used to identify the factors associated with insomnia among the fire service staff. All statistical analyses were performed using Stata version 17. Results Among the 406 participants, nearly one-fourth (22.9%) suffered from moderate to severe insomnia. The results of the multivariable regression analyses showed that the firefighting staff aged 30 to 45 years (adjusted odds ratio, AOR: 2.0; 95% CI: 1.075 to 3.663) and above 45 years (AOR: 4.3, 95% CI: 1.386 to 13.039)had higher odds of insomnia than those aged below 30 years. The participants who conducted over 1,000 rescue operations had higher odds of experiencing insomnia compared to their colleagues who conducted fewer than 500 rescue operations (AOR: 2.6, 95% CI: 1.451 to 4.529). The firefighting staff with severe (AOR: 2.5, 95% CI: 1.325 to 4.551) and potentially dangerous (AOR: 3.9, 95% CI: 1.928 to 8.012) levels of workplace stress had two 2times higher odds of suffering from insomnia compared to those with minimal/mild levels of workplace stress. Furthermore, those who reported moderate (AOR: 2.0, 95% CI: 1.314 to 3.083) and severe (AOR: 2.6, 95% CI: 1.558 to 4.506) levels of PTSD were more likely to suffer from insomnia than their counterparts who reported minimal/mild levels of PTSD. Conclusions The present study revealed that nearly one-fourth of firefighting staff working in the Dhaka division experienced moderate to severe insomnia. Several factors, including age, the number of rescue operations, workplace stress, PTSD, and chronic diseases. The findings of this study highlight the need for sleep health promotion programs in firefighting staff.

The maximum likelihood estimator (MLE) suffers from the instability problem in the presence of multicollinearity for a Poisson regression model (PRM). In this study, we propose a new estimator with some biasing parameters to estimate the regression coefficients for the PRM when there is multicollinearity problem. Some simulation experiments are conducted to compare the estimators' performance by using the mean squared error (MSE) criterion. For illustration purposes, aircraft damage data has been analyzed. The simulation results and the real-life application evidenced that the proposed estimator performs better than the rest of the estimators.

A 73-year-old man with gallstone disease was admitted with right upper quadrant abdominal pain. He was treated for cholecystitis with intravenous antibiotics. Two days later, he reported of new onset left iliac fossa pain, with tenderness and guarding. An abdominal X-ray demonstrated small bowel obstruction, a CT scan demonstrated an impacted gallstone within the proximal ileum. He was treated for a gallstone ileum and underwent an uncomplicated laparotomy, small bowel enterotomy and removal of a faceted gallstone. Three months later, the patient re-presented with generalised abdominal pain, guarding and rebound tenderness. Small bowel obstruction was again demonstrated with an impacted gallstone within the distal ileum seen on CT scan. A second laparotomy revealed two further faceted gallstones, which were removed through an enterotomy. The densely adherent gallbladder to the duodenum precluded a surgical repair of the cholecystoduodenal fistula. He made an uneventful recovery and was subsequently discharged home.

Instead of applying the commonly used parametric Almon or Beta lag distribution of MIDAS, Breitung and Roling (J Forecast 34:588–603, 2015) suggested a nonparametric smoothed least-squares shrinkage estimator (henceforth SLS1) for estimating mixed-frequency models. This SLS1 approach ensures a flexible smooth trending lag distribution. However, even if the biasing parameter in SLS1 solves the overparameterization problem, the cost is a decreased goodness-of-fit. Therefore, we suggest a modification of this shrinkage regression into a two-parameter smoothed least-squares estimator (SLS2). This estimator solves the overparameterization problem, and it has superior properties since it ensures that the orthogonality assumption between residuals and the predicted dependent variable holds, which leads to an increased goodness-of-fit. Our theoretical comparisons, supported by simulations, demonstrate that the increase in goodness-of-fit of the proposed two-parameter estimator also leads to a decrease in the mean square error of SLS2, compared to that of SLS1. Empirical results, where the inflation rate is forecasted based on the oil returns, demonstrate that our proposed SLS2 estimator for mixed-frequency models provides better estimates in terms of decreased MSE and improved R2, which in turn leads to better forecasts.