Narrow Results

We re-analyze historical daily atmospheric temperature time series for investigating long-range correlation. Such a problem is attracting much attention due to the deep importance of assessing statistical dependence of atmospheric phenomena on climatic scales in the context of Climate modeling. In particular, we adopt Detrended Fluctuation Analysis (DFA), which is one of the most used techniques for detecting scale invariance in stationary signals contaminated by external non-stationary disturbances. A very standard application of this methodology seems to evidence persistence power-law exponents close to 0.65 on time scales greater than the meteorological one (>15 days). Nevertheless, more careful investigations put into evidence the local character of this exponent whose value decays progressively with scale. Our results show that the simple detection of approximately straight lines in a log–log plot cannot be considered as a signature of scale invariance and local scale features have to be explicitly investigated.

The role of vegetation cover within the processes that link land and atmosphere is of stringent interest for the correct modeling of Climate dynamics. Temporal and spatial correlation of the terrestrial coverage varies according to Climate and acts as a major forcing on it through changes in surface energy and water balance as well as in the carbon cycle. Recent studies have enhanced the actual and potential impact of this forcing on the radiative balance thus evidencing effects that are at least comparable to that due to all the anthropogenic greenhouse gases together. At now, observational studies on land cover dynamics are strongly in progress thanks to satellite data. The availability of continuous observations of the land surface can allow us to understand the correlation structure, both in time and in space, that characterizes the land cover activity. Satellites provide time series of photosynthetic activity measures that can be regarded as a succession of observations of a two-dimensional scalar field. We exploited the paradigm of fluctuating surfaces as a mechanic analogue for our problem. To capture vegetation cover characteristic time-scales, persistence properties were evaluated by analysing annual maps of NDVI-AVHRR time series and persistence probability was estimated by using the sing-time distribution methodology. The analysis performed for ecoregions of Italian and Greek territories evidenced signatures of short range persistence with characteristic time scales that depend on land cover, climate, and anthropic activities. Our results confirm that such an approach can provide a useful parameterisation for including vegetation into climate models as a dynamical component.

In this journal, Gerhard Gerlich and Ralf D. Tscheuschner claim to have falsified the existence of an atmospheric greenhouse effect.¹ Here, we show that their methods, logic, and conclusions are in error. Their most significant errors include trying to apply the Clausius statement of the Second Law of Thermodynamics to only one side of a heat transfer process rather than the entire process, and systematically ignoring most non-radiative heat flows applicable to the Earth's surface and atmosphere. They claim that radiative heat transfer from a colder atmosphere to a warmer surface is forbidden, ignoring the larger transfer in the other direction which makes the complete process allowed. Further, by ignoring heat capacity and non-radiative heat flows, they claim that radiative balance requires that the surface cool by 100 K or more at night, an obvious absurdity induced by an unphysical assumption. This comment concentrates on these two major points, while also taking note of some of Gerlich and Tscheuschner's other errors and misunderstandings.

In this work we propose a method that exploits the feedback between empirical and theoretical knowledge of a complex macroscopic system in order to build a nonlinear model. We apply the method to the monthly earth's mean surface temperature time series. The problems of contamination and stationarity are considered noting the importance of observation and modeling scales. We construct a dynamical system of ordinary differential equations where the vector field relating the relevant degrees of freedom and their variations in time is expressed in terms of a polynomial base orthonormal to the measure associated to the time series under study. The optimal size of the model and the values of its parameters are estimated with the principle of minimum description length and the Adams–Molton predictor–corrector method. This procedure is self-consistent because it does not use any external parameter or assumption. We then present a first approach to find the closest chaotic dynamical system corresponding to the earth's mean surface temperature and compare it with scale consistent theoretical or phenomenological models of the lower atmosphere. This comparison allows us to obtain an explicit functional form of the heat capacity of the earth's surface as a function of the earth's mean surface temperature.

Detrended fluctuation analysis (DFA) is used to investigate correlations between the monthly average of the maximum daily temperatures for different locations in the continental United States and the different climates these locations have. When we plot the scaling exponents obtained from the DFA versus the standard deviation of the temperature fluctuations, we observe crowding of data points belonging to the same climates. Thus, we conclude that by observing the long-time trends in the fluctuations of temperature it would be possible to distinguish between different climates.

Biotechnology unveils secrets of Chinese medicine.

Climate change a mixed blessing for wheat, say experts.

NTOU identified germ cells and somatic cells in coral bodies for the first time in the world.

Researchers tabulate disturbing changes and disappearance in biodiversity in Southern Taiwan coral reefs over last 26 years.

NCKU research team discovers new complex in treating cancer.

Chinese scientists successfully crack the genome of diploid cotton.

Taiwan's healthcare market boosted by aging population.

Researchers develop mouse model of common motor neuron disease pinpointing TDP-43 protein as likely cause.

WuXi PharmaTech receives honorable mention at 2014 ISPE Facility of the Year Awards.

UCLA launches joint venture with Chinese firm to open sophisticated lab in Shanghai.

Mucosis enters strategic partnership with Changchun BCHT Biotechnology of China.

Catalent enters biosimilar development collaboration with Zhejiang Hisun Pharma.

Bio-Techne acquires Shanghai PrimeGene Bio-Tech.

Sinovac receives notification of China government grant for EV71 vaccine project.

Sinovac enters technology transfer agreement with intravacc to develop and commercialize Sabin Inactivated Polio Vaccine (sIPV).

WuXi PharmaTech breaks ground on new cell therapy manufacturing facility.

China study to see how Pacific current affects climate.

ResearchDx receives Strategic Partner Award from WuXi AppTec.

iBio receives patent allowance in China for fusion protein compositions and technology.

AUSTRALIA – Potential of Anisina to become major new chemotherapy.

AUSTRALIA – Phylogica peptide fusion kills aggressive breast cancer cells.

INDIA – South Asia running out of groundwater.

JAPAN – Fruit fly studies shed light on adaptability.

KOREA – Hanmi Pharm begins phase 2 clinical trials on new long-acting human growth hormone drug.

MALAYSIA – Researchers discover breakthrough treatment for depression.

MALAYSIA – New diagnosis tool pushed to cope with diabetes in Asia.

SINGAPORE – Scientists discover gene that allows a peek into the future on eye disease.

SINGAPORE – Babies from moms with hepatitis have better immune systems.

THE PHILIPPINES – Debate persists on pneumonia vaccine in the Philippines.

VIETNAM – Second hookworm genome hoped to lead to vaccines.

AFRICA – Shop-based malaria kit boosts testing for disease.

BRAZIL – Prospective Chagas vaccine proves effective in trials.

EUROPE – Vaccine against placental malaria to start human tests in Germany and Benin.

SRI LANKA – Sri Lankan farmers told to adapt to changing climate.

UNITED STATES – Genetic markers play a role in who benefits from aspirin, NSAIDs to lower colon cancer risk.

UNITED STATES – Potential Ebola treatment mechanism discovered.

UNITED STATES – Researchers develop new potential drug for rare leukemia.

UNITED STATES – Researchers find new link between neurodegenerative diseases and abnormal immune responses.

Pfizer Advances Biosimilars Leadership with Investment in a New World-Class Global Biotechnology Center in China

Global Experts Convene to Discuss China's Plan for Diabetes Prevention and Rehabilitation in 2016

Synthace Awarded Technology Pioneer by World Economic Forum

GSK Institute for Infectious Diseases and Public Health to Partner with Tsinghua University to Tackle Global Public Health Challenges

Fangyuan Pharmaceutical Invests Heavily in R&D of New Drugs for Treatment of Superbacteria

Research Centers to Boost New Zealand-China Science Collaboration

Dehaier Medical Systems Ltd. Cooperates with China Sciences Group (Holding) Co., Ltd. to Enter China's Elderly Sleep Apnea Market

Yisheng Biopharma and the United States Army Medical Research Institute of Infectious Diseases Announce Positive Animal Results of Vaccine against Ebola Virus

Ally Bridge Group (ABG) Expands Personalized Medicine Portfolio, Invests in LinkDoc Technology Limited, China's Oncology Big Data Company

Infinitus Establishes International Research Center to Promote Research into the Safety of Chinese Herbal Medicines

Local Scientists Invent Novel DNA Testing Technology to Raise Test Accuracy

Butterflies Offer Climate Scientists Ecological Insights

For the months of July and August 2022, APBN considers how technology, for better or for worse, allows us to engineer a future where we can grow crops in the ocean, save endangered animals, and possibly even bring extinct animals back to life.

Here, we have Alex Teo, Managing Director and Vice President of Siemens Digital Industries Software, Southeast Asia, on how we can look towards the ocean as a viable alternative to traditional farming; Dr Sebastian Diecke and Steven Seet on the use of stem cell-associated techniques to rescue the northern white rhino from extinction; and Vanessa Quek on considering what de-extinction really means for us and other animals.

Other highlights in this issue include highlights from a webinar hosted by Professor Michael S. Strano, Joint Principal Investigator at Disruptive & Sustainable Technologies for Agricultural Precision (DiSTAP), on the recent advancements in AgriTech to help meet the increasing demand for food, and an interview with Dr Mee-ran Kim, Professor in Obstetrics and Gynaecology at Korea Seoul St. Mary's Hospital, Chair of the APAC Consensus, on the gynaecological condition, endometriosis.

In the current era, entire global economies are transitioning to sustainable development because of global warming and climate change. Due to turbulence in environmental issues such as weather shocks, climate change and basic infrastructure and industrial planning, many countries are changing their approach and taking green steps. This paper assesses sustainable finance in India and the ways in which India can mitigate climate changing risk toward zero carbon policy and supporting SDGs 2030. It talks about the physical and transitional climate risks in India like surface temperature, heat wave concourse, etc., and provides a comprehensive analysis of how the corporate sector is imparting its role in sustainable nature through corporate social responsibility (CSR). India is an emerging economy where energy is an essential component. This study analyzes about supply of energy and how India is shifting from traditional energy sources to renewal energy sources.

Thus, the objective of this study is to focus on the initiatives taken by the Indian government for sustainable finance through green bond policy at national and international platforms using the Panchamrit framework adopted by India thereby focusing on India’s sustainable policy support for SDGs 2030. Also, this research proposes numerous recommendations for future sustainable finance research in the context to India, which includes developing and diffusing innovative sustainable financing instruments, magnifying, and managing the profitability and returns of sustainable financing, making sustainable finance more sustainable, and leveraging the power of new-age technologies for sustainable finance.

Observed near surface air and soil temperature time series reveal a long-term memory, which is associated with a power-law scaling of the frequency spectra, S(ω) ~ ω^{- β} with β ~ 0.6, lying between white and flicker noise, 0 < β < 1. As this power law scaling is not consistent with the Brownian motion concept of climate variability, Fickian diffusion is added to a Newtonian cooling relaxation to provide a more suitable analog of climatic fluctuations: (i) Diffusive plus random heat fluxes parametrise the turbulent mixing by synoptic scale eddy life cycles, affect tropospheric and near surface temperatures and excite a long-term memory regime with a β ~ 0.5 scaling. (ii) Newtonian cooling describes the near surface temperatures relaxing towards a global mean deep soil temperature and stabilises the system to a white noise response at very low frequencies. The long-term memory regime emerges from the high frequency scaling (β ~ 1.5), once temperatures become correlated in space due to diffusion, so that spatially averaged fluctuations correlate for times beyond the diffusion time scale. The long-term memory regime disappears into a white noise plateau (β ~ 0), when low frequencies exceed the damping time scale of Newtonian cooling. This system may be interpreted as a diffusive system relaxing towards the deep soil restoration temperature with an almost infinitely large time scale.

Innovation is an ever-increasing focus for modern organizations; yet research studies on organizational culture have tended to neglect this aspect. This paper specifies the key factors characterising a company's innovation culture and examines the top managers' executed impact as organizational leaders from the individual perspective on implementing and fostering it in their respective organizations. Based on a conceptual framework, the empirically identified key factors of the (1) formal embedment, (2) climate, (3) incentives and reward allocation, (4) integration into decision process, (5) cross-hierarchical communication and (6) communication style are presented. Within the empirical study, 37 top managers of 21 leading companies in the industries of “fashion and accessories” and “watch and jewelry” were interviewed. The results indicate that the impact of top managers lag behind their potential to advance innovativeness through innovation culture, and the detected deficit represents scope for improvement. The paper concludes by highlighting the implications of the study and its limitations.

Malaria parasites are strongly dependent on Anopheles mosquitoes for transmission; for this reason, mosquito population dynamics are a crucial determinant of malaria risk. However, temperature and rainfall play a significant role in both aquatic and adult stages of the Anopheles. Consequently, it is important to understand the biology of malaria vector mosquitoes in the study of malaria transmission. In this study, we develop a climate-based, ordinary-differential-equation model to analyze how rainfall and temperature determine mosquito population size. In the model, we consider in detail the influence of ambient temperature on gonotrophic and sporogonic cycles over Amajuba District, Kwazulu-Natal Province, South Africa. In particular, we further use the model to simulate the spatial distribution of the mosquito biting rate over the study region. Our results reflect high seasonality of the population of An. gambiae over the region and also demonstrate the influence of climatic factors on the mosquito population dynamics.

Atmospheric and oceanic climate factors and conditions play a crucial role in modulating seasonal/annual tropical cyclone activity in the North Atlantic Ocean Basin. In the following, correlations between North Atlantic tropical cyclone activity including frequency of occurrence and pathways are explored, with special emphasis on hurricanes. The value of two-dimensional and three-dimensional data sets representing climate patterns is investigated. Finally, the diagnostic study of historical tropical cyclone and hurricane temporal and spatial variability and relationships to climate factors lead to a statistical prognostic forecast, made in April, 2010, of the 2010 tropical cyclone and hurricane season. This forecast is tested both retrospectively and presently and is shown to be quite accurate. Knowing the probability of the frequency of occurrence, i.e. the numbers of named storms to form in general and the number of hurricanes (NHs) that are likely to form, is important for many societal sectors. However, the reliable forecasts of probable pathways of predicted events, specifically the likely NH land falls along the coastlines of the United States, should have great potential value to emergency planners, the insurance industry, and the public. The forecast provided in this study makes such a prognostication. As the 2010 hurricane season has progressed, an update of the goodness of the forecast is shown to be quite accurate in numbers of named events, hurricanes, major hurricanes (MHs), and landfalls. The mathematical and statistical methodology used in this study, which could be coupled to next generation "empirical modal decomposition," suggests that this may signal a new era in the future of tropical cyclone forecasting, including the reliable prognostication of numbers of events, intensities of events, and the pathways of those events. The ability to reliably predict the probability and location of land falls of these destructive events would be very powerful indeed.

In the recent United Nations Framework Convention on Climate Change (UNFCCC) negotiations, sectoral trading was proposed to encourage early action and spur investment in low carbon technologies in developing countries. This mechanism involves including a sector from one or more nations in an international cap-and-trade system. We analyze trade in carbon permits between the Chinese electricity sector and a US economy-wide cap-and-trade program using the MIT Emissions Prediction and Policy Analysis (EPPA) model. In 2030, the US purchases permits valued at $42 billion from China, which represents 46% of its capped emissions. In China, sectoral trading increases the price of electricity and reduces aggregate electricity generation, especially from coal. However, sectoral trading induces only moderate increases in generation from nuclear and renewables. We also observe increases in emission from other sectors. In the US, the availability of cheap emissions permits reduces the cost of climate policy and increases electricity generation.

Accounting for socioeconomic and demographic variables, as well as country-specific effects, households' marginal willingness to pay for climate is revealed using European data on life-satisfaction. Individuals located in areas with lower average levels of sunshine and higher average levels of relative humidity are less satisfied as are individuals in locations subject to significant seasonal variation in monthly mean temperatures and rain days. Ranking regions by climate households appear strongly to favor the Mediterranean climate over the climate of Northern Europe.

The climate sensitivity of electricity demand in India is likely to be highly sensitive to growth in income. Thus, both intensive and extensive adjustments in cooling and heating will play an important role in determining future climate change impacts on electricity demand. This paper utilizes a national level panel dataset of 28 Indian states for the period 2005–2009. The preferred estimates indicate that climate change will increase electricity demand by 6.7% with 4% p.a. GDP growth and 8.5% with 6% p.a. GDP growth in 2030 over the reference scenario of no climate change. This reflects the fact that the estimated marginal effect of a hotter climate is greater when income is higher. Over 50% of the climate change impacts will be due to extensive adjustments as the current penetration of space conditioning equipments such as air conditioners is very low.

Climate change will push the weather experienced by people affected outside the bounds of historic norms, resulting in unprecedented weather events. But people and firms should be able to learn from their experience of unusual weather and adjust their expectations about the climate distribution accordingly. The efficiency of this learning process gives an upper bound on the rate at which adaptation can occur and is therefore important in determining the adjustment costs associated with climate change. Learning about climate change requires people to infer the state of a changing probability distribution (climate) given annual draws from that distribution (weather). If the climate is stationary, it can be inferred from the distribution of historic weather observations, but if it is changing, the inference problem is more challenging. This paper first develops different learning models, including an efficient hierarchical Bayesian model in which the observer learns whether the climate is changing and, if it is, the functional form that describes that change. I contrast this with a less efficient but simpler learning model in which observers react to past changes but are unable to anticipate future changes. I propose a general metric of learning costs based on the average, discounted squared difference between beliefs and the true climate state and use climate model output to calculate this metric for two emissions scenarios, finding substantial relative differences between learning models and scenarios but small absolute values. Geographic differences arise from spatial patterns of warming rates and natural weather variability (noise). Finally, I present results from an experimental game simulating the adaptation decision, which suggests that people are able to learn about a trending climate and respond proactively.

Future carbon dioxide (CO₂) emissions under a carbon tax depend on the time-path of the economy under baseline (business-as-usual) conditions as well as the extent to which the policy reduces emissions relative to the baseline. Considerable uncertainties surround the baseline forecasts for fuel prices, energy efficiency (energy-GDP ratios), and GDP, as evidenced by the significant ranges in the forecasts by government agencies and research institutions in the U.S. This paper assesses the significance of these uncertainties to the path of CO₂ emissions under a carbon tax. We do this by examining the emissions levels and quantities of abatement that result from the E3 general equilibrium model under a range of alternative baseline forecasts for fuel prices, energy efficiency, and GDP, where the different baselines are produced through suitable changes to key model parameters. In addition, we consider how the time-profile of the carbon tax needed to achieve specified CO₂ abatement targets is affected by such forecast-linked changes in parameters.

We find that the sensitivity of baseline emissions to alternative forecasts depends on the particular forecasted variable under consideration. Baseline CO₂ emissions are highly sensitive to alternative scenarios related to the rate of energy efficiency improvements in the nonenergy sector and the rate of general economic growth. In contrast, such emissions are much less sensitive to alternative scenarios related to the productivity of fossil fuel production. The extent of abatement from the baseline is generally fairly insensitive to changes in the scenarios for time-paths of fuel prices, energy-efficiency and GDP. We also find that short-term emissions targets can be achieved with relatively moderate carbon taxes under all of the baseline scenarios considered.