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Climate change is a global problem, which affects the various geographical regions at different levels. It is also associated with a wide range of human health problems, which pose a burden to health systems, especially in regions such as Africa. Indeed, across the African continent public health systems are under severe pressure, partly due to their fragile socioeconomic conditions. This paper reports on a cross-sectional study in six African countries (Ghana, Nigeria, South Africa, Namibia, Ethiopia, and Kenya) aimed at assessing their vulnerabilities to climate change, focusing on its impacts on human health. The study evaluated the levels of information, knowledge, and perceptions of public health professionals. It also examined the health systems’ preparedness to cope with these health hazards, the available resources, and those needed to build resilience to the country’s vulnerable population, as perceived by health professionals. The results revealed that 63.1% of the total respondents reported that climate change had been extensively experienced in the past years, while 32% claimed that the sampled countries had experienced them to some extent. Nigerian respondents recorded the highest levels (67.7%), followed by Kenya with 66.6%. South Africa had the lowest level of impact as perceived by the respondents (50.0%) when compared with the other sampled countries. All respondents from Ghana and Namibia reported that health problems caused by climate change are common in the two countries. As perceived by the health professionals, the inadequate resources reiterate the need for infrastructural resources, medical equipment, emergency response resources, and technical support. The study’s recommendations include the need to improve current policies at all levels (i.e., national, regional, and local) on climate change and public health and to strengthen health professionals’ skills. Improving the basic knowledge of health institutions to better respond to a changing climate is also recommended. The study provides valuable insights which may be helpful to other nations in Sub-Saharan Africa.

Chromium (Cr) exposure during gestation causes malformations in animal experiments. In this multicenter case-control study, we initially involved 130 orofacial clefts (OFCs) and 260 controls to assess the association between Cr concentration and risk for OFCs. Then, umbilical cord serum (49 vs. 119) and cord tissue (84 vs. 142) were used to validate the association between Cr and OFCs. We found that maternal serum Cr concentrations in OFC cases were significantly higher than those in controls. Compared with the lowest tertile of maternal serum Cr concentration, the highest tertile of Cr increased the risk for OFCs [OR = 2.14 (1.14-4.05)]. In the validation cohort of umbilical cord serum and tissue, higher concentrations of Cr were associated with increased risks for OFCs in a dose-dependent manner (all Ps for trends <0.05). Cr concentrations in maternal serum and cord serum showed a positive correlation. The Cr concentration in cord serum was inversely correlated with egg and milk consumption frequencies, and the Cr concentration in cord tissue was positively associated with indoor coal burning. In conclusion, prenatal Cr exposure is a risk factor for OFCs, and indoor coal burning during pregnancy may be one of the sources of Cr exposure.

Datacenter power demand has been continuously growing and is the key driver of its cost. An accurate mapping of compute resources (CPU, RAM, etc.) and hardware types (servers, accelerators, etc.) to power consumption has emerged as a critical requirement for major Web and cloud service providers. With the global growth in datacenter capacity and associated power consumption, such models are essential for important decisions around datacenter design and operation. In this paper, we discuss two classes of statistical power models designed and validated to be accurate, simple, interpretable and applicable to all hardware configurations and workloads across hyperscale datacenters of Google fleet. To the best of our knowledge, this is the largest scale power modeling study of this kind, in both the scope of diverse datacenter planning and real-time management use cases, as well as the variety of hardware configurations and workload types used for modeling and validation. We demonstrate that the proposed statistical modeling techniques, while simple and scalable, predict power with less than 5% Mean Absolute Percent Error (MAPE) for more than 95% diverse Power Distribution Units (more than 2000) using only 4 features. This performance matches the reported accuracy of the previous started-of-the-art methods, while using significantly less features and covering a wider range of use cases.

Bioplastics are alternatives of conventional petroleum-based plastics. Bioplastics are polymers processed from renewable sources and are biodegradable. This study aims at conducting an environmental impact assessment of the bioprocessing of agricultural wastes into bioplastics compared to petro-plastics using an LCA approach. Bioplastics were produced from potato peels in laboratory. In a biochemical reaction under heating, starch was extracted from peels and glycerin, vinegar and water were added with a range of different ratios, which resulted in producing different samples of bio-based plastics. Nevertheless, the environmental impact of the bioplastics production process was evaluated and compared to petro-plastics. A life cycle analysis of bioplastics produced in laboratory and petro-plastics was conducted. The results are presented in the form of global warming potential, and other environmental impacts including acidification potential, eutrophication potential, freshwater ecotoxicity potential, human toxicity potential, and ozone layer depletion of producing bioplastics are compared to petro-plastics. The results show that the greenhouse gases (GHG) emissions, through the different experiments to produce bioplastics, range between 0.354 and 0.623 kg CO2 eq. per kg bioplastic compared to 2.37 kg CO2 eq. per kg polypropylene as a petro-plastic. The results also showed that there are no significant potential effects for the bioplastics produced from potato peels on different environmental impacts in comparison with poly-β-hydroxybutyric acid and polypropylene. Thus, the bioplastics produced from agricultural wastes can be manufactured in industrial scale to reduce the dependence on petroleum-based plastics. This in turn will mitigate GHG emissions and reduce the negative environmental impacts on climate change.

In the search for a nontoxic replacement of the commonly employed CdS buffer layer for Cu(In,Ga)(S,Se) $_\mathrm{2}$ based solar cells, chemically deposited Zn(O,S) thin films are a most promising choice. In this paper, we address the usually slow deposition speed of Zn(O,S) in a newly developed ammonia-free chemical bath process, resulting in a deposition of 30 nm in 3 min with good homogeneity on 30 cm × 30 cm sized substrates. Solar cells with buffer layers prepared from this process match the efficiency of CdS reference cells. In a second step, we address the light-soaking post-treatment, still needed for maximum efficiencies. By addition of aluminum to the deposition process, the initial efficiencies can be increased slightly. With the addition of boron, the light-soaking post-treatment is rendered unnecessary, while maintaining high efficiencies above 15%, surpassing reference cells with CdS buffer.

We begin by analyzing, using basic physics considerations, under what conditions it becomes energetically favorable to use aggressive regenerative braking to reach a lower speed over “coasting” where one relies solely on air drag to slow down. We then proceed to reformulate the question as an optimization problem to find the velocity profile that maximizes battery charge. Making a simplifying assumption on battery-charging efficiency, we express the recovered energy as an integral quantity, and we solve the associated Euler–Lagrange equation to find the optimal braking curves that maximize this quantity in the framework of variational calculus. Using Lagrange multipliers, we also explore the effect of adding a fixed-displacement constraint.