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Humans have unique cognitive abilities among primates, including language, but their molecular, cellular, and circuit substrates are poorly understood. We used comparative single nucleus transcriptomics in adult humans, chimpanzees, gorillas, rhesus macaques, and common marmosets from the middle temporal gyrus (MTG) to understand human-specific features of cellular and molecular organization. Human, chimpanzee, and gorilla MTG showed highly similar cell type composition and laminar organization, and a large shift in proportions of deep layer intratelencephalic-projecting neurons compared to macaque and marmoset. Species differences in gene expression generally mirrored evolutionary distance and were seen in all cell types, although chimpanzees were more similar to gorillas than humans, consistent with faster divergence along the human lineage. Microglia, astrocytes, and oligodendrocytes showed accelerated gene expression changes compared to neurons or oligodendrocyte precursor cells, indicating either relaxed evolutionary constraints or positive selection in these cell types. Only a few hundred genes showed human-specific patterning in all or specific cell types, and were significantly enriched near human accelerated regions (HARs) and conserved deletions (hCONDELS) and in cell adhesion and intercellular signaling pathways. These results suggest that relatively few cellular and molecular changes uniquely define adult human cortical structure, particularly by affecting circuit connectivity and glial cell function.

Summary Human accelerated regions (HARs) are the fastest-evolving regions of the human genome, and many are hypothesized to function as regulatory elements that drive human-specific gene regulatory programs. We interrogate the in vitro enhancer activity and in vivo epigenetic landscape of more than 3,100 HARs during human neurodevelopment, demonstrating that many HARs appear to act as neurodevelopmental enhancers and that sequence divergence at HARs has largely augmented their neuronal enhancer activity. Furthermore, we demonstrate PPP1R17 to be a putative HAR-regulated gene that has undergone remarkable rewiring of its cell type and developmental expression patterns between non-primates and primates and between non-human primates and humans. Finally, we show that PPP1R17 slows neural progenitor cell cycle progression, paralleling the cell cycle length increase seen predominantly in primate and especially human neurodevelopment. Our findings establish HARs as key components in rewiring human-specific neurodevelopmental gene regulatory programs and provide an integrated resource to study enhancer activity of specific HARs.

Because of the rapid growth of small-scale solar electricity generation over the past few years, forecasting solar power output is becoming more important. However, changes in weather conditions cause solar power generation to be highly volatile. This paper analyses the challenges of solar power forecasting and then presents a similar day-based forecasting tool to do 24-h-ahead forecasting for small-scale solar power output forecasting.

Demand response plays an important role in the development of the smart grid, which can effectively manage society's energy consumption. Cooling devices, such as refrigerators and freezers, are ideal devices for demand-response programs because their energy states can be controlled without reducing the lifestyle and comfort of the residents. Conversely, managing air conditioning and space heating would affect a resident's comfort level. Direct compressor control and thermostat control methods have been proposed in the past for controlling cooling devices but they are never studied concurrently. This paper proposes a new control mechanism and compares the effectiveness of the three control mechanisms for cooling devices in demand response. In addition, this paper illustrates the need for a damping strategy to mitigate demand oscillations that occur from synchronous fleet control.

The increasing demand for electricity and the emergence of smart grids have presented new opportunities for residential energy management systems (REMS) in demand response market. Several techniques are available for optimizing the operation schedules and decisions of REMS. However, it can be challenging for REMS to capture sufficient resolution and horizon to make good short-term and long-term decisions, respectively, under limited computing resources. We propose a two-horizon algorithm, which can achieve high resolution schedules with reasonable value of energy services while limiting computation time. Simulation results are provided to confirm the validity of the proposed approach.

Summary Despite substantial interest in the species diversity of the human microbiome and its role in disease, the scale of its genetic diversity, which is fundamental to deciphering human-microbe interactions, has not been quantified. Here, we conducted a cross-study meta-analysis of metagenomes from two human body niches, the mouth and gut, covering 3,655 samples from 13 studies. We found staggering genetic heterogeneity in the dataset, identifying a total of 45,666,334 non-redundant genes (23,961,508 oral and 22,254,436 gut) at the 95% identity level. Fifty percent of all genes were “singletons,” or unique to a single metagenomic sample. Singletons were enriched for different functions (compared with non-singletons) and arose from sub-population-specific microbial strains. Overall, these results provide potential bases for the unexplained heterogeneity observed in microbiome-derived human phenotypes. One the basis of these data, we built a resource, which can be accessed at https://microbial-genes.bio .

Innovations in the residential sector are required to reduce environmental impacts, as the sector is a contributor to greenhouse gas emissions. The increasing demand for electricity and the emergence of smart grids have presented new opportunities for home energy management systems (HEMS) in demand response markets. HEMS are demand response tools that shift and curtail demand to improve the energy consumption and production profile of a dwelling on behalf of a consumer. HEMS usually create optimal consumption and productions schedules by considering multiple objectives such as energy costs, environmental concerns, load profiles and consumer comfort. The existing literature has presented several methods, such as mathematical optimization, model predictive control and heuristic control, for creating efficient operation schedules and for making good consumption and production decisions. However, the effectiveness of the methods in the existing literature can be difficult to compare due to diversity in modelling parameters, such as appliance models, timing parameters and objectives. The present chapter provides a comparative analysis of the literature on HEMS, with a focus on modelling approaches and their impact on HEMS operations and outcomes. In particular, we discuss a set of HEMS challenges such as forecast uncertainty, modelling device heterogeneity, multi-objective scheduling, computational limitations, timing considerations and modelling consumer well-being. The presented work is organized to allow a reader to understand and compare the important considerations, approaches, nomenclature and results in prominent and new literary works without delving deeply into each one.

Wind and solar power generation is growing quickly around the world, mainly to mitigate some of the negative environmental impacts of the electricity sector. However, the variability of these renewable sources of electricity poses technical and economical challenges when integrated on a large scale. Energy storage is being widely regarded as one of the potential solutions to deal with the variations of variable renewable electricity sources (VRES). This paper presents an up-to-date review of the state of technology, installations and some challenges of electrical energy storage (EES) systems. It particularly focuses on the applicability, advantages and disadvantages of various EES technologies for large-scale VRES integration. This survey paper indicates that each challenge imposed by VRES requires a different set of EES characteristics to address the issue, and that there is no single EES technology that consistently outperforms the others in various applications. This paper also discusses external factors, such as mineral availability and geographic limitations, that may affect the success of the widespread implementation of EES technologies.

The 21st century brings a number of opportunities and challenges that require the innovative capacity of the engineering profession. Many of these challenges and opportunities are shaped by globalization, which has impacts on how the engineering profession is educated and ultimately practices. Engineers Without Borders Canada (EWB) has recently completed the development of two projects that enable engineering students to develop skills for engineering practice in a globalized world These projects are based on ‘complementary learning outcomes’-areas that complement CEAB outcomes while providing a 21st century perspective.The first project is a final year technical elective focused on enabling electrical engineering students to combine the skills they have learned in other courses with complementary 21st included the application of systems thinking to engineering problems, sustainability and its relation to electrical engineering, and globalization and its impacts on engineering practice. The outcome of this course is to expand their ability to practice in a globalized world. EWB’s second program is a certificate that is aimed at recognizing students who develop 21st century skills throughout their education through curricular and co-curricular engagement. For curricular measurement, the certificate offers a framework to determine which courses enable the development of 21st century skills. The co-curricular component applies best practices for leadership evaluation from a number of institutions worldwide to effectively measure a student’s leadership experiences. The certificate both serves to encourage students to explore unique educational and leadership opportunities while also to recognize students who have invested their time in developing 21st century skills.

Wind and solar power generation is growing quickly around the world, mainly to mitigate some of the negative environmental impacts of the electricity sector. However, the variability of these renewable sources of electricity poses technical and economical challenges when integrated on a large scale. Energy storage is being widely regarded as one of the potential solutions to deal with the variations of variable renewable electricity sources (VRES). This chapter presents an review of the state of technology, installations and some challenges of electrical energy storage (EES) systems. It particularly focuses on the applicability, advantages and disadvantages of various EES technologies for large-scale VRES integration. This chapter indicates that each challenge imposed by VRES requires a dierent set of EES characteristics to address the issue, and that there is no single EES technology that consistently outperforms the others in various applications. This chapter also discusses external factors, such as mineral availability and geographic limitations, that may aect the success of the widespread implementation of EES technologies.

Solar Photovoltaic power production has grown significantly over the past few years. California ISO is the first system operator in North America to make the data for aggre- gated system-level solar power production across its territory available on a regular basis. In this paper, we demonstrate the application of three well-established forecasting models to 24- hour-ahead prediction of solar power at the system level. The models investigated in this paper include Auto Regressive Inte- grated Moving Average (ARIMA), Radial Basis Function Neural Network (RBFNN), and Least Squares Support Vector Machine (LS-SVM). Numerical results and discussions are provided based on California ISO solar power data.

Residential energy management is an important research topic due to the opportunities offered by the coming of Smart Grid, and increasing concern of greenhouse gas emissions. This works demonstrates the formulation and use of optimization to improve energy consumption and production schedules for a single dwelling, and how to integrate it using a moving window error correcting algorithm. The presented numerical results demonstrate that the proposed algorithm outperforms a baseline model without the mowing window algorithm, and is more robust against forecast errors and fluctuations beyond the scope of the scheduling period.