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This Special Issue of the IEEE Journal of Solid-State Circuits highlights some of the best papers presented at the 32nd Symposium on VLSI Circuits, held on June 18–22, 2018, at the Hilton Hawaiian Village, Honolulu, HI, USA. Industry and university engineers from all over the world reported innovative new techniques and state-of-the-art results. Papers presented covered a broad range of topics important to VLSI circuit and system designers, including topics that are also important to integrated circuit technology developers. [ABSTRACT FROM AUTHOR]

The IEEE International Conference on Plasma Science (ICOPS) is held annually, sponsored by the Plasma Science and Applications Committee (PSAC) of the IEEE Nuclear and Plasma Science Society (NPSS). The 45th IEEE NPSS ICOPS was held on June 24–28, 2018, in Denver, CO, USA. The conference's technical program included a wide range of topics, which ranged from basic and fundamental research to traditional and emerging technology applications. Over 530 accepted abstracts, representing scientists from over 30 countries. The program also included a minicourse on “New Directions in Plasma Diagnostics for High Energy Density and Burning Plasmas.” This year, the ICOPS sessions were enhanced by several exceptional plenary talks and two IEEE Birdsall award talks. John Cary from the University of Colorado (the 2016 Birdsall Award Winner) gave a talk entitled “The Evolution of Computational Physics,” and Alex Friedman from the Lawrence Livermore National Laboratory (the 2017 Birdsall Award Winner) spoke on “Computer Simulations of Plasmas and Beams: A View From Multiple Angles.” Other plenary talks covered areas that included: particle accelerator technology; challenges of high-energy density physics and inertial fusion; the physics, chemistry, and interactions of plasma with liquids; radiation belt remediation; and electron beams, plasmas, and EM fields and waves. [ABSTRACT FROM AUTHOR]

This issue of the IEEE Transactions on Nuclear Science (TNS) is devoted to the 21st IEEE-NPSS Real Time Conference (RT2018) on Computing Applications in Nuclear and Plasma Sciences held in Williamsburg, VA, USA, in June 2018. A total of 98 papers presented at the conference have been submitted for possible publication in TNS. The conference accepted 54 papers as oral and 149 as poster presentations in 13 tracks. [ABSTRACT FROM AUTHOR]

In this paper, we consider the development of algorithms for the automatic detection of buried threats using ground penetrating radar (GPR) measurements. GPR is one of the most studied and successful modalities for automatic buried threat detection (BTD), and a large variety of BTD algorithms have been proposed for it. Despite this, large-scale comparisons of GPR-based BTD algorithms are rare in the literature. In this paper, we report the results of a multi-institutional effort to develop advanced BTD algorithms for a real-world GPR BTD system. The effort involved five institutions with substantial experience with the development of GPR-based BTD algorithms. In this paper, we report the technical details of the advanced algorithms submitted by each institution, representing their latest technical advances, and many state-of-the-art GPR-based BTD algorithms. We also report the results of evaluating the algorithms from each institution on the large experimental data set used for development. The experimental data set comprised 120 000 m2 of GPR data using surface area, from 13 different lanes across two U.S. test sites. The data were collected using a vehicle-mounted GPR system, the variants of which have supplied data for numerous publications. Using these results, we identify the most successful and common processing strategies among the submitted algorithms, and make recommendations for GPR-based BTD algorithm design. [ABSTRACT FROM AUTHOR]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM AUTHOR]

For the past decade, the discussion of occupational electrical safety in the U.S. has largely focused on compliance with NFPA70E, Standard for Electrical Safety in the Workplace. Without taking away from the importance of the requirements in the standard, this paper describes a more comprehensive solution, based on proven concepts derived from systems safety engineering. This paper provides an overview of systems safety engineering and includes a 20 $+$ year case history of a global Fortune 500 company's effort to change the electrical safety culture in its operations to demonstrate results of applying systems safety techniques to electrical safety. [ABSTRACT FROM PUBLISHER]

Researchers and educators in humanities such as computational linguists, digital humanists, and those doing historical reconstructions are increasingly heavy users of computational and/or data resources. Many know about activities, working groups, and initiatives around the findable, accessible, interoperable, reusable (FAIR) principles and are a driving force for improving the sharing of data and software. However, it seems humanities researchers are less aware of the science gateways community and the end-to-end solutions that science gateways could provide, therefore lacking a driving force for adoption of this technology. This small-scale exploratory study will clarify some of the challenges and needs faced by computational researchers in the humanities that may explain their relatively low participation in the science gateways community. For this paper, we present the results of 19 interviews with seven women, 11 men, and one nonbinary individual from seven states and DC in the United States with a range of professional backgrounds and roles. [ABSTRACT FROM AUTHOR]

This paper deals with the challenges of railgun electromagnetic compatibility. After some general remarks on integration issues, the current regulations, norms, and standards of a high-power railgun are discussed. Different standards for ordnance or staff are considered. Also, preliminary results from the French-German Research Institute of Saint-Louis are presented. Since these laboratory data are not yet representative for future weapon systems, scaling calculations were carried out concerning energy levels, electrical sources, and cabling architecture. The obtained signal profiles are compared to the values defined by U.S., EU, or NATO standards. [ABSTRACT FROM AUTHOR]

This Special Section of the IEEE Journal of Solid-State Circuits covers expanded papers from the 2018 Radio Frequency Integrated Circuits Symposium (RFIC Symposium), which was held on June 10–12, 2018, in Philadelphia, PA, USA. [ABSTRACT FROM AUTHOR]

Natural disasters affect structural health of buildings, thus directly impacting public safety. Continuous structural monitoring can be achieved by deploying an Internet of things network of distributed sensors in buildings to capture floor movement. These sensors can be used to compute the displacements of each floor, which can then be employed to assess building damage after a seismic event. The peak relative floor displacement is computed, which is directly related to damage level according to the United States federal agencies standards. With this information, the building inventory can be classified into immediate occupancy, life safety, or collapse prevention categories. In this paper, we propose a zero velocity update technique to minimize displacement estimation error. Theoretical derivation and experimental validation are presented. In addition, we investigate modeling sensor error and interstory drift ratio distribution. Moreover, we discuss the impact of sensor error on the achieved building classification accuracy. [ABSTRACT FROM AUTHOR]

During the 1980s, many software developers and enthusiasts reacted to software's unclear status within the U.S. copyright law by pursuing technical, rather than legislative fixes. This paper applies forensic methods to recover records of conflict between commercial software developers and the enthusiasts who sought to "crack" commercial programs' copy protection. [ABSTRACT FROM AUTHOR]

The standard for electrical safety in the workplace, National Fire Protection Association 70E, and relevant Occupational Safety and Health Act electrical safety standards evolved in the U.S. over the past 40 years to address the hazards of 60-Hz power that are faced primarily by electricians, linemen, and others performing facility and utility work. This leaves a substantial gap in the management of other types of electrical hazards including battery banks, dc power systems, capacitor banks, and solar power systems. Although many of these systems are fed by 50/60-Hz energy, we find substantial use of electrical energy, and the use of capacitors, inductors, batteries, solar, and radiofrequency (RF) power. The electrical hazards of these forms of electricity and their systems are different than for 50/60 Hz ac power. At the IEEE Electrical Safety Workshop in 2009, we presented a comprehensive approach to classifying the electrical shock hazards of all types of electricity, including various waveforms and various types of sources of electrical energy. That paper introduced a new comprehensive electrical shock hazard classification system that used a combination of voltage, shock current available, fault current available, power, energy, and waveform to classify all forms of electrical hazards with a focus on the shock hazard. That paper was based on research conducted over the past 100 years and on decades of experience. This paper continues the effort in understanding and managing all forms of injury from all forms of electricity with the introduction of a comprehensive approach to classifying all forms of injury from the electrical arc, including thermal, blast pressure, hearing, radiation, and shrapnel injury. The general term “arc” is divided into the arc, arc flash, and arc blast as a first subdivision of type of source of injury. Then, the parameters of voltage, short-circuit current, energy, waveform, gap distance, gap geometry, enclosure geometry, and time are used to choose various approaches to analysis. Recent efforts to understand, model, and estimate injury for these types of systems are reviewed. Most of the focus to understand and predict injury for dc, capacitor, solar, and RF arc hazards has been only in the past 10 years. A comprehensive approach to analyzing all forms of injury from all forms of electrical arcs is presented. [ABSTRACT FROM AUTHOR]

This Transactions’ Mini-Special Issue includes a selection of papers presented last year at the 2018 IEEE Radio Frequency Integrated Circuits (RFIC) Symposium held in the Pennsylvania Convention Center and Loews Philadelphia Hotel, Philadelphia, PA, USA, during June 10–12, 2018. The 2018 RFIC Symposium focused on the major themes of millimeter-wave 5G and autonomous vehicles in the plenary sessions. The first plenary talk was delivered by Zachary J. Lemnios, the Vice President of IBM Research, and discussed silicon-based mm-wave systems for 5G and beyond. The second plenary talk was presented by Lars Reger, CTO of NXP Semiconductors’ Automotive Business Unit, and focused on RF systems in the automotive industry, including the technical aspects related to future self-driving cars. [ABSTRACT FROM AUTHOR]

The papers in this special section focus on neuro-informatics which is considered one of the most attractive research fields for scientists, engineers, practitioners and physicians due to its profound importance in healthcare and in our lives. Human curiosity, the BRAIN project in USA with a very large funding budget, and the exponential evolution of computational informatics and nanotech during the last two decades have inspired and motivated many researchers around the globe to contribute with their research to the brain. The papers are associated with the IEEE BIBE-2017 Conference. [ABSTRACT FROM AUTHOR]

This paper presents a unified physics-based insulated-gate bipolar transistor (IGBT) compact model for circuit simulation that predicts the performance of both Si and SiC, n- and p-channel devices. The model can predict the detailed switching waveforms of these technologies based on its charge-based formulation. Further, this compact IGBT model is presented alongside a unique datasheet-driven parameter extraction process. The parameter extraction process enables users to quickly extract model parameters from data typically published without the need of taking physical measurements. The model has been validated with both Si and SiC devices for static and dynamic characteristics. The SiC IGBTs used for validation are a 12.5-kV n-channel device and a 13-kV p-channel device, while the Si IGBT chosen was IXDH30N120 from IXYS Corp. (Milpitas, CA, USA). This is the only IGBT model that predicts the performance of both n- and p-channel, Si and SiC devices, providing more freedom for the development of complex power electronics circuit designs. The convergence of the model has been verified by implementing a complex circuit consisting of both a dc–dc converter and a dc–ac inverter. The results presented here show that the unified model can be used to describe the behavior of a wide range of Si and SiC IGBT circuits. This paper is accompanied by a Verilog-A source code and a power point file demonstrating the model parameter extraction sequence. [ABSTRACT FROM AUTHOR]

Epilepsy is one of the most common neurological disorders affecting a significant portion of the world’s population and approximately 2.5 million people in the United States. Important biomedical research effort is focused on the development of energy efficient devices for the real-time detection of seizures. In this paper, we propose an Internet of Medical Things (IoMT)-based automated seizure detection system which will detect a seizure from electroencephalography (EEG) signals using a voltage level detector (VLD) and a signal rejection algorithm (SRA). The proposed system analyzes neural signals continuously and extracts the hyper-synchronous pulses for the detection of seizure onset. Within a time frame, if the number of pulses exceeds a predefined threshold value, a seizure is declared. The SRA reduces false detections, which in turn enhances the accuracy of the seizure detector. The design was validated using system-level simulations and consumer electronics proof of concept. The proposed seizure detector reports a sensitivity of 96.9% and specificity of 97.5%. The use of minimal circuitry can lead to reduction of power consumption compared to many contemporary approaches. The proposed approach can be generalized to other sensor modalities and the use of wearable or implantable solutions, or a combination of the two. [ABSTRACT FROM AUTHOR]

Epilepsy, which is characterized by recurrent spontaneous seizures, has a considerably negative impact on both the quality and the expectancy of life of the patient. Approximately 3.4 million individuals in the USA and up to 1% of the world population is afflicted by epilepsy. This necessitates the real-time detection of seizures which can be done by the use of an Internet of Things (IoT) framework for smart healthcare. In this paper, we propose an electroencephalogram (EEG)-based seizure detection system in the IoT framework which uses the discrete wavelet transform (DWT), Hjorth parameters (HPs), statistical features, and a machine learning classifier. Seizure detection is done in two stages. In the first stage, EEG signals are decomposed by the DWT into sub-bands and features (activity, signal complexity, and standard deviation) were extracted from each of these sub-bands. In the second stage, a deep neural network (DNN) classifier is used to classify the EEG data. A prototype of the proposed neuro-detect was implemented using the hardware-in-the-loop approach. The results demonstrate a significant difference in HP values between interictal and ictal EEG with ictal EEG being less complex than interictal EEG. In this approach, we report an accuracy of 100% for a classification of normal versus ictal EEG and 98.6% for normal and interictal versus ictal EEG. [ABSTRACT FROM AUTHOR]

The accurate estimation of the terrestrial latent heat flux (LE) from satellite observations at high spatial and temporal scales plays an important role in the assessment of the water and heat exchange between the earth’s surface and the atmosphere. Although a variety of data fusion methods have been proposed to merge different LE products for more reliable estimates, most of them have ignored the spatiotemporal consistency of LE products across different resolutions. In this paper, we apply the multiresolution tree (MRT) method to improve the accuracy and reduce the inconsistency between the Moderate Resolution Imaging Spectroradiometer (MODIS) LE (MOD16) product and the Landsat-based LE product at different resolutions. Eddy covariance (EC) ground measurements at five sites, MODIS and Landsat images from January 2005 to December 2005 in the north central USA, are used to evaluate the performance of the MRT method. The results show that the MRT method can improve the accuracy of the original LE products (MOD16 and Landsat), and it has the potential to significantly reduce the uncertainty and inconsistency of these products. The bias decreased by 38.3% on average, and the root-mean-square error (RMSE) decreased by approximately 49.2% after the MRT was applied at each scale. Further studies are still required to make the MRT method more universal on a variety of land cover types for long-time periods. [ABSTRACT FROM AUTHOR]

Higher fuel economy standards and more stringent limitations on greenhouse gas emissions for ground vehicles have been made due to public concerns about energy crisis and environmental issues. By organizing a group of automated vehicles into a platoon at a short intervehicular distance, the overall fuel consumption and greenhouse gas emissions of vehicle platoon can be decreased due to reduced aerodynamic drag, which is called the platooning technology. In addition, the eco-driving technology can help further increase the fuel efficiency of vehicle platoon by optimizing speed trajectories of vehicles. However, little research has been done into the combination of the eco-driving and platooning technologies. Based on distributed model predictive control (DMPC), this paper proposes an ecological cooperative look-ahead control strategy for a platoon of automated vehicles travelling on a freeway with varying slopes, where both the eco-driving and platooning technologies are used. To maximize the fuel efficiency of vehicle platoon, an ecological cooperative look-ahead control problem (Eco-CLC) is first formulated based on DMPC, where rotational inertia coefficient related to reduction ratio of gear box, aerodynamic drag related to spacing and model constraints are considered. Since the Eco-CLC problem is a nonconvex and nonlinear optimization problem with hard state constraints, it is very difficult to quickly obtain its optimal solution. To enhance real-time control performance, after the hard state constraints of the Eco-CLC problem are transformed into parts of the multi-objective function using the band-stop function, the improved ecological cooperative look-ahead control (iEco-CLC) based on DMPC is given. A particle swarm optimization algorithm with multiple dynamic populations is further presented to quickly solve the iEco-CLC problem online. Simulation results demonstrate, compared with benchmarks, the proposed strategy can save more than 21% of fuel for vehicle platoon. [ABSTRACT FROM AUTHOR]

Pancreatic ductal adenocarcinoma (PDAC) is the third most common cause of cancer death in the United States. Predicting tumors like PDACs (including both classification and segmentation) from medical images by deep learning is becoming a growing trend, but usually a large number of annotated data are required for training, which is very labor-intensive and time-consuming. In this paper, we consider a partially supervised setting, where cheap image-level annotations are provided for all the training data, and the costly per-voxel annotations are only available for a subset of them. We propose an Inductive Attention Guidance Network (IAG-Net) to jointly learn a global image-level classifier for normal/PDAC classification and a local voxel-level classifier for semi-supervised PDAC segmentation. We instantiate both the global and the local classifiers by multiple instance learning (MIL), where the attention guidance, indicating roughly where the PDAC regions are, is the key to bridging them: For global MIL based normal/PDAC classification, attention serves as a weight for each instance (voxel) during MIL pooling, which eliminates the distraction from the background; For local MIL based semi-supervised PDAC segmentation, the attention guidance is inductive, which not only provides bag-level pseudo-labels to training data without per-voxel annotations for MIL training, but also acts as a proxy of an instance-level classifier. Experimental results show that our IAG-Net boosts PDAC segmentation accuracy by more than 5% compared with the state-of-the-arts. [ABSTRACT FROM AUTHOR]

As the number of combined-cycle units increases, efficient modeling approaches for these units play important roles for independent system operators (ISOs). Based on various combinations of combustion turbines (CTs) and steam turbines (STs), the combined-cycle unit could work at different configurations (modes) with different efficiencies. In this paper, we propose an edge-based formulation for the combined-cycle units in the unit commitment problem to improve the accuracy and effectiveness of current modeling approaches. Our formulation can 1) clearly describe the transition processes among different configurations so as to satisfy the ISO financial offer submission requirements and (2) capture physical constraints of each turbine, including the exact min-up/down time and time dependent startup cost, in the combined-cycle units so as to increase the operational flexibility while ensuring system feasibility. This model fits well with the current U.S. deregulated electricity market. The final numerical studies show that our approaches perform better than the current configuration-based modeling approach. [ABSTRACT FROM AUTHOR]

Secondary or low-voltage (LV) networks are applied in many large cities throughout the U.S., and in some other countries. Various grounding schemes have been employed for the primary systems supplying these secondary networks. In this paper, characteristics of the different grounding schemes for the substation and primary feeders that supply the LV networks are reviewed. The impacts of primary system grounding on the ground fault currents, the unfaulted line-to-ground voltages, as well as the voltage sags in the LV network have been considered for the single line-to-ground fault on the primary. The differences between systems with network transformers having the delta- or grounded-wye-connected primary windings are discussed. The relationships between grounding characteristics and protection settings, including actual examples, have been documented. [ABSTRACT FROM AUTHOR]

In this paper, we analyze how carbon emissions affect the selection of transportation modes and social welfare by using a two-stage Stackelberg gaming model. Based on this model, the government’s optimal carbon-emission tax scheme and the company’s optimal transportation mode and production decisions are explored. We find that: 1) whether or not the transport carbon-emission tax can increase social welfare depends on the relationships among the social cost of carbon (SCC), the transportation mode shifting threshold (TMST), and the biggest carbon-emission tax that a company can afford (BCRA); 2) a greater SCC implies a higher probability of improving social welfare via imposing transportation carbon-emission tax; and 3) a smaller TMST or BCRA yields a higher probability of improving social welfare when a carbon-emission tax is imposed. Further study shows that imposing a carbon-emission tax on the product with a higher production cost, a bigger product volume, or a bigger product density can increase the probability of improving social welfare. [ABSTRACT FROM PUBLISHER]

Interconnection wide electric transmission system has been envisioned to facilitate the growth of renewable energy, enhance reliability, improve system operating efficiency, and reduce emissions. In this paper, we propose an explicit planning approach for transmission design at the national level. It has been applied to the U.S. system to design transmission overlays for multiple future generation scenarios. Associated simulation results suggest that a national transmission overlay provides economic, environmental, and system performance benefits. [ABSTRACT FROM PUBLISHER]

FERC Order 755 requires each RTO/ISO to use market-based mechanisms to select and compensate frequency regulation resources based on a two-part payment methodology, i.e., a capacity payment to keep the capacity in reserve and a performance payment to reflect the amount of work each resource performs in real-time in response to the system operator's dispatch signal. This paper discusses how MISO enhanced its market rules to implement a market-based regulation performance payment. The market enhancement includes four key areas: quantifying the movement of regulation, modifications to the market clearing processes, performance accuracy measurement, and performance-based two-part regulation compensation. [ABSTRACT FROM PUBLISHER]

The American automobile industry is facing substantial environmental and energy challenges driving the pursuit of alternative powertrain technologies—“The intervening mechanism by which power is transmitted from an engine to a propeller or axle that it drives,” which nominally includes engine, transmission, drive shafts, differentials, and the final drive. Emerging alternative fuel vehicles are showing their potential to address these challenges. However, diffusion of new technologies has many complications. This paper uses system dynamics modeling to investigate the impacts of individual and organizational parameters. This model embeds two traditional modeling methods for technology adoption, the Bass diffusion model and multinomial logit regression method. Five types of powertrain systems and three major stakeholders are considered. Mathematical relationships among different variables are elaborated. The qualitative impacts of government feebate, manufacturer willingness, and consumer purchasing preferences on economic and environmental issues are addressed using scenario analysis. [ABSTRACT FROM AUTHOR]

Today's power utilities, around the world, own multiple substations that are connected together to form a complex energy network. The functions within and between these substations are being automated according to globally accepted power utility automation standard IEC 61850. This automation results in efficient operation and enhanced protection of power network with the aid of the communication system. Implementing the protection schemes modeled using communication configurations of standardized information exchange will lead to digital power grid. Designing an IEC-61850-based protection scheme to take care of the faults outside the substations is a challenge, as the typical local-area-network-based generic-object-oriented substation events and sampled-value messages need to be transmitted over a wide-area network. This paper presents communication configuration for line current differential protection schemes applied between two automated substations. It presents the simulation results of communication configuration network between two substations. Its performance is evaluated using a network simulator tool. This study intends to guide the development of a robust protection scheme with IEC-61850-based communication configuration. [ABSTRACT FROM PUBLISHER]

In many radio-frequency identification (RFID) applications, one of the essential systematic functionalities is to quickly detect missing-tag event in case of misplacement or other incorrect operations. In this paper, we focus on probabilistically detecting the missing tags for the anonymous multicategory RFID systems without revealing the tag privacy. The main objective is to minimize the detection time while satisfying the required detection reliability of each category. First, we propose to use a multihash technique to sequentially detect the missing tags category-by-category, called segmented sequential detection approach, in which the frame segmentation is adopted to reduce the detection time. Then, we propose an enhanced segmented sequential detection approach to further improve the detection efficiency by deactivating the identified existing tags. We conduct extensive simulations to illustrate the effectiveness of our proposed two missing tag detection approaches. [ABSTRACT FROM AUTHOR]

In the context of the hierarchy of risk controls, warnings, administrative controls, and personal protective equipment are sometimes referred to as lower order controls. In practice, the effectiveness of these controls can be overestimated due to a misunderstanding of the human factors that affect error likelihood in their implementation. This paper explores one human factor: the limitation of attention on residual risk inherent in lower order controls. The limitation of attention has implications for identifying hazards, assessing risk, taking appropriate actions based on warnings, executing safe work practices, and proper selection and use of personal protective equipment. Raising awareness and improved understanding of these topics can enable more effective risk assessment, quantification of residual risk, and more effective selection and combination of risk controls to achieve risk as low as reasonably practicable. [ABSTRACT FROM PUBLISHER]

Energy efficiency policies require the implementation of strategies to reduce energy use during the operation of integrated urban water systems. In Italy, the overall amount of electrical energy consumption for water systems is close to 2.3% of the total national consumption, thus efforts to minimize costs are crucial. This paper analyzes a case study of urban water systems in a specific area in the Piedmont Region, Italy; the case study includes the data collection methodology and the analysis of the energy consumption of seven large water providers. The discussion includes the quantification of the energy consumption reduction, achievable with the replacement of existing transformers with high-efficiency transformers, power factor correction, and the use of variable-frequency drives for the centrifugal pumps. [ABSTRACT FROM PUBLISHER]

Load participation is vital for the smart grid development. As an effective tool to improve reliability, stability, and financial efficiency of the power grids, demand response (DR) has brought significant financial and technical benefits to power systems. As one of the price-based DR programs with less control costs, the time-of-use (TOU) program has been applied as the default rate structure by many utility companies. To avoid financial risks and make the most profit from the market, utility companies treat TOU as an effective strategy to change customers’ electricity consumption patterns. As reported in many literature, existing TOU programs are not as effective as expected in many developed countries due to the complexity of human behaviors and disparities of residential customers. To examine whether to obtain different outcomes of TOU on the residential customers in developing countries, actual utility usage data from residential consumers in Shanghai, China, are analyzed in this paper. The result shows current TOU in Shanghai, China, has similar trends as TOU in developed countries. With high penetration level of renewable energy, an effective TOU program is urgently needed in the utility industry. In recent years, a creative TOU pricing structure has been introduced at the Electrical Reliability Council of Texas deregulated market, and it shows that the introduced “zero pricing” strategy has a significant impact on customers’ consumption patterns. The purpose of this research is to examine the key reasons that underlie ineffectiveness/effectiveness of TOU programs at residential level. [ABSTRACT FROM PUBLISHER]

Urban landscapes present a variety of socio-topological environments that are associated to diverse human activities. As the latter affect the way individuals connect with each other, a bound exists between the urban tissue and the mobile communication demand. In this paper, we investigate the heterogeneous patterns emerging in the mobile communication activity recorded within metropolitan regions. To that end, we introduce an original technique to identify classes of mobile traffic signatures that are distinctive of different urban fabrics. Our proposed technique outperforms previous approaches when confronted to ground-truth information, and allows characterizing the mobile demand in greater detail than that attained in the literature to date. We apply our technique to extensive real-world data collected by major mobile operators in 10 cities. Results unveil the diversity of baseline communication activities across countries, but also provide evidence of the existence of a number of mobile traffic signatures that are common to all studied areas and specific to particular land uses. [ABSTRACT FROM AUTHOR]

This paper shows that extraction and analysis of various acoustic features from speech using mobile devices can allow the detection of patterns that could be indicative of neurological trauma. This may pave the way for new types of biomarkers and diagnostic tools. Toward this end, we created a mobile application designed to diagnose mild traumatic brain injuries (mTBI) such as concussions. Using this application, data were collected from youth athletes from 47 high schools and colleges in the Midwestern United States. In this paper, we focus on the design of a methodology to collect speech data, the extraction of various temporal and frequency metrics from that data, and the statistical analysis of these metrics to find patterns that are indicative of a concussion. Our results suggest a strong correlation between certain temporal and frequency features and the likelihood of a concussion. [ABSTRACT FROM AUTHOR]

Probabilistic flood mapping offers flood managers, decision makers, insurance agencies, and humanitarian relief organizations a useful characterization of uncertainty in flood mapping delineation. Probabilistic flood maps are also of high interest for data assimilation into numerical models. The direct assimilation of probabilistic flood maps into hydrodynamic models would be beneficial because it would eliminate the intermediate step of having to extract water levels first. This paper introduces a probabilistic flood mapping procedure based on synthetic aperture radar (SAR) data. Given a SAR image of backscatter values, we construct a total histogram of backscatter values and decompose this histogram into probability distribution functions of backscatter values associated with flooded (open water) and non-flooded pixels, respectively. These distributions are then used to estimate, for each pixel, its probability of being flooded. The new approach improves on binary SAR-based flood mapping procedures, which do not inform on the uncertainty in the pixel state. The proposed approach is tested using four SAR images from two floodplains, i.e., the Severn River (U.K.) and the Red River (U.S.). In all four test cases, reliability diagrams, with error values ranging from 0.04 to 0.23, indicate a good agreement between the SAR-derived probabilistic flood map and an independently available validation map, which is obtained from aerial photography. [ABSTRACT FROM PUBLISHER]

Alzheimer’s disease (AD) is a serious neurodegenerative condition that affects millions of individuals across the world. As the average age of individuals in the United States and the world increases, the prevalence of AD will continue to grow. To address this public health problem, the research community has developed computational approaches to sift through various aspects of clinical data and uncover their insights, among which one of the most challenging problem is to determine the biological mechanisms that cause AD to develop. To study this problem, in this paper we present a novel Joint Multi-Modal Longitudinal Regression and Classification method and show how it can be used to identify the cognitive status of the participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort and the underlying biological mechanisms. By intelligently combining clinical data of various modalities (i.e., genetic information and brain scans) using a variety of regularizations that can identify AD-relevant biomarkers, we perform the regression and classification tasks simultaneously. Because the proposed objective is a non-smooth optimization problem that is difficult to solve in general, we derive an efficient iterative algorithm and rigorously prove its convergence. To validate our new method in predicting the cognitive scores of patients and their clinical diagnosis, we conduct comprehensive experiments on the ADNI cohort. Our promising results demonstrate the benefits and flexibility of the proposed method. We anticipate that our new method is of interest to clinical communities beyond AD research and have open-sourced the code of our method online. 1 The code package for the proposed Joint Multi-Modal Longitudinal Regression and Classification model have been made publicly available online at https://github.com/minds-mines/jmmlrc. [ABSTRACT FROM AUTHOR]

In this paper, we propose bag of adversarial features (BAFs) for identifying mild traumatic brain injury (MTBI) patients from their diffusion magnetic resonance images (MRIs) (obtained within one month of injury) by incorporating unsupervised feature learning techniques. MTBI is a growing public health problem with an estimated incidence of over 1.7 million people annually in USA. Diagnosis is based on clinical history and symptoms, and accurate, concrete measures of injury are lacking. Unlike most of the previous works, which use hand-crafted features extracted from different parts of brain for MTBI classification, we employ feature learning algorithms to learn more discriminative representation for this task. A major challenge in this field thus far is the relatively small number of subjects available for training. This makes it difficult to use an end-to-end convolutional neural network to directly classify a subject from MRIs. To overcome this challenge, we first apply an adversarial auto-encoder (with convolutional structure) to learn patch-level features, from overlapping image patches extracted from different brain regions. We then aggregate these features through a bag-of-words approach. We perform an extensive experimental study on a dataset of 227 subjects (including 109 MTBI patients, and 118 age and sex-matched healthy controls) and compare the bag-of-deep-features with several previous approaches. Our experimental results show that the BAF significantly outperforms earlier works relying on the mean values of MR metrics in selected brain regions. [ABSTRACT FROM AUTHOR]

Organizational leaders routinely note the critical importance of human capital in enabling successful innovation outcomes. However, strategic human resource management (SHRM) practices, especially in large firms, often are not aligned with practices that facilitate breakthrough innovation (BI), where uncertainty, ambiguity, and risk of failure are high. In this paper, drawing from the motivation–opportunity–ability (MOA) framework, we identify and delineate SHRM practices that are likely to affect BI outcomes in firms. These include career risk mitigation, extrinsic rewards (motivation), idea generation facilitation (opportunity), innovation talent development, and managerial ambidexterity development (ability). We theorize that while these SHRM practices may directly affect BI outcomes, these effects are likely to be modulated by the broader organizational context, specifically, the degree of organizational conservatism. We test our hypotheses using survey data collected from 79 U.S.-based multinational firms and find that first, the impacts of career risk, extrinsic rewards, and innovation talent development on BI outcomes vary with the firm's degree of conservatism. Second, developing ambidextrous talent among middle managers positively influences BI outcomes, irrespective of the firm's level of conservatism. Overall, our study provides a basic framework to understand how SHRM practices can be leveraged to influence innovation under varying degrees of conservatism in organizations. [ABSTRACT FROM AUTHOR]

This paper explores the history of, and addresses certain perceived procedural misunderstandings in, Hush-A-Phone v. United States (D.C. Cir. 1956) and its progeny In re Carterfone (F.C.C. 1968) to illuminate what the Hush-A-Phone Corporation actually did in its dispute with AT&T prior to the breakup of AT&T's century-long telephone monopoly. [ABSTRACT FROM AUTHOR]

Hybrid energy storage system (HESS) based on Li-ion and supercapacitor (SC) can play a potential role to stabilize the grid by providing the fast frequency ancillary services. The SC helps to reduce the battery charge/discharge stress and, hence, assists to extend the battery lifespan. The power sharing method (PSM) is the heart in control part to improve the HESS performance and reduce the battery stress. This paper proposes a hybrid PSM and investigates its impact on battery life extension along with its relation to the system design and regulation signal. The performance of hybrid PSM is compared with three other PSMs (low-pass filter, first and second rule based) in a 10 MW/10 MWh full-active parallel HESS for frequency regulation service in two networks: U.K. (national grid) and USA (PJM). Considering maximum possible battery lifetime upto 25 years, result shows that the hybrid PSM approach allows a degree of the better performance for both grid while the sharing of SC is kept maximum 2.0% and 2.5% (for U.S. and U.K. grid, respectively) of the HESS capacity. This study also analyses the impact of PSM on shared capacity and design of HESS for different grid regulation signals. [ABSTRACT FROM AUTHOR]

Energetic electrons can be trapped in Earth’s magnetic field, forming the radiation belts (also known as the Van Allen Belts). These electrons, which can originate from the solar wind or a high-altitude nuclear explosion (HANE), have the potential to damage satellites in low-Earth orbit (LEO). For example, in 1962, the U.S. detonated a nuclear device at an altitude of about 400 km in the Starfish experiment. The resulting enhancement of the radiation belts disabled several satellites within a few months and energetic electrons remained in the radiation belts for up to several years. In order to address this potential vulnerability, schemes have been proposed to drain electrons from the radiation belts, with the most promising approaches based on using high-power very-low-frequency (VLF) waves to scatter the electrons into more field-aligned trajectories, forcing them to precipitate into Earth’s atmosphere. This paper will provide an overview of enhanced electron distributions in the radiation belts as well as approaches to VLF wave belt remediation including the use of either antennas or relativistic electrons beams in space to generate the VLF waves. [ABSTRACT FROM AUTHOR]

Heat conduction load test is a main and tough issue in the complex and huge general atom (GA) feeder system cold test. The GA feeder system is a superconducting system connects from the room temperature power supply system to the 4.5-K busbar of the magnet, supply 52 kA current and cryogenic provisions to the central solenoid magnet (CSM) coil, used for International Thermonuclear Experimental Reactor CSM magnet test at General Atomics Company, San Diego, CA, USA. This system have been developed and constructed at the Institute of Plasma Physics, Chinese Academy of Sciences, and completed the full cold test before transferred to the USA. In all the cold test items, the requirement of overall heat load 68 W is a big challenge, for its many leakage heat sources and cooling circuits: high temperature superconducting current lead, vacuum barrier, busbars, pipes, supports, etc. In order to get the steady results, many efforts were took to achieve the required value, such as reduce the inlet pressure of the 5-K loop as much as possible, minimize the flow rate of different 5-K cooling circuits, and at the same time, a dedicated program was developed to online calculate the density and the heat load while monitoring and analyzing the heat load. This paper will give a detail description of the 5-K overall heat load measurement. [ABSTRACT FROM AUTHOR]

Nineteenth and early twentieth century American governments—local, state, and national—profoundly shaped diffusion of novel mathematical instruments. The federal government ran an office that judged what inventions were patentable and a legal system for those who defended or challenged patent rights. Governments at all levels employed inventors. Sometimes new laws required extensive calculations promoting invention and sale of computing instruments. Governments were customers for mathematical instruments ranging from teaching apparatus to adding and calculating machines to harmonic analyzers to tabulating machines. They not only bought goods, but set standards for what they would purchase. Government buyers also offered testimonials to businesses. From the 1890s, ant.itrust legislation led to some federal government oversight of corporations. Historians usually rely on documents to tell this story. This paper begins from objects—a few known only from patent descriptions, but most surviving in museum collections. This perspective complements rich existing accounts. [ABSTRACT FROM AUTHOR]

The Interconnections Seam Study examines the potential economic value of increasing electricity transfer between the Eastern and Western Interconnections using high-voltage direct-current (HVDC) transmission and cost-optimizing both generation and transmission resources across the United States, proposing, assessing, justifying, and illustrating a major infrastructure change involving two of the world's largest power grids. The study conducted a multi-model analysis that used co-optimized generation and transmission expansion planning and production cost modeling. Four transmission designs under eight scenarios were developed and studied to estimate costs and potential benefits. The results show benefit-to-cost ratios that reach as high as 2.5, indicating significant value to increasing the transmission capacity between the interconnections under the cases considered, realized through sharing generation resources and flexibility across regions. [ABSTRACT FROM AUTHOR]

The use of alternate fuels and raw materials in the cement manufacturing process has evolved substantially over the last two to three decades, along with approaches to acquire regulatory approval. Most recently, the Environmental Protection Agency (EPA) has published new regulations as an overlay to what has been in place, having the potential to significantly impact this practice. Instead of dealing with the state agencies that usually have permitting authority, the new rules add another layer of regulatory hurdles by EPA. The new regulations also force a more significant overlap of the air permitting and solid waste rules. As a result, implementing an alternate material program and seeking approval has the potential to be considerably more challenging for the regulated community, in addition to adding complexity for the state agency permitting staff. This paper provides an update on the latest solid waste and air regulations applicable to alternative material use in cement plants, and potential implications for the U.S. industry. Early experiences will be explored, in addition to contemplating potential permitting and/or approval pathways expected to be encountered with the newest regulations in place. [ABSTRACT FROM AUTHOR]

Microinverter-based photovoltaic (PV) systems now represent about 8% of the U.S. residential market, and offer many advantages including safety, performance, and simplified installation. The next-generation of PV microinverter will include more ancillary functions to support grid stability and reliability in more distributed generation smart-grid systems. A commercial ready PV microinverter not only focuses on efficiency and cost, but also on reliability, manufacturability, compliance of various grid-code, and electromagnetic interference regulations. This paper presents a detailed design and development process of a microinverter system from concept all the way to final commercial-ready prototype. Various design tradeoffs such as topology, control, filter solutions and power supplies, and mechanical packaging are provided. The required prototype testing and final system field tests are also presented. The presented design and test process intends to accelerate the future microinverter system design and development toward a commercial ready product. [ABSTRACT FROM PUBLISHER]

Control is and will continue to be a key discipline for realizing the objectives of smart grid initiatives. Research in control science and engineering is not limited to one or a few application concepts but is pervasive across the smart grid ecosystem. The principal contribution of this paper is to review, from a system-architectural perspective, how control enables smart grid applications. Application “templates” are presented for direct load control, automated demand response, microgrid optimization, control for distribution grids, wide-area control, and market-centric control. Technological developments, including in power electronics, that are enabling smart grid control research and applications are also itemized and two cross-cutting needs/opportunities for future research discussed. We conclude with a summary of a recent status report on the progress that has been made in the United States, noting also the challenges to further progress, in renewable generation, energy efficiency, and carbon reduction. [ABSTRACT FROM PUBLISHER]

In this paper, a multiinput dc–dc converter is proposed and studied for hybrid electric vehicles. Compared to conventional works, the output gain is enhanced. Fuel cell (FC), photovoltaic panel, and energy storage system are the input sources for the proposed converter. The FC is considered as the main power supply, and roof-top PV is employed to charge the battery, increase the efficiency, and reduce fuel economy. The converter has the capability of providing the demanded power by load in absence of one or two resources. Moreover, the power management strategy is described and applied in a control method. A prototype of the converter is also implemented and tested to verify the analysis. [ABSTRACT FROM AUTHOR]

This paper examines the cultural climate faced by women in the American computer industry from the 1960s to the early 1980s, a period in which the percentage of the industry workforce that was female almost tripled. Drawing on a comprehensive study of articles and advertisements in the trade journal Datamation, sources from IBM, Control Data, and the Burroughs Corporation, and the records of the user group SHARE, Inc, the study argues that the cultural climate of the industry shifted radically in the early 1970s, from hostility in the 1960s to a more open one in the late 1970s and early 1980s. [ABSTRACT FROM PUBLISHER]

Prostate cancer is the most common cancer among men in the US, and yet most cases represent localized cancer for which the optimal treatment is unclear. Accumulating evidence suggests that the available treatment options, including surgery and conservative treatment, result in a similar prognosis for most men with localized prostate cancer. However, approximately 90% of patients choose surgery over conservative treatment, despite the risk of severe side effects like erectile dysfunction and incontinence. Recent medical research suggests that a key reason is the lack of patient-centered tools that can effectively communicate personalized risk information and enable them to make better health decisions. In this paper, we report the iterative design process and results of developing the PROgnosis Assessment for Conservative Treatment (PROACT) tool, a personalized health risk communication tool for localized prostate cancer patients. PROACT utilizes two published clinical prediction models to communicate the patients' personalized risk estimates and compare treatment options. In collaboration with the Maine Medical Center, we conducted two rounds of evaluations with prostate cancer survivors and urologists to identify the design elements and narrative structure that effectively facilitate patient comprehension under emotional distress. Our results indicate that visualization can be an effective means to communicate complex risk information to patients with low numeracy and visual literacy. However, the visualizations need to be carefully chosen to balance readability with ease of comprehension. In addition, due to patients' charged emotional state, an intuitive narrative structure that considers the patients' information need is critical to aid the patients' comprehension of their risk information. [ABSTRACT FROM PUBLISHER]