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This paper reports the discovery of a series of computational social media accounts (Fanbots) on Twitter that may have played a critical role in sustaining the entrepreneurial narratives of Tesla, the electric-vehicle maker. From 2010 to 2020—a period of trial, error, and eventual success for Tesla—these computational agents generated pro-firm tweets (Corporate Computational Propaganda, CCP), accounting for more than 10% of the total Twitter activity that included the cashtag, $TSLA, and 23% of activity that included the hashtag, #TSLA. Though similar to programmed social media content in the political sphere, the activities of these accounts predate the existence of political computational propaganda associated with foreign support for, for instance, Brexit in the United Kingdom (2016) and Donald Trump in the United States (2016). The paper (a) characterizes the extent of Fanbot content in two large Tesla tweet corpora, (b) identifies possible motivations for the creation of these accounts in relation to the firm’s entrepreneurial narratives, and (c) explores possible mechanisms by which the Fanbots might have acted. Although we are unable to directly observe the source or stated purpose of these accounts, based upon the timing of Fanbot creation and other indirect indicators, we infer that these accounts and the social media activity they generated were intended to influence social perception of Tesla. The conclusion assesses the generalizability of a Fanbot-based strategy, highlighting contextual limitations, while also pointing to ways that firms may already be using CCP to manage social approval in emerging-industry contexts.

In this paper, we show how volume rendering with a Programmable Transfer Function can be used for the effective and comprehensible visualization of WiFi signals. A traditional transfer function uses a low-dimensional lookup table to map the volumetric scalar field to color and opacity. In this paper, we present the concept of a Programmable Transfer Function. We then show how generalizing traditional lookup-based transfer functions to Programmable Transfer Functions enables us to leverage view-dependent and real-time attributes of a volumetric field to depict the data variations of WiFi surfaces with low and high-frequency components. Our Programmable Transfer Functions facilitate interactive knowledge discovery and produce meaningful visualizations.

Group testing idea is an efficient approach to detect prevalence of an infection in the test samples taken from a group of individuals. It is based on the idea of pooling the test samples and performing tests to the mixed samples. This approach results in possible reduction in the required number of tests to identify infections. Classical group testing works consider static settings where the infection statuses of the individuals do not change throughout the testing process. In our paper, we study a dynamic infection spread model, inspired by the discrete time SIR model, where infections are spread via non-isolated infected individuals, while infection keeps spreading over time, a limited capacity testing is performed at each time instance as well. In contrast to the classical, static group testing problem, the objective in our setup is not to find the minimum number of required tests to identify the infection status of every individual in the population, but to control the infection spread by detecting and isolating the infections over time by using the given, limited number of tests. In order to analyze the performance of the proposed algorithms, we focus on the average-case analysis of the number of individuals that remain non-infected throughout the process of controlling the infection. We propose two dynamic algorithms that both use given limited number of tests to identify and isolate the infections over time, while the infection spreads, while the first algorithm is a dynamic randomized individual testing algorithm, in the second algorithm we employ the group testing approach similar to the original work of Dorfman. By considering weak versions of our algorithms, we obtain lower bounds for the performance of our algorithms. Finally, we implement our algorithms and run simulations to gather numerical results and compare our algorithms and theoretical approximation results under different sets of system parameters.

This paper analyzed quarterly longitudinal data for 64,800 1 × 1 degree grids during 2000–2019 on sea surface temperatures, sea ice concentrations, and ocean surface current zonal and meridional velocities in the Northern and Southern hemispheres. The methodological framework addressed the processing of remote sensing signals, interdependence between sea surface temperatures and sea ice concentrations, and combining zonal and meridional velocities as the eddy kinetic energy. Dynamic and static random effects models were estimated by maximum likelihood and stepwise methods, respectively, taking into account the unobserved heterogeneity across grids. The main findings were that quarterly sea surface temperatures increased steadily in the Northern hemisphere, whereas cyclical patterns were apparent in Southern hemisphere; sea ice concentrations declined in both hemispheres. Second, sea surface temperatures were estimated with large negative coefficients in the models for sea ice concentrations for the hemispheres; previous sea ice concentrations were negatively associated with sea surface temperatures, indicating feedback loops. Third, sea surface temperatures were positively and significantly associated with eddy kinetic energy in Northern hemisphere. Overall, the results indicated the importance of reducing sea surface temperatures via reductions in greenhouse gas emissions and the dumping of pollutants into oceans for maintaining sea ice concentrations and enhancing global sustainability.

Food safety issues are a worldwide concern. Pathogens, toxins, pesticides, veterinary drugs, heavy metals, and illegal additives are frequently reported to contaminate food and pose a serious threat to human health. Conventional detection methods have difficulties fulfilling the requirements for food development in a modern society. Therefore, novel rapid detection methods are urgently needed for on-site and rapid screening of massive food samples. Due to the extraordinary properties of nanozymes and aptamers, biosensors composed of both of them provide considerable advantages in analytical performances, including sensitivity, specificity, repeatability, and accuracy. They are considered a promising complementary detection method on top of conventional ones for the rapid and accurate detection of food contaminants. In recent years, we have witnessed a flourishing of analytical strategies based on aptamers and nanozymes for the detection of food contaminants, especially novel detection models based on the regulation by single-stranded DNA (ssDNA) of nanozyme activity. However, the applications of nanozyme-based aptasensors in food safety are seldom reviewed. Thus, this paper aims to provide a comprehensive review on nanozyme-based aptasensors in food safety, which are arranged according to the different interaction modes of ssDNA and nanozymes: aptasensors based on nanozyme activity either inhibited or enhanced by ssDNA, nanozymes as signal tags, and other methods. Before introducing the nanozyme-based aptasensors, the regulation by ssDNA of nanozyme activity via diverse factors is discussed systematically for precisely tailoring nanozyme activity in biosensors. Furthermore, current challenges are emphasized, and future perspectives are discussed.

Adolescent years are a time of joy and can represent a challenge for parents and youth, especially for immigrants to the US who are adjusting to their host country. Programs focusing on family skills and positive youth development (PYD) can contribute to youth wellbeing especially, however, few exist for low-income immigrant families. (1) Background: The major goals of this project are to strengthen both PYD and healthy parenting practices by implementing an evidence-informed program, Familias Activas. A theory of change guided the development of Familias Activas in which three factors: parent training, positive youth development, and youth physical activity sessions (soccer) aimed to improve Latinx youth mental health. Youth participated in weekly soccer practices led by trained soccer coaches while parents/caregivers attended parent education. Both sessions lasted eight weeks. (2) Method: We describe the formative stage of the research project as well as the pilot implementation of the Familias Activas program, which provides critical insights for the development of a PYD program. (3) Results: Evaluation surveys were administered to youth and their parents. Thirty youths and sixteen parents completed the survey. The Kidscreen scale had a mean for most items ranging from 3.6 to 4.2. Participating youth were 11 years old and most affirmed they were Latinx. The feasibility program quality mean was 4.2 indicating an overall positive result for the pilot program.. Implications of PYD programs for Latinx youth are discussed. (4) Conclusions: The current paper presents a model for positively influencing the physical and mental wellbeing of Latinx youth and their parents. The model is culturally responsive in its involvement of both parents and youth in programming.

In recent years, Global Navigation Satellite System (GNSS) radio occultation (RO) has become a critical observation system for global operational numerical weather prediction. Constellation Observing System for Meteorology, Ionosphere, Climate (COSMIC) 2 (COSMIC-2) has been a backbone RO mission for NOAA. NOAA also began to purchase RO data from commercial sources in 2020. To ensure the consistent quality of RO data from different sources, NOAA Center for Satellite Applications and Research (STAR) has developed capabilities to process all available RO data from different missions. This paper describes the STAR RO processing systems which convert the pseudo-range and carrier phase observations to excess phases and bending angles (BAs). We compared our COSMIC-2 data products with those processed by the University Corporation for Atmospheric Research (UCAR) COSMIC Data Analysis and Archive Center (CDAAC). We processed more than twelve thousand COSMIC-2 occultation profiles. Our results show that the excess phase difference between UCAR and STAR is within a few centimeters at high altitudes, although the difference increases towards the lower atmosphere. The BA profiles derived from the excess phase are consistent with UCAR. The mean relative BA differences at impact height from 10 to 30 km are less than 0.1% for GLObal NAvigation Satellite System (GLONASS) L2C signals and Global Positioning System (GPS) L2C and L2P signals. The standard deviations are 1.15%, 1.15%, and 1.32% for GLONASS L2C signal and for GPS L2C and L2P signals, respectively. The BA profiles agree with those derived from European Center for Medium-range Weather Forecast (ECMWF) reanalysis version 5 (ERA5). The Signal-to-Noise-Ratio (SNR) plays an essential role in the processing. The STAR BA profiles with higher L1 SNRs (L1 at 80 km) tend to yield more consistent results than those from UCAR, with a negligible difference and a smaller deviation than lower SNR profiles. Profiles with lower SNR values t

Terrestrial snow is a vital freshwater resource for more than 1 billion people. Remotely-sensed snow observations can be used to retrieve snow mass or integrated into a snow model estimate; however, optimally leveraging remote sensing observations of snow is challenging. One reason is that no single sensor can accurately measure all types of snow because each type of sensor has its own unique limitations. Another reason is that remote sensing data is inherently discontinuous across time and space, and that the revisit cycle of remote sensing observations may not meet the requirements of a given snow applications. In order to quantify the feasible availability of remotely-sensed observations across space and time, this study simulates the sensor coverage for a suite of hypothetical snow sensors as a function of different orbital configurations and sensor properties. The information gleaned from this analysis coupled with a dynamic snow binary map is used to evaluate the efficiency of a single sensor (or constellation) to observe terrestrial snow on a global scale. The results show the efficacy achievable by different sensors over different snow types. The combination of different orbital and sensor configurations is explored to requirements of remote sensing missions that have 1-day, 3-day, or 30-day repeat intervals. The simulation results suggest that 1100 km, 550 km, and 200 km are the minimum required swath width for a polar-orbiting sensor to meet snow-related applications demanding a 1-day, 3-day, and 30-day repeat cycles, respectively. The results of this paper provide valuable input for the planning of a future global snow mission.

This paper presents a novel computational algorithm to estimate blood volume decompensation state based on machine learning (ML) analysis of multi-modal wearable-compatible physiological signals. To the best of our knowledge, our algorithm may be the first of its kind which can not only discriminate normovolemia from hypovolemia but also classify hypovolemia into absolute hypovolemia and relative hypovolemia. We realized our blood volume classification algorithm by (i) extracting a multitude of features from multi-modal physiological signals including the electrocardiogram (ECG), the seismocardiogram (SCG), the ballistocardiogram (BCG), and the photoplethysmogram (PPG), (ii) constructing two ML classifiers using the features, one to classify normovolemia vs. hypovolemia and the other to classify hypovolemia into absolute hypovolemia and relative hypovolemia, and (iii) sequentially integrating the two to enable multi-class classification (normovolemia, absolute hypovolemia, and relative hypovolemia). We developed the blood volume decompensation state classification algorithm using the experimental data collected from six animals undergoing normovolemia, relative hypovolemia, and absolute hypovolemia challenges. Leave-one-subject-out analysis showed that our classification algorithm achieved an F1 score and accuracy of (i) 0.93 and 0.89 in classifying normovolemia vs. hypovolemia, (ii) 0.88 and 0.89 in classifying hypovolemia into absolute hypovolemia and relative hypovolemia, and (iii) 0.77 and 0.81 in classifying the overall blood volume decompensation state. The analysis of the features embedded in the ML classifiers indicated that many features are physiologically plausible, and that multi-modal SCG-BCG fusion may play an important role in achieving good blood volume classification efficacy. Our work may complement existing computational algorithms to estimate blood volume compensatory reserve as a potential decision-support tool to provide guidance on context-sen

The research in this paper entails the design of material systems with tunable energy-absorbing properties. Hollow glass microspheres of different densities are layered using dry powder printing and subsequently sintered to form a cellular structure. The tunability of the bilayer foams is investigated using various combinations of hollow microspheres with different densities and different thickness ratios of the layers. The mechanical responses to quasi-static uniaxial compression of the bilayer foams are also investigated. These bilayer samples show different mechanical responses from uniform samples with a distinctive two-step stress–strain profile that includes a first and second plateau stress. The strain where the second plateau starts can be tuned by adjusting the thickness ratio of the two layers. The resulting tunable stress–strain profile demonstrates tunable energy absorption. The tunability is found to be more significant if the density values of each layer differ largely. For comparison, bilayer samples are fabricated using epoxy at the interface instead of a sintering process and a different mechanical response is shown from a sintered sample with the different stress–strain profile. Designing the layered foams allows tuning of the stress–strain profile, enabling desired energy-absorbing properties which are critical in diverse impact conditions.

Designers perform early-stage formative usability tests with low-fidelity prototypes to improve the design of new products. This low-tech prototype style reduces the manufacturing resources but limits the functions that can be assessed. Recent advances in technology enable designers to create low-fidelity 3D models for users to engage in a virtual environment. Three-dimensional models communicate design concepts and are not often used in formative usability testing. The proposed method discusses how to create a virtual replica of a product by assessing key human interaction steps and addresses the limitations of translating those steps into a virtual environment. In addition, the paper will provide a framework to evaluate the usability of a product in a virtual setting, with a specific emphasis on low-resource online testing in the user population. A study was performed to pilot the subject’s experience with the proposed approach and determine how the virtual online simulation impacted the performance. The study outcomes demonstrated that subjects were able to successfully interact with the virtual replica and found the simulation realistic. This method can be followed to perform formative usability tests earlier and incorporate subject feedback into future iterations of their design, which can improve safety and product efficacy.

It is well-established that increasing demands for fresh water are paving the way for desalination technologies. However, this correlates with an increase in brine production whose treatment and disposal can be complicated and expensive. This paper presents a thermodynamic model to bound the operation and development of a novel Humidification–Dehumidification-based system featuring Zero-Liquid Discharge and off-grid capabilities. The model employs conservation laws to find feasible state points to meet a baseline operation of 10 kg/h of product water separated from a hypersaline feed stream with 100 g/kg salt concentration. The system incurs in a 1039 kWh/m3 energy intensity that can be supplied completely by an electric source or in combination with heating steam. Follow-up sensitivity analysis highlights the robustness of the system in handling variations of 25% in product flowrate and 75% in feed salinity, practically without incurring any additional energy demands. The proposed system operating costs between 72 USD/m3 and 96 USD/m3 are comparable to those of existing brine disposal techniques. Furthermore, an operational map of existing desalination technologies suggests a niche characterized by high recovery rates and high feed salinities that are generally unfulfilled by conventional desalination methods. Overall, the proposed system shows potential for off-grid hypersaline brine treatment. This study sets the stage for future development of physics-based and data-driven predictive models as the proposed system iterates into a pilot plant deployment.

This paper presents the various ways that lithium-ion batteries are being counterfeited, the problems that counterfeit batteries present, how they enter the consumer market, and the difficulties of detection. Simple external visual inspection of the battery is unreliable. As shown in the presented case study, even for the same brand batteries, their internal structures are different. The current counterfeit prevention methods focus on the manufacturing step. To reduce the risk of counterfeit batteries, device manufacturers and retail stores should characterize the batteries they receive. In addition, related authorities or organizations should set standards to enable a universal battery tracking method along the supply chain to prevent counterfeit lithium-ion batteries from entering the market.

Soil moisture operational product system (SMOPS) is developed by National Oceanic and Atmospheric Administration (NOAA) to provide the real-time blended soil moisture (SM) for numeric weather prediction and national water model applications. However, all individual satellite SM data ingested into the current operational SMOPS are scaled to global land data assimilation system (GLDAS) 0–10 cm SM climatology before the combination. As a result, the useful information from the original microwave SM retrievals could be lost, and the GLDAS model errors could be brought into the final SMOPS blended product. In this paper, we propose to scale the individual SM retrievals to the soil moisture active passive (SMAP) data through building regression models. The rescaled individual SM data and the SMAP observations then have similar climatology and dynamics, which allows producing the SMOPScdr (distinguishing with the current operational SMOPSopr) data using an equal-weight averaging approach. With respect to the in situ SM measurements, the developed SMOPScdr is more successful tracking the surface SM status than the individual satellite SM products with significantly decreased errors. The proposed method also preserves the climatology of the reference SMAP data for the period when SMAP is not available, allowing us to produce a long-term SMOPScdr data product.

In order to meet increasing power-dissipation requirements of the electronics industry, compact, low-cost, and lightweight heat exchangers (HXs) are desired. With proper design, materials, and manufacture, polymer composite heat exchangers could meet these requirements. This paper presents a novel crossflow air-to-water, low-cost, and lightweight metal-polymer composite HX. This HX, which is entirely additively manufactured, utilizes a novel cross-media approach that provides direct heat exchange between air and liquid sides by using connecting fins. A robust numerical model was developed, which includes the dimensional effects of additive manufacturing. The study consists of a simplified 3D CFD model based on ellipsoidal-shaped staggered tube banks for the laminar range. It then uses an analytical approach to compute entire HX performance. The model is validated experimentally within 8% for thermal performance, 12% for air-side impedance, and 18% for water-side impedance. Finally, HX is compared with a conventional CPU radiator and performs within 10% of the conventional unit for reasonable flow rates and pressure-drop ranges. Moreover, HX also provides added design and cost advantages over the conventional unit, which makes the HX a potential candidate for electronic cooling applications.

This paper studies the privacy of wireless communications from an eavesdropper that employs a deep learning (DL) classifier to detect transmissions of interest. There exists one transmitter that transmits to its receiver in the presence of an eavesdropper. In the meantime, a cooperative jammer (CJ) with multiple antennas transmits carefully crafted adversarial perturbations over the air to fool the eavesdropper into classifying the received superposition of signals as noise. While generating the adversarial perturbation at the CJ, multiple antennas are utilized to improve the attack performance in terms of fooling the eavesdropper. Two main points are considered while exploiting the multiple antennas at the adversary, namely the power allocation among antennas and the utilization of channel diversity. To limit the impact on the bit error rate (BER) at the receiver, the CJ puts an upper bound on the strength of the perturbation signal. Performance results show that this adversarial perturbation causes the eavesdropper to misclassify the received signals as noise with a high probability while increasing the BER at the legitimate receiver only slightly. Furthermore, the adversarial perturbation is shown to become more effective when multiple antennas are utilized.

Analog circuits are a critical part of industrial electronics and systems. Estimates in the literature show that, even though analog circuits comprise less than 20% of all circuits, they are responsible for more than 80% of faults. Hence, analog circuit fault diagnosis and isolation can be a valuable means of ensuring the reliability of circuits. This paper introduces a novel technique of learning time–frequency representations, using learnable wavelet scattering networks, for the fault diagnosis of circuits and rotating machinery. Wavelet scattering networks, which are fixed time–frequency representations based on existing wavelets, are modified to be learnable so that they can learn features that are optimal for fault diagnosis. The learnable wavelet scattering networks are developed using the genetic algorithm-based optimization of second-generation wavelet transform operators. The simulation and experimental results for the diagnosis of analog circuit faults demonstrates that the developed diagnosis scheme achieves greater fault diagnosis accuracy than other methods in the literature, even while considering a larger number of fault classes. The performance of the diagnosis scheme on benchmark datasets of bearing faults and gear faults shows that the developed method generalizes well to fault diagnosis in multiple domains and has good transfer learning performance, too.