Recent publications
Industrial and agricultural Internet of Things (IoT) are emerging in very large-scale and wide-area applications (e.g., oil-field management, smart farming) that may spread over hundreds of square miles (e.g., 45mi×12mi East Texas Oil-field). Although a single Low-Power Wide-Area Network (LPWAN) covers several miles, it faces coverage challenge in such extremely large-area IoT applications, especially in rural or remote areas with no/limited infrastructure, requiring an in-band integration of multiple LPWANs. We consider a seamless integration of multiple SNOW LPWANs. SNOW (Sensor Network Over White spaces) is an LPWAN architecture over the TV white spaces, avoiding overcrowding problems in the limited ISM band and the cost of licensed band and infrastructure. It offers high scalability through concurrent and bi-directional communication between a base station and numerous nodes. Existing integration of multiple SNOW LPWANs does not consider minimizing network latency and is less suitable for delay-sensitive or real-time applications. In this work, we propose the
first latency-minimizing scalable in-band integration of multiple SNOWs
. Considering the impact of bandwidth on latency and base station power dissipation, low-latency integration of multiple SNOWs as a constrained spectrum allocation problem is formulated. A novel greedy latency- and traffic- aware spectrum allocation to allocate each link's bandwidth is proposed, achieving an integrated network. To enable low-latency integration, we propose two medium access control protocols for multiple SNOWs, RI-TDMA and TDMA, and estimate their latency. We have implemented the proposed integration both on SNOW hardware and in NS-3 simulator. The physical experiments show up to 44% reduction in the maximum network latency under our approach compared to existing approach. The simulation results show at least 62.3% reduction of maximum network latency by the proposed approach with RI-TDMA and 86.7% with TDMA.
Sociotechnical imaginaries of gene editing in food and agriculture reflect and shape culturally particular understandings of what role technology should play in an ideal agrifood future. This study employs a comparative media content analysis to identify sociotechnical imaginaries of agricultural gene editing and the actors who perform them in five countries with contrasting regulatory and cultural contexts: Canada, Japan, New Zealand, the Netherlands, and the United States. We find that news media in these countries reinforce a predominantly positive portrayal of the technology’s future, although variations in which imaginaries are most mobilized exist based on the regulatory status of gene editing and unique histories of civil society engagement around biotechnology in each country. We argue that by granting legitimacy to some narratives over others, the media supports gene editing as a desirable and necessary component of future agrifood systems, thereby limiting consideration of broader issues related to the technology’s development and application.
As FPGAs are being deployed in the cloud infrastructure for acceleration, the technology of multi-tenant FPGA has emerged as a topic of interest. This development has drawn considerable attention to its security issues. While previous research primarily focused on the security of applications, there has been limited exploration of the vulnerabilities inherent in FPGA IPs. In our work, we examine the vulnerabilities of two widely used data transmission protocols in modern FPGAs: the Advanced eXtensible Interface (AXI) and Peripheral Component Interconnect Express (PCIe). Our experiments, conducted with commercial FPGA development kits, launched fault injection attacks through the shared power distribution network (PDN). Through non-invasive electromagnetic (EM) trace measurement, we characterize the voltage fluctuation across various attack patterns. Subsequently, we simulate real-world data transfers using two crafted datasets with different statistical characteristics. The experimental results demonstrate the unique security vulnerabilities of the current AXI and PCIe protocols in the context of a multi-tenant cloud-FPGA. In response to such vulnerability, we further propose two defense strategies: InChAXI that utilizes integrity checking for AXI-based data, and FCPCIe that employs frequency scaling for PCIe-based data. The performance evaluation demonstrates that our proposed defenses can significantly reduce the fault injections on the AXI-based data transmission by 705 times with small overheads – 0.5% in hardware footprint and 7.9% in latency, respectively. On the other hand, FCPCIe effectively prevents the fault injection attack during the PCIe-based data transmission by reducing the user clock frequency, while incurring a 10.13% overhead on data throughput.
Pain and cannabis use are highly prevalent among emerging adults but research regarding how pain is associated with cannabis-related expectancies is limited. Emerging adults who reported past three-month cannabis use ( N = 173) were recruited through an online sampling platform. Participants completed the Graded Chronic Pain Scale, Cannabis Use Disorders Identification Test – Revised, and Marijuana Effect Expectancy Questionnaire. Multiple linear regressions indicated that pain intensity and disability were associated with hazardous cannabis use and expectancies for global negative effects ( ps < .001). Sex did not moderate any of these relationships ( ps > .14). Findings suggest that emerging adults who experience pain report greater hazardous cannabis use and may expect more negative effects of cannabis use. Researchers and clinicians should consider assessing pain in the context of cannabis studies and interventions.
Gas-filled hollow-core fibers have over the last three decades emerged as a key technology for ultrafast nonlinear optics, attosecond science, and strong-field physics. Today, noble gas-filled capillary and microstructured fibers are used to generate broadband, coherent supercontinuum spectra through self-phase modulation in the gas medium, which can then be compressed to yield few- (or even sub-) cycle pulses for driving lasers spanning the ultraviolet to mid-infrared. More recently, the use of molecular gases for spectral broadening has attracted significant interest, due to the interplay of the rotational and vibrational degrees of freedom with the electronic nonlinearity. Depending on the pulse duration of the driving laser, the complex interplay between instantaneous Kerr effect and the “delayed” rotational and vibrational Raman nonlinearities can induce novel behavior, such as four-wave mixing, stimulated Raman scattering, and soliton self-frequency shifting, which can combine with self-phase modulation to realize unique few-cycle sources in spectral regions inaccessible to available laser gain media. In this Invited Paper, we discuss new routes to spectral broadening in molecular gas-filled hollow-core capillary fibers, with a particular focus on the generation of few-cycle, red-shifted pulses from ytterbium-doped laser amplifiers. We review the physics underlying the rotational enhancement of optical nonlinearity in linear molecules, and explore the effects of nonlinear propagation molecular gas-filled fibers through both simulations and experiments. We further describe the compression of the broadband output spectrum, using dispersive mirrors and bulk media to produce few-cycle output pulses in different spectral regions, and discuss the challenges and opportunities for power scaling of few-cycle sources based on rotational nonlinearity. Finally, we describe the prospects for generating few-cycle sources in the mid- to long-wave infrared through red-shifted broadening initiated by new long-wavelength laser sources.
Purpose
This study examines the preliminary reliability and validity of the PedsQL™ Family Impact Module (PedsQL™ FIM) in parents of children with congenital muscular dystrophy (CMD).
Methods
The participants in this study were 28 parents of children with CMD and 39 parents of unaffected children. Both groups of parents completed the PedsQL™ FIM and a demographic information form. Cronbach’s alpha was used to examine the internal consistency reliability, and the known-groups method was used to assess construct validity. Three distinct models were used to estimate the mean score differences of the PedsQL™ FIM between the two groups: an unadjusted model, a multivariate regression model, and propensity score matching with inverse probability of treatment weighting.
Results
Cronbach’s alpha coefficients for all scales exceeded 0.70, supporting evidence for the internal consistency reliability of the PedsQL™ FIM. The construct validity of the PedsQL™ FIM demonstrated that the mean differences between the CMD and unaffected groups were significantly different ( p < .05). This indicated that the instrument could discriminate between the two groups.
Conclusions
The results of this study demonstrated the good preliminary reliability and validity of the PedsQL™ FIM in assessing parental health-related quality of life and family functioning of children with CMD.
Recent advances in machine learning research have produced powerful neural graph embedding methods, which learn useful, low-dimensional vector representations of network data. These neural methods for graph embedding excel in graph machine learning tasks and are now widely adopted. However, how and why these methods work—particularly how network structure gets encoded in the embedding—remain largely unexplained. Here, we show that node2vec—shallow, linear neural network—encodes communities into separable clusters better than random partitioning down to the information-theoretic detectability limit for the stochastic block models. We show that this is due to the equivalence between the embedding learned by node2vec and the spectral embedding via the eigenvectors of the symmetric normalized Laplacian matrix. Numerical simulations demonstrate that node2vec is capable of learning communities on sparse graphs generated by the stochastic blockmodel, as well as on sparse degree-heterogeneous networks. Our results highlight the features of graph neural networks that enable them to separate communities in the embedding space.
The microfossil record contains abundant, diverse, and well‐preserved fossils spanning multiple trophic levels from primary producers to apex predators. In addition, microfossils often constitute and are preserved in high abundances alongside continuous high‐resolution geochemical proxy records. These characteristics mean that microfossils can provide valuable context for understanding the modern climate and biodiversity crises by allowing for the interrogation of spatiotemporal scales well beyond what is available in neo‐ecological research. Here, we formalize a research framework of “micropaleoecology,” which builds on a holistic understanding of global change from the environment to ecosystem level. Location: Global. Time period: Neoproterozoic‐Phanerozoic. Taxa studied: Fossilizing organisms/molecules. Our framework seeks to integrate geochemical proxy records with microfossil records and metrics, and draws on mechanistic models and systems‐level statistical analyses to integrate disparate records. Using multiple proxies and mechanistic mathematical frameworks extends analysis beyond traditional correlation‐based studies of paleoecological associations and builds a greater understanding of past ecosystem dynamics. The goal of micropaleoecology is to investigate how environmental changes impact the component and emergent properties of ecosystems through the integration of multi‐trophic level body fossil records (primarily using microfossils, and incorporating additional macrofossil data where possible) with contemporaneous environmental (biogeochemical, geochemical, and sedimentological) records. Micropaleoecology, with its focus on integrating ecological metrics within the context of paleontological records, facilitates a deeper understanding of the response of ecosystems across time and space to better prepare for a future Earth under threat from anthropogenic climate change.
Binding of arbitrary information into distinct memory representations that can be used to guide behavior is a hallmark of relational memory. What is and is not bound into a memory representation and how those things influence the organization of that representation remain topics of interest. While some information is intentionally and effortfully bound—often the information that is consistent with task goals or expectations about what information may be required later—other information appears to be bound automatically. The present set of experiments sought to investigate whether spatial memory would be systematically influenced by the presence and absence of distinct categories of stimuli on a spatial reconstruction task. In this task, participants must learn multiple item-location bindings and place each item back in its studied location after a short delay. Across three experiments, participants made significantly more within-category errors (i.e., misassigning one item to the location of a different item from the same category) than between-category errors (i.e., misassigning one item to the location of an item from a different category) when categories were perceptually or semantically distinct. These data reveal that category information contributed to the organization of the memory representation and influenced spatial reconstruction performance. Together, these results suggest that categorical information can influence memory organization, and not always to the benefit of overall task performance.
After receiving a diagnosis of a rare form of blood cancer in my mid 30s, in the midst of my first year working full-time as an assistant professor, I reflect on the ways such a life-altering event gave me a new vantage point on my own career as a management scholar. I highlight ways that both a) the nature of the cancer treatment I had to receive and b) being forced to mentally face prospects of a reduced life expectancy, had the unforeseen value of giving me specific insights on effective ways to conduct research in our field, while still appreciating deeper issues of personal values and relationships. I specifically utilize the conceptual framework of the ‘protean’ career in my narrative essay to elucidate important truths about the nature of management scholarship which I propose all my colleagues should personally consider as they pursue their own career goals.
How does a candidate's racial background affect the inferences voters make about them? Prior work finds that Black candidates are perceived to be more liberal. Using two survey experiments, we test whether this effect persists when candidate partisanship and issue positions are specified and also consider other consequential voter perceptions. We make two contributions. First, we show that while Black candidates are perceived to be more liberal than White candidates with the same policy positions, this difference is smaller for Black candidates who adopt more conservative positions on race-related issues. Second, we find that voters, both Black and White, believe Black candidates will prioritize the interests of Black constituents over those of White constituents, regardless of candidate positions.
This chapter addresses the concept of “intersectionality” and its uses in archaeological analysis and practice. Intersectionality is broadly understood to encompass approaches that consider the complexity of human experience, particularly oppression and privilege, along multiple axes of identity simultaneously. Consistent with its development from Black feminism, much intersectional archaeological research has focused on race and gender in recent times; however, an increasing number of explicitly intersectional approaches are also addressing class, status, sexuality, age, dis/ability, and other aspects of identity. Intersectional investigation of the discipline is also growing, investigating the demographics of archaeology and knowledge production, and supporting social justice activism in academia.
The electrochemical oxygen reduction reaction (ORR) is critical for fuel cell application, and modifying surface structures of electrocatalysts has proven effective in improving their catalytic performances. In this study, we investigated surface‐engineered Pt−Ni nano‐octahedra subjected to annealing in various atmospheres. All octahedral nanocrystals retained their Pt−Ni {111} facets at an elevated temperature following the annealing treatments. Air annealing led to the formation of nickel‐rich shells on the Pt−Ni surface. In contrast, hydrogen (H₂) as a reducing gas facilitated the reduction of surface Ni species, incorporating them into the Pt−Ni bulk alloy, which resulted in superior mass activity and specific activity for ORR‐approximately 2.4 and 2.3 times as high as those from the unmodified counterpart, respectively. After 20,000 potential cycles, the H₂/Ar‐annealed Pt−Ni nano‐octahedra maintained a mass activity of 3.92 A/ mgPt , surpassing the initial mass activity of the unannealed counterparts (2.95 A/ mgPt ). These findings demonstrate a viable approach for tailoring catalyst surfaces to enhance performance in various energy storage and conversion applications.
The paper focuses on self-knowledge of attitudes like belief, desire, and intention. It motivates a constraint on when it is rational to treat a source as an authority. Then, drawing on the “transparency” of belief and other attitudes, it argues that the constraint is not satisfied in the case of knowing one’s own mind by relying on AI or any other technology. Against some AI optimists, the paper argues that there are principled reasons why the task of knowing one’s mind should not be outsourced to computers. Neither is AI a threat to the practice of deferring to avowing subjects.
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