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Direct emissions of soil nitrous oxide during a growing season (N2Ogs) can be quantified with process-based models considering interactions between management, climate, and soil moisture when key data are available. We used an adapted “parameterized CENTURY/DAYCENT-model” (pCENTURY) calibrated with crop growth and soil organic matter decay coefficients at the county-level for the estimation of N2Ogs in the United States Corn Belt. Model estimated N2O-emissions from corn-based biofuels scenarios considering crop rotation, fertilizer inputs, tillage, and weather were compared against meta-summary of field observations from 55 studies. Both model and meta-summary ranked N2Ogs-emissions to be corn > wheat > soybean phase while model likely underestimated cover crop N2Ogs-emissions. The N2Ogs-emissions and the associated emission factors (EFs) were modeled and summarized to be greater after anhydrous ammonia than urea application and from conventional tilled than non-tilled fields. Modeled and observed N2Ogs-emissions after organic and inorganic fertilizer amendment did not differ due to high variability associated with the treatments. However, the organic fertilizer associated EFs were greater according to meta-summary data because of N input rates. Regionalized weather scenarios indicate hotspots for N2Ogs-emissions can occur where crop N uptake is limited during dry years and in eastern states also during normal or wet seasons. The pCENTURY-derived N2Ogs EFs (0.91 ± 0.19%) for counties investigated were only slightly lower than literature (1.07 ± 0.57%) or Tier-1 (1%) values. Our preliminary evaluation of regional soil moisture estimates showed reasonable agreement between monthly soil moisture estimates and the North American Soil Moisture Dataset during the growing season, but overestimation of soil moisture in winter-spring can influence the estimates of annual N2O emissions so future work is needed to calibrate soil moisture-associated model parameters. Our work provided scenario-based estimates of climate and management impacts on soil N2Ogs-emissions together with valuable spatial insights into EFs that will be improved by more accurate information of fertilizer inputs and more temporally refined model evaluation.

Whistler-mode chorus waves in the magnetosphere play a crucial role in space weather via wave–particle interactions. The past two decades have observed tremendous advances in theory and simulations of chorus generation; however, several details of the generation mechanism are still actively contended. To simulate chorus generation, a new envelope particle-in-cell code is introduced. The model produces a rising tone chorus element in a parabolic geomagnetic field. The initial chorus element “embryo” frequency is shown to initialize near the equator at the frequency of maximum linear growth. A backward resonant current is then observed to propagate upstream of the equator. The trajectory of the backward current follows that of a freely falling electron that has been de-trapped at the equator superimposed with forward motion at the group velocity. The backward current iteratively radiates a rising tone element where the highest frequency components are generated furthest upstream. The work provides new advancements in modeling chorus and corroborates other recent work that has also demonstrated a backward-moving source during the generation of coherent whistler-mode waves.

Fully Homomorphic Encryption (FHE) allows computations on encrypted data without decryption, providing strong security for sensitive information. However, computational and memory demands for FHE are significant challenges, particularly in the Number Theoretic Transform (NTT) phase. This paper presents three efficient Twiddle Factor Generators (TFGs) to address these challenges: the Half-Memory TFG, the On-the-fly Serial TFG, and the On-the-fly Parallel TFG. The Half-Memory TFG reduces memory usage by storing only half of the twiddle factors and calculating the rest as needed. The On-the-fly Serial TFG eliminates memory requirements by computing twiddle factors, while the On-the-fly Parallel TFG enhances computational speed through parallel processing. Implemented on the FPGA KCU105 board, these TFGs demonstrated significant improvements in hardware resource utilization and computational efficiency. The Half-Memory TFG effectively reduces memory footprint, the On-the-fly Serial TFG eliminates memory usage with acceptable computational overhead, and the On-the-fly Parallel TFG offers superior performance for high-throughput applications. These innovations make FHE more practical for real-world applications, contributing to the broader goal of enabling secure, privacy-preserving computations on encrypted data. [ABSTRACT FROM AUTHOR]

A lot of research on deep learning and big data has led to efficient methods for processing large volumes of data and research on conserving computing resources. Particularly in domains like the IoT (Internet of Things), where the computing power is constrained, efficiently processing large volumes of data to conserve resources is crucial. The processing-in-memory (PIM) architecture was introduced as a method for efficient large-scale data processing. However, PIM focuses on changes within the memory itself rather than addressing the needs of low-cost solutions such as the IoT. This paper proposes a new approach using the PIM architecture to overcome memory bottlenecks effectively in domains with computing performance constraints. We adopt the RISC-V instruction set architecture for our proposed PIM system's design, implementation, and comprehensive performance evaluation. Our proposal expects to efficiently utilize low-spec systems like the IoT by minimizing core modifications and introducing PIM instructions at the ISA level to enable solutions that leverage PIM capabilities. We evaluate the performance of our proposed architecture by comparing it with existing structures using convolution operations, the fundamental unit of deep-learning and big data computations. The experimental results show our proposed structure achieves a 34.4% improvement in processing speed and 18% improvement in power consumption compared to conventional von Neumann-based architectures. This substantiates its effectiveness at the application level, extending to fields such as deep learning and big data. [ABSTRACT FROM AUTHOR]

Data acquisition systems that receive analog signals, convert them to digital, and perform signal processing are used in a variety of systems that use acoustics, radar, sonar, indoor localization, and navigation. The previous systems, such as NI USRP-RIO, are expensive to build, and the number of signals a single device can receive is limited to between two and four. In order to receive more channels of signals, multi-channel data acquisition systems using ADCs operating at tens of MSPS have been proposed. However, these systems require additional processing time because data acquisition and signal processing are performed on different devices. In this paper, we propose a multi-channel data acquisition system using a 16-channel ADC that can support up to 100 MSPS. In particular, to reduce unnecessary signal transmission time, we propose a one-chip structure where all processes are performed on a single chip. Also, we propose a data acquisition system that applies pipelining techniques to enable real-time processing. To verify the proposed system, we used TI ADS52J90 and a Kintex UltraScale KCU105 evaluation board, and confirmed that it is possible to receive and process all channels simultaneously. Furthermore, it is possible to configure a real-time system by adjusting the speed of the signal-processing operation and the speed of the communication interface. Therefore, the proposed system is expected to reduce the cost of system construction by performing signal reception and processing with a single chip, and to reduce the time required for overall signal processing. [ABSTRACT FROM AUTHOR]

AbstractThe rapid changes in interest rates experienced during and following the COVID-19 pandemic posed significant challenges for management committed to enhancing the ESG strategies of firms. This study explores how interest rates moderate the relationship between ESG factors and firm performance within the US restaurant sector. Using 240 observations from US publicly-traded restaurant firms, regression analyses are conducted using Tobin’s q as the dependent variable, incorporating interaction terms between each of the environmental (E), social (S), and governance (G) factors with interest rates as moderating variables. The findings indicate that interest rates significantly amplify the impacts of the environmental (E) and social (S) factors on company performance but do not significantly moderate the effect of the governance (G) factor on performance. This research enriches the hospitality and tourism literature by providing novel insights into the interplay between ESG factors and restaurant firms’ performance, particularly under varying interest rate conditions. Additionally, this study offers practical guidance to management in the restaurant industry, suggesting that restaurant firms should intensify their environmental and social initiatives in response to rising interest rates to enhance their financial performance. [ABSTRACT FROM AUTHOR]

This paper explores how writers in the South Korean broadcasting industry have collectively struggled against their precarious working conditions and faced distinctive difficulties in taking their collective actions, including unionization. Based on semi-structured, in-depth interviews with 23 research participants, this paper examines why and how these South Korean writers have established various forms of collective worker organization: from a professional association to informal grassroots communities, to a women-only trade union, to two unions of media workers who are freelance or on contract. I argue that the activism of these writers reflects the intersection of different identities, ranging from women to freelancers to cultural workers and that their activism promotes the idea that working creatively, autonomously and equitably should be recognized as a basic labour right. [ABSTRACT FROM AUTHOR]

In this study, we introduce an innovative methodology aimed at enhancing Fisher's Linear Discriminant Analysis (LDA) in the context of high‐dimensional data classification scenarios, specifically addressing situations where each feature exhibits distinct variances. Our approach leverages Nonparametric Maximum Likelihood Estimation (NPMLE) techniques to estimate both the mean and variance parameters. By accommodating varying variances among features, our proposed method leads to notable improvements in classification performance. In particular, unlike numerous prior studies that assume the distribution of heterogeneous variances follows a right‐skewed inverse gamma distribution, our proposed method demonstrates excellent performance even when the distribution of heterogeneous variances takes on left‐skewed, symmetric, or right‐skewed forms. We conducted a series of rigorous experiments to empirically validate the effectiveness of our approach. The results of these experiments demonstrate that our proposed methodology excels in accurately classifying high‐dimensional data characterized by heterogeneous variances. [ABSTRACT FROM AUTHOR]

Understanding the interplay between charge, nematic, and structural ordering tendencies in cuprate superconductors is critical to unraveling their complex phase diagram. Using pump-probe time-resolved resonant X-ray scattering on the (0 0 1) Bragg peak at the Cu L3 and O K resonances, we investigate nonequilibrium dynamics of Qa = Qb = 0 nematic order and its association with both charge density wave (CDW) order and lattice dynamics in La1.65Eu0.2Sr0.15CuO4. The orbital selectivity of the resonant X-ray scattering cross-section allows nematicity dynamics associated with the planar O 2p and Cu 3d states to be distinguished from the response of anisotropic lattice distortions. A direct time-domain comparison of CDW translational symmetry breaking and nematic rotational-symmetry breaking reveals that these broken symmetries remain closely linked in the photoexcited state, consistent with the stability of CDW topological defects in the investigated pump fluence regime. [ABSTRACT FROM AUTHOR]

Persistent key-value stores have been widely adopted as storage engines for modern IT infrastructures because they provide high performance with simple design principles. Moreover, many key-value stores commonly employ LSM-tree as their index structure due to its attractive features such as high write throughput and storage space efficiency. Unfortunately, LSM-tree has critical drawbacks in that it leads to write/read amplification problem. One of the prevalent solutions for remedying the write amplification problem is the tiering merge policy that reduces the number of rewrites by delaying merge operations. However, in spite of this advantage, the tiering merge policy may lead to a side-effect that induces high read amplification, increasing search/scan cost for upcoming read operations. In this paper, we concentrate on mitigating the high read amplification problem of the tiering merge policy, while maintaining its low write amplification. To achieve this, we propose a novel LSM-tree scheme, called Spatially Fragmented LSM-tree (SFM), which delays merge operations only for the non-read-intensive key-spaces. For this, SFM identifies the read intensity of each key-spaces by dynamically estimating their read/write hotness. We have implemented SFM based on PebblesDB and evaluated the performance benefits of our scheme under real-world workloads of Facebook. Experimental results clearly show that our scheme improves throughput by up to $1.67\times $ compared with the conventional schemes while maintaining low write amplification, and also indicate that its latency is lowered by up to 41.41% on average by mitigating the read amplification problem of the existing schemes, by up to 43.68%.

While health effects of conventional tobacco are well defined, data on vaping devices, including one of the most popular e-cigarettes which have high nicotine levels, are less established. Prior acute e-cigarette studies have demonstrated inflammatory and cardiopulmonary physiology changes while chronic studies have demonstrated extra-pulmonary effects, including neurotransmitter alterations in reward pathways. In this study we investigated the impact of inhalation of aerosols produced from pod-based, flavored e-cigarettes (JUUL) aerosols three times daily for 3 months on inflammatory markers in the brain, lung, heart, and colon. JUUL aerosol exposure induced upregulation of cytokine and chemokine gene expression and increased HMGB1 and RAGE in the nucleus accumbens in the central nervous system. Inflammatory gene expression increased in the colon, while gene expression was more broadly altered by e-cigarette aerosol inhalation in the lung. Cardiopulmonary inflammatory responses to acute lung injury with lipopolysaccharide were exacerbated in the heart. Flavor-specific findings were detected across these studies. Our findings suggest that daily e-cigarette use may cause neuroinflammation, which may contribute to behavioral changes and mood disorders. In addition, e-cigarette use may cause gut inflammation, which has been tied to poor systemic health, and cardiac inflammation, which leads to cardiovascular disease.

Epidemiological and experimental evidence has implicated a potent link between antibiotic exposure and susceptibility to various diseases. Clinically, antibiotic treatment during platinum chemotherapy is associated with poor prognosis in patients with malignancy. In the present study, mucosal antibiotic exposure was assessed for its impact on renal distress as a sequela of platinum-based chemotherapy. Clinical transcriptome dataset-based evaluations demonstrated that levels of dysbiosis-responsive genes were elevated during renal distress, indicating pathological communications between gut and kidney. Experimentally, mucosal exposure to streptomycin aggravated platinum-induced renal tubular lesions in a mouse model. Moreover, antibiotic-induced dysbiosis increased susceptibility to gut mucosal inflammation, epithelial disruption, and bacterial exposure in response to cisplatin treatment. Further investigation of the luminal microbes indicated that antibiotic-induced dysbiosis promoted the dominance of Bacteroides species. Moreover, the functional assessment of dysbiotic microbiota predicted tryptophan metabolic pathways. In particular, dysbiosis-responsive Bacteroides acidifaciens was associated with the production of the uremic toxin indoxyl sulfate and renal injuries. The results of this study including bacterial community-based evaluations provide new predictive insights into the interorgan communications and interventions against dysbiosis-associated disorders.