Search Results (176)

Culinary rhubarb is grown for its large, thick leaf petioles. Red-stalked cultivars and selection are more attractive for the fresh market and processing industry. In vitro cultures are important for rapidly multiplying value genotypes and producing plants free of viruses. This study aimed to develop an in vitro propagation method for six rhubarb selections from the Raspberry (R1, R2, R3), Leader (L1, L2), and Karpow Lipskiego (KL) groups. In addition, the planting material of six rhubarb selections was subjected to phytochemical analysis using the HPLC method to assess the content of bioactive compounds. The cultures were initiated from underground buds on the crowns. The initial growth was obtained for 45 to 75% of initial explants because of bacterial contamination and low bud activity of some genotypes. The type and concentration of cytokinin and its interaction with gibberellin acid (GA₃) were shown to have a significant effect on the cyclic multiplication and commercially interesting rate of all rhubarb genotypes. A high in vitro rooting frequency, 93.7 to 95.8% for rhubarb Raspberry, 94.3 to 100% for rhubarb Leader, and 96.7% KP selections were obtained after two-cycle rooting on a medium containing IBA and NAA. The polyphenolic compounds, such as cyanidin-3-O-rutinoside, rhaponticin, resveratrol, caffeic acid, p-coumaric acid, cinnamic acid, syringic acid, and ferulic acid were detected in selected rhubarb genotypes. The highest content of anthocyanins (2.9 mg·1 g⁻¹ DM) and rhaponticin (107.8 µg·1 g⁻¹ DM) was found in Raspberry selections. On the other hand, Leader selections were characterized by the highest content of resveratrol (0.25 µg·1 g⁻¹ DM) and phenolic acids (1.3 µg·1 g⁻¹ DM). The less attractive for functional food production seems to be KL selection. Full article

The main goal of the paper is to characterize families of $[r,s]$-(upper) superporous subsets of $\mathbb{R}$, which generalize well-known notions of superporosity and strong superporosity of subsets of $\mathbb{R}$. Definitions and properties of $[r,s]$-superporosity are symmetric to definitions and properties of superporosity and strong superporosity. The purpose in all cases is to define small subsets of the line using the notion of porosity. Superporous sets preserve positive porosity, and strongly superporous sets preserve strong porosity; i.e., if E is superporous (correspondingly, E is strongly superporous), then for every $x\in E$ and for every F such that porosity of F at x is greater than 0 (correspondingly, is equal to 1), porosity of $E\cup F$ at x is greater than 0 (correspondingly, is equal to 1). Taking arbitrary positive porosity, instead of 0 or 1, we obtain the symmetric definition as follows: $[r,s]$-superporosity for $0<r\le s<1$ transfers s-porosity to r-porosity; i.e., if E is $[r,s]$-superporous, then for every $x\in E$ and for every F such that porosity of F at x is not less than s, porosity of $E\cup F$ at x is not less than r. Even though the definition and properties of $[r,s]$-superporosity, superporosity and strong superporosity are symmetric and all of them consist of very small sets, the families of these sets are essentially different. In the paper, we focus on relationships between $[r,s]$-superporous sets for different indices $[r,s]$. Furthermore, we compare $[r,s]$-superporosity to superporosity and strong superporosity. We apply the notion of $[r,s]$-superporosity to find multipliers and adders of porouscontinuous functions. Full article

This work presents the design of optimal and multiplierless compensators for Chebyshev sharpened comb decimation filters. The narrowband and wideband compensators are proposed. For the narrowband, the compensator with a magnitude response is proposed in a sinusoidal form, while for the wideband, two compensators with magnitude responses of two sinusoidal functions are introduced. The optimum design is performed using particle swarm optimization (PSO), while the multiplierless design is realized by presenting optimum parameters in a signed-power-of-two (SPT) form. Unlike the methods in the literature, this approach presents flexibility in design, allowing for an exchange between the quality of optimization and the complexity. Comparisons with the compensators from the literature demonstrated that the proposed method provides much better compensation while requiring fewer or slightly increased number of adders. Possible practical applications and potential future research work are also included. Full article

Recent studies have shown that by using channel-correlation and cosparsity in a centralized framework, the accuracy of reconstructing multichannel EEG signals can be improved. A single-channel electroencephalogram (EEG) signal is intrinsically non-sparse in both the converted and raw time domains, which presents a number of important issues. However, this is ignored by contemporary compressive sensing (CS) algorithms, resulting in less recovery quality than is ideal. To address these constraints, we provide a novel CS method that takes advantage of Nonlocal Low-Rank and Cosparse priors (NLRC). By utilizing low-rank approximations and block operations, our method aims to improve the CS recovery process and take advantage of channel correlations. The Alternating Direction Method of Multipliers (ADMM) are also used to efficiently solve the resulting non-convex optimization problem. The outcomes of the experiments unequivocally demonstrate that by using NLRC, the quality of signal reconstruction is significantly enhanced. Full article

A multi-level 2D Discrete wavelet transform (DWT) architecture for JPEG2000 is proposed, enhancing speed through parallel processing multiple tile blocks. Based on the lifting scheme, folded architecture and unfolded architecture achieving critical path delay with only one multiplier are designed to increase throughput rate. Connecting the folded and unfolded architecture through a pipeline architecture ensures uniform throughput rates across all DWT levels within a singular clock domain. Computational resource consumption is reduced by adjusting the timing to allow one folded architecture to process three tile blocks of three to five levels of DWT, and a transposing module requiring merely six registers is devised to decrease storage resource consumption. The quantization module, crucial for code-word control in JPEG2000, is integrated into the scaling module with minimal additional resource expenditure. Compared to the existing architecture, the analysis demonstrates that the proposed architecture exhibits enhanced hardware efficiency, with a reduction in transistor-delay-product (TDP) of no less than 14.69%. Synthesis results further reveal an area reduction of at least 26.64%, and a decrease in area-delay-product (ADP) by a minimum of 29.89%. Results from FPGA implementation indicate a significant decrease in resource utilization. Full article

The maritime industry is under increasing pressure to reduce CO₂ emissions, with the International Maritime Organization (IMO) setting a target to reduce greenhouse gas emissions, including emissions from the port sector, by 40% by 2030. However, accurate and reliable methods for estimating CO₂ emissions at container ports, which are significant contributors to maritime emissions, are still lacking. This study aims to address this by evaluating a novel method for estimating CO₂ emissions at container ports. The proposed method utilizes the cargo handling equipment movement theory, quantifying both vertical and horizontal movements based on the amount of container handling equipment at the port. The emissions for each piece of equipment are estimated by multiplying the movement quantity by the respective emission factor. To validate the model, a robustness test compares the estimated CO₂ emissions with actual energy consumption data from the port. A case study was conducted at a container port with an annual capacity of over 500,000 TEUs and a parallel layout type. The estimated CO₂ emissions were approximately 8183 tons per year, with container cranes contributing 56%, rubber-tire gantry cranes contributing 27%, terminal trucks contributing 14%, and reach stackers contributing 3%. The method demonstrated accuracy, with a deviation of less than 1%. This method offers a fast and reliable approach for estimating baseline CO₂ emissions at container ports, providing valuable insights for port authorities and policymakers to develop more effective emission-reduction strategies. Full article

Plant pathogenic Pseudomonas species cause a variety of diseases in plants. Each Pseudomonas species employs different virulence factors and strategies for successful infection. Moreover, even the same bacterial pathogens can differentially utilize virulence factors against various host plants. However, there has been relatively less emphasis on comparing the infection strategies of a single bacterial pathogen on different hosts and different bacterial pathogens on a single host. Here, we investigated plant–pathogen interactions using two Pseudomonas species, Pseudomonas cannabina pv. alisalensis (Pcal) KB211 and Pseudomonas syringae pv. tomato (Pst) DC3000, and their host plants, cabbage and tomato. Our findings reveal distinct behaviors and virulence patterns across different host plants. Pcal multiplies to greater levels in cabbage compared to tomato, suggesting that Pcal is more adaptive in cabbage than tomato. Conversely, Pst showed robust multiplication in tomato even at lesser inoculum levels, indicating its aggressiveness in the apoplastic space. Gene expression analyses indicate that these pathogens utilize distinct virulence-related gene expression profiles depending on the host plant. These insights highlight the importance of revealing the spatiotemporal regulation mechanisms of virulence factors. Full article

Object detection architectures struggle to detect small objects across applications including remote sensing and autonomous vehicles. Specifically, for unmanned aerial vehicles, poor detection of small objects directly limits this technology’s applicability. Objects both appear smaller than they are in large-scale images captured in aerial imagery and are represented by reduced information in high-altitude imagery. This paper presents a new architecture, CR-CNN, which predicts independent regions of interest from two unique prediction branches within the first stage of the network: a conventional R-CNN convolutional backbone and an hourglass backbone. Utilizing two independent sources within the first stage, our approach leads to an increase in successful predictions of regions that contain smaller objects. Anchor-based methods such as R-CNNs also utilize less than half the number of small objects compared to larger ones during training due to the poor intersection over union (IoU) scores between the generated anchors and the groundtruth—further reducing their performance on small objects. Therefore, we also propose artificially inflating the IoU of smaller objects during training using a simple, size-based Gaussian multiplier—leading to an increase in the quantity of small objects seen per training cycle based on an increase in the number of anchor–object pairs during training. This architecture and training strategy led to improved detection overall on two challenging aerial-based datasets heavily composed of small objects while predicting fewer false positives compared to Mask R-CNN. These results suggest that while new and unique architectures will continue to play a part in advancing the field of object detection, the training methodologies and strategies used will also play a valuable role. Full article

Telomere length is an index of cellular aging. Healthy lifestyles are associated with reduced oxidative stress and longer telomeres, whereas unhealthy behaviors are related to shorter telomeres and greater biological aging. This investigation was designed to determine if strength training accounted for differences in telomere length in a random sample of 4814 US adults. Data from the National Health and Nutrition Examination Survey (NHANES) were employed to answer the research questions using a cross-sectional design. Time spent strength training was calculated by multiplying days of strength training per week by minutes per session. Participation in other forms of physical activity was also calculated based on reported involvement in 47 other activities. Weighted multiple regression and partial correlation were used to calculate the mean differences in telomere length across levels of strength training, adjusting for differences in potential confounders. With the demographic covariates controlled, strength training and telomere length were linearly related (F = 14.7, p = 0.0006). Likewise, after adjusting for all the covariates, the linear association remained strong and significant (F = 14.7, p = 0.0006). In this national sample, 90 min per week of strength training was associated with 3.9 years less biological aging, on average. Regular strength training was strongly related to longer telomeres and less biological aging in 4814 US adults. Full article

This article lies at the intersection of migration studies and disability studies and aims to contribute to redressing the considerable gap in knowledge regarding disabled voluntary migrants. These two areas, migration and disability, respectively, have rarely been considered together, a significant gap given the situation faced by disabled migrants and crosscutting issues confronting disabled people and migrants. Dynamics of exclusion are viewed as a shared experience of migrants, disabled people and disabled migrants. This paper is based on qualitative semi-structured interviews with voluntary EU migrants to the UK, disabled British people and disabled EU migrants, which are supplemented by key informant interviews. All were conducted in 2019 in the north of England. The findings highlight that when migration and disability are considered concurrently, barriers multiply and result in a nuanced disadvantage and experiences of social marginalisation. Migrant and disability experience translates into social vulnerability and is a contributing cause of exclusion in relation to social expectations and mainstream services. This paper concludes that there is an urgent need to change the narrative and perception that migrant and disabled people are less worthy of attention and bring their needs to the fore. Full article

The radio spectrum is a scarce and extremely valuable resource that demands careful real-time monitoring and dynamic resource allocation. Dynamic spectrum access (DSA) is a new paradigm for managing the radio spectrum, which requires AI/ML-driven algorithms for optimum performance under rapidly changing channel conditions and possible cyber-attacks in the electromagnetic domain. Fast sensing across multiple directions using array processors, with subsequent AI/ML-based algorithms for the sensing and perception of waveforms that are measured from the environment is critical for providing decision support in DSA. As part of directional and wideband spectrum perception, the ability to finely channelize wideband inputs using efficient Fourier analysis is much needed. However, a fine-grain fast Fourier transform (FFT) across a large number of directions is computationally intensive and leads to a high chip area and power consumption. We address this issue by exploiting the recently proposed approximate discrete Fourier transform (ADFT), which has its own sparse factorization for real-time implementation at a low complexity and power consumption. The ADFT is used to create a wideband multibeam RF digital beamformer and temporal spectrum-based attention unit that monitors 32 discrete directions across 32 sub-bands in real-time using a multiplierless algorithm with low computational complexity. The output of this spectral attention unit is applied as a decision variable to an intelligent receiver that adapts its center frequency and frequency resolution via FFT channelizers that are custom-built for real-time monitoring at high resolution. This two-step process allows the fine-gain FFT to be applied only to directions and bands of interest as determined by the ADFT-based low-complexity 2D spacetime attention unit. The fine-grain FFT provides a spectral signature that can find future use cases in neural network engines for achieving modulation recognition, IoT device identification, and RFI identification. Beamforming and spectral channelization algorithms, a digital computer architecture, and early prototypes using a 32-element fully digital multichannel receiver and field programmable gate array (FPGA)-based high-speed software-defined radio (SDR) are presented. Full article

To promote cooperation and strengthen the individual impact on the collective outcome in social dilemmas, we propose the Environmental-impact Multi-Agent Reinforcement Learning (EMuReL) method where each agent estimates the “environmental impact” of every other agent, that is, the difference in the current environment state compared to the hypothetical environment in the absence of that other agent. Inspired by the inequity aversion model, the agent then compares its own reward with that of its fellows multiplied by their environmental impacts. If its reward exceeds the scaled reward of one of its fellows, the agent takes “social responsibility” toward that fellow by reducing its own reward. Therefore, the less influential an agent is in reaching the current state, the more social responsibility is taken by other agents. Experiments in the Cleanup (resp. Harvest) test environment demonstrated that agents trained based on EMuReL learned to cooperate more effectively and obtained $54\%$ ($39\%$) and $20\%$ ($44\%$) more total rewards while preserving the same cooperation levels compared to when they were trained based on the two state-of-the-art reward reshaping methods: inequity aversion and social influence. Full article

(1) Background: Rare skin cancers include epithelial, neuroendocrine, and hematopoietic neoplasias as well as cutaneous sarcomas. Ultraviolet (UV) radiation and sunburns are important drivers for the incidence of certain cutaneous sarcomas; however, the pathogenetic role of UV light is less clear in rare skin cancers compared to keratinocyte cancer and melanoma. In this study, we compared the degree of actinic elastosis (AE) as a surrogate for lifetime UV exposure among selected rare skin cancers (atypical fibroxanthoma [AFX], pleomorphic dermal sarcoma [PDS], dermatofibrosarcoma protuberans [DFSP], Kaposi sarcoma [KS], Merkel cell carcinoma [MCC], and leiomyosarcoma [LMS]) while taking into account relevant clinical variables (age, sex, and body site). (2) Methods: We newly established a semi-quantitative score for the degree of AE ranging from 0 = none to 3 = total loss of elastic fibers (basophilic degeneration) and multiplied it by the perilesional vertical extent (depth), measured histometrically (tumor-associated elastosis grade (TEG)). We matched the TEG of n = 210 rare skin cancers from 210 patients with their clinical variables. (3) Results: TEG values were correlated with age and whether tumors arose on UV-exposed body sites. TEG values were significantly higher in AFX and PDS cases compared to all other analyzed rare skin cancer types. As expected, TEG values were low in DFSP and KS, while MCC cases exhibited intermediate TEG values. (4) Conclusions: High cumulative UV exposure is more strongly associated with AFX/PDS and MCC than with other rare skin cancers. These important results expand the available data associated with rare skin cancers while also offering insight into the value of differentiating among these tumor types based on their relationship with sun exposure, potentially informing preventative, diagnostic and/or therapeutic approaches. Full article

In recent years, the variability in the composition of cement raw materials has increasingly impacted the quality of cement products. However, there has been relatively little research on the homogenization effects of equipment in the cement production process. Existing studies mainly focus on the primary functions of equipment, such as the grinding efficiency of ball mills, the thermal decomposition in cyclone preheaters, and the thermal decomposition in rotary kilns. This study selected four typical pieces of equipment with significant homogenization functions for an in-depth investigation: ball mills, pneumatic homogenizing silos, cyclone preheaters, and rotary kilns. To assess the homogenization efficacy of each apparatus, scaled-down models of these devices were constructed and subjected to simulated experiments. To improve experimental efficiency and realistically simulate actual production conditions in a laboratory setting, this study used the uniformity of the electrical capacitance of mixed powders instead of compositional uniformity to analyze homogenization effects. The test material in the experiment consisted of a mixture of raw meal from a cement factory with a high dielectric constant and Fe₃O₄ powder. The parallel plate capacitance method was employed to ascertain the capacitance value of the mixed powder prior to and subsequent to treatment by each equipment model. The fluctuation of the input and output curves was analyzed, and the standard deviation (S), coefficient of variation (R), and homogenization multiplier (H) were calculated in order to evaluate the homogenization effect of each equipment model on the raw meal. The findings of the study indicated that the pneumatic homogenizer exhibited an exemplary homogenization effect, followed by the ball mill. For the ball mill, a higher proportion of small balls in the gradation can significantly enhance the homogenization effect without considering the grinding efficiency. The five-stage cyclone preheater also has a better homogenization effect, while the rotary kiln has a less significant homogenization effect on raw meal. Finally, the raw meal processed by each equipment model was used for clinker calcination and the preparation of cement mortar samples. After curing for three days, the compressive and flexural strengths of the samples were tested, thereby indirectly verifying the homogenization effect of each equipment model on the raw meal. This study helps to understand the homogenization process of raw materials by equipment in cement production and provides certain reference and data support for equipment selection, operation optimization, and quality control in the cement production process. Full article