Benne: A Modular and Self-Optimizing Algorithm for Data Stream Clustering

In various real-world applications, ranging from the Internet of Things (IoT) to social media and financial systems, data stream clustering is a critical operation. This paper introduces Benne, a modular and highly configurable data stream clustering algorithm designed to offer a nuanced balance between clustering accuracy and computational efficiency. Benne distinguishes itself by clearly demarcating four pivotal design dimensions: the summarizing data structure, the window model for handling data temporality, the outlier detection mechanism, and the refinement strategy for improving cluster quality. This clear separation not only facilitates a granular understanding of the impact of each design choice on the algorithm's performance but also enhances the algorithm's adaptability to a wide array of application contexts. We provide a comprehensive analysis of these design dimensions, elucidating the challenges and opportunities inherent to each. Furthermore, we conduct a rigorous performance evaluation of Benne, employing diverse configurations and benchmarking it against existing state-of-the-art data stream clustering algorithms. Our empirical results substantiate that Benne either matches or surpasses competing algorithms in terms of clustering accuracy, processing throughput, and adaptability to varying data stream characteristics. This establishes Benne as a valuable asset for both practitioners and researchers in the field of data stream mining

Transcriptomics and metabolomics reveal the primary and secondary metabolism changes in Glycyrrhiza uralensis with different forms of nitrogen utilization

The roots and rhizomes of Glycyrrhiza uralensis Fisch. represent the oldest and most frequently used herbal medicines in Eastern and Western countries. However, the quality of cultivated G. uralensis has not been adequate to meet the market demand, thereby exerting increased pressure on wild G. uralensis populations. Nitrogen, vital for plant growth, potentially influences the bioactive constituents of plants. Yet, more information is needed regarding the effect of different forms of nitrogen on G. uralensis. G. uralensis seedlings were exposed to a modified Hoagland nutrient solution (HNS), varying concentrations of nitrate (KNO3), or ammonium (NH4)2SO4. We subsequently obtained the roots of G. uralensis for physiology, transcriptomics, and metabolomics analyses. Our results indicated that medium-level ammonium nitrogen was more effective in promoting G. uralensis growth compared to nitrate nitrogen. However, low-level nitrate nitrogen distinctly accelerated the accumulation of flavonoid ingredients. Illumina sequencing of cDNA libraries prepared from four groups—treated independently with low/medium NH4+ or NO3- identified 364, 96, 103, and 64 differentially expressed genes (DEGs) in each group. Our investigation revealed a general molecular and physiological metabolism stimulation under exclusive NH4+ or NO3- conditions. This included nitrogen absorption and assimilation, glycolysis, Tricarboxylic acid (TCA) cycle, flavonoid, and triterpenoid metabolism. By creating and combining putative biosynthesis networks of nitrogen metabolism, flavonoids, and triterpenoids with related structural DEGs, we observed a positive correlation between the expression trend of DEGs and flavonoid accumulation. Notably, treatments with low-level NH4+ or medium-level NO3- positively improved primary metabolism, including amino acids, TCA cycle, and glycolysis metabolism. Meanwhile, low-level NH4+ and NO3- treatment positively regulated secondary metabolism, especially the biosynthesis of flavonoids in G. uralensis. Our study lays the foundation for a comprehensive analysis of molecular responses to varied nitrogen forms in G. uralensis, which should help understand the relationships between responsive genes and subsequent metabolic reactions. Furthermore, our results provide new insights into the fundamental mechanisms underlying the treatment of G. uralensis and other Glycyrrhiza plants with different nitrogen forms

A novel solar-assisted ground-source heat pump (SAGSHP) with seasonal heat-storage and heat cascade utilization: field test and performance analysis

To maintain the energy quality with high temperature and reduce the energy loss of seasonal heat-storage in solar-assisted ground-source heat pumps (SAGSHPs), a novel SAGSHP system with the heat-cascading of borehole heat-exchangers was designed and its field-test was conducted in this paper. The borehole heat-exchangers were divided into two regions: the core region and the peripheral region. The core region can maintain a high temperature (e.g. 45 ℃), which is much higher than in previous studies, and the heat from this region can be used directly, without the operation of a heat pump. The field-test was conducted in a community in the province Shandong, China. The results indicate that a sufficient soil-temperature gradient (the temperature is high in the core but low at the periphery) can be created and maintained. The monthly averaged borehole-wall-temperature difference between the borehole heat-exchangers (BHEs) at the core and the periphery can be as high as 30.1 ℃. This means that both cascaded heat-storage and heat-utilization can be realized. In addition, an average performance of CCOP=5.15 and SCOP=4.66 can be achieved. Compared with previous studies, despite the lower CCOP, a higher SCOP can be attained, thanks to heat cascade storage and -utilization. The novel approach described in this paper represents a viable alternative for space heating in North China

Photocatalytic degradation of gaseous toluene over ZnAl(2)O(4) prepared by different methods: A comparative study

The development of a " green" treatment process for typical indoor pollutants such as toluene is greatly desirable. In this study, ZnAlO nanoparticles were prepared via three different routes, i.e., solvothermal, citrate precursor and hydrothermal methods. Their structural properties were systematically investigated by X-ray powder diffraction (XRD), scanning electronic microscopy (SEM), energy-dispersive X-ray spectra (EDX), Brunauer-Emmett-Teller (BET), UV-vis diffuse reflectance spectroscopy (DRS), and Fourier transform infrared spectroscopy (FT-IR) techniques. The photo-induced charge separation in the samples was demonstrated by surface photovoltage (SPV) measurement. The photocatalytic performances of the ZnAlO samples and nanostructured TiO samples were comparatively studied by the degradation of gaseous toluene under UV lamp irradiation in in situ FTIR reactor. The results indicated that the sample synthesized by facile solvothermal method exhibited about 90% photocatalytic efficiency of toluene. The toluene was mineralized into carbon dioxide and water as the major species. The photocatalytic oxidation of gaseous pollutant over UV-illuminated ZnAlO is a promising technique for air purification

Vitreous Decompression Combined with Phacoemulsification for Medically Unresponsive Acute Angle Closure

The management of acute angle closure combined with an extremely shallow anterior chamber and cataract remains complex. This study evaluated a technique of vitreous needle aspiration combined with phacoemulsification for the treatment of acute angle closure with continuous high intraocular pressure (IOP). We retrospectively reviewed the results of vitreous needle aspiration combined with phacoemulsification in 17 eyes (17 patients) with acute angle closure with continuous high IOP and coexisting visually significant cataracts between September 2018 and April 2020 at the glaucoma unit of the affiliated Changshu Hospital of Xuzhou Medical University. The main outcomes were the best corrected visual acuity (BCVA), IOP, anterior chamber depth (ACD), angle open distance 500 (AOD500), number of antiglaucoma medications, and surgery-associated complications. There were no complications during phacoemulsification and a foldable acrylic intraocular lens was implanted in the capsular bag in all 17 patients. For all patients, vitreous needle aspiration was successful at the first attempt. The BCVA improved from 2.02 ± 0.54 logMAR preoperatively to 0.73 ± 0.57 logMAR postoperatively at the final examination (p<0.001). The mean IOP was 54.47 ± 5.33 mmHg preoperatively and 15.59 ± 2.35 mmHg at the final examination (p<0.001) without any medication. The ACD was 1.70 ± 0.16 mm preoperatively and 3.35 ± 1.51 mm at the final examination (p<0.001). The AOD500 was 0.07 ± 0.02 mm preoperatively and 0.51 ± 0.04 mm at the final examination (p<0.001). Our vitreous needle aspiration technique can be performed safely in phacoemulsification for the management of acute angle closure with continuous high IOP

A Novel Class-Imbalanced Ship Motion Data-Based Cross-Scale Model for Sea State Estimation

Sea state estimation (SSE) is significant to the development of autonomous ships, which can enhance the sustainable development of maritime transportation. Traditional model-based methods are limited by their drawbacks, such as high costs and inaccurate estimations. The deep learning model shows superior performance, but it requires that the sample quantity for each sea state should be almost the same. Since the occurrence probability of each state is different, the ships mainly work in low sea states, and the collected ship motion data for different sea states are highly imbalanced. This work proposes a novel class-imbalanced ship motion data-based cross-scale model for SSE. The model consists of three major components: a multi-scale feature learning module, a cross-scale feature learning module, and a prototype classifier module. The multi-scale and cross-scale feature learning modules are designed to learn abundant coarse and fine-level features from the ship motion data. The prototype classifier is utilized to overcome the limitation of the conventional softmax classifier to produce better estimates. Our research highlights our model’s remarkable scalability and versatility with 30 publicly available datasets in time series classification, demonstrating superior performance over baseline methods in 21 cases. Notably, it outperformed ShapeNet by 5.72% and EDI by 26.3%. We further validated our model’s proficiency using ship motion datasets, consistently surpassing eight state-of-the-art baselines and five class-imbalanced learning methods. Ablation and sensitivity studies, emphasize the critical role of each model component. Our findings underscore the model’s robustness and its potential to advance time series classification in diverse domains