Gaussian filter to process tracer breakthrough curves

Breakthrough curves in hydrogeology are similar to seismograms in containing a variety of undesired noises and regular interferences characterized with high frequency. In this paper, Gaussian filter for processing seismic waves is used to retain low-frequency trend of breakthrough curves and remove away high-frequency fluctuations. At first, the mathematical fundamental of the filter is introduced. Then the filter is applied to process four breakthrough curves measured in laboratory experiments, in which Gaussian parameter is set to be 0.2 and 0.5. Finally, a breakthrough curve in field test is processed with different Gaussian parameters. The results demonstrate how the parameter controls the cutting-off frequency and the filter is well controllable and very efficient in acquiring the primary trend of the curves.Key words: Gaussian filter; convolution; breakthrough curves, cutting-off frequency, noises.Analiza sledilnih krivulj z Gaussovimi filtriSledilne krivulje (krivulja časovne odvisnosti koncentracije povrnjenega sledila) v hidrogeologiji so podobno kot seizmogrami v geofiziki obremenjene z nezaželenimi visokofrekvenčnimi šumi in interferencami. V tem članku uporabimo Gaussov filter, primarno namenjen obdelavi seizmičnih podatkov, za odstanitev visokofrekvenčnih šumov iz sledilnih krivulj. Najprej predstavimo matematične osnove filtriranja, potem Gaussov filter z parametrom 0,2 in 0,5 uporabimo na štirih sledilnih krivuljah, dobljenih v laboratorijskih pogojih. Na koncu z različnimi Gaussovimi parametri obravnavamo sledilno krivuljo, dobljeno pri sledenju v naravi. Z rezultati prikažemo vpliv izbranih parametrov na mejno frekvenco ter prilagodljivost , učinkovitost in uporabnost filtra za izluščenje primarnih značilnosti sledilnih krivulj.Ključne besede: Gaussov filter, konvolucija, sledilne krivulje, mejne frekvence, šum

Isolation of anticancer constituents from flos genkwa (Daphne genkwa Sieb.et Zucc.) through bioassay-guided procedures

BACKGROUND: Flos Genkwa (yuanhua in Chinese), the dried flower buds of Daphne genkwa Sieb.et Zucc. (Thymelaeaceae), is a traditional Chinese medicinal herb mainly used for diuretic, antitussive, expectorant, and anticancer effects. However, systematic and comprehensive studies on Flos Genkwa and its bioactivity are limited. RESULTS: After confirmation of the anti-tumor activity, the 95% ethanolic extract was subjected to successive solvent partitioning to petroleum ether, dichloromethane, n-butanol, and water soluble fractions. Each fraction was tested using the same biological activity model, and the dichloromethane fraction had the highest activity. The dichloromethane fraction was subjected to further chromatographic separation for the isolation of compounds 1–13. Among the 13 compounds, the diterpene esters (compounds 10–13) showed anticancer activity, whereas the flavonoids, lignanoids, and peptides showed moderate activity. Compound 13 was a new daphnane diterpenoid, which was named genkwanin VIII. The preliminary antitumor mechanism of yuanhuacine was studied by protein expression and cell cycle analysis in MCF-7 cancer cells. CONCLUSION: The present investigation tends to support the traditional use of Flos Genkwa for treating cancer. Through bioassay-guided fractionation and isolation techniques, the CH(2)Cl(2) fraction was determined as the active fraction of the flower buds of D. genkwa, and the anti-tumor activity was ascribable to the compounds 10–13

Eliminating Reasoning via Inferring with Planning: A New Framework to Guide LLMs' Non-linear Thinking

Chain-of-Thought(CoT) prompting and its variants explore equipping large language models (LLMs) with high-level reasoning abilities by emulating human-like linear cognition and logic. However, the human mind is complicated and mixed with both linear and nonlinear thinking. In this work, we propose \textbf{I}nferential \textbf{E}xclusion \textbf{P}rompting (IEP), a novel prompting that combines the principles of elimination and inference in order to guide LLMs to think non-linearly. IEP guides LLMs to plan and then utilize Natural Language Inference (NLI) to deduce each possible solution's entailment relation with context, commonsense, or facts, therefore yielding a broader perspective by thinking back for inferring. This forward planning and backward eliminating process allows IEP to better simulate the complex human thinking processes compared to other CoT-based methods, which only reflect linear cognitive processes. We conducted a series of empirical studies and have corroborated that IEP consistently outperforms CoT across various tasks. Additionally, we observe that integrating IEP and CoT further improves the LLMs' performance on certain tasks, highlighting the necessity of equipping LLMs with mixed logic processes. Moreover, to better evaluate comprehensive features inherent in human logic, we introduce \textbf{M}ental-\textbf{A}bility \textbf{R}easoning \textbf{B}enchmark (MARB). The benchmark comprises six novel subtasks with a total of 9,115 questions, among which 1,685 are developed with hand-crafted rationale references. We believe both \textsc{IEP} and \textsc{MARB} can serve as a promising direction for unveiling LLMs' logic and verbal reasoning abilities and drive further advancements. \textsc{MARB} will be available at ~\texttt{anonymity link} soon

Effects of Hydrological and Climatic Variables on Cyanobacterial Blooms in Four Large Shallow Lakes Fed by the Yangtze River

Shallow lakes, one of the most widespread water bodies in the world, are easily shifted to a new trophic state due to external interferences. Shifting hydrologic conditions and climate change can cause cyanobacterial harmful algal blooms (CyanoHABs) in shallow lakes, which pose serious threats to ecological integrity and human health. This study analyzed the effects of hydrologic and meteorological variables on cyanobacterial blooms in Yangtze-connected lakes (Lake Dongting and Poyang) and isolated lakes (Lake Chao and Tai). The results show that (i) chlorophyll-a (Chl-a) concentration tends to decrease exponentially with increasing relative lake level fluctuations (RLLF) and precipitation, but to increase linearly with increasing wind speed and air temperature; (ii) Chl-a concentrations in lakes were significantly higher when RLLF \u3c 100, precipitation \u3c 2.6 mm, wind speed \u3e 2.6 m s−1, or air temperature \u3e 17.8 °C; (iii) the Chl-a concentration of Yangtze-isolated lakes was more significantly affected by water level amplitude, precipitation, wind speed and air temperature than the Yangtze-connected lakes; (iv) the RLLF and the ratio of wind speed to mean water depth could be innovative coupling factors to examine variation characteristics of Chl-a in shallow lakes with greater correlation than single factors

Neurologic Abnormalities in Workers of a 1-Bromopropane Factory

We reported recently that 1-bromopropane (1-BP; n-propylbromide, CAS Registry no. 106-94-5), an alternative to ozone-depleting solvents, is neurotoxic and exhibits reproductive toxicity in rats. The four most recent case reports suggested possible neurotoxicity of 1-BP in workers. The aim of the present study was to establish the neurologic effects of 1-BP in workers and examine the relationship with exposure levels. We surveyed 27 female workers in a 1-BP production factory and compared 23 of them with 23 age-matched workers in a beer factory as controls. The workers were interviewed and examined by neurologic, electrophysiologic, hematologic, biochemical, neurobehavioral, and postural sway tests. 1-BP exposure levels were estimated with passive samplers. Tests with a tuning fork showed diminished vibration sensation of the foot in 15 workers exposed to 1-BP but in none of the controls. 1-BP factory workers showed significantly longer distal latency in the tibial nerve than did the controls but no significant changes in motor nerve conduction velocity. Workers also displayed lower values in sensory nerve conduction velocity in the sural nerve, backward recalled digits, Benton visual memory test scores, pursuit aiming test scores, and five items of the Profile of Mood States (POMS) test (tension, depression, anxiety, fatigue, and confusion) compared with controls matched for age and education. Workers hired after May 1999, who were exposed to 1-BP only (workers hired before 1999 could have also been exposed to 2-BP), showed similar changes in vibration sense, distal latency, Benton test scores, and depression and fatigue in the POMS test. Time-weighted average exposure levels in the workers were 0.34–49.19 ppm. Exposure to 1-BP could adversely affect peripheral nerves or/and the central nervous system

Open Design and 3D Printing of Face Shields: The Case Study of a UK-China Initiative

At the start of the COVID-19 outbreak, many countries lacked personal protective equipment (PPE) to protect healthcare workers. To address this problem, open design and 3D printing technologies were adopted to provide much-in-need PPEs for key workers. This paper reports an initiative by designers and engineers in the UK and China. The case study approach and content analysis method were used to study the stakeholders, the design process, and other relevant issues such as regulation. Good practice and lessons were summarised, and suggestions for using distributed 3D printing to supply PPEs were made. It concludes that 3D printing has played an important role in producing PPEs when there was a shortage of supply, and distributed manufacturing has the potential to quickly respond to local small-bench production needs. In the future, clearer specification, better match of demands and supply, and quicker evaluation against relevant regulations will provide efficiency and quality assurance for 3D printed PPE supplies

Leveraging Large Language Models for Pre-trained Recommender Systems

Recent advancements in recommendation systems have shifted towards more comprehensive and personalized recommendations by utilizing large language models (LLM). However, effectively integrating LLM's commonsense knowledge and reasoning abilities into recommendation systems remains a challenging problem. In this paper, we propose RecSysLLM, a novel pre-trained recommendation model based on LLMs. RecSysLLM retains LLM reasoning and knowledge while integrating recommendation domain knowledge through unique designs of data, training, and inference. This allows RecSysLLM to leverage LLMs' capabilities for recommendation tasks in an efficient, unified framework. We demonstrate the effectiveness of RecSysLLM on benchmarks and real-world scenarios. RecSysLLM provides a promising approach to developing unified recommendation systems by fully exploiting the power of pre-trained language models.Comment: 13 pages, 4 figure