Miracle-dz/UniFAR-FTUs huggingface dataset:
Inferring the dynamics of ionic currents from recursive piecewise data assimilation of approximate neuron models

We construct neuron models from data by transferring information from an observed time series to the state variables and parameters of Hodgkin-Huxley models. When the learning period completes, the model will predict additional observations and its parameters uniquely characterise the complement of ion channels. However, the assimilation of biological data, as opposed to model data, is complicated by the lack of knowledge of the true neuron equations. Reliance on guessed conductance models is plagued with multi-valued parameter solutions. Here, we report on the distributions of parameters and currents predicted with intentionally erroneous models, over-specified models, and an approximate model fitting hippocampal neuron data. We introduce a recursive piecewise data assimilation (RPDA) algorithm that converges with near-perfect reliability when the model is known. When the model is unknown, we show model error introduces correlations between certain parameters. The ionic currents reconstructed from these parameters are excellent predictors of true currents and carry a higher degree of confidence,>95.5%, than underlying parameters,>53%. Unexpressed ionic currents are correctly filtered out even in the presence of mild model error. When the model is unknown, the covariance eigenvalues of parameter estimates are found to be a good gauge of model error. Our results suggest that biological information may be retrieved from data by focussing on current estimates rather than parameters.
20 years of Greedy Randomized Adaptive Search Procedures with Path Relinking

This is a comprehensive review of the Greedy Randomized Adaptive Search Procedure (GRASP) metaheuristic and its hybridization with Path Relinking (PR) over the past two decades. GRASP with PR has become a widely adopted approach for solving hard optimization problems since its proposal in 1999. The paper covers the historical development of GRASP with PR and its theoretical foundations, as well as recent advances in its implementation and application. The review includes a critical analysis of variants of PR, including memory-based and randomized designs, with a total of ten different implementations. It describes these advanced designs both theoretically and practically on two well-known optimization problems, linear ordering and max-cut. The paper also explores the hybridization of GRASP with PR and other metaheuristics, such as Tabu Search and Scatter Search. Overall, this review provides valuable insights for researchers and practitioners seeking to utilize GRASP with PR for solving optimization problems.
Light-responsive active particles in a thermotropic liquid crystal

The development of synthetic microswimmers has advanced our understanding of the fundamental self-propelled mechanisms of living systems. However, there are scarce studies at the microscale within highly structured anisotropic media, such as bacteria or cellular receptors that swim in concentrated solutions of filamentous proteins or lipids with viscoelastic properties. Synthetic liquid crystals (LCs) have the potential to serve as biomimetic surrogates to study the structure and dynamics of living systems. Nevertheless, studies on thermotropic LCs have mainly focused on electro and magneto-phoretic effects, with a few others on diffusiophoresis or light-driven distortions of the LC nematic director. To the best of our knowledge, here we report self-thermophoretic experiments on thermotropic LCs for the first time. Our system consists of 2D confined Janus particles in 5CB with homeotropic anchoring on the particle and LC cell surfaces. The Janus particles include a conductive titanium coating that, upon exposure to an LED source, is heated and induces a local steady nematic-isotropic phase transition, leading to the self-propulsion of the particles orthogonally to the LC director. The trajectories of the Janus particles were tracked at different intensities of the applied light. A model is developed to describe the mean-squared displacement of a Janus particle suspended in a nematic LC. The model assumes that the Janus particle feels the LC as a continuum with the anisotropic viscosity of the bulk nematic phase. Moreover, the viscoelasticity of the LC is also considered. The model describes the experimental data well, and the fitting parameter related to the magnitude of the swimming force increases with the intensity of the applied light. Our approach suggests the possibility of utilizing Janus particles for microrheological measurements within nematic LCs.
CORECODE: A Common Sense Annotated Dialogue Dataset with Benchmark Tasks for Chinese Large Language Models

As an indispensable ingredient of intelligence, commonsense reasoning is crucial for large language models (LLMs) in real-world scenarios. In this paper, we propose CORECODE, a dataset that contains abundant commonsense knowledge manually annotated on dyadic dialogues, to evaluate the commonsense reasoning and commonsense conflict detection capabilities of Chinese LLMs. We categorize commonsense knowledge in everyday conversations into three dimensions: entity, event, and social interaction. For easy and consistent annotation, we standardize the form of commonsense knowledge annotation in open-domain dialogues as "domain: slot = value". A total of 9 domains and 37 slots are defined to capture diverse commonsense knowledge. With these pre-defined domains and slots, we collect 76,787 commonsense knowledge annotations from 19,700 dialogues through crowdsourcing. To evaluate and enhance the commonsense reasoning capability for LLMs on the curated dataset, we establish a series of dialogue-level reasoning and detection tasks, including commonsense knowledge filling, commonsense knowledge generation, commonsense conflict phrase detection, domain identification, slot identification, and event causal inference. A wide variety of existing open-source Chinese LLMs are evaluated with these tasks on our dataset. Experimental results demonstrate that these models are not competent to predict CORECODE's plentiful reasoning content, and even ChatGPT could only achieve 0.275 and 0.084 accuracy on the domain identification and slot identification tasks under the zero-shot setting. We release the data and codes of CORECODE at https://github.com/danshi777/CORECODE to promote commonsense reasoning evaluation and study of LLMs in the context of daily conversations.
Behind the Intent of Extract Method Refactoring: A Systematic Literature Review

Code refactoring is widely recognized as an essential software engineering practice to improve the understandability and maintainability of the source code. The Extract Method refactoring is considered as"Swiss army knife"of refactorings, as developers often apply it to improve their code quality. In recent years, several studies attempted to recommend Extract Method refactorings allowing the collection, analysis, and revelation of actionable data-driven insights about refactoring practices within software projects. In this paper, we aim at reviewing the current body of knowledge on existing Extract Method refactoring research and explore their limitations and potential improvement opportunities for future research efforts. Hence, researchers and practitioners begin to be aware of the state-of-the-art and identify new research opportunities in this context. We review the body of knowledge related to Extract Method refactoring in the form of a systematic literature review (SLR). After compiling an initial pool of 1,367 papers, we conducted a systematic selection and our final pool included 83 primary studies. We define three sets of research questions and systematically develop and refine a classification schema based on several criteria including their methodology, applicability, and degree of automation. The results construct a catalog of 83 Extract Method approaches indicating that several techniques have been proposed in the literature. Our results show that: (i) 38.6% of Extract Method refactoring studies primarily focus on addressing code clones; (ii) Several of the Extract Method tools incorporate the developer's involvement in the decision-making process when applying the method extraction, and (iii) the existing benchmarks are heterogeneous and do not contain the same type of information, making standardizing them for the purpose of benchmarking difficult.
Learning Performance Maximizing Ensembles with Explainability Guarantees

In this paper we propose a method for the optimal allocation of observations between an intrinsically explainable glass box model and a black box model. An optimal allocation being defined as one which, for any given explainability level (i.e. the proportion of observations for which the explainable model is the prediction function), maximizes the performance of the ensemble on the underlying task, and maximizes performance of the explainable model on the observations allocated to it, subject to the maximal ensemble performance condition. The proposed method is shown to produce such explainability optimal allocations on a benchmark suite of tabular datasets across a variety of explainable and black box model types. These learned allocations are found to consistently maintain ensemble performance at very high explainability levels (explaining 74% of observations on average), and in some cases even outperform both the component explainable and black box models while improving explainability.
Automated Testing for Text-to-Image Software

Recently, creative generative artificial intelligence software has emerged as a pivotal assistant, enabling users to generate content and seek inspiration rapidly. Text-to-image (T2I) software, being one of the most widely used among them, is used to synthesize images with simple text input by engaging in a cross-modal process. However, despite substantial advancements in several fields, T2I software often encounters defects and erroneous, including omitting focal entities, low image realism, and mismatched text-image information. The cross-modal nature of T2I software makes it challenging for traditional testing methods to detect defects. Lacking test oracles further increases the complexity of testing. To address this deficiency, we propose ACTesting, an Automated Cross-modal Testing Method of Text-to-Image software, the first testing method designed specifically for T2I software. We construct test samples based on entities and relationship triples following the fundamental principle of maintaining consistency in the semantic information to overcome the cross-modal matching challenges. To address the issue of testing oracle scarcity, we first design the metamorphic relation for T2I software and implement three types of mutation operators guided by adaptability density. In the experiment, we conduct ACTesting on four widely-used T2I software. The results show that ACTesting can generate error-revealing tests, reducing the text-image consistency by up to 20% compared with the baseline. We also conduct the ablation study that effectively showcases the efficacy of each mutation operator, based on the proposed metamorphic relation. The results demonstrate that ACTesting can identify abnormal behaviors of T2I software effectively.
Intermediation and Voluntary Exposure to Counterparty Risk ∗

I develop a model of financial sector in which endogenous intermediation among debt financed banks generates excessive systemic risk. The central idea is to explore the possibility that certain financial institutions are able to use their lending and borrowing decisions to tilt the division of surplus in their own favor through capturing intermediation spreads, even if the implied change in the structure of financial system hurts the total surplus of the economy. The paper predicts that there is excessive connection among banks who make risky investments and too little connection among those who mainly provide funding. Inefficiency arises because the financial institutions who intermediate among other institutions are exposed to excessive counterparty risk: replacing them with certain other banks mitigates the extent of failure when it is inevitable without hurting the optimal level of investment. In equilibrium, intermediators choose to over expose themselves to other risky banks and suffer the cost of failure due to contagion if they absorb enough rents when they survive. ∗I am extremely grateful to my advisors Douglas Diamond, Lars Hansen, Zhigou He and Raghuram Rajan for their invaluable input. I would also like to thank Ana Babus, Simcha Barkai, Alex Frankel, Micheal Gofman, Amir Kermani, Peter Kondor, Matthew Jackson, Amin Jafarian, Fabrice Tourre, Rasool Zandvakil and Luigi Zingales for their feedback and comments. Finally, I am grateful to the participants of the Networks in Macroeconomics and Finance Conference, Financial and Economic Networks Conference and Financial Markets and Contracts Graduate Student Conference, as well as the participants of the Applied Macro Working Group and Economics Dynamics Working Group at The University of Chicago. Research support from the Deutsche Bank Doctoral Fellowship, Bradley Foundation Fellowship, the Stevanovich Center for Financial Mathematics Fellowship and MFM Dissertation Support Fellowship is gratefully acknowledged; any opinions expressed herein are the author’s. †University of Chicago Booth School of Business and Department of Economics. Email: farboodi@chicagobooth.edu
Peri-operative radiotherapy for the prevention of post-operative fibrosis on rats

Background: Post-operative fibrosis related pains and functional anomalies can be serious problems for patients. Radiotherapy (RT) could inhibit fibrosis which may occurs postoperation especially following laminectomy. Therefore, this study was designed to investigate the effect of peri-operative RT on the prevention of post-operative fibrosis and scar formation on rats. Materials and Methods: Twenty three male Wistar albino rats, were divided into three experimental groups. Laminectomy was performed to L3 and L4 lomber vertebral regions of group L and L+R rats. The rats in L+R group in addition to this procedure also received peri-operative RT on the laminectomy area. A total dose of 700 cGy of RT was applied as a single fraction using electrons. Control rats received no treatment. A rotarod test was performed at 20, 30 and 40 rpm/min speed rates to determine the physical performances of the rats. Results: The results have indicated statistically significant (P<0.05) differences for the 30 rpm/min rotarod test between the L and L+R groups. Histopatologically, significant differences were observed in epidural fibrosis, fibroblast cell density and arachnoid adhesion between the L and L+R groups. Conclusion: It was concluded that peri-operative RT may provide a significant advantage for prevention post-laminectomic of scar
Indonesian village tourism: a bibliometric review

In Indonesia, tourism in rural or village settings is growing as a kind of tourist development that supports regional culture and history. These communities support the economic development of rural regions while providing tourists with a comprehensive rural experience. Low pay and unpleasant working conditions are obstacles, though. This study measures the local communities' preparedness for the creation of a tourism town and rates its efficacy. The study investigates the trends and clusters in village tourism research using bibliometric analysis. The results provide stakeholders in Indonesia information to support national growth and alleviate regional inequities.
Systems thinking approach for strategy evolution in the Indonesian energy corporation towards sustainable organization

Addressing the urgent global need for sustainable energy solutions, this pioneering study carves a transformative path for Indonesian Energy Corporations, with a spotlight on PARAMEL, a leading B2B downstream fuel company. By employing an innovative combination of the Analytical Hierarchy Process (AHP) and Systems Thinking, this research tackles the energy trilemma—security, affordability, and sustainability—through a mixed-methods approach integrating content analysis, AHP, and Causal Loop Diagram (CLD) methodologies to provide actionable insights for organizational change. The study identifies three crucial clusters for sustainable transition: Organizational Strategy and Alignment (OSA), Stakeholder Relations and Engagement (SRE), and Innovation, Culture, and Financial Performance (ICF). By understanding the hierarchical relationships between these factors, organizations can design targeted strategies for sustainability. The research advocates for PARAMEL's adoption of the Visionary Sustainability Pathway (VSP), emphasizing a comprehensive sustainability strategy supported by leadership commitment and effective communication. By leveraging innovative solutions and technologies, PARAMEL can enhance environmental performance and resource efficiency. Additionally, the study introduces PARENT, a business ecosystem shifting from traditional energy trading to solutions addressing the energy trilemma. The CANVAS+ Model of PARENT is presented, along with challenges of measuring organizational sustainability progress. By examining the Sustainable Corporate Story for PARENT, the extended KPIs that covered Environmental and Social aspect, Key Value Indicators (KVIs), and Key Decision-Making Indicators (KDIs) can be developed to guide organizations. In conclusion, this paper offers a blueprint for Indonesian Energy Corporations to adopt sustainable practices, exemplified by PARAMEL. The proposed mixed-methods approach identifies key factors driving sustainable change, and by pursuing the VSP and innovative solutions, corporations can enhance their environmental performance, resource efficiency, and commitment to sustainability.
Experimental investigation of removal of sulfur dioxide from exhaust gas by using semi-dry flue gas desulfurization (FGD)

Many coal-fired power generators use moderate exhaust gas systems or components to eliminate hydrogen sulphide from exhaust gases. The SO2 is eliminated by potassium adsorbent in a responsive dryer in mid FGD devices; however, the amount of dehydrogenation is smaller than that in wet FGD structures. The impacts of eight chemicals in exhaust gas dehydrogenation are studied in this study: mg, NaOH, potassium nitrate, ammonia solution, ammonium salts, hypochlorite, acetic acid, as well as nitrogen. Drilling fluids containing 2 and 8 wt% of addition with magnesium carbonate levels of 10 and 20 wt% were utilized to remove sulphur dioxide. Ammonium-containing additives accelerated sulphur dioxide elimination as well as boosted feedback and comments. Whenever a solution comprising 4% anhydrous ammonia and 12% CaO was applied to the exhaust gases, this same carbon dioxide level was decreased by 69.8%. Compared to the most significant win achieved with ca (OH)2 slurry with no addition, this solution boosted sulphur dioxide reduction by about 18.5%.
Virtual Routing Model and PI Rate Control Algorithm in High-band width Wireless LAN

Transmission accuracy of traditional Transmission Control Protocol (TCP) in high-bandwidth wireless network is low, and its efficiency is low. For these problems, the thesis proposes a virtual router model from the perspective of end-to-end, and on this basis of using the classical control theory the thesis proposes an end-to-end PI rate control algorithm--SPI RCP on the transmitter. According to the RTT information collected by the transmitter, the real-time queue length of virtual router is calculated, and by using the PI control theory the transmission rate of the transmitter is calculated to make the queue length of the bottleneck node is stabilized at a target location, at the same time to avoid the wrong adjust of congestion windows caused by wireless link packet loss. The end-to-end design of new algorithm saves router resources, enhances the stability of the queue length, and reduces network congestion packet loss. The simulation results show that compared with the traditional congestion control protocol, the new mechanism can better control the queue length of the router and improve the stability and network throughput of the network load.
Radiation and chemical reaction effects in MHD Casson fluid flow over a vertical porous surface

This article discusses the steady MHD Casson fluid past a vertical porous surface incorporating the effects of radiation, chemical reaction. Using similarity analysis the nonlinear system of PDEs are transformed to ODEs and then calculated by applying 4th and 5th order Runge-Kutta method. The graphs plotted illuminates the characteristics of heat and mass transfer flow, and their dependency upon the several parameters. Hear falling of velocity observed in Casson fluid parameter. Mathematical values of skin friction, rate of heat and mass transfer coefficients are examined in a tabular form.
How to Effectively Build the Image of an Emerging Destination

Abstract Only a few studies have focused on building the image of emerging destinations. The region’s image is influenced by its tourist attractiveness (image-related perception) and the knowledge about its tourist attributes (recognition). To build the image of emerging destinations, the attributes of attractiveness should be identified. In this study, such an attempt was made for Lublin Province. A diagnostic survey and statistical methods were used to assess the perception of its attractiveness and the recognition of the attractiveness attributes by visitors. Lublin Province has no tourism image, and its attractiveness attributes are identified only vaguely. The landscape is the only attribute on which the image of this destination can be built.
Dry Needling of Myofascial Trigger Points: Quantification of the Biomechanical Response Using a Myotonometer.

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A Blockchain Architecture to Increase the Resilience of Industrial Control Systems from the Effects of a Ransomware Attack: A Proposal and Initial Results

The motivation of this research (and also one of the nation’s cyber goals) is enhancing the resilience of Industrial Control Systems (ICS)/Supervisory Control and Data Acquisition (SCADA) systems against ransomware attacks. ICS and SCADA systems run some of the most important networks in the country: our critical infrastructure (i.e. water flow, power grids, etc.). Disruption of these systems causes confusion, panic, and in some cases loss of life. We propose a SCADA architecture that uses blockchain to help protect ICS data from ransomware. We focus on the historian. In a SCADA system, the historian collects events from devices in the control network for real-time and future analysis. We choose to use Ethereum and its Proof of Stake (PoS) consensus protocol. The other goal of this research focuses on the resilience of blockchain. There is very little research in protecting the blockchain itself. By performing encryption testing on an Ethereum private network, we explore how vulnerable blockchain is and discuss potential ways to make a blockchain client more resilient.