Mental Visual Imagery, Authenticity and Consumers' Attitude Formation Towards Licensed Brands

Licensed brand refers broadly to any brands that are manufactured and marketed by someone other than the brand owner. In order for the licensed brand to be perceived as authentic, the cues for communicating authenticity are crucial between marketers and consumers. A person construes the cues via the formation of mental visual image before further deriving his/her perception about the authenticity of a licensed brand. This research examines different types of visual mental image, attributes and assessment of authenticity and consumers' attitude formation associated with licensed brand

Toona Sinensis Extracts Induced Cell Cycle Arrest and Apoptosis in the Human Lung Large Cell Carcinoma

Toona sinensis extracts have been shown to exhibit anti-cancer effects in human ovarian cancer cell lines, human promyelocytic leukemia cells and human lung adenocarcinoma. Its safety has also been confirmed in animal studies. However, its anti-cancer properties in human lung large cell carcinoma have not been studied. Here, we used a powder obtained by freeze-drying the super-natant of centrifuged crude extract from Toona sinensis leaves (TSL-1) to treat the human lung carcinoma cell line H661. Cell viability was evaluated by the 3-(4-,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide assay. Flow cytometry analysis revealed that TSL-1 blocked H661 cell cycle progression. Western blot analysis showed decreased expression of cell cycle proteins that promote cell cycle progression, including cyclin-dependent kinase 4 and cyclin D1, and increased the expression of proteins that inhibit cell cycle progression, including p27. Furthermore, flow cytometry analysis showed that TSL-1 induced H661 cell apoptosis. Western blot analysis showed that TSL-1 reduced the expression of the anti-apoptotic protein B-cell lymphoma 2, and degraded the DNA repair protein, poly(ADP-ribose) polymerase. TSL-1 shows potential as a novel therapeutic agent or for use as an adjuvant for treating human lung large cell carcinoma

GPT-4 Is Too Smart To Be Safe: Stealthy Chat with LLMs via Cipher

Safety lies at the core of the development of Large Language Models (LLMs). There is ample work on aligning LLMs with human ethics and preferences, including data filtering in pretraining, supervised fine-tuning, reinforcement learning from human feedback, and red teaming, etc. In this study, we discover that chat in cipher can bypass the safety alignment techniques of LLMs, which are mainly conducted in natural languages. We propose a novel framework CipherChat to systematically examine the generalizability of safety alignment to non-natural languages -- ciphers. CipherChat enables humans to chat with LLMs through cipher prompts topped with system role descriptions and few-shot enciphered demonstrations. We use CipherChat to assess state-of-the-art LLMs, including ChatGPT and GPT-4 for different representative human ciphers across 11 safety domains in both English and Chinese. Experimental results show that certain ciphers succeed almost 100% of the time to bypass the safety alignment of GPT-4 in several safety domains, demonstrating the necessity of developing safety alignment for non-natural languages. Notably, we identify that LLMs seem to have a ''secret cipher'', and propose a novel SelfCipher that uses only role play and several demonstrations in natural language to evoke this capability. SelfCipher surprisingly outperforms existing human ciphers in almost all cases. Our code and data will be released at https://github.com/RobustNLP/CipherChat.Comment: 13 pages, 4 figures, 9 table

Determinant Role of Aerosols From Industrial Sources in Hurricane Harvey's Catastrophe

The destructive power of tropical cyclones is driven by latent heat released from water condensation and is inevitably linked to the abundance of aerosols as cloud condensation nuclei. However, the aerosol effects are unaccounted for in most operational hurricane forecast models. We combined multisource measurements and cloud‐resolving model simulations to show fundamentally altered cloud microphysical and thermodynamic processes by anthropogenic aerosols during Hurricane Harvey. Our observational analyses reveal intense lightning and precipitation in the proximity of Houston industrial areas, and these hot spots exhibit a striking geographic similarity to a climatological maximum of lightning flash density in the south‐central United States. Our ensemble cloud‐resolving simulations of Hurricane Harvey indicate that aerosols increase precipitation and lightning by a factor of 2 in the Houston urban area, unraveling the key anthropogenic factor in regulating flooding during this weather extreme

The Earth is Flat? Unveiling Factual Errors in Large Language Models

Large Language Models (LLMs) like ChatGPT are foundational in various applications due to their extensive knowledge from pre-training and fine-tuning. Despite this, they are prone to generating factual and commonsense errors, raising concerns in critical areas like healthcare, journalism, and education to mislead users. Current methods for evaluating LLMs' veracity are limited by test data leakage or the need for extensive human labor, hindering efficient and accurate error detection. To tackle this problem, we introduce a novel, automatic testing framework, FactChecker, aimed at uncovering factual inaccuracies in LLMs. This framework involves three main steps: First, it constructs a factual knowledge graph by retrieving fact triplets from a large-scale knowledge database. Then, leveraging the knowledge graph, FactChecker employs a rule-based approach to generates three types of questions (Yes-No, Multiple-Choice, and WH questions) that involve single-hop and multi-hop relations, along with correct answers. Lastly, it assesses the LLMs' responses for accuracy using tailored matching strategies for each question type. Our extensive tests on six prominent LLMs, including text-davinci-002, text-davinci-003, ChatGPT~(gpt-3.5-turbo, gpt-4), Vicuna, and LLaMA-2, reveal that FactChecker can trigger factual errors in up to 45\% of questions in these models. Moreover, we demonstrate that FactChecker's test cases can improve LLMs' factual accuracy through in-context learning and fine-tuning (e.g., llama-2-13b-chat's accuracy increase from 35.3\% to 68.5\%). We are making all code, data, and results available for future research endeavors

Mechanics of Optimal Structural Design for Extreme Loads to Peak System Responses

[[abstract]]Over the past decades, with the development of modern manufacturing and information technology, demands of smart and economical structural designs have been increasing considerably. Central to this engineering issue is that a good structural design needs to embrace both necessary capabilities to afford critical load distributions and the best arrangement of materials serving the performance criteria using limited resources. Here, a new analysis technique is proposed to achieve optimal structural designs considering peak system responses as design constraints respective to extreme load distributions. We anticipate that the technique will open a door for designing efficient structural systems which satisfy safety requirements under various sophisticated loadings from the environment.[[sponsorship]]Tamkang University[[sponsorship]]Taiwan Association of Wind Engineering[[sponsorship]]Institute of Theoretical and Applied Mechanics, Academy of Sciences of the Czech Republic[[conferencetype]]國際[[conferencetkucampus]]淡水校園[[conferencedate]]20151101~20151102[[booktype]]紙本[[iscallforpapers]]Y[[conferencelocation]]New Taipe

Hepatocellular carcinoma detected by regular surveillance: Does timely confirmation of diagnosis matter?

AbstractBackgroundAlthough current guidelines recommended surveillance of hepatocellular carcinoma, prognosis in patients undergoing enhanced follow-up has yet to be evaluated.AimsExamine outcomes of hepatocellular carcinoma diagnosed during enhanced follow-up.MethodsDuring 2010–2012, 194 patients underwent ultrasonography surveillance were diagnosed with hepatocellular carcinoma and divided into: (A) immediate diagnosis (N=105, 54.1%) after positive ultrasonography, (B) enhanced follow-up: (N=38, 19.6%) for initial negative recall procedures, (C) late call back: (N=28, 14.4%) recall procedures were deferred after positive ultrasonography, and (D) beyond ultrasonography: (N=23, 11.9%) surveillance ultrasonography had been negative.ResultsMedian time from positive ultrasonography to confirmation of hepatocellular carcinoma were 9.5 months (2–67) in the Group B and 6.5 months (3–44) in the Group C. Stage distribution and 3-year survival rates were similar amongst all Groups. Surveillance intervals longer than 6 months were associated with the non-curative stage (3.7% vs. 12.5%, p=0.04). Nine (4.6%) patients underwent surveillance were diagnosed as Barcelona-Clinic Liver Cancer stage C.ConclusionEnhanced follow-up by current guidelines is appropriate that treatment can be deferred until a definite diagnosis. Despite optimal surveillance interval and recall policies, few non-curative stage diagnoses seemed inevitable under current standard of care

Computational Framework for Optimal Carbon Taxes Based on Electric Supply Chain Considering Transmission Constraints and Losses

A modeling and computational framework is presented for the determination of optimal carbon taxes that apply to electric power plants in the context of electric power supply chain with consideration of transmission constraints and losses. In order to achieve this goal, a generalized electric power supply chain network equilibrium model is used. Under deregulation, there are several players in electrical market: generation companies, power suppliers, transmission service providers, and consumers. Each player in this model tries to maximize its own profit and competes with others in a noncooperative manner. The Nash equilibrium conditions of these players in this model form a finite-dimensional variational inequality problem (VIP). By solving this VIP via an extragradient method based on an interior point algorithm, the optimal carbon taxes of power plants can be determined. Numerical examples are provided to analyze the results of the presented modeling