One-step synthesis of amino-functionalized fluorescent carbon nanoparticles by hydrothermal carbonization of chitosan

Electrochemical biosensors and associated lab-on-a-chip devices are the analytical system of choice when rapid and on-site results are needed in medical diagnostics and food safety, for environmental protection, process control, wastewater treatment, and life sciences discovery research among many others. A premier example is the glucose sensor used by diabetic patients. Current research focuses on developing sensors for specific analytes in these application fields and addresses challenges that need to be solved before viable commercial products can be designed. These challenges typically include the lowering of the limit of detection, the integration of sample preparation into the device and hence analysis directly within a sample matrix, finding strategies for long-term in vivo use, etc. Today, functional nanomaterials are synthesized, investigated, and applied in electrochemical biosensors and lab-on-a-chip devices to assist in this endeavor. This review answers many questions around the nanomaterials...

Functional Nanomaterials and Nanostructures Enhancing Electrochemical Biosensors and Lab-on-a-Chip Performances: Recent Progress, Applications, and Future Perspective.

While large language models (LLMs) have demonstrated remarkable capabilities across a range of downstream tasks, a significant concern revolves around their propensity to exhibit hallucinations: LLMs occasionally generate content that diverges from the user input, contradicts previously generated context, or misaligns with established world knowledge. This phenomenon poses a substantial challenge to the reliability of LLMs in real-world scenarios. In this paper, we survey recent efforts on the detection, explanation, and mitigation of hallucination, with an emphasis on the unique challenges posed by LLMs. We present taxonomies of the LLM hallucination phenomena and evaluation benchmarks, analyze existing approaches aiming at mitigating LLM hallucination, and discuss potential directions for future research.

pdf/siren-s-song-in-the-ai-ocean-a-survey-on-hallucination-in-2t9sebemsy.pdf

Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models

Vast numbers of studies and developments in the nanotechnology area have been conducted and many nanomaterials have been utilized to detect cancers at early stages. Nanomaterials have unique physical, optical and electrical properties that have proven to be very useful in sensing. Quantum dots, gold nanoparticles, magnetic nanoparticles, carbon nanotubes, gold nanowires and many other materials have been developed over the years, alongside the discovery of a wide range of biomarkers to lower the detection limit of cancer biomarkers. Proteins, antibody fragments, DNA fragments, and RNA fragments are the base of cancer biomarkers and have been used as targets in cancer detection and monitoring. It is highly anticipated that in the near future, we might be able to detect cancer at a very early stage, providing a much higher chance of treatment.

/pdf/nanotechnology-for-early-cancer-detection-2clfex6qqk.pdf

Nanotechnology for Early Cancer Detection

To the best of our knowledge, this was the first report on the integration of a signal amplification strategy into a microfluidic paper-based electrochemical immunodevice for the multiplexed measurement of cancer biomarkers. Signal amplification was achieved through the use of graphene to modify the immunodevice surface to accelerate the electron transfer and the use of silica nanoparticles as a tracing tag to label the signal antibodies. Accurate, rapid, simple, and inexpensive point-of-care electrochemical immunoassays were demonstrated using a photoresist-patterned microfluidic paper-based analytical device (μPAD). Using the horseradish peroxidase (HRP)–O-phenylenediamine–H2O2 electrochemical detection system, the potential clinical applicability of this immunodevice was demonstrated through its ability to identify four candidate cancer biomarkers in serum samples from cancer patients. The novel signal-amplified strategy proposed in this report greatly enhanced the sensitivity of the detection of cance...

Paper-Based Microfluidic Electrochemical Immunodevice Integrated with Nanobioprobes onto Graphene Film for Ultrasensitive Multiplexed Detection of Cancer Biomarkers

CdTe quantum dot functionalized silica nanosphere labels for ultrasensitive detection of biomarker

Many text generation applications require the generated text to be factually consistent with input information. Automatic evaluation of factual consistency is challenging. Previous work has developed various metrics that often depend on specific functions, such as natural language inference (NLI) or question answering (QA), trained on limited data. Those metrics thus can hardly assess diverse factual inconsistencies (e.g., contradictions, hallucinations) that occur in varying inputs/outputs (e.g., sentences, documents) from different tasks. In this paper, we propose AlignScore, a new holistic metric that applies to a variety of factual inconsistency scenarios as above. AlignScore is based on a general function of information alignment between two arbitrary text pieces. Crucially, we develop a unified training framework of the alignment function by integrating a large diversity of data sources, resulting in 4.7M training examples from 7 well-established tasks (NLI, QA, paraphrasing, fact verification, information retrieval, semantic similarity, and summarization). We conduct extensive experiments on large-scale benchmarks including 22 evaluation datasets, where 19 of the datasets were never seen in the alignment training. AlignScore achieves substantial improvement over a wide range of previous metrics. Moreover, AlignScore (355M parameters) matches or even outperforms metrics based on ChatGPT and GPT-4 that are orders of magnitude larger.

pdf/alignscore-evaluating-factual-consistency-with-a-unified-3hnl22rl.pdf

AlignScore: Evaluating Factual Consistency with A Unified Alignment Function

Piezocatalysis is regarded as a fascinating technology for water remediation and possible disease treatment. A high piezoelectric coefficient (d33) is one of the most important parameters to determine piezocatalytic performance, which can be manipulated via phase boundary design. Herein, a novel strategy for excellent piezocatalytic activity in lead-free BaTiO3-based materials via manipulating the multiphase coexistence is proposed. The piezocatalyst of 0.82Ba(Ti0.89Sn0.11)O3-0.18(Ba0.7Ca0.3)TiO3 (0.82BTS-0.18BCT) with multiphase coexistence is prepared, and a large d33 can be obtained. As a result, 0.82BTS-0.18BCT exhibits excellent piezocatalytic performance for the degradation of Rhodamine B (RhB). Furthermore, the removal rate of RhB could reach more than 90% after vibration for 30 min, and the reaction rate constant (k) could reach 0.0706 min-1, which is much superior to that of most other representative perovskite-structured piezoelectric materials. Excellent piezocatalytic performance can be attributed to the strong local ferro-/piezoelectric response induced by the multiphase coexistence, as confirmed by the in situ piezoresponse force microscopy (PFM). Finally, the piezocatalytic degradation mechanism is analyzed systemically and proposed. This work not only provides a high-efficiency piezocatalyst but also sheds light on developing efficient BT-based piezocatalysts by manipulating the multiphase coexistence.

A Novel Strategy for Excellent Piezocatalytic Activity in Lead-Free BaTiO3-Based Materials via Manipulating the Multiphase Coexistence.

A machine learning strategy for fast prediction of cardiac function based on peripheral pulse wave

In this paper, we briefly introduce our submission to the Valence-Arousal Estimation Challenge of the 3rd Affective Behavior Analysis in-the-wild (ABAW) competition. Our method utilizes the multi-modal information, i.e., the visual and audio information, and employs a temporal encoder to model the temporal context in the videos. Besides, a smooth processor is applied to get more reasonable predictions, and a model ensemble strategy is used to improve the performance of our proposed method. The experiment results show that our method achieves 65.55% ccc for valence and 70.88% ccc for arousal on the validation set of the Aff-Wild2 dataset, which prove the effectiveness of our proposed method.

Multi-modal Emotion Estimation for in-the-wild Videos

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