Application of Sigma metrics in assessing the clinical performance of verified versus non-verified reagents for routine biochemical analytes

Introduction: Sigma metrics analysis is considered an objective method to evaluate the performance of a new measurement system. This study was designed to assess the analytical performance of verified versus non-verified reagents for routine biochemical analytes in terms of Sigma metrics. Materials and methods: The coefficient of variation (CV) was calculated according to the mean and standard deviation (SD) derived from the internal quality control for 20 consecutive days. The data were measured on an Architect c16000 analyser with reagents from four manufacturers. Commercial reference materials were used to estimate the bias. Total allowable error (TEa) was based on the CLIA 1988 guidelines. Sigma metrics were calculated in terms of CV, percent bias and TEa. Normalized method decisions charts were built by plotting the normalized bias (biasa: bias%/ TEa) on the Y-axis and the normalized imprecision (CVa: mean CV%/TEa) on the X-axis. Results: The reagents were compared between different manufacturers in terms of the Sigma metrics for relevant analytes. Abbott and Leadman’s verified reagents provided better Sigma metrics for the alanine aminotransferase assay than non-verified reagents (Mindray and Zybio). All reagents performed well for the aspartate aminotransferase and uric acid assays with a sigma of 5 or higher. Abbott achieved the best performance for the urea assay, evidenced by the sigma of 2.83 higher than all reagents, which were below 1-sigma. Conclusion: Sigma metrics analysis system is helpful for clarifying the performance of candidate non-verified reagents in clinical laboratory. Our study suggests that the quality of non-verified reagents should be assessed strictly

Quantum Key Distribution (QKD) over Software-Defined Optical Networks

Optical network security is attracting increasing research interest. Currently, software-defined optical network (SDON) has been proposed to increase network intelligence (e.g., flexibility and programmability) which is gradually moving toward industrialization. However, a variety of new threats are emerging in SDONs. Data encryption is an effective way to secure communications in SDONs. However, classical key distribution methods based on the mathematical complexity will suffer from increasing computational power and attack algorithms in the near future. Noticeably, quantum key distribution (QKD) is now being considered as a secure mechanism to provision information-theoretically secure secret keys for data encryption, which is a potential technique to protect communications from security attacks in SDONs. This chapter introduces the basic principles and enabling technologies of QKD. Based on the QKD enabling technologies, an architecture of QKD over SDONs is presented. Resource allocation problem is elaborated in detail and is classified into wavelength allocation, time-slot allocation, and secret key allocation problems in QKD over SDONs. Some open issues and challenges such as survivability, cost optimization, and key on demand (KoD) for QKD over SDONs are discussed

Supervised coordinate descent method with a 3D bilinear model for face alignment and tracking

Face alignment and tracking play important roles in facial performance capture. Existing data-driven methods for monocular videos suffer from large variations of pose and expression. In this paper, we propose an efficient and robust method for this task by introducing a novel supervised coordinate descent method with 3D bilinear representation. Instead of learning the mapping between the whole parameters and image features directly with a cascaded regression framework in current methods, we learn individual sets of parameters mappings separately step by step by a coordinate descent mean. Because different parameters make different contributions to the displacement of facial landmarks, our method is more discriminative to current whole-parameter cascaded regression methods. Benefiting from a 3D bilinear model learned from public databases, the proposed method can handle the head pose changes and extreme expressions out of plane better than other 2D-based methods. We present the reliable result of face tracking under various head poses and facial expressions on challenging video sequences collected online. The experimental results show that our method outperforms state-of-art data-driven methods

Characterization of physico-chemical and bio-chemical compositions of selected four strawberry cultivars

The physico-chemical and bio-chemical compositions of Hongyan, Tiangxiang, Tongzi Ι and Zhangji strawberries inChinawere analyzed. Their values were pH 3.42~3.73, titration acidity 0.63~0.79%, total soluble sugars 5.26~8.95 g/100 gfresh weight (FW), ascorbic acid 21.38~42.89 mg/100 gFW, total phenolics 235.12~444.73 mg/100 gFW, pectin 82.84~96.13 mg/100 gFW, total organic acids 874.30~1216.27 mg/100 gFW, Individual phenolic compounds other than anthocyanins 7.60~12.18 mg/100 gFW, free amino acids 13.35~32.66 mg/100 gFW, monomeric anthocyanins 4.47~47.19 mg/100gFW, antioxidant capacity of ·DPPH 14.14~18.87 and FRAP 7.97~10.54 equal to mg/100 gVc, polyphenol oxidase (PPO) activity 0~0.42 Abs/min, peroxidase (POD) activity 0.17~0.34 Abs/min and pectin methyl esterase (PME) activity 0.012~0.018 mL/min. Tongzi Ι was most suitable for food processing due to the highest titration acidity, total phenolics, pectin, total organic acids, monomeric anthocyanins, antioxidant capacity of ·DPPH and FRAP with lower PPO, POD and PME activity

CapsFusion: Rethinking Image-Text Data at Scale

Large multimodal models demonstrate remarkable generalist ability to perform diverse multimodal tasks in a zero-shot manner. Large-scale web-based image-text pairs contribute fundamentally to this success, but suffer from excessive noise. Recent studies use alternative captions synthesized by captioning models and have achieved notable benchmark performance. However, our experiments reveal significant Scalability Deficiency and World Knowledge Loss issues in models trained with synthetic captions, which have been largely obscured by their initial benchmark success. Upon closer examination, we identify the root cause as the overly-simplified language structure and lack of knowledge details in existing synthetic captions. To provide higher-quality and more scalable multimodal pretraining data, we propose CapsFusion, an advanced framework that leverages large language models to consolidate and refine information from both web-based image-text pairs and synthetic captions. Extensive experiments show that CapsFusion captions exhibit remarkable all-round superiority over existing captions in terms of model performance (e.g., 18.8 and 18.3 improvements in CIDEr score on COCO and NoCaps), sample efficiency (requiring 11-16 times less computation than baselines), world knowledge depth, and scalability. These effectiveness, efficiency and scalability advantages position CapsFusion as a promising candidate for future scaling of LMM training.Comment: CVPR 2024. Code & Dataset: https://github.com/baaivision/CapsFusio

Reinforcement learning optimized look-ahead energy management of a parallel hybrid electric vehicle

This paper presents a predictive energy management strategy for a parallel hybrid electric vehicle (HEV) based on velocity prediction and reinforcement learning (RL). The design procedure starts with modeling the parallel HEV as a systematic control-oriented model and defining a cost function. Fuzzy encoding and nearest neighbor approaches are proposed to achieve velocity prediction, and a finite-state Markov chain is exploited to learn transition probabilities of power demand. To determine the optimal control behaviors and power distribution between two energy sources, a novel RL-based energy management strategy is introduced. For comparison purposes, the two velocity prediction processes are examined by RL using the same realistic driving cycle. The look-ahead energy management strategy is contrasted with shortsighted and dynamic programming based counterparts, and further validated by hardware-in-the-loop test. The results demonstrate that the RL-optimized control is able to significantly reduce fuel consumption and computational time

Simultaneous observation of hybrid states for cyber-physical systems: a case study of electric vehicle powertrain

As a typical cyber-physical system (CPS), electrified vehicle becomes a hot research topic due to its high efficiency and low emissions. In order to develop advanced electric powertrains, accurate estimations of the unmeasurable hybrid states, including discrete backlash nonlinearity and continuous half-shaft torque, are of great importance. In this paper, a novel estimation algorithm for simultaneously identifying the backlash position and half-shaft torque of an electric powertrain is proposed using a hybrid system approach. System models, including the electric powertrain and vehicle dynamics models, are established considering the drivetrain backlash and flexibility, and also calibrated and validated using vehicle road testing data. Based on the developed system models, the powertrain behavior is represented using hybrid automata according to the piecewise affine property of the backlash dynamics. A hybrid-state observer, which is comprised of a discrete-state observer and a continuous-state observer, is designed for the simultaneous estimation of the backlash position and half-shaft torque. In order to guarantee the stability and reachability, the convergence property of the proposed observer is investigated. The proposed observer are validated under highly dynamical transitions of vehicle states. The validation results demonstrates the feasibility and effectiveness of the proposed hybrid-state observer

Energy management in plug-in hybrid electric vehicles: recent progress and a connected vehicles perspective

Plug-in hybrid electric vehicles (PHEVs) offer an immediate solution for emissions reduction and fuel displacement within the current infrastructure. Targeting PHEV powertrain optimization, a plethora of energy management strategies (EMSs) have been proposed. Although these algorithms present various levels of complexity and accuracy, they find a limitation in terms of availability of future trip information, which generally prevents exploitation of the full PHEV potential in real-life cycles. This paper presents a comprehensive analysis of EMS evolution toward blended mode (BM) and optimal control, providing a thorough survey of the latest progress in optimization-based algorithms. This is performed in the context of connected vehicles and highlights certain contributions that intelligent transportation systems (ITSs), traffic information, and cloud computing can provide to enhance PHEV energy management. The study is culminated with an analysis of future trends in terms of optimization algorithm development, optimization criteria, PHEV integration in the smart grid, and vehicles as part of the fleet