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Today, as deep learning (DL) is applied more often in daily life, dedicated processors such as CPUs and GPUs have become very important for accelerating model executions. With the growth of technology, people are becoming accustomed to using edge devices, such as mobile phones, smart watches, and VR devices in their daily lives. A variety of technologies using DL are gradually being applied to these edge devices. However, there is a large number of computations in DL. It faces a challenging problem how to provide solutions in the edge devices. In this article, the proposed method enables a flow with the RISC-V Packed extension (P extension) in TVM. TVM, an open deep learning compiler for neural network models, is growing as a key infrastructure for DL computing. RISC-V is an open instruction set architecture (ISA) with customized and flexible features. The Packed-SIMD extension is a RISC-V extension that enables subword single-instruction multiple-data (SIMD) computations in RISC-V architectures to support fallback engines in AI computing. In the proposed flow, a fixed-point type that is supported by an integer of 16-bit type and saturation instructions is added to replace the original 32-bit float type. In addition, an auto-tuning method is proposed to use a uniform selector mechanism (USM) to find the binary point position for fixed-point type use. The tensorization feature of TVM can be used to optimize specific hardware such as subword SIMD instructions with RISC-V P extension. With our experiment on the Spike simulator, the proposed method with the USM can improve performance by approximately 2.54 to 6.15× in terms of instruction counts with little accuracy loss.

For system manufacturers, packaging of the ball grid array (BGA) of wireless communication modules can be performed via solder paste printing. As reballing can be conducted via the pretinning process, it can be compatible with its existing surface mount technology (SMT). This study referred to the Taguchi experimental design to plan its experiment. Three quality characteristics related to the consistency of the solder volume are applied for printing, that is, the standard deviation of the deposition volume of solder paste on all bonding pads of a single component, the coplanarity of the solder balls of the component, and the height range of the solder ball of a single component, which were regarded as the response values. Important design and process parameters related to the pretinning process and the relative location of the bonding pads of components on a connecting board are considered as the controlling factors. Then, this study applied and integrated the results of two multivariate analysis methods, i.e., gray correlation of the principal components and a technique for order preference by similarity to ideal solution (TOPSIS), in order to determine the ideal solution and propose the optimal parameter combination: general squeegee: pressure 40 N and speed 30 mm/s; steel: separation distance 3 mm, speed 2 mm/s, and aspect ratio 0.17; and cleaning frequency: cleaning of stencils after the printing of every two boards. This method effectively improves the control of the solder volume in the pretinning process and meets the acceptance criteria of manufacturers.

We retrospectively analyzed 82 adult patients with MALA admitted to the ICU over 20 years. The association between the clinical parameters and mortality post-MALA was estimated using logistic regression analysis.Patients with MALA admitted to the ICU presented with clinical symptoms mainly associated with the head (40.24%), chest (41.46%), and abdomen (35.37%). Additionally, the PLL distribution significantly varied with age, APACHE II = Acute Physiology and Chronic Health Evaluation II (APACHE II) score, various laboratory parameters like nadir arterial bicarbonate level, multiple treatment modalities such as renal replacement therapy, and mortality. The overall mortality rate was 17.07%. After adjustment of age and gender, the significant predictors of mortality were APACHE II score, PLL, vasoactive support, ventilator support, and cardiopulmonary resuscitation.Despite MALA being a rare event, it is necessary to evaluate its clinical characteristics, especially the associated PLL and mortality. In the current study, higher levels of APACHE II score and PLL show a greater likelihood of mortality in MALA patients.

TVM is a AI compiler supports both graph-level and operator-level optimization on machine learning models. It provides an optimizable flow to deploy on diverse target devices. By exploiting TVM schedule, we can optimize the codegen behavior for our RISC-V architecture. Since RISCV is configurable with the selection on different extension, we can enable multiple extensions depends on the application scene. In our work, we present the flow for enabling and optimizing the RISC-V P extension toward QNN models from TVM. With support from LLVM and a customized deep learning runtime (DLR), we verified our work on both FLOAT32 and prequantized models from Tensorflow Lite. Experiments shows that comparing with FLOAT32 models, our work can achieve 2.7-7.0 times of performance improvement with regard to total instruction count at runtime for pre-quantized version with a set of benchmarks including Mobilenet and Inception-v3. As for accuracy issue, the degradation is tiny for quantization version among 500 images. All experiments are running on RISC-V simulator, Spike with P extension support.

Heterogeneous systems that consist of multiple CPUs and GPUs for high-performance computing are becoming increasingly popular, and OpenCL (Open Computing Language) provides a framework for writing programs that can be executed across heterogeneous devices. Compared with OpenCL 1.2, the new features of OpenCL 2.0 provide developers with better expressive power for programming heterogeneous computing environments. Currently, gem5-gpu, which includes gem5 and GPGPU-Sim, can offer an experimental simulation environment for OpenCL. In gem5-gpu, gem5 only supports CUDA, although GPGPU-Sim can support OpenCL by compiling an OpenCL kernel code to PTX code using real GPU drivers. However, this compilation flow in GPGPU-Sim can only support up to OpenCL 1.2. OpenCL 2.0 provides new features such as workgroup built-in functions, extended atomic built-in functions, and device-side enqueue. To support OpenCL 2.0, the compiler must be extended to enable the compilation of OpenCL 2.0 kernel code to PTX code. In this paper, the proposed compiler is modified from the low level virtual machine (LLVM) compiler to extend such features to enhance the emulator to support OpenCL 2.0. The proposed compiler creates local buffers for each workgroup to enable workgroup built-in functions and adds atomic built-in functions with memory order and memory scope for OpenCL 2.0 in NVPTX. Furthermore, the APIs available in CUDA are utilized to implement the OpenCL 2.0 device-side enqueue kernel and compilation schemes in Clang are revised. The AMD APP SDK 3.0 and NTU OpenCL benchmarks are used to verify that the proposed compiler can support the features of OpenCL 2.0.

Invasive pulmonary Aspergillus infection during or after severe influenza infection in an immunocompromised patient or a previously healthy person has been reported in the literature [1-2]. In addition, coinfections of Staphylococcus aureus or Streptococcus pneumoniae with influenza are common, whereas simultaneous infections of legionellosis with aspergillosis are unusual in patients with influenza [2]. We herein report a diabetic patient who presented with a community-acquired pneumonia (CAP). The initial sputum culture yielded Aspergillus species and S. aureus, which were coinfected with influenza A, Pneumococcus and Legionella, with a deteriorated fatal course. A 63-year-old man with diabetes mellitus felt general malaise and cough with sputum for 3 days. He was admitted to the intensive care unit (ICU) of the hospital on June 20, 2015 due to pneumonia with respiratory failure. There was no fever, diarrhea, headache, or skin rash. He denied travel and contact history. On examination, a blood pressure of 80/60 mmHg was noticed. The breath sounds revealed bilateral fine rales. Laboratory data included WBC 15,700/μL; band, 18%; CRP, 91.9 mg/L; BUN, 50 mg/dL; creatinine, 2.38 mg/dL; blood sugar, 221 mg/dL and HbA1C, 9.1%. The initial chest x-ray (CXR) film showed mild infiltration in perihilar area of left lung on June 20 (Figures 1A). The result of urinary Legionella Ag test was negative. Empirical antibiotic therapy included piperacillin-tazobactam and levofloxacin.

Previous studies have revealed higher mortality rates in patients with severe influenza who are coinfected with invasive pulmonary aspergillosis (IPA) than in those without IPA coinfection, nonetheless, the clinical impact of IPA on economic burden and risk factors for mortality in critically ill influenza patients remains undefined. The study was retrospectively conducted in three institutes. From 2016 through 2018, all adult patients with severe influenza admitted to an intensive care unit (ICU) were identified. All patients were classified as group 1, patients with concomitant severe influenza and IPA, group 2, severe influenza patients without IPA, and group 3, severe influenza patients without testing for IPA. Overall, there were 201 patients enrolled, including group 1 (n = 40), group 2 (n = 50), and group 3 (n = 111). Group 1 patients had a significantly higher mortality rate (20/40, 50%) than that of group 2 (6/50, 12%) and group 3 (18/11, 16.2%), p &lt, 0.001. The risk factors for IPA occurrence were solid cancer and prolonged corticosteroid use in ICU of &gt, 5 days. Group 1 patients had significantly longer hospital stay and higher medical expenditure than the other two groups. The risk factors for mortality in group 1 patients included patients’ Charlson comorbidity index, presenting APACHE II score, and complication of severe acute respiratory distress syndrome. Overall, IPA has a significant adverse impact on the outcome and economic burden of severe influenza patients, who should be promptly managed based on risk host factors for IPA occurrence and mortality risk factors for coinfection with both diseases.

The heterogeneous system architecture (HSA), announced by the HSA Foundation, is an approach to integrate central processing unit (CPU) and graphics processing unit (GPU) architectures. The open computing language (OpenCL) is a programming framework that can help utilize heterogeneous architectures. The well-known OpenCL framework, currently in version 1.2, provides programming models for heterogeneous computing. The proposed specifications of OpenCL 2.0 can help utilize HSA features, such as shared virtual memory (SVM). In previous work, we helped enable Portable Computing Language (PoCL)-based OpenCL 1.2 runtime frameworks on the HSA. In this paper, we further extend the PoCL-based runtime on the HSA to support OpenCL 2.0 features. In addition, this is the first work, to our best knowledge, to support PoCL-based OpenCL 2.0 features on HSA. Compared with the widely used OpenCL 1.2, OpenCL 2.0 will support SVM, nested parallelism, pipes, and atomic operations. It can further support parallel design patterns such as tree searches, pointer-based programming and nested parallelism models. Note that PoCL is a widely used open source implementation of OpenCL. Our design flow can help academics to enable OpenCL 2.0 flow on the HSA and benefit further from advanced academic research. The experimental results indicate that our framework provides adequate features to support advanced research.

Aspergillus-associated infection might comprise up to 23–29% of severe influenza patients from the community throughout stay in an intensive care unit (ICU). In Taiwan, cases of severe influenza with aspergillosis are increasingly reported. Therefore, we describe the relative risk of mortality among severe influenza patients with aspergillosis and other coinfections compared to severe influenza patients without Aspergillus coinfections. Methods: We retrospectively reviewed 124 adult patients with severe influenza in a tertiary medical center in southern Taiwan from January 2015 through March 2016. The definition of probable aspergillosis required abnormal radiological findings and positive Aspergillus galactomannan (GM) antigen and/or Aspergillus isolation. Results: Probable aspergillosis (detected throughout the whole course) and other coinfections (only community-acquired) were diagnosed in 21 (17%) and 38 (31%) of all patients respectively. Klebsiella pneumoniae (36.8%), Pseudomonas aeruginosa (31.6%) and Staphylococcus aureus (31.6%) were the most frequent isolates of other coinfections. In-ICU mortality of Aspergillus group (66.7%) was significantly higher than other coinfections (23.7%, p = 0.001) or control group without coinfections (15.4%, p

With the growing computing complexity of 5G baseband, parallel computing and OpenCL become a promising solution to help with the computing needs for baseband computing. In this paper, the issues are investigated in supporting the complex class library with OpenCL C++ class for baseband computing. Using templates and overloaded C++ features, the proposed complex class library works with many data types. This work also addresses the issue with fast math, and can work with standard C++ math with complex number as well as fast-math option in the complex class that the check of Not a Number (NaN) and infimum (INF) are removed. Our work is experimented with two environments. One is with Intel OpenCL 2.1 environment with complex float and complex half, and the other one runs a minimum mean-square error (MMSE) equalizer application in Massive Multi-input Multi-output (MIMO). As compared to the C++ sequential code, the complex class in OpenCL can reduce total execution time. Based on the preliminary results, the kernel execution time could be reduced by about 5% to 45% with fast math to omit NaN and INF check. The C++ class header file will also be illustrated to fit C++ complex class with OpenCL C++ linguistics.

This paper investigates the phenomenon of mode switching for GaAs-based high index contrast photonic crystal surface emitting lasers (PCSELs) with deeply etched air holes. The room-temperature lasing characteristics and angular resolved photoluminescence spectra of PCSELs show that the lasing mode would change from the fundamental guided mode to a high-order guided mode as the filling factor (FF) increased, resulting from the diffraction of deeply etched semiconductor/air arrays with high index contrast. Higher order guided modes could coherently couple out of photonic crystal and would significantly influence the laser output of PCSELs with a larger air FF, leading to rich far-field characteristics.

In recent decades, the global incidence of dengue has increased. Affected countries have responded with more effective surveillance strategies to detect outbreaks early, monitor the trends, and implement prevention and control measures. We have applied newly developed machine learning approaches to identify laboratory-confirmed dengue cases from 4,894 emergency department patients with dengue-like illness (DLI) who received laboratory tests. Among them, 60.11% (2942 cases) were confirmed to have dengue. Using just four input variables [age, body temperature, white blood cells counts (WBCs) and platelets], not only the state-of-the-art deep neural network (DNN) prediction models but also the conventional decision tree (DT) and logistic regression (LR) models delivered performances with receiver operating characteristic (ROC) curves areas under curves (AUCs) of the ranging from 83.75% to 85.87% [for DT, DNN and LR: 84.60% ± 0.03%, 85.87% ± 0.54%, 83.75% ± 0.17%, respectively]. Subgroup analyses found all the models were very sensitive particularly in the pre-epidemic period. Pre-peak sensitivities (, Author summary Identifying dengue cases early is crucial but challenging for healthcare professionals. This challenge is increased during large epidemics and is a particular problem in non-endemic areas with limited experienced staff. To improve dengue diagnosis, we investigated how to exploit machine learning (ML)-based prediction models and identified four key variables [age, fever, white blood cell counts (WBCs), and platelet counts], which are compatible with clinical and epidemiological knowledge. With these variables, the ML prediction models [decision tree (DT), deep neural network (DNN)] and the logistic regression model developed for identifying laboratory-confirmed dengue cases produced areas under curve (AUCs) of the receiver operating characteristic (ROC) curves ranging from 83.75% to 85.87%. This implies that the prediction models may serve as a pivotal component of an integrated dengue surveillance system and they required only a single complete blood count (CBC) examination. The sensitivities, positive prediction values, and accuracies for major risk factors in the two machine learning models were close to those of the regression models. For future applications, the DNN models with superior performance can be employed at epidemic sites with adequate computer facilities, while the DT and regression models with interpretable prediction logic can be employed at sites with limited or no computer facilities. Artificial intelligence and clinical parameters identified from this study may aid when laboratories are overwhelmed, but should never replace laboratory confirmation.

In recent years, the parallel computing performance on GPUs (graphics processing units) has grown rapidly. As a result, GPUs have been widely applied in computationally intensive applications such as image processing, deep learning, and artificial intelligence. Because these applications can be modeled by multiple GPU kernels, some of which might even be dependent, it is essential to identify an efficient method for scheduling dependent kernels on GPU cores. Simply observing kernel dependencies by executing them in sequence results in performance degradation. Furthermore, dependent kernels generally need to share data. Consequently, without properly scheduling dependent kernels, unnecessary memory accesses and copies will be generated. Neural network model environments include many operators that are suitable for both parallel and dependent kernels. This paper proposes an efficient and clear method for creating a framework to analyze the ONNX (open neural network exchange) model and find the pattern of dependent kernels, which can then be used for scheduling with the GPU architecture. The preliminary experimental results show that this technique improves the overall performance by 8% and reduces the cache miss rate by 14% on average by combining neural network operators and appropriate memory policies.

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GPUs have now been widely used in various computation-intensive applications, such as image processing, deep learning, artificial intelligence, etc. As these applications could be modeled by multiple GPU kernels, some of which might even be dependent, it is essential to find an efficient method to schedule dependent kernels on GPU cores. Simply observing dependences of kernels by executing them in sequence will result in performance degradation. Furthermore, dependent kernels generally need to share data. Consequently, without properly scheduling dependent kernels, unnecessary memory accesses and copies will be generated. This paper proposes an efficient method for scheduling dependent kernels on GPUs. Preliminary experimental results show that this technique improves performance by 43% on average when combining with appropriate memory write-back policies.

Computation on heterogeneous multi-core systems has great opportunities for optimization which may include the compute resource scheduling such as workload distribution between CPU and GPU, as well as finding the best combination of tasks and compute devices for best performance. Currently, OpenCL, the parallel programming standard for heterogeneous computing, contains mainly low-level APIs to interact with the runtime and hardware device of each individual vendor. To apply efficient scheduling algorithm, the overall execution flow and information of OpenCL kernels must be considered. In this paper, we proposed computational graph support for OpenCL. The framework features computational graphs that store meta-data and execution dependencies of kernels. We then provide scheduling framework for OpenCL programs based on the graph information. In our optimization framework, the kernel task scheduling is based on the graph model. In addition, we have kernel code analysis for target device decision as well as runtime work-group size optimization. The preliminary experimental results show that our scheme enables significant performance enhancement, achieving about 1.59 times speedup relative to our neural network program baseline.

We have explored a novel molecular framework with a lysine core to prepare a probe library for protein kinases. Active site-directed probes, identified through screening of the library, displayed desirable labelling properties, including target specificity, good linear response to the corresponding enzymatic activity and cell permeability. Finally, a two-stage protocol utilizing the newly developed probe with an immunoprecipitation step was successfully established to monitor intracellular Src kinase activity in RK3E-v-Src cells.

A class of neutral tris-bidentate Ir(III) metal complexes incorporating a diphosphine as a chelate is prepared and characterized here for the first time. Treatment of [Ir(dppb)(tht)Cl3] (1) with fppzH afforded the dichloride complexes, trans-(Cl,Cl)[Ir(dppb)(fppz)Cl2] (2) and cis-(Cl,Cl)[Ir(dppb)(fppz)Cl2] (3). The reaction of 3 with the dianionic chelate precursor bipzH2 or mepzH2, in DMF gave the complex [Ir(dppb)(fppz)(bipz)] (4) or [Ir(dppb)(fppz)(mepz)] (5), respectively. In contrast, a hydride complex [Ir(dppb)(fppz)(bipzH)H] (6) was isolated instead of 4 in protic solvent, namely: DGME. All complexes 2 - 6 are luminescent in powder forms and thin films where the dichlorides (2, 3) emit with maxima at 590-627 nm (orange) and quantum yields (Q.Y.s) up to 90% whereas the tris-bidentate (4, 5) and hydride (6) complexes emit at 455-458 nm (blue) with Q.Y.s up to 70%. Hybrid TD-DFT calculations showed considerable MLCT contribution to the orange-emitting 2 and 3 but substantial ligand-centered 3ππ* transition character in the blue-emitting 4 - 6. The dppb does not participate to these radiative transitions in 4 - 6, but it provides the rigidity and steric bulk needed to promote the luminescence by suppressing the self-quenching in the solid state. Fabrication of an OLED with dopant 5 gave a deep blue CIE chromaticity of (0.16, 0.15). Superior blue emitters, which are vital in OLED applications, may be found in other neutral Ir(III) complexes containing phosphine chelates.

Due to continuous and unplanned urbanization, biases and probability of occurrences of non-line-of-sight (NLOS) errors can be drastically enlarged in macro-cellular smart urban environments. This paper presents a new robust estimation approach to mobile tracking improvements based on adequately tackling NLOS errors. To cover the dynamics of a mobile station, we cast a wireless localization problem into a Markov state transitioned system framework. We then introduce a screening scheme for reducing NLOS effects on received measurements, and a M-estimator. These enable us to develop an enhanced robust-regression-based interacting multiple-model (enhanced R-IMM) algorithm. Simulations demonstrate the advantages and superior performance of the enhanced R-IMM.

OpenCL continues to gather momentum on both desktop and mobile devices. The new features of OpenCL 2.0 provides developers better expressive power in programming heterogeneous computing environments. Currently in the experimental simulation environment, gem5-gpu only supports CUDA, but GPGPU-Sim can support OpenCL by compiling OpenCL kernel code to PTX using real GPU driver. However, this driver compilation in GPGPU-Sim only can support up to OpenCL 1.2. To support OpenCL 2.0, it is necessary to extend the compiler to enable the compilation of OpenCL 2.0 kernel code to PTX. In this paper, our experience in enabling the compiler flow is reported. In OpenCL 2.0, it provides new features such as dynamic parallelism, work-group built-in functions, extend atomic builtin functions, and so on. The proposed compiler that is modified from Low Level Virtual Machine (LLVM) extends such features for enhancing the emulator to support OpenCL 2.0. After the compiler is modified, it can support dynamic parallelism, workgroup built-in functions and extend atomic built-in functions. Using existing dynamic parallelism APIs in CUDA to implement OpenCL 2.0 enqueue kernel and revise compilation scheme in clang. Furthermore, the proposed compiler also creates local buffers for each work group to use for work-group builtin functions, and adds atomic built-in functions with memory order and memory scope for OpenCL 2.0 in NVPTX. From benchmarks, the proposed compiler can support the claim target.

Despite the fast boom of thermally activated delayed fluorescence (TADF) emitters bearing borane-based acceptor, so far, no TADF emitter with a direct B-N linkage between N-donor and boryl acceptor has been reported. The latter should simplify the molecular architecture and hence facilitate the synthetic design and versatility. We report here the preparation and characterization of a new series of N-borylated compounds with functional acridine donor unit; namely: ACBM, PACBM, and SACBM. Spectroscopic studies were performed to explore their photophysical properties that exhibited prominent solvatochromism and thermally activated delayed fluorescence. The time-dependent DFT calculation indicated the involvement of substantial intramolecular charge transfer character for which HOMO and LUMO are spatially separated. For compound SACBM, fabrication of green emitting OLED gave CIE chromaticity of (0.22, 0.59) and maximum external quantum efficiency, luminance efficiency and power efficiency of 19.1%, 60.9 cd/A, and 43.6 lm/W, respectively, demonstrating for the first time the highly efficient OLEDs using N-borylated TADF emitters.

In this research, a multispectral fluorescence-based imaging algorithm was developed to detect frass contamination on mature Campari tomatoes. Tomato images were acquired using a hyperspectral fluorescence line-scan imaging system with violet LED excitation, then analyzed for wavelength selection. The fluorescence intensities at five wavelengths, 515 nm, 640 nm, 664 nm, 690 nm, and 724 nm, were used to compute three simple ratio functions to detect frass contamination. The contamination spots were created on the tomato surfaces using four low-concentration frass dilutions. The algorithms detected over 99% of the 0.2 kg/L and 0.1 kg/L frass contamination spots and successfully differentiated these spots from tomato skin surfaces, stem scars, and stems. However, differentiation of the 0.05 kg/L and 0.02 kg/L frass contamination spots was more difficult. Adjusting the algorithm to successfully detect 95% of the 0.05 kg/L spots also resulted in false-positive pixel detections occurring on 28% of the tomatoes. This study demonstrates that a simple multispectral fluorescence imaging algorithm based on violet LED excitation could be useful for rapid postharvest detection of frass contamination on tomatoes in processing lines.

Purpose: A multispectral algorithm for detection and differentiation of defective (defects on apple skin) and normal Red Delicious apples was developed from analysis of a series of hyperspectral line-scan images. Methods: A fast line-scan hyperspectral imaging system mounted on a conventional apple sorting machine was used to capture hyperspectral images of apples moving approximately 4 apples per second on a conveyor belt. The detection algorithm included an apple segmentation method and a threshold function, and was developed using three wavebands at 676 nm, 714 nm and 779 nm. The algorithm was executed on line-by-line image analysis, simulating online real-time line-scan imaging inspection during fruit processing. Results: The rapid multispectral algorithm detected over 95% of defectiv e apples and 91% of normal apples investigated. Conclusions: The multispectral defect detection algorithm can potentially be used in commercial apple processing lines.

The lattice approach derived by Broadie and Kaya (J Financ Quant Anal 42(2):279–312, 2007) has traditionally been used to determine the capital structure of a firm in economies with constant interest rates; however, this study argues that the capital structure of a firm should be determined by considering the state of its debt simultaneously with the randomness of interest rates. This study extends the Hilliard et al. (J Financ Res 19(4):585–602, 1996) bivariate binomial model to determine the capital structure of firms, taking into account stochastic interest rates and their correlation with the asset value of the firm. Our simulation results suggest that taking stochastic interest rates into consideration reduces the equity value of a firm while increasing its debt value. The stronger the correlation between variations in the asset value of the firm and the short rate, the stronger the impact of this correlation on the capital structure of the firm.

The popularity of online games has generated research interest in the flow experiences they may induce and the possibility of game addiction. Our study focuses on the dynamics of progression within online games, a perspective largely overlooked in previous investigations. Recreation specialization is used for exploring progression among online gamers and clarifying relationships between flow experiences and addiction tendencies. Data from online questionnaires completed by 357 players of Massively Multiplayer Role-Playing Games showed that specialized players are more likely to experience flow and exhibit game addiction than inexperienced players. Additionally, recreation specialization intensifies the effect of flow experiences on addiction tendencies.

Protein kinases are involved in many important cellular signaling pathways. Development of activitybased probes targeting this enzyme family would be of great contemporary values. In this study, we established a synthetic route that allows parallel synthesis for the preparation of a 2 × 2 probe library. The probes feature a reactive fluorosulfonylbenzoyl moiety on an adenosine framework to investigate the effect of two structural variables on the labeling performance toward kinases. Preliminary labeling results indicated that probe 3, which is the best candidate in the probe library, is indeed an activity-based probe for kinases. The results also revealed that the ester linkage between the fluorosulfonylbenzoyl moiety and the sugar plays an important role in the labeling ability of the probes, and the meta-substituted sulfonyl fluoride could help improving the target specificity. The valuable information obtained in this study could be applied for probe improvement in the future.

We found significant correlation between the incidence of severe influenza and Aspergillus antigenemia among medical intensive care unit patients for 7-month observation (coefficient γ=0.976, p

This paper deals with mobile localization in environments with probabilistic occurrences of non-line-of-sight (NLOS) errors. We introduce the construction of validated measurement regions using real-time LOS-assisted information. To ameliorate both NLOS errors and mobility variations of a mobile target, we propose an intelligent robust extended-Kalman filter-based interacting multiple-model (intelligent IMM-REKF). The proposed approach is distinct from other existing methods. Simulation results illustrate the superior performance of the proposed algorithm under various NLOS occurrence probabilities.

Lactic acidosis is common in critical care; by contrast, a subtype called alcohol-associated lactic acidosis (AALA) is rarely encountered. The primary purpose of this study was to determine the prognosis of AALA in critically ill patients and the second aim was to determine whether the survival was associated to the peak blood lactate concentration. An 8-year retrospective analysis of adult patients admitted to the intensive care unit (ICU) with AALA between January 2007 and December 2014 was considered in a tertiary care hospital. In total, 23 patients were analyzed and the median peak blood lactate level was 15.9 mmol/L. Only 2 patients (8.7%) presented peak blood lactate levels

This research developed and evaluated three multispectral algorithms derived from hyperspectral line-scan fluorescence imaging using violet LED excitation for the detection of fecal contamination on Golden Delicious apples. The algorithms utilized the fluorescence intensities at four wavebands, 680, 684, 720, and 780 nm, for computation of simple functions for effective detection of contamination spots created on the apple surfaces using four concentrations of aqueous fecal dilutions. The algorithms detected more than 99% of the fecal spots. The effective detection of feces showed that a simple multispectral fluorescence imaging algorithm based on violet LED excitation may be appropriate to detect fecal contamination on high-speed apple processing lines. This fast and non-destructive method for detection of fecal contamination can be implemented in the food and agricultural industries to help in risk reduction and food safety assurance for preventing or minimizing the potential foodborne illness.

A line-scan spectral imaging system was developed for online identification of wholesome and unwholesome freshly slaughtered chicken carcasses on commercial processing lines. Hyperspectral images acquired by the imaging system for 5,549 wholesome and 93 unwholesome chicken carcasses on a 140 bird per minute (bpm) processing line were analyzed to optimize region of interest size and location and determine key wavebands by which to implement online multispectral inspection based on single-waveband intensity and a two-waveband ratio. Multispectral inspection algorithms were developed for real-time online chicken inspection. The imaging system inspected over 100,000 chickens on the processing line during continuous operation and accurately identified over 99% of wholesome and over 96% of unwholesome chickens. A system of this type can perform food safety inspection tasks accurately and consistently on high-speed processing lines (e.g., at least 140 bpm), to help poultry processors improve production efficiency and satisfy increasing consumer demand for poultry products. PRACTICAL APPLICATIONS The line-scan spectral imaging system was designed as a tool to assist poultry processors in meeting the requirements of the HACCP-Based Inspection Models Project as implemented by Food Safety and Inspection Service (FSIS). The system is particularly well suited for presorting poultry carcasses on high-speed processing lines by removing systemically diseased birds prior to the inspection stations. This can increase efficiency and reduce cross-contamination risks by minimizing the presence and unnecessary processing of unwholesome birds on the processing line. Real-time data collection and prompt removal of unwholesome poultry carcasses can enhance the ability of FSIS to certify U.S. poultry products for both domestic and export markets. In addition, the spectral imaging methodology of this system has the potential for easy adaptation to other high-speed food processing tasks, particularly those involving automated inspection for quality indicators.

Foodborne diseases are of serious concern to public health. It is necessary to develop fast and reliable non-destructive detection methods to improve food product monitoring for the food industry. This research was conducted to investigate hyperspectral fluorescence imaging using violet/blue LED excitation to develop a multispectral algorithm for detection of fecal contamination on Golden Delicious apples. From the hyperspectral image data, four wavebands, 680, 684, 720, and 780 nm, were selected for potential use in a multispectral detection algorithm. The algorithm could detect 96–100% of different dilutions of feces on apples. The highly successfully detection of feces showed that a simple multispectral fluorescence imaging algorithm based on violet/blue LED excitation can be used to detect fecal contamination on apple processing lines.

A spectral line-scan imaging system was developed for automated online wholesomeness inspection of broilers and evaluated in a commercial chicken processing plant. Real-time online hyperspectral images acquired by the system on a 140 bird-per-minute processing line were analyzed to optimize Region of Interest (ROI) size and location and to determine key wavebands by which to implement online high-speed multispectral inspection. Multispectral imaging algorithms were implemented to automatically recognize individual carcasses entering and exiting the field of view, to locate the ROI on the bird, and to determine the condition for each carcass as being wholesome or unwholesome. The high accuracy obtained from the in-plant evaluation results showed that the system can effectively perform food safety inspection tasks on high-speed processing lines. The system is being adapted for commercial use in pre-sorting chicken during initial processing operations, to help poultry processors improve production efficiency and satisfy increasing consumer demand for poultry products.

Epstein-Barr virus (EBV)-positive diffuse large B cell lymphoma (DLBCL) of the elderly is a recently defined subgroup of DLBCL in the 2008 WHO classification of lymphoid neoplasms. It is characterized by an EBV-positive clonal B cell lymphoproliferation that occurs in patients aged over 50 years without any known immunodeficiency. This disease shows frequent extranodal involvement, a morphological spectrum from polymorphous to monomorphic large cells, and a poor prognosis. We reported the case of a 75-year-old male who had a primary intestinal multicentric EBV(+) DLBCL and presented with perforation, the first multicentric case in the literature. The monomorphic tumor cells exhibited extensive necrosis and expressed CD20, CD138, and EBNA2 with diffuse positivity for EBV by in situ hybridization. Quantitative real-time PCR revealed a low serum EBV viral load. The patient passed away in 10 days. His poor outcome was probably due to perforation, age over 70, the presence of B symptoms, poor general condition, and high performance score.

Electronic business (e-business) plays a major role in modern economic networks due to its shorter cycle time and faster information transactions. Building enterprise applications that can coordinate activities, decisions, and knowledge across many functions is a good solution for e-business. Hence, how to implement enterprise applications successfully has become an increasingly important management issue. Although success of information systems (IS) has received fairly extensive attention from prior research, issues regarding whether functions and service delivered by systems can be justifiably relied on by users are seriously ignored, especially complex systems like enterprise applications. Thus, this paper attempts to extend this kind of concept, perceived dependability , into DeLone and McLean's IS success model to explore how it influences success of enterprise applications. Results from a survey of 170 respondents taken from six internationalized e-businesses in Taiwan strongly support that perceived dependability is indeed an important factor for success of enterprise applications. Additionally, over and beyond the effects of dependability on success of enterprise applications, it can help practitioners and managers get deep insights into how to implement e-business successfully.

A machine vision system was developed and evaluated for the automation of online inspection to differentiate freshly slaughtered wholesome chickens from systemically diseased chickens. The system consisted of an electron-multiplying charge-coupled-device (EMCCD) camera used with an imaging spectrograph and controlled by a computer to obtain line-scan images quickly on a chicken processing line of a commercial poultry plant. The system scanned chicken carcasses on an eviscerating line operating at a speed of 140 chickens per minute. An algorithm was implemented in the system to automatically recognize individual carcasses entering and exiting the field of view, to locate the region of interest (ROI) of each chicken, to extract useful spectra from the ROI as inputs to the differentiation method, and to determine the condition for each carcass as being wholesome or systemically diseased. The system can acquire either hyperspectral or multispectral images without any cross-system calibration. The essential spectral features were selected from hyperspectral images of chicken samples. The differentiation of chickens on the processing line was then carried out using multispectral imaging. The high accuracy obtained from the evaluation results showed that the machine vision system can be applied successfully to automatic online inspection for chicken processing.

High speed networks and rapidly improving microprocessor performance make the network of workstations an extremely important tool for parallel computing in order to speedup the execution of scientific applications. Shared memory is an attractive programming model for designing parallel and distributed applications, where the programmer can focus on algorithmic development rather than data partition and communication. Based on this important characteristic, the design of systems to provide the shared memory abstraction on physically distributed memory machines has been developed, known as Distributed Shared Memory (DSM). DSM is built using specific software to combine a number of computer hardware resources into one computing environment. Such an environment not only provides an easy way to execute parallel applications, but also combines available computational resources with the purpose of speeding up execution of these applications. DSM systems need to maintain data consistency in memory, which usually leads to communication overhead. Therefore, there exists a number of strategies that can be used to overcome this overhead issue and improve overall performance. Strategies as prefetching have been proven to show great performance in DSM systems, since they can reduce data access communication latencies from remote nodes. On the other hand, these strategies also transfer unnecessary prefetching pages to remote nodes. In this research paper, we focus on the access pattern during execution of a parallel application, and then analyze the data type and behavior of parallel applications. We propose an adaptive data classification scheme to improve prefetching strategy with the goal to improve overall performance. Adaptive data classification scheme classifies data according to the accessing sequence of pages, so that the home node uses past history access patterns of remote nodes to decide whether it needs to transfer related pages to remote nodes. From experimental results, we can observe that our proposed method can increase the accuracy of data access in effective prefetch strategy by reducing the number of page faults and misprefetching. Experimental results using our proposed classification scheme show a performance improvement of about 9---25% over the same benchmark applications running on top of an original JIAJIA DSM system.

In recent years, cluster computing has been widely investigated and there is no doubt that it can provide a cost-effective computing infrastructure by aggregating computational power, communication, and storage resources. Moreover, it is also considered to be a very attractive platform for low-cost supercomputing. Distributed shared memory (DSM) systems utilize the physical memory of each computing node interconnected in a private network to form a global virtual shared memory. Since this global shared memory is distributed among the computing nodes, accessing the data located in remote computing nodes is an absolute necessity. However, this action will result in significant remote memory access latencies which are major sources of overhead in DSM systems. For these reasons, in order to increase overall system performance and decrease this overhead, a number of strategies have been devised. Prefetching is one such approach which can reduce latencies, although it always increases the workload in the home nodes. In this paper, we propose a scheme named Agent Home Scheme. Its most noticeable feature, when compared to other schemes, is that the agent home distributes the workloads of each computing nodes when sending data. By doing this, we can reduce not only the workload of the home nodes by balancing the workload for each node, but also the waiting time. Experimental results show that the proposed method can obtain about 20% higher performance than the original JIAJIA, about 18% more than History Prefetching Strategy (HPS), and about 10% higher than Effective Prefetch Strategy (EPS).

We have recently developed a rapid online line-scan imaging system capable of both hyperspectral Vis/NIR reflectance and fluorescence in the Vis with UV-A excitation. The hyperspectral online line-scan system was integrated with a commercial apple-sorting machine and evaluated to inspect apples for fecal contamination and defects at a processing line speed of over three apples per second. Results showed that fluorescence imaging (using a two-band ratio) could achieve detection of fecal spots on artificially contaminated apples with a 100% detection rate and no false positives regardless of the presence of defects. A NIR two-band reflectance ratio coupled with a simple classification method based on the mean intensity and homogeneity of the ratio achieved a 99.5% apple defect classification accuracy with a false positive rate of only 2%. The presented NIR processing regime overcame the presence of stem/calyx on apples that typically has been a problematic source for false positives in the detection of defects. The most significant and important outcome of this investigation is a line-scan inspection system that can potentially provide the capability for current sorting mechanisms, such as by size and color, as well as additional sorting for quality and safety attributes of food products. This line-scan based online imaging system offers great potential as a value-added dynamic inspection system due to its capability for multi-tasking to meet a variety of inspection objectives. A multi-tasking inspection system that can meet current industry sorting needs with the added benefit of safety inspection without requiring significant modification of existing infrastructure or incurring significant costs may lead the apple industry to consider adopting voluntary measures to further enhance safe production and processing of fruits.

The development of accurate, rapid, and non-invasive inspection technologies are needed to help poultry processors meet food safety regulations and rising consumer demand while increasing productivity and economic competitiveness. This paper reports on a novel two narrow-band color-mixing technique for identification of broiler chicken carcass conditions. Spectra were collected for samples cut from the breast area of 103 wholesome chicken carcasses, 66 systemically diseased chicken carcasses, and 40 cadaver chicken carcasses using a photodiode array spectrophotometer system. Waveband pairs in the range of 416–715 nm were evaluated for identifying chicken conditions using the two-band color-mixing technique, and the pair of (453 nm, 589 nm) was selected based on color difference index calculations in CIELUV color space. Significant differences in the color characteristics of wholesome, systemically diseased, and cadaver chicken conditions, based on color-mixing using the two selected wavebands, were confirmed by one-way analysis of variance. Decision-tree classification models using the calculated color difference indexes were evaluated first by using the spectral data divided into a validation set and a testing set, and second by 10-fold cross-validation of the entire data set. Classification accuracies achieved for the wholesome, systemically diseased, and cadaver samples were 95.8%, 95.5%, and 100%, respectively, for the validation set; 94.6%, 100%, and 90.6%, respectively, for the testing set; and 98.1%, 97.5%, and 93.9%, respectively, when using 10-fold cross-validation. Published by Elsevier Ltd.

The USDA Agricultural Research Service has developed a fast line-scan imaging system for differentiating wholesome and systemically diseased fresh chickens. The imaging system was used to acquire hyperspectral line-scan images of 250 chicken carcasses on a laboratory processing line moving at 70 birds per minute. A method appropriate for line-scan imaging was developed for automated sensing of birds and locating the Region of Interest (ROI) within the line-scan images most suited for differentiation. From analysis of wholesome and systemically diseased chicken spectra in the ROI, four key wavelengths for differentiating between wholesome and systemically diseased chickens were selected: 424, 465, 515, and 546 nm. The key wavelengths and their ratios with a reference wavelength (689 nm) were investigated for a fuzzy logic based differentiation algorithm. Classification using the key wavelengths correctly identified 98 and 95% of wholesome and systemically diseased chickens for model development, and 98 and 93% of wholesome and systemically diseased chickens for model testing. Although band ratios reduced variation within each chicken category, the resulting classification accuracies were not significantly improved over those for classification by key wavelengths.

DRAINMOD-N, a mathematical model to predict nitrate-N concentrations in surface runoff and drain outflows from subsurface-drained farmlands, has been tested against field data collected in southern Ontario. The data was collected in a corn field from 16 conventional drainage and subirrigation plots in Woodslee, Ontario, from 1992 to 1994. The model performance was evaluated by comparing the observed and simulated nitrate-N concentrations in surface runoff and drain outflows. A precise calculation of water-table depth is an essential prerequisite for a model to obtain a proper prediction of nitrate-N movement. For the simulation of water-table depth, the lowest root mean square error and the highest correlation coefficient of linear regression were 173 mm and 0.51 for the subirrigation plots; and 178 mm and 0.84 for the subsurface drainage plots. Therefore, the performance of DRAINMOD-N for soil hydrologic simulations was satisfactory and it could be used for assessing nitrogen fate and transport. For the simulation of nitrate-N losses in the subirrigation plots, the lowest root mean square error and the highest correlation coefficient of linear regression were 0.74 kg/ha and 0.98 for surface runoff; and 6.53 kg/ha and 0.91 for drain outflow. For the simulation in the subsurface drainage plots, the lowest root mean square error and the highest correlation coefficient of linear regression were 0.70 kg/ha and 0.96 for surface runoff; and 6.91 kg/ha and 0.92 for drain outflow. The results show that DRAINMOD-N can perform satisfactory simulation of soil hydrology and nitrate-N losses in surface runoff under various water-table management practices. The model can, therefore, be used to evaluate different water pollution scenarios and help in the development and testing of various pollution control strategies for fields in cold weather such as that in southern Canada.

Cardiotoxin III (CTX III), a basic polypeptide with 60 amino acid residues isolated from Naja naja atra venom, has been reported to have anticancer activity. The molecular effects of CTX III on HL-60 cells were dissected in the present study. We found that the antiproliferative action of CTX III on HL-60 cells was mediated through apoptosis, as characterized by an increase of sub G1 population, DNA fragmentation and poly(ADP-ribose) polymerase (PARP) cleavage. Upregulation of Bax, downregulation of Bcl-2, the release of mitochondrial cytochrome c to cytosol and the activations of capase-9 and -3 were noted, while CTX III had no appreciable effect on the levels of Bcl-X(L) and Bad proteins. Moreover, c-Jun N-terminal kinase (JNK) was activated shortly after CTX III treatment in HL-60 cells. Consistently, the SP600125 compound, an anthrapyrazolone inhibitor of JNK, suppressed apoptosis induced by CTX III. As expected, this JNK inhibitor also attenuated the modulation of Bax and Bcl-2, as well as the cytosolic appearance of cytochrome c and the activation of caspase-3 and caspase-9 that induced by CTX III. These findings suggest that CTX III can induce apoptosis in HL-60 cells via the mitochondrial caspase cascade and the activation of JNK is critical for the initiation of the apoptotic death of HL-60 cells.