A case study study of implementing value creation strategy in a Chinese manufacturing firm

Value creation is a fundamental concept for business organisations. Recently, academics, consultants and practitioners have advocated to re-examine this concept to better understand competitive advantage in business organisations. However, little empirical study has been conducted on the value creation implementation. This paper focuses on the value creation implementation at a Chinese manufacturing firm, the first company in the Chinese steel industry implementing value creation strategy, to illustrate how value has been created in the firm from a managerial perspective. It describes a rich account of the implementation process of value creation, discusses its theoretical and managerial implications, and presents the challenges faced by practicing managers in such a process

Reliability analysis of coherent systems subject to internal failures and external shocks

In reality, a system and its components, apart from internal failures, are often exposed to external shocks as well. Since external shocks have significant effects on the performance of the system, neglecting their effects during reliability analysis of the system leads to large prediction errors and even misleading conclusions. In this paper, we present a new method for reliability analysis of coherent systems subject to internal failures and random external shocks. The cumulative probability of failure (CPF) is used as an index to quantify the effects of random external shocks on the reliability of the components and a three-moment saddlepoint approximation approach is proposed to predict the CPF. In addition, the theory of survival signature is applied to assess the associations of the components of the system to calculate the reliability of the system efficiently. Finally, two numerical examples are utilized to demonstrate the validity and effectiveness of the proposed method

A high precision robotic system design for microsurgical applications.

The introduction of robotic systems in medical surgery has achieved the goal of decreasing procedures invasiveness positively impacting the patient’s prognosis by reducing the incisions size, surgical infections, and hospitalization time. Nowadays robotic surgery is used as an integral part of urology, gynaecology, abdominal and cardiac interventions. Despite its adoption in several surgical specialties, robotic technology remains limited in the area of microsurgery. In this paper, we present the development of a robotic system providing sub-millimeter motion resolution for the potential manipulation of fine structures. The design is based on a linear delta robotic geometry. The motion, resolution and repeatability of the developed system were simulated followed by a proof-of-concept experimental testing. The developed system achieved a motion resolution of 3.37 ±0.17μm in both the X and Y axes and 1.32 ±0.2μm in the Z axis considering individual steps. We evaluated the system navigation setting a zigzag trajectory having dimensions below those found in blood vessels (300 to 800 μm) and found that the system is capable of achieving a maximum resolution of 3.06 ±0.03 μm. These results demonstrate the potential application of the here presented robotic system for its use in microsurgical applications such as neurosurgery, plastic, and breast cancer surgeries.</p

Measurements of electrical resistivity and Seebeck coefficient for disc-shaped samples

Herein we develop a methodology to measure the resistivity of discs by deriving a mathematic formula between resistivity and resistance from solving the electrostatic Laplace equation in polar coordinates. Then the resistivity and Seebeck coefficient of disc samples of p- and n-type bismuth telluride are measured experimentally either in nitrogen or helium atmosphere. The validity of Seebeck coefficient is demonstrated by the excellent linearity between Seebeck voltages and temperature differences. The bar samples are also measured for comparison. Finite element simulation is utilized to display the two-dimensional potentials and currents and have an error analysis. Furthermore, the resistivity error due to the probe distance error is discussed analytically based on the mathematic formula, and the probe distance can be optimized to minimize the resistivity error. The disclosed approach would extend the applicability of the present instruments to the disc-shaped samples and be useful in the emerging transverse thermoelectricity

Sketch-supervised Histopathology Tumour Segmentation: Dual CNN-Transformer with Global Normalised CAM

Deep  learning  methods  are  frequently  usedin segmenting histopathology images with high-quality an-notations  nowadays.  Compared  with  well-annotated  data,coarse, scribbling-like labelling is more cost-effective andeasier to obtain in clinical practice. The coarse annotationsprovide  limited  supervision,  so  employing  them  directlyfor  segmentation  network  training  remains  challenging.We  present  a  sketch-supervised  method,  called  DCTGN-CAM,  based  on  a  dual  CNN-Transformer  network  and  amodified global normalised class activation map. By mod-elling global and local tumour features simultaneously, thedual  CNN-Transformer  network  produces  accurate  patch-based  tumour  classification  probabilities  by  training  onlyon lightly annotated data. With the global normalised classactivation map, more descriptive gradient-based represen-tations of the histopathology images can be obtained, andinference of tumour segmentation can be performed withhigh accuracy. Additionally, we collect a private skin cancerdataset named BSS, which contains fine and coarse anno-tations for three types of cancer. To facilitate reproducibleperformance  comparison,  experts  are  also  invited  to  la-bel coarse annotations on the public liver cancer datasetPAIP2019. On the BSS dataset, our DCTGN-CAM segmenta-tion outperforms the state-of-the-art methods and achieves76.68 % IOU and 86.69 % Dice scores on the sketch-basedtumour  segmentation  task.  On  the  PAIP2019  dataset,  ourmethod achieves a Dice gain of 8.37 % compared with U-Netas the baseline network. The dataset, annotation and codewill be published athttps://github.com/skdarkless/DCTGN–CAM.</p

Social media mining under the COVID-19 context: Progress, challenges, and opportunities

Social media platforms allow users worldwide to create and share information, forging vast sensing networks that allow information on certain topics to be collected, stored, mined, and analyzed in a rapid manner. During the COVID-19 pandemic, extensive social media mining efforts have been undertaken to tackle COVID-19 challenges from various perspectives. This review summarizes the progress of social media data mining studies in the COVID-19 contexts and categorizes them into six major domains, including early warning and detection, human mobility monitoring, communication and information conveying, public attitudes and emotions, infodemic and misinformation, and hatred and violence. We further document essential features of publicly available COVID-19 related social media data archives that will benefit research communities in conducting replicable and reproducible studies. In addition, we discuss seven challenges in social media analytics associated with their potential impacts on derived COVID-19 findings, followed by our visions for the possible paths forward in regard to social media-based COVID-19 investigations. This review serves as a valuable reference that recaps social media mining efforts in COVID-19 related studies and provides future directions along which the information harnessed from social media can be used to address public health emergencies

Oral Immunization of Chickens with Probiotic Lactobacillus crispatus Constitutively Expressing the α-β2-ε-β1 Toxoids to Induce Protective Immunity

Clostridium perfringens (C. perfringens) is a bacterium that commonly causes zoonotic disease. The pathogenicity of C. perfringens is a result of the combined action of α, β, and ε exotoxins. In this study, Lactobacillus crispatus (pPG-T7g10/L. crispatus) expressing the main toxoids of C. perfringens, α, ε, β1, and β2, with EGFP-labeling, was constructed, and the protective effect was estimated in chickens. The α-β2-ε-β1 toxoid was constitutively expressed for confirmation by laser confocal microscopy and western blotting, and its immunogenicity was analyzed by enzyme-linked immunosorbent assay (ELISA) and immunohistochemical assays. After booster immunization, the probiotic vaccine group showed significantly higher levels (p < 0.05) of specific secretory IgA (sIgA) and IgY antibodies in the serum and intestinal mucus. Furthermore, the levels of cytokines, including interferon (IFN)-γ, interleukin (lL)-2, IL-4, IL-10, IL-12, and IL-17, and the proliferation of spleen lymphocytes in chickens orally immunized with pPG-E-α-β2-ε-β1/L. crispatus increased significantly. Histopathological observations showed that the intestinal pathological changes in chickens immunized with pPG-E-α-β2ε-β1/L. crispatus were significantly alleviated. These data reveal that the probiotic vaccine could stimulate mucosal, cellular, and humoral immunity and provide an active defense against the toxins of C. perfringens, suggesting a promising candidate for oral vaccines against C. perfringens

CARDIAN: A Novel Computational Approach for Real-Time End-Diastolic Frame Detection in Intravascular Ultrasound Using Bidirectional Attention Networks

Introduction: Changes in coronary artery luminal dimensions during the cardiac cycle can impact the accurate quantification of volumetric analyses in intravascular ultrasound (IVUS) image studies. Accurate ED-frame detection is pivotal for guiding interventional decisions, optimizing therapeutic interventions, and ensuring standardized volumetric analysis in research studies. Images acquired at different phases of the cardiac cycle may also lead to inaccurate quantification of atheroma volume due to the longitudinal motion of the catheter in relation to the vessel. As IVUS images are acquired throughout the cardiac cycle, end-diastolic frames are typically identified retrospectively by human analysts to minimize motion artefacts and enable more accurate and reproducible volumetric analysis. Methods: In this paper, a novel neural network-based approach for accurate end-diastolic frame detection in IVUS sequences is proposed, trained using electrocardiogram (ECG) signals acquired synchronously during IVUS acquisition. The framework integrates dedicated motion encoders and a bidirectional attention recurrent network (BARNet) with a temporal difference encoder to extract frame-by-frame motion features corresponding to the phases of the cardiac cycle. In addition, a spatiotemporal rotation encoder is included to capture the IVUS catheter's rotational movement with respect to the coronary artery. Results: With a prediction tolerance range of 66.7 ms, the proposed approach was able to find 71.9%, 67.8%, and 69.9% of end-diastolic frames in the left anterior descending, left circumflex and right coronary arteries, respectively, when tested against ECG estimations. When the result was compared with two expert analysts’ estimation, the approach achieved a superior performance. Discussion: These findings indicate that the developed methodology is accurate and fully reproducible and therefore it should be preferred over experts for end-diastolic frame detection in IVUS sequences.</p

CARDIAN: A Novel Computational Approach for Real-Time End-Diastolic Frame Detection in Intravascular Ultrasound Using Bidirectional Attention Networks

Introduction: Changes in coronary artery luminal dimensions during the cardiac cycle can impact the accurate quantification of volumetric analyses in intravascular ultrasound (IVUS) image studies. Accurate ED-frame detection is pivotal for guiding interventional decisions, optimizing therapeutic interventions, and ensuring standardized volumetric analysis in research studies. Images acquired at different phases of the cardiac cycle may also lead to inaccurate quantification of atheroma volume due to the longitudinal motion of the catheter in relation to the vessel. As IVUS images are acquired throughout the cardiac cycle, end-diastolic frames are typically identified retrospectively by human analysts to minimize motion artefacts and enable more accurate and reproducible volumetric analysis. Methods: In this paper, a novel neural network-based approach for accurate end-diastolic frame detection in IVUS sequences is proposed, trained using electrocardiogram (ECG) signals acquired synchronously during IVUS acquisition. The framework integrates dedicated motion encoders and a bidirectional attention recurrent network (BARNet) with a temporal difference encoder to extract frame-by-frame motion features corresponding to the phases of the cardiac cycle. In addition, a spatiotemporal rotation encoder is included to capture the IVUS catheter's rotational movement with respect to the coronary artery. Results: With a prediction tolerance range of 66.7 ms, the proposed approach was able to find 71.9%, 67.8%, and 69.9% of end-diastolic frames in the left anterior descending, left circumflex and right coronary arteries, respectively, when tested against ECG estimations. When the result was compared with two expert analysts’ estimation, the approach achieved a superior performance. Discussion: These findings indicate that the developed methodology is accurate and fully reproducible and therefore it should be preferred over experts for end-diastolic frame detection in IVUS sequences.</p

Functional vulnerability of liver macrophages to capsules defines virulence of blood-borne bacteria

Many encapsulated bacteria use capsules to cause invasive diseases. However, it remains largely unknown how the capsules enhance bacterial virulence under in vivo infection conditions. Here we show that the capsules primarily target the liver to enhance bacterial survival at the onset of blood-borne infections. In a mouse sepsis model, the capsules enabled human pathogens Streptococcus pneumoniae and Escherichia coli to circumvent the recognition of liver-resident macrophage Kupffer cells (KCs) in a capsular serotype-dependent manner. In contrast to effective capture of acapsular bacteria by KCs, the encapsulated bacteria are partially (low-virulence types) or completely (high-virulence types) "untouchable" for KCs. We finally identified the asialoglycoprotein receptor (ASGR) as the first known capsule receptor on KCs to recognize the low-virulence serotype-7F and -14 pneumococcal capsules. Our data identify the molecular interplay between the capsules and KCs as a master controller of the fate and virulence of encapsulated bacteria, and suggest that the interplay is targetable for therapeutic control of septic infections