Critical Care of Acute Heart Failure

Acute heart failure is a life-threatening medical condition. Improving acute heart failure care is important. Early diagnosis and evaluating the etiology are important in acute heart failure. Patients with suspected acute heart failure should have a diagnostic workup, and appropriate pharmacological and nonpharmacological management should be started promptly and in parallel. Diagnosis of acute heart failure should be based on history and symptoms. The physical examination typically presents with some combination of increased congestion and decreased peripheral perfusion, further confirmed by electrocardiogram, chest radiograph, biomarkers, and echocardiogram. The first step in the management of a patient is to address life-threatening issues. Patients with respiratory failure or cardiogenic shock should be treated soon. The next step is the identification of precipitants that needs urgent management. Evidence-based medication to reduce morbidity and mortality for patients with heart failure includes an angiotensin converting enzyme inhibitor, angiotensin receptor blocker, or angiotensin receptor-neprilysin inhibitor; a beta blocker; and a mineralocorticoid receptor antagonist. During an acute heart failure episode, management of these agents depends upon whether the patient has contraindications to therapy such as hemodynamic instability or acute kidney injury. Once the patient is stable, therapies are carefully initiated, reinitiated, or titrated with appropriate follow-up

Family firm and analyst forecasts in an emerging economy

Purpose: The purpose of this paper is to examine how family firms affect analyst forecast dispersion, accuracy and optimism and how earnings smoothness as the moderating factor, affects these relationships in an emerging market context. Design/methodology/approach: This paper uses the population sample of firms listed on the Taiwan Stock Exchange from 2009 to 2010 as the research sample, which includes 963 firm-year observations. Findings: The findings show that analysts following family firms are more likely to have more dispersed, less accurate and more optimism biased forecasts than those following nonfamily firms. Earning smoothness is mainly used by nonfamily firms as a signalling strategy to improve analyst forecast quality. In contrast, earnings smoothness is mainly used by families as a garbling strategy, stimulating forecast optimism. Only earnings smoothness in family firms with a high level of family ownership concentration is likely to be signalling-oriented to improve analyst forecast accuracy and mitigate analyst optimism biases. Originality/value: Emerging markets are not only featured by prevailing principal-principal conflicts but also have multiple levels of agency conflicts among large shareholders, minority shareholders and professionally hired managers. This research reveals the multiple governance roles of family owners in affecting analyst forecast quality, including their entrenchment role in extracting private benefits of control through opaque environments and market discipline distortion role in aligning interests between managers and families without prioritising meeting or beating analyst forecasts, both at the cost of minority shareholders. This research further disentangles the intertwined signaling oriented and garbiling oriented incentives associated with earnings smoothness under family governance

Nanotargeted Radionuclides for Cancer Nuclear Imaging and Internal Radiotherapy

Current progress in nanomedicine has exploited the possibility of designing tumor-targeted nanocarriers being able to deliver radionuclide payloads in a site or molecular selective manner to improve the efficacy and safety of cancer imaging and therapy. Radionuclides of auger electron-, α-, β-, and γ-radiation emitters have been surface-bioconjugated or after-loaded in nanoparticles to improve the efficacy and reduce the toxicity of cancer imaging and therapy in preclinical and clinical studies. This article provides a brief overview of current status of applications, advantages, problems, up-to-date research and development, and future prospects of nanotargeted radionuclides in cancer nuclear imaging and radiotherapy. Passive and active nanotargeting delivery of radionuclides with illustrating examples for tumor imaging and therapy are reviewed and summarized. Research on combing different modes of selective delivery of radionuclides through nanocarriers targeted delivery for tumor imaging and therapy offers the new possibility of large increases in cancer diagnostic efficacy and therapeutic index. However, further efforts and challenges in preclinical and clinical efficacy and toxicity studies are required to translate those advanced technologies to the clinical applications for cancer patients

Retraction and Generalized Extension of Computing with Words

Fuzzy automata, whose input alphabet is a set of numbers or symbols, are a formal model of computing with values. Motivated by Zadeh's paradigm of computing with words rather than numbers, Ying proposed a kind of fuzzy automata, whose input alphabet consists of all fuzzy subsets of a set of symbols, as a formal model of computing with all words. In this paper, we introduce a somewhat general formal model of computing with (some special) words. The new features of the model are that the input alphabet only comprises some (not necessarily all) fuzzy subsets of a set of symbols and the fuzzy transition function can be specified arbitrarily. By employing the methodology of fuzzy control, we establish a retraction principle from computing with words to computing with values for handling crisp inputs and a generalized extension principle from computing with words to computing with all words for handling fuzzy inputs. These principles show that computing with values and computing with all words can be respectively implemented by computing with words. Some algebraic properties of retractions and generalized extensions are addressed as well.Comment: 13 double column pages; 3 figures; to be published in the IEEE Transactions on Fuzzy System

A multi-product FPR model with rework and an improved delivery policy

A multi-item finite production rate (FPR) model with rework and an improved delivery policy is examined in this paper. Unlike the classic FPR model whose purpose is to derive the most economic lot size for a single-product production system with perfect quality and a continuous issuing policy, this paper considers a production of multiple products on a single machine, rework of all nonconforming items produced, and a cost-reduction, multi-delivery policy. We extend the work of Chiu et al. [1] by incorporating an improved n+1 shipment policy into their model. According to such a policy, one extra delivery of finished items is made during vendor’s production uptime to satisfy product demands during the period of vendor’s uptime and rework time. When the rest of the production lot is quality assured and the rework has been finished as well, n fixed-quantity installments of finished items are delivered to customers. The objectives are to determine an optimal, common-production cycle time that minimizes the long-run average system cost per time unit, study the effects of rework and the improved delivery policy on the optimal production. Mathematical modelling and analysis is used to derive a closed-form, optimal, common-cycle time. Finally, practical usages of the obtained results are demonstrated by a numerical example

AVATAR: Robust Voice Search Engine Leveraging Autoregressive Document Retrieval and Contrastive Learning

Voice, as input, has progressively become popular on mobiles and seems to transcend almost entirely text input. Through voice, the voice search (VS) system can provide a more natural way to meet user's information needs. However, errors from the automatic speech recognition (ASR) system can be catastrophic to the VS system. Building on the recent advanced lightweight autoregressive retrieval model, which has the potential to be deployed on mobiles, leading to a more secure and personal VS assistant. This paper presents a novel study of VS leveraging autoregressive retrieval and tackles the crucial problems facing VS, viz. the performance drop caused by ASR noise, via data augmentations and contrastive learning, showing how explicit and implicit modeling the noise patterns can alleviate the problems. A series of experiments conducted on the Open-Domain Question Answering (ODSQA) confirm our approach's effectiveness and robustness in relation to some strong baseline systems

Mandatory restatement, family dominance and management turnover: the evidence from an emerging economy

Due to the uniqueness of mandatory restatements, this paper examines whether family dominance affects the relationship between mandatory restatements and management turnover in an emerging economy – Taiwan. This paper adopts logistic regression models along with reporting the marginal effect of all explanatory variables to examine management turnover in different years around the year of mandatory restatement announcement. The findings show that family directorship weakens the positive relationship between mandatory restatements and management turnover in one year after the year of mandatory restatement announcement whereas do not show that family shareholding can affect the above relationship in any observed years. The findings have essential policy implications for security regulators and firms to strengthen family governance practices and financial reporting quality