Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Strategies of Firms in High-Velocity Environments: The Adaptive Strategies of Firms in High-Velocity Environments: The Case of B2B Electronic Marketplaces Case of B2B Electronic Marketplaces

Electronic marketplaces operate in highly dynamic environments. B2B (Business-to-business) e-Commerce (Electronic Commerce) is expanding rapidly, but Independent Internet based Electronic Marketplaces (IBEM) have passed through periods of boom and bust. In start-up entrepreneurial ventures such as IBEMs, adaptation is critical than at any other stage in the life cycle and hence the ability to learn and adapt becomes a key competency. The research uses the resource-based theory as a means of analyzing the evolution and adaptation of the resources and capabilities of IBEMs and the sustainability of competitive advantage. We use four case studies to trace the pattern of adaptation as well as identify the variables. Based on the inputs from this, we use a comprehensive sample of 135 IBEMs across various geographic regions covering 15 industry segments. The findings of this study provide key managerial insights into various issues that are important to IBEMs in particular and start-up entrepreneurial firms operating in highly dynamic environments in general. These include the stages of evolution and the sources or the lack of competitive advantage at each stage, the type of resources and capabilities, which need to be built, the type of complementary assets, which needs to be leveraged and the degrees of adaptation at various stages.
Who Should I Approach? Knowledge Sourcing in Enterprise Who Should I Approach? Knowledge Sourcing in Enterprise System Implementation System Implementation

During enterprise system implementation, organizations transfer system-related knowledge to end users through training sessions and by having them involved in the system design and implementation processes. However, during actual use of the system, end users acquire informal knowledge from co-workers through their social networks. Existing research has focused on the structural features of social networks and their impact on regulating knowledge flow between end users and across the network. The personal attributes and preferences of end users that could impact their knowledge acquisition and processing capabilities have not been considered. This paper examines the expertise level of end users, their gender, and the complexity of their job tasks in influencing system-related knowledge acquisition and its impact on performance outcomes. Data for this study was collected from active users of a new Enterprise Resource Planning (ERP) system that had been implemented across multiple business units of diversified business conglomerate in the United States.
POSITION: THE CASE OF THE CZECH MORTGAGE MARKET 1

The study analyses mass media communication by mortgage lenders in the Czech market and builds on Kotler’s competitive strategies concept and aims to answer the question of whether there is a relationship between the mortgage lenders’ market position and their mass media communication profile. Furthermore, it analyses the mass media communication of mortgage lenders according to their financial performance. The analysis utilises complete monitoring data from the mass media market in the Czech Republic. As a result of this study, the different advertising patterns of the market leaders and challengers on a highly commoditised market are shown. Market leaders do not utilise defensive strategies to protect their market shares but employ more offensive strategies to increase the size of the market. Market challengers have no resources to implement a massive and offensive communication strategy to challenge the market leaders. Such a conclusion contributes to the debate on communication strategies for distinct types of companies according to their position on the market and their market share.
Laser‐Printed Organic Thin‐Film Transistors

Solution deposition of organic optoelectronic materials enables fast roll‐to‐roll manufacturing of photonic and electronic devices on any type of substrate and at low cost. But controlling the film microstructure when it crystallizes from solution can be challenging. This represents a major limitation of this technology, since the microstructure, in turn, governs the charge transport properties of the material. Further, the solvents typically used are hazardous, which precludes their incorporation in large‐scale manufacturing processes. Here, the first ever organic thin‐film transistor fabricated with an electrophotographic laser printing process using a standard office laser printer is reported. This completely solvent‐free additive manufacturing method allows for simultaneous deposition, purification, and patterning of the organic semiconductor layer. Laser‐printed transistors using triisopropylsilylethynyl pentacene as the semiconductor layer are realized on flexible substrates and characterized, making this a successful first demonstration of the potential of laser printing of organic semiconductors.
Regional Stiffness Reduction Using Lamina Emergent Torsional Joints for Flexible Printed Circuit Board Design

Flexible printed circuit boards (PCBs) make it possible for engineers to design devices that use space efficiently and can undergo changes in shape and configuration. However, they also suffer from tradeoffs due to nonideal material properties. Here, a method is presented that allows engineers to introduce regions of flexibility in otherwise rigid PCB substrates. This method employs geometric features to reduce local stiffness in the PCB, rather than reducing the global stiffness by material selection. Analytical and finite element models are presented to calculate the maximum stresses caused by deflection. An example device is produced and tested to verify the models.
A proof-of-concept study showing improved for

Objectives: Obtaining body temperature is a quick and easy method to screen for acute infection such as COVID-19. Currently, the predictive value of body temperature for acute infection is inhibited by failure to account for other readily available variables that affect temperature values. In this proof-of-concept study, we sought to improve COVID-19 pretest probability estimation by incorporating covariates known to be associated with body temperature, including patient age, sex, comorbidities, month, time of day. Methods: For patients discharged from an academic hospital emergency department after testing for COVID-19 in March and April of 2020, we abstracted clinical data. We reviewed physician documentation to retrospectively generate estimates of pretest probability for COVID-19. Using patients’ COVID-19 PCR test results as a gold standard, we compared AUCs of logistic regression models predicting COVID-19 positivity that used: 1) body temperature alone; 2) body temperature and pretest probability; 3) body temperature, pretest probability, and body temperature-relevant covariates. Calibration plots and bootstrap validation were used to assess predictive performance for model #3. Results: Data from 117 patients were included. The models’ AUCs were: 1) 0.69 2) 0.72, and 3) 0.76, respectively. The absolute difference in AUC was 0.029 (95%CI -0.057 to 0.114, p=0.25) between model 2 and 1 and 0.038 (95%CI -0.021 to 0.097, p=0.10) between model 3 and 2. Conclusions: By incorporating covariates known to affect body temperature, we demonstrated improved pretest probability estimates of acute COVID-19 infection. Future work should be undertaken to further develop and validate our model in a larger, multi-institutional sample.
The applicability of Business Intelligence systems in the support of managerial decisions in the international enterprises.

— The aim of the paper is to presents research results concerning application and identification of the role of Business Intelligence (BI) systems in the support of managerial decision processes at strategic, tactical and operational level of management in the international enterprises. It was presented the notion and characteristic of BI systems and benefits resulting from its application in the support of decision processes in the management of enterprises. The empirical research is followed by review of foreign research and case studies in selected subjects.
Modelling and analysis of a cooperative production network

In this study, we examine the cooperative production business model for a group of producers serving their own customers and also have access to external customers who can make an agreement to buy products at a lower price if a desired service level can be guaranteed. When the producers cannot meet the desired service level requirement of the external customers at the offered price on their own, they participate in a cooperative network. The network consolidates the external customers for its members and routes an arriving external customer to one of the participants. We determine the optimal production and rationing policies for each participating manufacturer as well as the optimal routing policy for the network. We also propose an accurate approximate method to analyse a network with a high number of homogeneous producers using a single queue approximation method. We show that, based on the parameters of the producers and the external market, the network can provide the desired service level for the external customers at the offered price and makes all the members increase their profit by better utilising their capacity and serving more external customers.
DeephageTP: A Convolutional Neural Network  Framework for Identifying Phage-specific Proteins from metagenomic sequencing data

Background: Bacteriophage (phage) is the most abundant and diverse biological entity on the Earth. This makes it a challenge to identify and annotate phage genomes efficiently on a large scale. Portal (portal protein), TerL (large terminase subunit protein) and TerS (small terminase subunit protein) are the three specific proteins of the tailed phage. Here, we develop a CNN (convolutional neural network)-based framework, DeephageTP, to identify these three proteins from metagenome data. The framework takes one-hot encoding data of the original protein sequences as the input and extracts the predictive features in the process of modeling. The cutoff loss value for each protein category was determined by exploiting the distributions of the loss values of the sequences within the same category. Finally, we tested the efficacy of the framework using three real metagenomic datasets. Result: The proposed multiclass classification CNN-based model was trained by the training datasets and shows relatively high prediction performance ( A ccuracy : Portal, 98.8%; TerL, 98.6%; TerS, 97.8%) for the three protein categories, respectively. The experiments using the independent mimic dataset demonstrate that the performance of the model could become worse along with the increase of the data size. To address this issue, we determined and set the cutoff loss values (i.e., TerL: -5.2, Portal: -4.2, TerS: -2.9) for each of the three categories, respectively. With these values, the model obtains high performance in terms of Precision in identifying the TerL and Portal sequences (i.e, ~ 94% and ~ 90%, respectively) from the mimic dataset that is 20 times larger than the training dataset. More interestingly, the framework identified from the three real metagenomic datasets many novel phage sequences that are not detectable by the two alignment-based methods (i.e., DIAMOND and HMMER). Conclusion: Compared to the conventional alignment-based methods, our proposed framework shows high performance in identifying phage-specific protein sequences with a particular advantage in identifying the novel protein sequences with remote homology to their known counterparts in public databases. Indeed, our method could also be applied for identifying the other protein sequences with the characteristic of high complexity and low conservation. The DeephageTP is available at https://github.com/chuym726/DeephageTP .
Analysis of Wavelet Transform-Domain LMS-Newton Adaptive Filtering Algorithms with Second-Order Autoregressive (AR) Process

This paper analyze the stability, misadjustment, and convergence performance of the Wavelet Transform (WT) domain least mean square (LMS) Newton adaptive filtering algorithm with first order and second order autoregressive (AR) process. The wavelet transform domain signal provides a means of constructing of more orthonormal correlated input signals than other transform. The wiener filter with AR input process are assumed to be stationary, and the Stationary Wavelet Transform (SWT) is used as transform algorithm to provide more correlated input signal than other integral transform. The simulation result of this work shows that Wavelet-domain LMS-Newton algorithm provides better performance result than other transform domain algorithm for both first order and second order AR process. Structure of the SWT based LMS-Newton adaptive algorithm for time-varying process has been also discussed in this paper. Finally, Computer simulations on the SWT based LMS-Newton adaptive algorithms are demonstrated to validate the analysis presented in this paper, and the simulation result shows that the structure of SWT domain LMS-Newton adaptive algorithm provides better denoising of a noisy signal than other transform domain LMS-Newton adaptive algorithm.
Techniques for input ripple current cancellation: classification and implementation [in SMPS]

Switching power supplies, due to their high-frequency switching operation, draw a substantial amount of input ripple current. Such ripple current manifests itself as electromagnetic interference (EMI). Active and passive ripple cancellation methods have been proposed in the past. This paper discusses the cancellation methods, and classifies the basic circuit configurations for ripple current cancellation. The basis of discussion is a "nullification" process, which can be described effectively in terms of the ideal circuit element nullor. The initial focus is on differential-mode noise cancellation, but the basic technique can be applied equally to cancelling common-mode noise. The classification provides a clear guideline for synthesizing practical cancellation circuits. Both active and passive implementation examples are given and experimentally demonstrated.
The Recurrence Interval Difference of Power Load in Heavy/Light Industries of China

The significant fluctuation of industrial electricity consumption has a high impact on power load, which makes the research on recurrence intervals between extreme events of theoretical and practical significance. The study uses a high-frequency data of heavy and light industries and employs recurrence interval analysis in different thresholds. We find that the reoccurrence interval of volatility can fit with the stretched exponential function and the probability density functions of recurrence intervals in various thresholds shows a scaling behavior. Then, the conditional probability density function and the multifractal detrended fluctuation analysis demonstrate the existence of short-range correlation, long-range correlation, and multifractal properties, respectively. We further construct a hazard function, introduce recurrence intervals into VaR calculation and establish a functional relationship between average recurrence interval and threshold. Following this result, we also shed light on policy discussion for multi-industrial electricity supply management.
Research Online Research Indoor Air Temperature and Agitation of Nursing Home Residents Indoor Air Temperature and Agitation of Nursing Home Residents With Dementia With Dementia

Abstract This study aimed to determine the specific correlation between indoor air temperature and agitation of nursing home residents with dementia. Agitated behaviors of 21 residents, living in 1 nursing home, were assessed for a 10-month period using the Cohen-Mansfield Agitation Inventory (CMAI). The CMAI Total Frequency scores were found to increase significantly when indoor average temperatures deviated from 22.6°C. In addition, cumulative exposure to temperatures higher than 26°C and lower than 20°C was linearly correlated with CMAI Total Frequency scores. Results showed that agitated behaviors not only affected the person manifesting them but were found to be disruptive for other residents and the delivery of care. Agitation can, therefore, be potentially reduced by limiting the range of indoor air temperature variations, and aged care providers should ensure that a thermally comfortable environment is provided in nursing homes to enhance comfort and well-being of all occupants. Abstract This study aimed to determine the specific correlation between indoor air temperature and agitation of nursing home residents with dementia. Agitated behaviors of 21 residents, living in one nursing home, were assessed for a 10-month period using the Cohen-Mansfield Agitation Inventory (CMAI). CMAI Total Frequency scores were found to increase significantly when indoor average temperatures deviated from 22.6°C. In addition, cumulative exposure to temperatures higher than 26°C and lower than 20°C was linearly correlated with CMAI Total Frequency scores. Results showed that agitated behaviors not only affected the person manifesting them but were found to be disruptive for other residents and the delivery of care. Agitation can, therefore, be potentially reduced by limiting the range of indoor air temperature variations and aged care providers should ensure that a thermally comfortable environment is provided in nursing homes to enhance comfort and well-being of all occupants.
Owner’s Risk Allocation and Contractor’s Role Behavior in a Project: A Parallel-mediation Model

Abstract Risk allocation decisions are of critical importance in project management. The present study proposes an explanation for how risk allocation in a contract motivates a contractor to cooperate with a project owner. Theories of risk allocation and trust were used to motivate the research. Using a survey methodology, we collected data concerning 124 construction projects in China. We found that risk allocation influenced the contractor’s role behavior through the contractor’s feeling of being trusted but not the contractor’s trust in the owner. Feeling of being trusted partially mediated the effect of risk allocation on the contractor’s in-role (i.e., contractual) behavior and fully mediated the effect on extra-role behavior. The study introduces a social and psychological view of the impacts of risk allocation to the project and engineering management literature. We contribute to theory by arguing and demonstrating the mediating effect of trust on the relationship between risk allocation and contractor behavior. From a practical standpoint, we conclude that contractual risk allocation has a significant impact on building a trusting relationship between owners and contractors and that contractors who feel trusted perform both contractually mandated actions and actions external to the contract more diligently, resulting in the likelihood of improved outcomes for both parties.
The old world salsola as a source of valuable secondary metabolites endowed with diverse pharmacological activities: a review

Abstract Salsola is an important genus in the plant kingdom with diverse traditional, industrial, and environmental applications. Salsola species are widely distributed in temperate regions and represent about 45% of desert plants. They are a rich source of diverse phytochemical classes, such as alkaloids, cardenolides, triterpenoids, coumarins, flavonoids, isoflavonoids, and phenolic acids. Salsola spp. were traditionally used as antihypertensive, anti-inflammatory, and immunostimulants. They attracted great interest from researchers as several pharmacological activities were reported, including analgesic, antipyretic, antioxidant, cytotoxic, hepatoprotective, contraceptive, antidiabetic, neuroprotective, and antimicrobial activities. Genus Salsola is one of the most notorious plant genera from the taxonomical point of view. Our study represents a comprehensive review of the previous phytochemical and biological research on the old world Salsola secies. It is designed to be a guide for future research on different plant species that still belong to this genus or have been transferred to other genera. Graphical Abstract
The Role of Epicardial Adipose Tissue in the Development of Atrial Fibrillation, Coronary Artery Disease and Chronic Heart Failure in the Context of Obesity and Type 2 Diabetes Mellitus: A Narrative Review

Cardiovascular diseases (CVDs) are a significant burden globally and are especially prevalent in obese and/or diabetic populations. Epicardial adipose tissue (EAT) surrounding the heart has been implicated in the development of CVDs as EAT can shift from a protective to a maladaptive phenotype in diseased states. In diabetic and obese patients, an elevated EAT mass both secretes pro-fibrotic/pro-inflammatory adipokines and forms intramyocardial fibrofatty infiltrates. This narrative review considers the proposed pathophysiological roles of EAT in CVDs. Diabetes is associated with a disordered energy utilization in the heart, which promotes intramyocardial fat and structural remodeling. Fibrofatty infiltrates are associated with abnormal cardiomyocyte calcium handling and repolarization, increasing the probability of afterdepolarizations. The inflammatory phenotype also promotes lateralization of connexin (Cx) proteins, undermining unidirectional conduction. These changes are associated with conduction heterogeneity, together creating a substrate for atrial fibrillation (AF). EAT is also strongly implicated in coronary artery disease (CAD); inflammatory adipokines from peri-vascular fat can modulate intra-luminal homeostasis through an “outside-to-inside” mechanism. EAT is also a significant source of sympathetic neurotransmitters, which promote progressive diastolic dysfunction with eventual cardiac failure. Further investigations on the behavior of EAT in diabetic/obese patients with CVD could help elucidate the pathogenesis and uncover potential therapeutic targets.
Influence of the Somatic Rubber Hand Illusion on Maximum Grip Aperture

Abstract The classic rubber hand illusion (RHI), based on visual, proprioceptive, and tactile feedback, can affect actions. However, it is not known whether these effects still occur if the paradigm is administered without visual feedback. In this study, we used the somatic RHI to test in thirty-two healthy individuals whether the incorporation of the rubber hand based on proprioceptive and tactile information only is sufficient to generate changes in actions. We measured maximum grip aperture (GA) changes towards a target and associated brain activations within the dorsal stream before and after the somatic RHI. Behavioural and neuroimaging data do not support an effect on maximum GA when the RHI is based on proprioceptive and tactile information only.