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Purpose This paper examines the influence of three dimensions of customer knowledge management – knowledge from customer, knowledge for customer and knowledge about customer – on innovation capabilities (speed and quality) and new service market performance. Design/methodology/approach The model links three dimensions of customer knowledge management to two dimensions of innovation capabilities. Further, the model links two dimensions of innovation capabilities to new service market performance. Analysis was conducted through structural equation modelling using SmartPLS software, using data from 253 managers representing 26 banks in Bangladesh. Findings The findings of this study show that knowledge from customer and knowledge for customer are the most influential predictors of new service market performance. Of the three dimensions of customer knowledge management, knowledge from customer turns out to be the strongest predictor of innovation quality and speed. Innovation quality has a greater impact on new service market performance than innovation speed. Innovation capability (quality and speed) plays a mediating role in this study. Practical implications Managing knowledge from, for and about customer should be systematically considered as a synergy approach to firms’ processes and activities to co-create value with customers. In particular, managers should put more emphasis on knowledge from and for customer to enhance innovation capacity and achieve success in the development of a new service. Originality/value This paper empirically supports the significant influence of knowledge from, for and about customer on innovation capabilities (quality and speed) and new service market performance. While the results provide guidance for researchers and practitioners, it also adds value to innovation-related research.

This research is a literature study that aims to find a model to examine factors that play major role in forming a college student as well as business owner to become a successful entrepreneur. Literature study has been done by grouping the individual factors which encourage someone to become entrepreneurs through research that has been done by previous researchers, and then selected factors that effect in the successful journey of an adult entrepreneur. Based on the conducted study it was found that the personality factor is a strong motivation for an individual person who is able to determine someone success. Another interesting factor is the style of parents in educating this individual, in which the authoritative parenting style plays more important role in the success of adult entrepreneurs than other parenting styles. Besides, for an entrepreneur, entrepreneurial competence is needed to support the individual’s success. In addition to an entrepreneur, entrepreneurial competence is needed to support the individual’s success. Therefore the model generated from this research is the entrepreneurial personality using the big five personality approach so that has five variable manifest and authoritative parenting style as an independent variable to entrepreneurial success, whereas entrepreneurial competence becomes the mediating variable between an independent variable and dependent variable where entrepreneurial competence has six manifest variables.

Technology adoption by the marginalized community is indeed an important issue in global poverty reduction. To realize it, this paper contributes by doing an empirical test of the technology acceptance model, amongst those users typically always deprived in the existing social structure. Firstly, the study aims to examine the influence of Technology Readiness Index (TRI) 2.0′s four indicators (optimism, innovativeness, discomfort and insecurity) on bKash entrepreneurs' perceived ease of use (PEOU) and perceived usefulness (PU) of bKash technology. Secondly, it aims to investigate the effect of ‘bKash’ entrepreneurs' PEOU and PU on their well-being. A total of 360 usable data were collected from the bKash agents in Bangladesh. We employed SEM-PLS3.0 for data analysis. The results indicate that optimism and innovativeness strongly drive perceived ease of use and perceived usefulness. Subjective well-being was found to be strongly predicted by perceived ease of use and perceived usefulness. The study indicates that poor people are somewhat ready to use and accept technology that leads to a sense of well-being. Bringing together the concept of subjective well-being with TRI and Technology Acceptance Model (TAM) is this paper's contribution to the knowledge domain.

2,4-dichlorophanoxyacetic acid (2,4-D), a functional analogue of auxin, is used as an exogenous source of auxin as it evokes physiological responses like the endogenous auxin, Indole-3-acetic acid (IAA). Previous molecular analyses of the auxin response pathway revealed that IAA and 2,4-D share a common mode of action to elicit downstream physiological responses. However, recent findings with 2,4-D specific mutants suggested that 2,4-D and IAA might also use distinct pathways to modulate Arabidopsis root growth. Using genetic and cellular approaches, we demonstrate that the distinct effects of 2,4-D and IAA on actin filament organization partly dictate the differential responses of roots to these two auxin analogues. 2,4-D but not IAA altered the actin structure in long term and short term assays. Analysis of the 2,4-D specific mutant aar1-1 revealed that Small Acidic Protein 1 (SMAP1) functions positively to facilitate the 2,4-D-induced depolymerization of actin. The ubiquitin proteasome mutants, tir1-1 and axr1-12, which show enhanced resistance to 2,4-D compared with IAA for root growth inhibition, were also found to have less disrupted actin filament networks after 2,4-D exposure. Consistently, chemical inhibitor of the ubiquitin proteasome pathway mitigated the disrupting effects of 2,4-D on the organization of actin filaments. Roots of the double mutant aar1-1 tir1-1 also showed enhanced resistance to 2,4-D-induced root growth inhibition and actin degradation compared with their respective parental lines. Collectively, these results suggest that the effects of 2,4-D on actin filament organization and root growth is mediated through synergistic interactions between SMAP1 and SCFTIR1 ubiquitin proteasome components. This article is protected by copyright. All rights reserved.

In the multifaceted as well as vibrant telecommunications industry, service innovation management is a relatively new paradigm, which has attracted the attention of scholars and practitioners. Thus, this study embarks on to compare the practices of service innovation management between Malaysia and Bangladesh telecommunications industry. The strategy, process, organization, tool/technology and system (SPOTS) model with 26 indicators was employed to compare the management practices among these two countries. A total of 176 managers (98 from Malaysia and 78 from Bangladesh) participated in this research through survey questionnaires. An independent-samples t-test was conducted to compare the 26 indictors on management practices for Malaysian and Bangladesh telecommunications industry. The findings indicated that the management practices were operationalized differently in these two countries. Bangladesh put more emphasis on each of the innovation management practices when compared to Malaysia. It indicates that innovation management is practiced more in the country where the market is in the process of development. The results enable the firms to better compete in an environment which requires innovative changes and global discipline. In fact, the findings provide guidelines for the service sectors in both countries in terms of implementation of innovative enterprises, especially in Bangladesh where innovation is the basis for survival.

Purpose The purpose of the study is to delve the influencing factors of sustainable entrepreneurship among SMEs in Malaysia. The heightened awareness in sustainable development coupled with globalisation has created immense aspiration, enthusiasm and interest in the trajectory of sustainable entrepreneurship. With this set of circumstances, the objective of the study is to explore the possible predictive factors that enhance sustainable entrepreneurship among Malaysian small and medium enterprises (SMEs). Design/methodology/approach This study examined the relationships between sustainable entrepreneurship, entrepreneurial orientation, sustainability orientation and entrepreneurial bricolage through the lens of a resource-based view, upper echelons theory and corporate social responsibility. A total of 102 responses from a survey instrument from Malaysian SMEs were analysed using partial least squares-structural equation modelling. Findings The results indicated that the entrepreneurial orientation is associated with the degree of sustainable entrepreneurship, and is mediated by the role and degree of entrepreneurial bricolage. Originality/value This study highlights the importance of sustainable entrepreneurship among SMEs with respect to the heightened societal and environmental awareness among consumers and international regulation concerning environmental protection.

Radiocesium, accumulated in the soil by nuclear accidents is a major environmental concern. The transport process of cesium (Cs+) is tightly linked to the indispensable plant nutrient potassium (K+) as they both belong to the group I alkali metal with similar chemical properties. Most of the transporters that had been characterized to date as Cs+ transporters are directly or indirectly linked to K+. Using a combinatorial approach of physiology, genetics, cell biology and root uptake assay, here we identified two ATP-Binding Cassette (ABC) proteins, ABCG37 and ABCG33 as facilitators of Cs+ influx. The gain-of-function mutant of ABCG37 (abcg37-1) showed hypersensitive response to Cs+-induced root growth inhibition, while the double knock out mutant of ABCG33 and ABCG37 (abcg33-1abcg37-2) showed resistance. Single loss-of-function mutant of ABCG33 and ABCG37 did not show any alteration in Cs+ response. Short term uptake experiment with radioactive Cs+ revealed reduced Cs+ uptake in abcg33-1abgc37-2 compared with wild type in presence or absence of K+. Potassium response and content were unaffected in the double mutant background confirming that Cs+ uptake by ABCG33 and ABCG37 is independent of K+. Collectively, this work identified two ABC proteins as new Cs+ influx carriers, which act redundantly and independent of K+ uptake pathway.

Background Pain volatility is an important factor in chronic pain experience and adaptation. Previously, we employed machine-learning methods to define and predict pain volatility levels from users of the Manage My Pain app. Reducing the number of features is important to help increase interpretability of such prediction models. Prediction results also need to be consolidated from multiple random subsamples to address the class imbalance issue. Objective This study aimed to: (1) increase the interpretability of previously developed pain volatility models by identifying the most important features that distinguish high from low volatility users; and (2) consolidate prediction results from models derived from multiple random subsamples while addressing the class imbalance issue. Methods A total of 132 features were extracted from the first month of app use to develop machine learning–based models for predicting pain volatility at the sixth month of app use. Three feature selection methods were applied to identify features that were significantly better predictors than other members of the large features set used for developing the prediction models: (1) Gini impurity criterion; (2) information gain criterion; and (3) Boruta. We then combined the three groups of important features determined by these algorithms to produce the final list of important features. Three machine learning methods were then employed to conduct prediction experiments using the selected important features: (1) logistic regression with ridge estimators; (2) logistic regression with least absolute shrinkage and selection operator; and (3) random forests. Multiple random under-sampling of the majority class was conducted to address class imbalance in the dataset. Subsequently, a majority voting approach was employed to consolidate prediction results from these multiple subsamples. The total number of users included in this study was 879, with a total number of 391,255 pain records. Results A threshold of 1.6 was established using clustering methods to differentiate between 2 classes: low volatility (n=694) and high volatility (n=185). The overall prediction accuracy is approximately 70% for both random forests and logistic regression models when using 132 features. Overall, 9 important features were identified using 3 feature selection methods. Of these 9 features, 2 are from the app use category and the other 7 are related to pain statistics. After consolidating models that were developed using random subsamples by majority voting, logistic regression models performed equally well using 132 or 9 features. Random forests performed better than logistic regression methods in predicting the high volatility class. The consolidated accuracy of random forests does not drop significantly (601/879; 68.4% vs 618/879; 70.3%) when only 9 important features are included in the prediction model. Conclusions We employed feature selection methods to identify important features in predicting future pain volatility. To address class imbalance, we consolidated models that were developed using multiple random subsamples by majority voting. Reducing the number of features did not result in a significant decrease in the consolidated prediction accuracy.

People suffering from Alzheimer's disease (AD) and their caregivers seek different approaches to cope with memory loss. Although AD patients want to live independently, they often need help from caregivers. In this situation, caregivers may attach notes on every single object or take out the contents of a drawer to make them visible before leaving the patient alone at home. This study reports preliminary results on an Ambient Assisted Living (AAL) real-time system, achieved through the Internet of Things (IoT) and Augmented Reality (AR) concepts, aimed at helping people suffering from AD. The system has two main sections: the smartphone or windows application allows caregivers to monitor patients' status at home and be notified if patients are at risk. The second part allows patients to use smart glasses to recognize QR codes in the environment and receive information related to tags in the form of audio, text, or three-dimensional image. This work presents preliminary results and investigates the possibility of implementing such a system., 5 pages, 5 figures

Arsenic contamination is a major environmental issue, as it may lead to serious health hazard. The reduced trivalent form of inorganic arsenic, arsenite, is in general more toxic to plants compared with the fully oxidized pentavalent arsenate. The uptake of arsenite in plants has been shown to be mediated through a large subfamily of plant aquaglyceroporins, nodulin 26-like intrinsic proteins (NIPs). However, the efflux mechanisms, as well as the mechanism of arsenite-induced root growth inhibition, remain poorly understood. Using molecular physiology, synchrotron imaging, and root transport assay approaches, we show that the cellular transport of trivalent arsenicals in Arabidopsis thaliana is strongly modulated by PIN FORMED 2 (PIN2) auxin efflux transporter. Root transport assay using radioactive arsenite, X-ray fluorescence imaging (XFI) coupled with X-ray absorption spectroscopy (XAS), and inductively coupled plasma mass spectrometry analysis revealed that pin2 plants accumulate higher concentrations of arsenite in roots compared with the wild-type. At the cellular level, arsenite specifically targets intracellular sorting of PIN2 and thereby alters the cellular auxin homeostasis. Consistently, loss of PIN2 function results in arsenite hypersensitivity in roots. XFI coupled with XAS further revealed that loss of PIN2 function results in specific accumulation of arsenical species, but not the other metals such as iron, zinc, or calcium in the root tip. Collectively, these results suggest that PIN2 likely functions as an arsenite efflux transporter for the distribution of arsenical species in planta., The cellular efflux mechanism of arsenite, the more toxic form of arsenic, remains elusive. Using molecular and cellular physiology, root transport assay, and synchrotron imaging, this study identified and characterized PIN Formed 2 (PIN2) as a new, putative arsenite efflux facilitator.

Arsenic contamination is a major environmental issue as it may lead to serious health hazard. Reduced trivalent form of inorganic arsenic, arsenite, is in general more toxic to plants compared with the fully oxidized pentavalent arsenate. The uptake of arsenite in plants has been shown to be mediated through a large subfamily of plant aquaglyceroporins, nodulin 26-like intrinsic proteins (NIPs). However, the efflux mechanisms, as well as the mechanism of arsenite-induced root growth inhibition, remain poorly understood. Using molecular physiology, synchrotron imaging, and root transport assay approaches, we show that the cellular transport of trivalent arsenicals inArabidopsis thalianais strongly modulated by PIN FORMED 2 (PIN2) auxin efflux transporter. Direct transport assay using radioactive arsenite, X-ray fluorescence imaging (XFI) coupled with X-ray absorption spectroscopy (XAS), and ICP-MS analysis revealed thatpin2plants accumulate higher concentrations of arsenite in root compared to wild-type. At the cellular level, arsenite specifically targets intracellular cycling of PIN2 and thereby alters the cellular auxin homeostasis. Consistently, loss of PIN2 results in aresenite hypersensitivity in root. XFI coupled with XAS further revealed that loss of PIN2 results in specific accumulation of arsenical species, but not the other metals like iron, zinc or calcium in the root tip. Collectively, these results demonstrate that PIN2 serves as a putative transporter of arsenical speciesin planta.

Orthogonal frequency division multiplexing with Index Modulation (OFDM-IM) has been used to transmit extra bits by indexing subcarriers using the ON-OFF keying modulation. In this paper, the closed-form of average bit error probability (ABEP) for BPSK and M-ary QAM modulations with OFDM-IM are derived in the absence and presence of co-channel interference (CCI) over composite Nakagami-m and Gamma (NG) shadowing channel. Simple closed-form expressions are introduced by using the Meijer G function. The theoretical results are validated through comparisons with existing research studies.

Background Acute gastroenteritis is defined as a sudden onset of diarrhoea which in itself is the passage of 3 or more loose stools in a 24 hour period, or the passage of one or more bloody stools in the presence or absence of abdominal pain, fever, nausea and vomiting. Acute gastroenteritis is self-limiting lasting no longer than 2 weeks, however, it is a major cause of morbidity and mortality worldwide. Current primary treatment modalities include restoration of the acid-base balance, correction of electrolyte disturbances, and oral rehydration therapies. The aforementioned therapeutic regimens help to decrease morbidity and mortality, however, it seldom has an impact on the duration of the infection and its symptoms. Probiotics are believed to help reduce both the duration and severity of symptoms of gastroenteritis, however strain specific efficacy and inter-strain comparison has not yet been established. Objective The aim of this systematic review was to examine established original research to determine the efficacy of probiotics in treating acute gastroenteritis in the paediatric population. We examined the different strains of probiotics utilized and their associated outcomes in treating gastroenteritis. Methods An Embase search was carried out with the help of a medical librarian. Two independent reviewers screened title and abstract, followed by full text review using the programme Covidence. All reviewer conflicts were resolved by a third party. Data was extracted from the included articles to determine correlation and effects of probiotics. Results Of the 581 results obtained from the search, 11 studies were included for data extraction after applying the inclusion and exclusion criteria. Majority of the studies showed probiotics reduced duration of diarrhoea (8 of 11 studies), and a reduced duration of hospitalisation (6 of 11 studies). Notably, 2 papers reported adverse effects, such as fungaemia, in immunocompromised and ICU patients. The different strains of probiotics that were examined in the selected papers include Saccharomyces boulardii, Lactobacillus casei, and Lactobacillus acidophilus. Conclusion Probiotics reduced the duration diarrhoea symptoms and hospitalization. Usage of probiotics was however accompanied by minimal side effects but not indicated in immunocompromised patients with gastroenteritis. However, further research needs to be conducted to determine the strain specific efficacy and dosage requirements for treatment of gastroenteritis using large scale double blinded randomized control trials.

Background Necrotizing enterocolitis (NEC) is an inflammatory gastrointestinal disease primarily affecting preterm neonates. Neonates with NEC suffer from a degree of neurodevelopmental delay that is not explained by prematurity alone. There is a need to understand the pathogenesis of neurodevelopmental delay in NEC. In this study, we assessed the macroscopic and microscopic changes that occur to brain cell populations in specific brain regions in a neonatal mouse model of NEC. Moreover, we investigated the role of intestinal inflammation as part of the mechanism responsible for the changes observed in the brain of pups with NEC. Methods Brains of mice were assessed for gross morphology and cerebral cortex thickness (using histology). Markers for mature neurons, oligodendrocytes, neural progenitor cells, microglia, and astrocytes were used to quantify their cell populations in different regions of the brain. Levels of cell apoptosis in the brain were measured by Western blotting and immunohistochemistry. Endoplasmic reticulum (ER) stress markers and levels of pro-inflammatory cytokines (in the ileum and brain) were measured by RT-qPCR and Western blotting. A Pearson test was used to correlate the levels of cytokines (ELISA) in the brain and ileum and to correlate activated microglia and astrocyte populations to the severity of NEC. Results NEC pups had smaller brain weights, higher brain-to-body weight ratios, and thinner cortices compared to control pups. NEC pups had increased levels of apoptosis and ER stress. In addition, NEC was associated with a reduction in the number of neurons, oligodendrocytes, and neural progenitors in specific regions of the brain. Levels of pro-inflammatory cytokines and the density of activated microglia and astrocytes were increased in the brain and positively correlated with the increase in the levels pro-inflammatory cytokines in the gut and the severity of NEC damage respectively. Conclusions NEC is associated with severe changes in brain morphology, a pro-inflammatory response in the brain that alters cell homeostasis and density of brain cell populations in specific cerebral regions. We show that the severity of neuroinflammation is associated with the severity of NEC. Our findings suggest that early intervention during NEC may reduce the chance of acute neuroinflammation and cerebral damage. Electronic supplementary material The online version of this article (10.1186/s12974-019-1481-9) contains supplementary material, which is available to authorized users.

In this paper a model is proposed and explained in brief on how to manage loads efficiently from the utility end using Power Line Communications (PLC). The model employs the principle of PLC along with the industry based technology called X10 to serve the purpose. The loads at the user end are classified according to its importance and power consuming characteristics, and are allotted X10 device codes. Each power consuming utility is assigned an IP address, which aids in selecting a particular load or a group of loads as per requirements. A simple schematic diagram that illustrates the process is also provided. Moreover, since the used PLC scheme is the Narrow-Band PLC (NB-PLC), a simulation based result is also provided that defines a small segment of the entire NB-PLC frequency range that is suitable for the purpose.

Distributed file system (DFS) is a transformation of traditional file systems to perform file read, write and execution of petabyte or larger-sized datasets with high-velocity and different structures. In order to process these large amounts of data in an inexpensive and efficient way, Hadoop Distributed File System (HDFS) is used and designed to scale up from a single server to hundreds of servers, with a very high degree of fault tolerance. Secured and optimized distributed file system for ISP user log management is a centralized log analytics platform that supports collaboration in a distributed environment. DFS for ISP log provides a uniform name space for all ISP along with location independence and location transparency. Any user’s internet access log can be accessed and analyzed using apache sparks data brick file system on stored log in DFS. In this paper, at first, limitation of popular DFS like NFS and CIFS is explored. ISP log management, DFS file architecture and security is considered for designing proposed ISP log management system. Finally, simulation on proposed distributed file system and query processing is performed to obtain the desired result related to optimization and security of the proposed distributed log analysis system.

In this paper a new technique has been proposed to minimize the effects of impulsive noise in power line communication for low data rate conditions. The model proposed employs Orthogonal Frequency Division Multiplexing (OFDM) as multiplexing technique and peak-to-average power (PAPR) calculation of the individual sub-carriers. The signal with minimum PAPR is transmitted as it remains below the cut-off threshold level. All the signals (mostly impulsive noises) above the cut-off level are blanked at the receiver, which results in a cleaner received signal. Two prominent existing methods that are described in literature have been exploited to create a hybrid model of impulsive noise attenuation. It contains only the advantages of the two method eliminating their shortcomings, thus making it a more feasible approach.

In this work, a relative thermal shutdown circuit is implemented to monitor abrupt junction temperature rise and provide reliable solution to keep device operation within its SOA limit for high in-rush current applications like light-bulb driving in an automotive system by effectively limiting energy accumulation, which turns into heat and causes thermal runaway in power switch devices. Relative thermal shutdown in a MCM (multi-chip-module) package solution is implemented by independently sensing and comparing the thermal diode voltages on both controller die and power-MOSFET die, and when the temperature of the power-MOSFET exceeds the temperature of controller by a certain threshold, the relative thermal shutdown is triggered to shut down and protect the device. Depending on the relative thermal shutdown threshold level, the total energy accumulation in the 8mohm high side switch during short-circuit test with 85A output current clamping is effectively limited under 80mJ (with 30° C threshold) to 145mJ (with 65° C threshold). Silicon validation results show that 8mΩ automotive high-side switch with the relative thermal shutdown protection circuit can pass >160k cycles of 300ms enable pulses to short circuit loading condition (AECQ100-12) with 85A current limit.

Introduction: Chronic pancreatitis (CP) is characterised by pain, functional deficits, nutritional and mechanical complications. Frequently managed in out-patient settings, the clinical course is unpredictable and requires multi-disciplinary care. There remains substantial variation in management. In contrast to acute pancreatitis, there are no globally accepted classification or severity scores to predict the disease course or compare interventions. We conducted a systematic review to determine the scope and clinical use of existing scoring systems. Methods: A systematic search was developed with a medical librarian using the Embase, Medline and Cochrane databases. Original articles and conference abstracts describing an original or modified classification or scoring system in CP that stratified patients into clinical and/or severity categories were included. To assess clinical application/validation, studies using all or part of a score as a stratification tool to measure another parameter or outcome were selected. Studies reporting on diagnosis or aetiology only were excluded. Four authors performed the search in independent pairs and conflicts were resolved by a fifth author using CovidenceTM systematic review software. Results: Following screening 6,652 titles and 235 full-text reviews, 48 papers were analysed. Eleven described original scores and 6 described modifications of published scores. Many were comprehensive but limited in capturing the full spectrum of disease. In 31 studies, a score was used to categorise patients to compare or correlate various outcome measures. Exocrine and endocrine dysfunction and pain were included in 6, 5, and 4 scoring systems, respectively. No score included other nutrition parameters, such as bone health, malnutrition, or nutrient deficiency. Only one score has been objectively validated prospectively and independently for monitoring clinical progression and prognosis, but this had been applied to an in-patient population. Conclusion: Available systems and scores do not reflect recent advances and guidelines in CP and are not commonly used. A practical clinical classification and scoring system, validated prospectively for prognostication would be useful for the meaningful analysis in observational and interventional studies in CP.

Aim To define patterns in the management of pediatric appendicitis. Methods A total of 169 delegates from 42 (24 European) countries completed a validated survey administered at the EUPSA 2017 annual congress. Results In the work-up of children with suspected acute appendicitis, most surgeons rely on full blood count (92%), C-reactive protein (82%), and abdominal ultrasonography (76%), but rarely on computed tomography scans or magnetic resonance imaging. In suspected simple appendicitis, most surgeons (76%) do not perform appendectomy at night in clinically stable patients and start antibiotic preoperatively (64%), but only 15% offer antibiotic therapy alone (no appendectomy). In suspected perforated appendicitis, 96% start antibiotic preoperatively, and 92% perform an appendectomy. Presence of phlegmon/abscess is the main contraindication to immediate surgery. In case of appendix mass, most responders (75%) favor a conservative approach and perform interval appendectomy always (56%) or in selected cases (38%) between 2 and 6 months from the first episode (81%). Children with large intraperitoneal abscesses are managed by percutaneous drainage (59% responders) and by surgery (37% responders). Laparoscopy is the preferred surgical approach for both simple (89%) and perforated appendicitis (81%). Most surgeons send the appendix for histology (96%) and pus for microbiology, if present (78%). At the end of the operation, 58% irrigate the abdominal cavity only if contaminated using saline solution (93%). In selected cases, 52% leave a drain in situ. Conclusion Some aspects of appendicitis management lack consensus, particularly appendix mass and intraperitoneal abscess. Evidence-based guidelines should be developed, which may help standardize care and improve clinical outcomes.

Figure S2. Effect of hypoxia on brain morphology and apoptosis. (PDF 8950 kb)

Figure S1. Level of brain histology sections with outline of specific brain regions. (PDF 4450 kb)

The objectives of this study are to examine the factors affecting the intention and actual usage behavior on mHealth adoption, investigate the effect of actual usage behavior of mHealth on mental well-being of the end-users, and investigate the moderating role of self-quarantine on the intention-actual usage of mHealth under the coronavirus disease (COVID-19) pandemic situation. The required primary data were gathered from the end-users of mHealth in Bangladesh. Using the Unified Theory of Acceptance and Use of Technology (UTAUT2), this study has confirmed that performance expectancy, effort expectancy, social influence, hedonic motivation, and facilitating conditions have a positive influence on behavioral intention whereas health consciousness has an impact on both intention and actual usage behavior. mHealth usage behavior has an affirmative and meaningful effect on the mental well-being of the service users. Moreover, self-quarantine has strong influence on actual usage behavior but does not moderate the intention-behavior relationship. In addition, due to the existence of a non-linearity problem in the data set, the Artificial Neural Network (ANN) approach was engaged to sort out relatively significant predictors acquired from Structural Equation Modeling (SEM). However, this study contributes to the emergent mHealth literature by revealing how the use of the mHealth services elevates the quality of patients' mental well-being under this pandemic situation.

A colossal amount of construction and demolished waste are generated daily due to rapid urbanisation and construction works. To manage this, recycled aggregate concrete (RAC) might draw a sustainab...

The objective of this study is to reveal required entrepreneurial competencies to achieve financial and non-financial performance of the BoP entrepreneurs in Bangladesh. This study includes 213 BoP entrepreneurs who signaled that opportunity competencies, commitment competencies and innovative competencies enhance financial and non-financial performance. On the other hand, strategic competencies and analytical competencies were found to be insignificant in this context. The study adds value to academia and policy-making, and to revamp and revitalise entrepreneurship among those who are at the base of the economic pyramid (BoP). Furthermore, this research will guide private and non-government organisations who intend to develop micro-entrepreneurship business through training for developing entrepreneurial abilities of BoP entrepreneurs.

The purpose of this study is to find critical determinants of Malaysian Small Medium Enterprises (SMEs) intention to continue website as a business innovation. CEO, organizational, technological, and environmental characteristics are considered as cohesive significant predictors. Further, it is aimed to examine the influence of existing level of web adoption as a moderator in small to medium firm website continuance intention. A total number of 108 SMEs participated in this research. The website continuance intention was found to be depended on CEO's innovativeness, CEO's IT attitude, relative advantage, and cost in Malaysian SMEs. Surprisingly, CEO IT knowledge, firm's size, employee IS knowledge, compatibility, security, external pressure and support do not play any role on the continuance of website adoption. In addition, the moderator effect of web adoption level is revealed to have an insignificant role in this study. Understanding the influential factors of websites continuance intention would help the government to devise appropriate policies and support programmes on promoting information technology (IT) acceptance and extension among the SMEs. Likewise website owner or solution provider can gain considerable value from a better insight into key determinants of businesses website retention intention.

Sustainability is an issue that has a profound effect on large companies and also among SMEs. The research framework for this study is built upon this triple bottom line agenda. The study integrates individual factors that consist of sustainable orientation, organisational factors that consist of intellectual capital (human, structural and relational capital) and institutional factors (government support and social norms) as the antecedents of sustainable entrepreneurship practices. Sustainable performance of SMEs is treated as the dependent variable in this research. A total of 203 responses were obtained and analyses were carried out using Smart-PLS software. The results show that five out of eight hypotheses were accepted. The result indicates that Malaysian SMEs entrepreneurs are clearly aware and understand the importance of sustainability concepts in their business performance. In addition, this study aims to provide insight and knowledge for designing policies and promote sustainable practices in Malaysian manufacturing SMEs.

Adoption of green practices within an organisation can take many different forms, through developing green practices and processes as its core competences and subsequently promoting a green corporate image. This study aims to examine the predictors of green entrepreneurship image and the mediating effect of green innovation on green organisational identity, green core competences and green entrepreneurship image of Malaysian manufacturing SMEs. This empirical study was conducted using a structured instrument, analysed by the Partial Least Square (PLS) technique. The results show that green organisational identity and green core competences significantly enhance the firms' green entrepreneurship image and that green process innovation performance underpins green entrepreneurship image. This study also offers practitioners a guide on what kind of green practice they need to adopt, which can bring advantages to them. It highlights the importance of engaging in green innovation as a differentiation strategy to promote a sustainable agenda within SME sectors.

Nowadays competition among company to the company very serves, but most of the companies present nearly the same product or service with their competitors. To comprehend from competitors a firm need or offer superior services. Customer satisfaction is now one of the important key factors in the business sector. When it comes to banking sector customer satisfaction level is differentiated to one bank to another, thus measuring customer satisfaction is awfully important. without satisfied customers, the bank cannot exist. This work focus on the customer satisfaction in the retail bank on city bank limited. This research is conducted to affirm customer expectation on retail banking, which is decisive for the growth of the company.

The traditional grid system is evolving towards smart grid at a rapid rate. All possible measures are being taken in order to reduce the energy usage in residential, commercial & industrial feeders. Peak demand of the residential users of Dhaka city is growing rapidly over the last few years and it is mostly due to the extensive usage of heavy loads such as air conditioner, washing machines, electric cooker, room heater, geyser etc. This paper proposes a revision of the present residential tariff scheme of utility, which encourages the customers to shift their heavy loads to off peak hours. Results show that the proposed scheme successfully lowers the customer bills without burdening the low energy consumers.

Purpose – The purpose of this study is to reveal the significant consumption values of consumers that drive them towards online purchasing. The study conceptualised and analysed a consumption value model as a predictor for intention to purchase online in the context of Malaysian school leavers familiar with the internet. Design/methodology/approach – In order to achieve its aim, this study carried out empirical research using a structured questionnaire sent to the potential respondents. In addition, the data were analysed using the structural equation modelling technique. Findings – The result of the study indicates that functional and emotional values have a strong relationship and predict online purchasing intention. However, social, epistemic, and conditional values do not predict intentions to purchase online. Originality/value – The result will increase the understanding of owners and managers of e-commerce businesses and help them to formulate their business strategies in order to retain users and attract new ones. Furthermore, the study will also enable e-commerce stakeholders to offer products/services to consumers in order to enhance e-commerce activities. Objetivo – O objetivo deste estudo é revelar os valores significativos de consumo dos consumidores que os direcionam para compras online. O estudo conceituou e analisou um modelo de valor de consumo como indicador da intenção de comprar online no contexto de alunos que deixam a escola na Malásia e estão familiarizados com a internet. Metodologia – Para atingir seu objetivo, este estudo realizou uma pesquisa empírica utilizando um questionário estruturado enviado aos potenciais entrevistados. Além disso, os dados foram analisados utilizando a técnica de modelagem de equações estruturais. Resultados – O resultado do estudo indica que os valores funcionais e emocionais têm uma forte relação com e preveem a intenção de compra online. Valores sociais, epistêmicos e condicionais, no entanto, não preveem intenções de compra online. Contribuições – O resultado aumentará a compreensão dos proprietários e gestores de empresas de comércio eletrônico e os ajudará a formular suas estratégias de negócios para reter e atrair usuários. Além disso, o estudo também permitirá que os atores do comércio eletrônico ofereçam produtos/serviços aos consumidores para melhorar as atividades desse tipo de comércio. Objetivo – O objetivo deste estudo é revelar os valores significativos de consumo dos consumidores que os direcionam para compras online. O estudo conceituou e analisou um modelo de valor de consumo como indicador da intenção de comprar online no contexto de alunos que deixam a escola na Malásia e estão familiarizados com a internet. Metodologia – Para atingir seu objetivo, este estudo realizou uma pesquisa empírica utilizando um questionário estruturado enviado aos potenciais entrevistados. Além disso, os dados foram analisados utilizando a técnica de modelagem de equações estruturais. Resultados – O resultado do estudo indica que os valores funcionais e emocionais têm uma forte relação com e preveem a intenção de compra online. Valores sociais, epistêmicos e condicionais, no entanto, não preveem intenções de compra online. Contribuições – O resultado aumentará a compreensão dos proprietários e gestores de empresas de comércio eletrônico e os ajudará a formular suas estratégias de negócios para reter e atrair usuários. Além disso, o estudo também permitirá que os atores do comércio eletrônico ofereçam produtos/serviços aos consumidores para melhorar as atividades desse tipo de comércio.

Background: Measuring and predicting pain volatility (fluctuation or variability in pain scores over time) can help improve pain management. Perceptions of pain and its consequent disabling effects are often heightened under the conditions of greater uncertainty and unpredictability associated with pain volatility. Objective: This study aimed to use data mining and machine learning methods to (1) define a new measure of pain volatility and (2) predict future pain volatility levels from users of the pain management app, Manage My Pain, based on demographic, clinical, and app use features. Methods: Pain volatility was defined as the mean of absolute changes between 2 consecutive self-reported pain severity scores within the observation periods. The k-means clustering algorithm was applied to users’ pain volatility scores at the first and sixth month of app use to establish a threshold discriminating low from high volatility classes. Subsequently, we extracted 130 demographic, clinical, and app usage features from the first month of app use to predict these 2 volatility classes at the sixth month of app use. Prediction models were developed using 4 methods: (1) logistic regression with ridge estimators; (2) logistic regression with Least Absolute Shrinkage and Selection Operator; (3) Random Forests; and (4) Support Vector Machines. Overall prediction accuracy and accuracy for both classes were calculated to compare the performance of the prediction models. Training and testing were conducted using 5-fold cross validation. A class imbalance issue was addressed using a random subsampling of the training dataset. Users with at least five pain records in both the predictor and outcome periods (N=782 users) are included in the analysis. Results: k-means clustering algorithm was applied to pain volatility scores to establish a threshold of 1.6 to differentiate between low and high volatility classes. After validating the threshold using random subsamples, 2 classes were created: low volatility (n=611) and high volatility (n=171). In this class-imbalanced dataset, all 4 prediction models achieved 78.1% (611/782) to 79.0% (618/782) in overall accuracy. However, all models have a prediction accuracy of less than 18.7% (32/171) for the high volatility class. After addressing the class imbalance issue using random subsampling, results improved across all models for the high volatility class to greater than 59.6% (102/171). The prediction model based on Random Forests performs the best as it consistently achieves approximately 70% accuracy for both classes across 3 random subsamples. Conclusions: We propose a novel method for measuring pain volatility. Cluster analysis was applied to divide users into subsets of low and high volatility classes. These classes were then predicted at the sixth month of app use with an acceptable degree of accuracy using machine learning methods based on the features extracted from demographic, clinical, and app use information from the first month.

BACKGROUND Measuring and predicting pain volatility (fluctuation or variability in pain scores over time) can help improve pain management. Perceptions of pain and its consequent disabling effects are often heightened under the conditions of greater uncertainty and unpredictability associated with pain volatility. OBJECTIVE This study aimed to use data mining and machine learning methods to (1) define a new measure of pain volatility and (2) predict future pain volatility levels from users of the pain management app, Manage My Pain, based on demographic, clinical, and app use features. METHODS Pain volatility was defined as the mean of absolute changes between 2 consecutive self-reported pain severity scores within the observation periods. The k-means clustering algorithm was applied to users’ pain volatility scores at the first and sixth month of app use to establish a threshold discriminating low from high volatility classes. Subsequently, we extracted 130 demographic, clinical, and app usage features from the first month of app use to predict these 2 volatility classes at the sixth month of app use. Prediction models were developed using 4 methods: (1) logistic regression with ridge estimators; (2) logistic regression with Least Absolute Shrinkage and Selection Operator; (3) Random Forests; and (4) Support Vector Machines. Overall prediction accuracy and accuracy for both classes were calculated to compare the performance of the prediction models. Training and testing were conducted using 5-fold cross validation. A class imbalance issue was addressed using a random subsampling of the training dataset. Users with at least five pain records in both the predictor and outcome periods (N=782 users) are included in the analysis. RESULTS k-means clustering algorithm was applied to pain volatility scores to establish a threshold of 1.6 to differentiate between low and high volatility classes. After validating the threshold using random subsamples, 2 classes were created: low volatility (n=611) and high volatility (n=171). In this class-imbalanced dataset, all 4 prediction models achieved 78.1% (611/782) to 79.0% (618/782) in overall accuracy. However, all models have a prediction accuracy of less than 18.7% (32/171) for the high volatility class. After addressing the class imbalance issue using random subsampling, results improved across all models for the high volatility class to greater than 59.6% (102/171). The prediction model based on Random Forests performs the best as it consistently achieves approximately 70% accuracy for both classes across 3 random subsamples. CONCLUSIONS We propose a novel method for measuring pain volatility. Cluster analysis was applied to divide users into subsets of low and high volatility classes. These classes were then predicted at the sixth month of app use with an acceptable degree of accuracy using machine learning methods based on the features extracted from demographic, clinical, and app use information from the first month.

Orthogonal frequency division multiplexing with Index Modulation (OFDM-IM) has been utilized to transmit extra bits by indexing subcarriers using the ON-OFF keying modulation. In this paper, the performance of the OFDM-IM system is studied in terms of bit error probability, over a composite Nakagami-m Gamma (NG) shadowed fading channel. The closed form expressions of approximate and exact average BEP (ABEP) of M-ary QAM modulation and pairwise error probability (PEP) are derived for OFDM-IM over the aforementioned channel. The theoretical results are compared with existing research studies and verified using simulation.

Casparian strip (CS) is an impregnation of endodermal cell wall, forming an apoplastic diffusion barrier which forces the symplastic and selective transport of nutrients across endodermis. This extracellular structure can be found in the roots of all higher plants and is thought to provide the protection of vascular tissues. In Arabidopsis, a genetic toolbox regulating the formation of Casparian strips has emerged recently. However, Arabidopsis has the stereotypical root which is much simpler than most other plant species. To understand the Casparian strip formation in a more complex root system, we examined CS regulatory pathways in tomato. Our results reveal a spatiotemporally conserved expression pattern of most essential components of CS machinery in tomato. Further functional analyses verify the role of homologous CS genes in the Casparian strip formation in tomato, indicating the functional conservation of CS regulatory cascade in tomato.

Phytohormones play an important role in every aspect of plant growth and development. Studies of hormonal biosynthesis, signaling, and transportation pathway facilitate our understanding for the basic developmental mechanisms. As a sessile organism, inability of plants to escape the adverse conditions is manifested through the alteration of growth parameters. These growth parameters are mainly regulated through phytohormone content, perception, and transport. Hormonal perception and regulation as a response of biotic and abiotic stresses have been studied from the model plant Arabidopsis thaliana to crop plants. In the era of uncertain climate condition, temperature stress (both high and low) has become a major limiting factor for plant growth and crop productivity. Recent progress in hormonal study revealed important roles of majority of the phytohormones in low-temperature stress response. However, the mechanistic explanation of responses of different hormones under cold stress is far from understood. In this chapter, we tried to provide a comprehensive summary of our existing knowledge linking hormones and cold stress and discuss about the possible mechanistic basis of response pathways and the future research direction that may help to develop cold-resistant crop varieties to meet the upcoming global food crisis.

Necrotizing enterocolitis (NEC) remains the leading cause of gastrointestinal surgical emergency in preterm neonates. Over the last five decades, a variety of experimental models have been developed to study the pathophysiology of this disease and to test the effectiveness of novel therapeutic strategies. Experimental NEC is mainly modeled in neonatal rats, mice and piglets. In this review, we focus on these experimental models and discuss the major advantages and disadvantages of each. We also briefly discuss other models that are not as widely used but have contributed to our current knowledge of NEC.

Purpose – Large private organisations (LPOs) creating entrepreneurship opportunities for people at the base of the economic pyramid (BoP) is a thought-provoking issue for researchers. The main purpose of this paper is to empirically explore the influence of support from LPOs on entrepreneurship business success among the BoP segment through entrepreneurial competencies. Design/methodology/approach – The paper follows a quantitative research approach. Data were collected from a sample of 134 BoP entrepreneurs in Bangladesh using a structured survey instrument. Partial least square has been employed as a statistical technique to analyse the data. Findings – The influence of support provided by LPOs, in terms of technical and training, increased entrepreneurial competencies among the BoP entrepreneurs. Further, entrepreneurial competencies contribute to the proliferation of the BoP entrepreneurship business success and play a mediating role to achieve business success as long as technical and training supports are provided. Practical implications – It is expected that business organisations can embrace this framework as a business model and contribute to the social initiatives. Originality/value – This paper invokes research on the entrepreneurship domain particularly focusing on the BoP segment of the society. The significant relationship between support of LPOs and success of small-scale entrepreneurship business can be viewed as a novel contribution in BoP domain.

Hypertrophic cardiomyopathy (HCM) is a cardiovascular disease where the heart muscle is partially thickened and blood flow is (potentially fatally) obstructed. A test based on electrocardiograms (ECG) that record the heart electrical activity can help in early detection of HCM patients. This paper presents a cardiovascular-patient classifier we developed to identify HCM patients using standard 10-seconds, 12-lead ECG signals. Patients are classified as having HCM if the majority of their recorded heartbeats are recognized as characteristic of HCM. Thus, the classifier’s underlying task is to recognize individual heartbeats segmented from 12-lead ECG signals as HCM beats, where heartbeats from non-HCM cardiovascular patients are used as controls. We extracted 504 morphological and temporal features - both commonly used and newly-developed ones - from ECG signals for heartbeat classification. To assess classification performance, we trained and tested a random forest classifier and a support vector machine classifier using 5-fold cross validation. We also compared the performance of these two classifiers to that obtained by a logistic regression classifier, and the first two methods performed better than logistic regression. The patient-classification precision of random forests and of support vector machine classifiers is close to 0.85. Recall (sensitivity) and specificity are approximately 0.90. We also conducted feature selection experiments by gradually removing the least informative features; the results show that a relatively small subset of 264 highly informative features can achieve performance measures comparable to those achieved by using the complete set of features.

This study examines the relationship between base of the pyramid (BoP) entrepreneurship success and wellbeing. The major contention is that the success of the BoP entrepreneurs is influenced by information and training support provided by large private organizations. A total of 134 BoP entrepreneurs, who run community information centres in remote areas of Bangladesh with the support of a leading private telecommunications company, participated in this research. The result revealed that support from a large private organization is important to ensure business success, and that this subsequently enhances the wellbeing of BoP entrepreneurs. Not only has this study empirically confirmed that assistance provided by large private organizations to BoP entrepreneurs influences business success, assistance also enhances the wellbeing of this vulnerable group and thus facilitate to reduce poverty within the local community. This study therefore can be a guideline for large private organizations that are willing to carry out pragmatic corporate social initiatives.

Implementation of demand side management (DSM) ensures a dynamic energy management for residential domain by permitting consumers to initiate early informed decisions concerning their energy utilization, which facilitates the utility providers to scale down the peak load demand and remodel the load curve, rather than constructing new generation and transmission units. Consequently, a better sustainability and decrement of net operational cost along with carbon emission level is achieved. DSM can be expanded by applying various optimization methods to handle large scale appliances of disparate power ratings. In this paper, for a residential community a unique DSM algorithm is proposed, where the Peak to Average Ratio (PAR) reduction is the foremost focus, which will eventually escalate the efficiency of Smart Grid and also reduce the cost of electricity consumption. Regarding this, a framework of DSM is suggested utilizing load shifting and peak clipping techniques, which is mathematically formulated and optimized by implementing the proposed algorithm in MATLAB. From this methodology, utility providers and consumers could reach to a mutually beneficial agreement; moreover, achieve significant savings and reduction in peak period requirement.

BackgroundPain is one of the most prevalent health-related concerns and is among the top 3 most common reasons for seeking medical help. Scientific publications of data collected from pain tracking and monitoring apps are important to help consumers and healthcare professionals select the right app for their use. ObjectiveThe main objectives of this paper were to (1) discover user engagement patterns of the pain management app, Manage My Pain, using data mining methods; and (2) identify the association between several attributes characterizing individual users and their levels of engagement. MethodsUser engagement was defined by 2 key features of the app: longevity (number of days between the first and last pain record) and number of records. Users were divided into 5 user engagement clusters employing the k-means clustering algorithm. Each cluster was characterized by 6 attributes: gender, age, number of pain conditions, number of medications, pain severity, and opioid use. Z tests and chi-square tests were used for analyzing categorical attributes. Effects of gender and cluster on numerical attributes were analyzed using 2-way analysis of variances (ANOVAs) followed up by pairwise comparisons using Tukey honest significant difference (HSD). ResultsThe clustering process produced 5 clusters representing different levels of user engagement. The proportion of males and females was significantly different in 4 of the 5 clusters (all P ≤.03). The proportion of males was higher than females in users with relatively high longevity. Mean ages of users in 2 clusters with high longevity were higher than users from other 3 clusters (all P