Transforming strategies in the digital era: The role of social media in customer value analysis and crisis management for tourism firms

Social media is gaining popularity nowadays and is increasingly being used by many small and large organisations. Organisations are adopting new social platforms and technologies to achieve their key and effective management strategies. However, there are still opportunities to explore the role of new technologies in developing useful strategies. In current research, the utilisation of technological tools especially social media was examined to improve the customer value analysis in the organisations. Besides, the research of social media use for crisis management is also increasing and the relevant strategies are still being-investigated. To overcome this gap, this research aims to evaluate the impact of the use of social media on customer value analysis and crisis management. To attain this, a detailed questionnaire was adapted from several different studies. Data were collected from a diverse targeted sample of tourism-related firms from all over Malaysia, including hotels, resorts, travel agencies and transportation companies. The model was tested using Smart PLS software and the results were generalised. Overall, this research will add a noteworthy contribution to the literature by highlighting the significance of social media and recognising its urgency during crisis for businesses. It will also help in answering questions regarding the role of social media usage towards customer value analysis and crisis management of organisations in the Malaysian tourism sector. Moreover, the practitioners will use the findings to make strategies for crisis management and build customer value chain

Wearable armband device for daily life electrocardiogram monitoring

A wearable armband electrocardiogram (ECG) monitor has been used for daily life monitoring. The armband records three ECG channels, one electromyogram (EMG) channel, and tri-axial accelerometer signals. Contrary to conventional Holter monitors, the armband-based ECG device is convenient for long-term daily life monitoring because it uses no obstructive leads and has dry electrodes (no hydrogels), which do not cause skin irritation even after a few days. Principal component analysis (PCA) and normalized least mean squares (NLMS) adaptive filtering were used to reduce the EMG noise from the ECG channels. An artifact detector and an optimal channel selector were developed based on a support vector machine (SVM) classifier with a radial basis function (RBF) kernel using features that are related to the ECG signal quality. Mean HR was estimated from the 24-hour armband recordings from 16 volunteers in segments of 10 seconds each. In addition, four classical HR variability (HRV) parameters (SDNN, RMSSD, and powers at low and high frequency bands) were computed. For comparison purposes, the same parameters were estimated also for data from a commercial Holter monitor. The armband provided usable data (difference less than 10% from Holter-estimated mean HR) during 75.25%/11.02% (inter-subject median/interquartile range) of segments when the user was not in bed, and during 98.49%/0.79% of the bed segments. The automatic artifact detector found 53.85%/17.09% of the data to be usable during the non-bed time, and 95.00%/2.35% to be usable during the time in bed. The HRV analysis obtained a relative error with respect to the Holter data not higher than 1.37% (inter-subject median/interquartile range). Although further studies have to be conducted for specific applications, results suggest that the armband device has a good potential for daily life HR monitoring, especially for applications such as arrhythmia or seizure detection, stress assessment, or sleep studies

Corporate capital structure effects on corporate performance pursuing a strategy of innovation in manufacturing companies

Within the sphere of finance, the concept of capital structure has long been a subject of intense debate, serving as a quantitative depiction of the balance between debt, preference shares, and common stock within a company. This structure serves a crucial role in optimizing the utilization of a company's existing resources while simultaneously elevating the revenue streams for stakeholders. This particular study delves into the intricate relationship between corporate performance and capital structure, focusing on 78 publicly listed firms within the Dhaka Stock Exchange (DSE). Bangladesh holds the 29th position globally in terms of purchasing power, lending significant weight to this investigation. To comprehensively analyze this correlation, panel data encompassing the span from 2017 to 2021 was collected for these 78 sample companies operating within the DSE. Several key determinants of capital structure were considered in this analysis, namely the debt-to-equity ratio, short-term leverage ratio, long-term leverage ratio, and total debt ratio. Meanwhile, the performance of these firms was gauged using key metrics such as Return on Assets (ROA), Return on Equity (ROE), and Earnings Per Share (EPS). To ensure a robust analysis, factors such as inflation, liquidity, growth rate, tax rate, and firm size were meticulously controlled for. The findings unveiled a compelling narrative: all forms of debt ratios—be it short-term, long-term, or the total debt ratio—exhibited a substantial negative impact on ROA at a significant level of 1 %. Conversely, specific debt ratios, like the short-term total debt and the total debt-to-total asset ratio, displayed a notable positive correlation with ROE at a 1 % significance level. Intriguingly, the long-term total debt ratio yielded a negative and insignificant effect on ROE. Moreover, within the spectrum of predictors influencing a firm's performance, the liquidity ratio emerged as a non-significant factor—a notable discovery that highlights the nuanced nature of the interplay between capital structure and performance within these companies.</p

A robust ECG denoising technique using variable frequency complex demodulation

Background and Objective Electrocardiogram (ECG) is widely used for the detection and diagnosis of cardiac arrhythmias such as atrial fibrillation. Most of the computer-based automatic cardiac abnormality detection algorithms require accurate identification of ECG components such as QRS complexes in order to provide a reliable result. However, ECGs are often contaminated by noise and artifacts, especially if they are obtained using wearable sensors, therefore, identification of accurate QRS complexes often becomes challenging. Most of the existing denoising methods were validated using simulated noise added to a clean ECG signal and they did not consider authentically noisy ECG signals. Moreover, many of them are model-dependent and sampling-frequency dependent and require a large amount of computational time. Methods This paper presents a novel ECG denoising technique using the variable frequency complex demodulation (VFCDM) algorithm, which considers noises from a variety of sources. We used the sub-band decomposition of the noise-contaminated ECG signals using VFCDM to remove the noise components so that better-quality ECGs could be reconstructed. An adaptive automated masking is proposed in order to preserve the QRS complexes while removing the unnecessary noise components. Finally, the ECG was reconstructed using a dynamic reconstruction rule based on automatic identification of the severity of the noise contamination. The ECG signal quality was further improved by removing baseline drift and smoothing via adaptive mean filtering. Results Evaluation results on the standard MIT-BIH Arrhythmia database suggest that the proposed denoising technique provides superior denoising performance compared to studies in the literature. Moreover, the proposed method was validated using real-life noise sources collected from the noise stress test database (NSTDB) and data from an armband ECG device which contains significant muscle artifacts. Results from both the wearable armband ECG data and NSTDB data suggest that the proposed denoising method provides significantly better performance in terms of accurate QRS complex detection and signal to noise ratio (SNR) improvement when compared to some of the recent existing denoising algorithms. Conclusions The detailed qualitative and quantitative analysis demonstrated that the proposed denoising method has been robust in filtering varieties of noises present in the ECG. The QRS detection performance of the denoised armband ECG signals indicates that the proposed denoising method has the potential to increase the amount of usable armband ECG data, thus, the armband device with the proposed denoising method could be used for long term monitoring of atrial fibrillation

Unlocking the power of social media marketing: Investigating the role of posting, interaction, and monitoring capabilities in building brand equity

Given the extensive utilisation of social media, brands have grown increasingly dependent on it to build brand equity. As a result, acquiring specific capabilities in the realm of digital marketing has become a necessity. This research aims to investigate the essence of Social Media Marketing Capabilities (SMMC) and assess the forecasting of its capability on Consumer-Based Brand Equity using qualitative and quantitative research methods. The results demonstrate that the ability to post and interact on social media positively correlates with consumer-based brand equity. Conversely, the monitoring capabilities of social media marketing (SMM) did not establish a significant association with Consumer-Based Brand Equity. These findings have important implications for marketing, branding, and community management professionals who can leverage these insights to optimise their social media strategies and maximise their returns by focusing on enhancing specific SMM capabilities

Using the redundant convolutional encoder–decoder to denoise QRS complexes in ECG signals recorded with an armband wearable device

Long-term electrocardiogram (ECG) recordings while performing normal daily routines are often corrupted with motion artifacts, which in turn, can result in the incorrect calculation of heart rates. Heart rates are important clinical information, as they can be used for analysis of heart-rate variability and detection of cardiac arrhythmias. In this study, we present an algorithm for denoising ECG signals acquired with a wearable armband device. The armband was worn on the upper left arm by one male participant, and we simultaneously recorded three ECG channels for 24 h. We extracted 10-s sequences from armband recordings corrupted with added noise and motion artifacts. Denoising was performed using the redundant convolutional encoder–decoder (R-CED), a fully convolutional network. We measured the performance by detecting R-peaks in clean, noisy, and denoised sequences and by calculating signal quality indices: signal-to-noise ratio (SNR), ratio of power, and cross-correlation with respect to the clean sequences. The percent of correctly detected R-peaks in denoised sequences was higher than in sequences corrupted with either added noise (70–100% vs. 34–97%) or motion artifacts (91.86% vs. 61.16%). There was notable improvement in SNR values after denoising for signals with noise added (7–19 dB), and when sequences were corrupted with motion artifacts (0.39 dB). The ratio of power for noisy sequences was significantly lower when compared to both clean and denoised sequences. Similarly, cross-correlation between noisy and clean sequences was significantly lower than between denoised and clean sequences. Moreover, we tested our denoising algorithm on 60-s sequences extracted from recordings from the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database and obtained improvement in SNR values of 7.08 ± 0.25 dB (mean ± standard deviation (sd)). These results from a diverse set of data suggest that the proposed denoising algorithm improves the quality of the signal and can potentially be applied to most ECG measurement devices

Disease Burden of Stroke in Bangladesh from 2015 to 2023 in Patients Receiving Rehabilitation: A Protocol for a Retrospective Cohort Study

Background: Stroke is a serious public health concern that has a significant impact on the global disease burden. It has significant social, economic, and healthcare consequences worldwide. To assess the total number of healthy years lost due to premature death and disability-related limitations, the World Health Organization (WHO) developed the disability-adjusted life years (DALYs) measure. Methods: We will conduct a retrospective cohort study and include all stroke patients who received rehabilitation services at the Centre for Rehabilitation of Paralysed (CRP) in Bangladesh from 2015 to 2019. Relevant data will be extracted from The CRP PDMS database, which includes data for 1835 patients and covers all divisions of Bangladesh. The primary outcome will be to calculate the disease burden of stroke by using DALYs, the level of disability, and the reason for the mortality rate in Bangladesh. Demographic characteristics and study outcomes will be summarised using descriptive statistics, Inferential statistics will be conducted, employing Pearson correlation for parametric data and either chi-square or Spearman rank correlation for nonparametric data. Multivariable logistic regression will be performed to determine the clinical variables associated with a worse clinical outcome. Ethics and dissemination: The study was approved by the Institute of Physiotherapy, Rehabilitation & Research (IPRR) (The Academic Institute of Bangladesh Physiotherapy Association) Ethics Committee (BPAIPRR/IRB/992/07/2023/663). The study's results will be published in peer-reviewed scientific journals and showcased at national and international conferences. Study Implication: Stroke is one of the major causes of prolonged disability. The prolongation of disease and disability leads to health-related, social and economic burdens. Usually, it's difficult to determine by the person and family level about the disease burden. On the other hand, the severity of stroke and post-stroke complications can be prevented by avoiding the risk factors. So, the study of finding the disease burden of stroke and the result of the implemented protocol of stroke can guide the management and awareness of prevention

Detecting Autism Spectrum Disorder Using Spectral Analysis of Electroretinogram and Machine Learning: Preliminary results

Autism spectrum disorder (ASD) is a neurodevelopmental condition that impacts language, communication and social interactions. The current diagnostic process for ASD is based upon a detailed multidisciplinary assessment. Currently no clinical biomarker exists to help in the diagnosis and monitoring of this condition that has a prevalence of approximately 1%. The electroretinogram (ERG), is a clinical test that records the electrical response of the retina to light. The ERG is a promising way to study different neurodevelopmental and neurodegenerative disorders, including ASD. In this study, we have proposed a machine learning based method to detect ASD from control subjects using the ERG waveform. We collected ERG signals from 47 control (CO) and 96 ASD individuals. We analyzed ERG signals both in the time and the spectral domain to gain insight into the statistically significant discriminating features between CO and ASD individuals. We evaluated the machine learning (ML) models using a subject independent cross validation-based approach. Time-domain features were able to detect ASD with a maximum 65% accuracy. The classification accuracy of our best ML model using time-domain and spectral features was 86%, with 98% sensitivity. Our preliminary results indicate that spectral analysis of ERG provides helpful information for the classification of ASD

Feasibility of atrial fibrillation detection from a novel wearable armband device

BACKGROUND: Atrial fibrillation (AF) is the world’s most common heart rhythm disorder and even several minutes of AF episodes can contribute to risk for complications, including stroke. However, AF often goes undiagnosed owing to the fact that it can be paroxysmal, brief, and asymptomatic. OBJECTIVE: To facilitate better AF monitoring, we studied the feasibility of AF detection using a continuous electrocardiogram (ECG) signal recorded from a novel wearable armband device. METHODS: In our 2-step algorithm, we first calculate the R-R interval variability–based features to capture randomness that can indicate a segment of data possibly containing AF, and subsequently discriminate normal sinus rhythm from the possible AF episodes. Next, we use density Poincaré plot-derived image domain features along with a support vector machine to separate premature atrial/ventricular contraction episodes from any AF episodes. We trained and validated our model using the ECG data obtained from a subset of the MIMIC-III (Medical Information Mart for Intensive Care III) database containing 30 subjects. RESULTS: When we tested our model using the novel wearable armband ECG dataset containing 12 subjects, the proposed method achieved sensitivity, specificity, accuracy, and F1 score of 99.89%, 99.99%, 99.98%, and 0.9989, respectively. Moreover, when compared with several existing methods with the armband data, our proposed method outperformed the others, which shows its efficacy. CONCLUSION: Our study suggests that the novel wearable armband device and our algorithm can be used as a potential tool for continuous AF monitoring with high accuracy

Prioritizing Business Quality Improvement of Fresh Agri-Food SMEs through Open Innovation to Survive the Pandemic: A QFD-Based Model

It is important that SMEs are able to prioritize business quality by identifying business requirements based on customer requirements. This strategy is able to help SMEs generate innovation in the form of improved business quality activities to meet customer requirements. This approach uses the “quality function deployment (QFD)” method to identify the priority business requirements and improvement actions to generate business quality. As a result, we managed to identify five priority variable business requirements (BReqs) based on seven variable customer requirements (CReqs) with the lowest satisfaction score. We proposed some improvement actions in perspective quality based on five priority business requirements. Moreover, the final quality matrix of business quality improvement priority generally makes it easier for users to read and find out which variables need to be improved. This research also presents a solution so that users can perform business actions effectively and efficiently in allocating their resources