[What happens if algorithmic music meets medicine].

Since ancient times there has been recognition of music's therapeutic powers, inherent in the properties of sound and its effects on human beings at a psychophysical level. Literature showed the development of therapeutic applications of music in numerous clinical settings. Music-listening itself can qualify as an effective therapeutic means within clinical contexts.

How Does Gender Moderate Customer Intention of Shopping via Live-Streaming Apps during the COVID-19 Pandemic Lockdown Period?

Shopping through Live-Streaming Shopping Apps (LSSAs) as an emerging consumption phenomenon has increased dramatically in recent years, especially during the COVID-19 lockdown period. However, insufficient studies have focused on the psychological processes undergone in different customer demographics while shopping via LSSAs under pandemic conditions. This study integrated the Unified Theory of Acceptance and Use of Technology 2 with Flow Theory into a Stimulus-Organism-Response framework to investigate the psychological processes of different customer demographics during the COVID-19 lockdown period.

Impact of COVID-19 lockdown measures on waste production behavior in Lisbon.

The recent restrictions on mobility and economic activities imposed by governments due to the COVID-19 pandemic have significantly affected waste production and recycling patterns in cities worldwide. This effect differed both between cities and within cities as the measures of confinement adopted by governments had diverse impacts in different areas of cities, depending on their characteristics (e.g.

Air-travelers' concerns emerging from online comments during the COVID-19 outbreak.

This research aims to assess air travelers' concerns affected by the Coronavirus pandemic, expressed in the comments they wrote online. A sample of 639 comments written on the Italian National Consumer Union website and related to the airline industry was assessed through an automated sentiment analysis in this study. The achieved results showed that travelers' concerns were directed mainly towards compensations, cancellations, and COVID-19 and at the same time, they had mixed and unpredictable feelings.

Scaling behavior of public procurement activity.

Public procurement refers to the purchase by public sector entities-such as government departments or local authorities-of Services, Goods, or Works. It accounts for a significant share of OECD countries' expenditures. However, while governments are expected to execute them as efficiently as possible, there is a lack of methodologies for an adequate comparison of procurement activity between institutions at different scales, which represents a challenge for policymakers and academics.

Object detection for automatic cancer cell counting in zebrafish xenografts.

Cell counting is a frequent task in medical research studies. However, it is often performed manually; thus, it is time-consuming and prone to human error. Even so, cell counting automation can be challenging to achieve, especially when dealing with crowded scenes and overlapping cells, assuming different shapes and sizes.

Applications of Blockchain Technology to Higher Education Arena: A Bibliometric Analysis.

Research related to blockchain is rapidly gaining importance in the higher education. This opportunity collaborates with a proposal for a review of papers on the main blockchain topic. The bibliometric analysis included 61 peer-reviewed articles published in the Scopus database during the period of 2016 to 2021.

Generative adversarial networks for generating synthetic features for Wi-Fi signal quality.

Wireless networks are among the fundamental technologies used to connect people. Considering the constant advancements in the field, telecommunication operators must guarantee a high-quality service to keep their customer portfolio. To ensure this high-quality service, it is common to establish partnerships with specialized technology companies that deliver software services in order to monitor the networks and identify faults and respective solutions.

Impact of GAN-based lesion-focused medical image super-resolution on the robustness of radiomic features.

Robust machine learning models based on radiomic features might allow for accurate diagnosis, prognosis, and medical decision-making. Unfortunately, the lack of standardized radiomic feature extraction has hampered their clinical use. Since the radiomic features tend to be affected by low voxel statistics in regions of interest, increasing the sample size would improve their robustness in clinical studies.

Extending the notion of customer value to surfing camps.

This study applies the germane principles of service-dominant logic by investigating how different dimensions of service value impact customers' satisfaction and related behavioral intentions in the surf camps context. An empirical model was developed and tested via survey responses from 300 Portuguese surf camps tourists who profiled their experience. Data were analyzed using Structural Equation Modeling, specifically Partial Least Squares (PLS-PM).

Comparative Study of First Order Optimizers for Image Classification Using Convolutional Neural Networks on Histopathology Images.

The classification of histopathology images requires an experienced physician with years of experience to classify the histopathology images accurately. In this study, an algorithm was developed to assist physicians in classifying histopathology images; the algorithm receives the histopathology image as an input and produces the percentage of cancer presence. The primary classifier used in this algorithm is the convolutional neural network, which is a state-of-the-art classifier used in image classification as it can classify images without relying on the manual selection of features from each image.

Musculoskeletal Images Classification for Detection of Fractures Using Transfer Learning.

The classification of the musculoskeletal images can be very challenging, mostly when it is being done in the emergency room, where a decision must be made rapidly. The computer vision domain has gained increasing attention in recent years, due to its achievements in image classification. The convolutional neural network (CNN) is one of the latest computer vision algorithms that achieved state-of-the-art results.

Understanding the factors of mobile payment continuance intention: empirical test in an African context.

Few studies have been conducted about individuals' continuance to use m-payment, especially in an African context. This study investigates the continuance intention to use m-payment, employing two theoretical models: the DeLone and McLean information system (D&M IS) success model and the expectation-confirmation model (ECM) in an African context. We collected 338 questionnaires through an online survey to evaluate and validate the proposed theoretical model, using partial least squares - structural equation modelling (PLS-SEM).

Improving convolutional neural networks performance for image classification using test time augmentation: a case study using MURA dataset.

Bone fractures are one of the main causes to visit the emergency room (ER); the primary method to detect bone fractures is using X-Ray images. X-Ray images require an experienced radiologist to classify them; however, an experienced radiologist is not always available in the ER. An accurate automatic X-Ray image classifier in the ER can help reduce error rates by providing an instant second opinion to the emergency doctor.

Mumbai's business landscape: A spatial analytical approach to urbanisation.

India has proven to be one of the most diverse and dynamic economic regions in the world. Its industry focuses predominantly on the service sector and immediate economic growth seems to steer India into the economic superpower. India's unique business landscape is felt at a regional level, where massive urbanization has become an unavoidable consequence of population growth and spatial allocation to the economic hubs of metropolitan cities.

Carbon sequestration scenarios in Portugal: which way to go forward?

Assessing carbon storage and sequestration is key for defining effective conservation actions to mitigate climate change. Forest species changes have direct impacts on carbon stocks and may lead to undesirable climate trade-offs. In this paper, we measure aboveground biomass (AGB) and the impact of forest changes on climate regulation through three land policy scenarios by 2030 in continental Portugal.

Comparing Stacking Ensemble Techniques to Improve Musculoskeletal Fracture Image Classification.

Bone fractures are among the main reasons for emergency room admittance and require a rapid response from doctors. Bone fractures can be severe and can lead to permanent disability if not treated correctly and rapidly. Using X-ray imaging in the emergency room to detect fractures is a challenging task that requires an experienced radiologist, a specialist who is not always available.

On the Relation Between Over-Indebtedness and Well-Being: An Analysis of the Mechanisms Influencing Health, Sleep, Life Satisfaction, and Emotional Well-Being.

This paper aims to explore the association between over-indebtedness and two facets of well-being - life satisfaction and emotional well-being. Although prior research has associated over-indebtedness with lower life satisfaction, this study contributes to the extant literature by revealing its effects on emotional well-being, which is a crucial component of well-being that has received less attention. Besides subjective well-being (SWB), reported health, and sleep quality were also assessed.

Stable leaders pave the way for cooperation under time-dependent exploration rates.

The exploration of different behaviours is part of the adaptation repertoire of individuals to new environments. Here, we explore how the evolution of cooperative behaviour is affected by the interplay between exploration dynamics and social learning, in particular when individuals engage on prisoner's dilemma along the edges of a social network. We show that when the population undergoes a transition from strong to weak exploration rates a decline in the overall levels of cooperation is observed.

Gamification and reputation: key determinants of e-commerce usage and repurchase intention.

Nowadays, companies know that keeping customers engaged is essential to increase their loyalty. The main goal of this study is to understand the impact of gamification and reputation on the intention of repurchase in e-commerce. Gamification corresponds to the regular usage of game elements in non-game context.

How Does the Pandemic Facilitate Mobile Payment? An Investigation on Users' Perspective under the COVID-19 Pandemic.

Owing to the convenience, reliability and contact-free feature of Mobile payment (M-payment), it has been diffusely adopted in China during the COVID-19 pandemic to reduce the direct and indirect contacts in transactions, allowing social distancing to be maintained and facilitating stabilization of the social economy. This paper aims to comprehensively investigate the technological and mental factors affecting users' adoption intentions of M-payment under the COVID-19 pandemic, to expand the domain of technology adoption under the emergency situation. This study integrated Unified Theory of Acceptance and Use of Technology (UTAUT) with perceived benefits from Mental Accounting Theory (MAT), and two additional variables (perceived security and trust) to investigate 739 smartphone users' adoption intentions of M-payment during the COVID-19 pandemic in China.

Students' long-term orientation role in e-learning success: A Brazilian study.

E-learning can play an important role in the solution to educate a large quota of the population in significant countries. Studies point out that cultural characteristics can influence e-learners' performance. Our main goal is to understand the e-learning success drivers in Brazil.

Reliability Evaluation of the Data Acquisition Potential of a Low-Cost Climatic Network for Applications in Agriculture.

Temperature, humidity and precipitation have a strong influence on the generation of diseases in different crops, especially in vine. In recent years, advances in different disciplines have enabled the deployment of sensor nodes on agricultural plots. These sensors are characterised by a low cost and so the reliability of the data obtained from them can be compromised, as they are built from low-confidence components.

Using artificial intelligence to overcome over-indebtedness and fight poverty.

This research examines how artificial intelligence may contribute to better understanding and to overcome over-indebtedness in contexts of high poverty risk. This research uses Automated Machine Learning (AutoML) in a field database of 1654 over-indebted households to identify distinguishable clusters and to predict its risk factors. First, unsupervised machine learning using Self-Organizing Maps generated three over-indebtedness clusters: low-income (31.

What factors determining customer continuingly using food delivery apps during 2019 novel coronavirus pandemic period?

Food delivery apps (FDAs) as an emerging online-to-offline mobile technology, have been widely adopted by catering businesses and customers. Especially, as they have provided two-way beneficial catering delivery services in rescuing catering enterprises and satisfying customers' technological and mental exceptions under the COVID-19 global pandemic condition. This study proposes a comprehensive model integrating UTAUT, ECM and TTF with the trust factor and examines 532 valid FDA users' continuance intention of using FDAs during the COVID-19 pandemic period in China.