Miracle-dz/UniFAR-FTUs huggingface dataset:
Palliative Professionals’ Views on the Importance of Religion, Belief, and Spiritual Identities toward the End of Life

Abundant literature has argued the significance of religion, belief, and spirituality at the end of life. This study aims to add to this literature by exploring palliative professionals’ views in this area. By means of an in-depth interviewing method, this paper reports data from 15 hospice and palliative care professionals. Participants were recruited from five hospice and palliative care organisations, and the data were managed and analysed with thematic analysis and NVivo (version 11). This study found three main reasons that make religion, belief, and spirituality important for patients and their loved ones when facing imminent death: the sense of comfort and security, meaning making, and closure. These reasons are not independent from one another, but complementary. This paper offers some implications for practice and concludes with a call for further research.
On the Discrete Phase Shifts Design for Distributed RIS-aided Downlink MIMO-NOMA Systems

In this paper, we study a distributed reconfigurable intelligent surface-aided downlink multiple-input multiple-output non-orthogonal multiple access systems with random distributed users, where the signal alignment can be achieved within the paired users by controlling discrete phase shifts. In particular, a unified precoder and decoder are provided to cancel inter-cluster interference. To evaluate the performance of proposed framework, the near and far users’ channel statistics with Nakagami-m fading are derived. Further, the approximate expressions of average outage probability are obtained by utilizing the proposed channel statistics, and the corresponding diversity orders can also be obtained to acquire more insights. Finally, simulation results reveal that not only discrete phase shifts design by selecting a 3bit resolution can realize the near optimal performance but also the diversity gain can be significantly improved by increasing the number of reflection elements.
Isoload vs isokinetic eccentric exercise: a direct comparison of exercise-induced muscle damage and repeated bout effect

Purpose This study compared the exercise induced muscle damage and repeated bout effect after isoload vs isokinetic eccentric supra-maximal single session. Methods Thirty sport science male students were randomly divided in isokinetic (IK) and isoload (IL) eccentric training. Creatin kinase (CK) serum activity, muscle soreness and strength decrement measured both in dynamic and isometric modalities were recorded at baseline, immediately after and up to 4 days following 48 supramaximal IK or IL eccentric contractions. Same protocol was repeated after 4 weeks. A three-way repeated measures ANOVA was used to detect differences in dependent variables comparing group 9 bout 9 time. Results No three-way interaction occurred in dependent variables. Bout 9 time resulted in a significant interaction in all dependent variables. Muscle damage markers resulted significantly altered compared to baseline up to 4 days. However, IL showed significantly greater CK, muscle soreness and strength deficit compared to IK. All parameters were significantly reduced after second compared to first bout. Difference between IL and IK after second bout was not overall significant. Conclusion IK vs IL supra-maximal eccentric contraction is showed to have different muscle damage symptoms. Protection conferred by first bout reduced muscle damage after 4 weeks and decreased difference between IL and IK.
Kultura organizacji wirtualnych. Charakterystyka e-kultury

Socio-economical transformation changes the way organizations are managed. Virtual organization is a new type of enterprise that adjusts to these changes. Effective implementation of vitality requires sufficient actions that refer to the social aspect of organization. It is represented by management by organizational culture. In virtual organizations there is a new type of culture – e-culture. The specifics of e-cultures based on organizational adjustment to new conditions. This paper describes the key elements of e-culture. It points the main areas that specify e-culture. The main purpose of this paper is to describe the e-culture in polish IT enterprises. Based on quantities research, conducted on the 92 organizations, the exploratory factor analysis allows to characterize core elements of e-culture in IT enterprises in Poland.
Understanding the effect of scale and resolution on predictive habitat maps

Summary High intensity, localised, and targeted surveys using multibeam and drop down video have been carried out around Shetland aiming to map nationally and internationally important habitats of maerl and horse mussel (Modiolus modiolus) beds. These surveys have resulted in targeted closures to mobile dredging gear in order to conserve these habitats. However, the surveys were based on historical information and there is a growing requirement to identify additional suitable habitats which could be considered for protection. In order to achieve this, predictive habitat maps, using the MaxEnt species distribution model (SDM), were run and analysed at different spatial scales and data resolutions. Tide, bathymetry, and bathymetric associations such as slope and aspect were used as variables in the model. Comparisons were made at a large- (regional level) and small-spatial scale (site-specific) and between different bathymetry resolutions (e.g. localised high resolution versus broad scale lower resolution) to assess the impact of modelling resolution. Depth and tide had the greatest influence in the SDM outputs for both habitats. Access to high resolution data, in this case bathymetry, greatly increased the confidence of the SDMs.
Industrial internet platforms: applications in BF ironmaking

The industrial internet brings new trends and technologies to traditional industries. This paper analyses the current data situation and intelligent transformation needs of the blast furnace ironmaking industry; proposes a reference architecture for the blast furnace ironmaking industrial internet and implements corresponding technical support given a large amount of industrial information in the blast furnace ironmaking field, the fast update rate and the difficulties in data analysis and mining. To further illustrate the reference architecture, the implementation and operation mechanism of the industrial internet platform for the blast furnace ironmaking is discussed from the perspectives of data transmission, storage, analysis and processing, task scheduling, data warehouse and intelligent applications. Finally, the application development and operation process of the industrial internet platform in the blast furnace ironmaking environment is illustrated by building the application, using the hot metal temperature prediction application and the material size monitoring application as examples.
State of Art IoT and Edge Embedded Systems for Real-time Machine Vision Applications

IoT and edge devices dedicated to run machine vision algorithms are usually few years lagging currently available state-of-the-art technologies for hardware accelerators. This is mainly due to the non-negligible time delay required to implement and assess related algorithms. Among possible hardware platforms which are potentially being explored to handle real-time machine vision tasks, multi-core CPU and Graphical Processing Unit (GPU) platforms remain the most widely used ones over Field Programmable Gate Array (FPGA) and Application Specific Integrated Circuit (ASIC)-based platforms. This is mainly due to the availability of powerful and user friendly software development tools, in addition to their lower cost, and obviously their high computation power with reasonable form factor and power consumption. Nevertheless, the trend now is towards a System-On-Chip (SOC) processors which combine ASIC/FPGA accelerators with GPU/multicore CPUs. This paper presents different state of the art IoT and edge machine vision technologies along with their performance and limitations. It can be a good reference for researchers involved in designing state of the art IoT embedded systems for machine vision applications.
Customer Churn Prediction in B2B Non-Contractual Business Settings Using Invoice Data

Customer churn is a problem virtually all companies face, and the ability to predict it reliably can be a cornerstone for successful retention campaigns. In this study, we propose an approach to customer churn prediction in non-contractual B2B settings that relies exclusively on invoice-level data for feature engineering and uses multi-slicing to maximally utilize available data. We cast churn as a binary classification problem and assess the ability of three established classifiers to predict it when using different churn definitions. We also compare classifier performance when different amounts of historical data are used for feature engineering. The results indicate that robust models for different churn definitions can be derived by using invoice-level data alone and that using more historical data for creating some of the features tends to lead to better performing models for some classifiers. We also confirm that the multi-slicing approach to dataset creation yields better performing models compared to the traditionally used single-slicing approach.
Using Online Grocery Applications during the COVID-19 Pandemic: Their Relationship with Open Innovation

This present research examines the behavioral intentions of Filipinos to use online grocery applications during the novel COVID-19 pandemic. The study proposes an integration of the health belief model (HBM) and the Unified Theory of Acceptance and Use of Technology (UTAUT2) to identify the factors affecting the acceptance and usage of Filipinos of online grocery applications in terms of the impact of health risk for COVID-19. To accurately measure the factors and their relationship to behavioral intentions and usage behavior, a questionnaire was developed and distributed to 373 residents in the Philippines. Partial least squares structural equation modeling (PLS-SEM) was applied as an analytical method for this study. The results revealed that performance expectancy, perceived benefits, perceived severity, and cues to action significantly influenced the behavioral intentions and usage of online grocery apps during the COVID-19 pandemic. The study’s findings can be utilized as a theoretical framework for future researchers of consumer behavior; e-commerce developers; and grocery industry retailers, to enhance the innovation and services of online grocery applications. The results of this study may also be used and capitalized on by investors and managers to apply in strategizing when developing and marketing online grocery applications among consumers. Moreover, the framework of this study may be adopted and utilized by other online markets, even in different counties. Further theoretical and practical aspects are discussed in this paper.
Application of Evolutionary Computation for Identification of Parameters of Sky Luminance Distributions

Abstract The International Commission on Illumination has adopted a set of fifteen standard sky luminance distributions (SSLD). One of our previous papers uses a least squares method to sort sky luminance measurements from a station located north of Bangkok into fifteen slots, each corresponding to each type of SSLD, to appraise the statistics of occurrence of each sky type at the location. The paper also uses Kittler's method (Kittler; and others, 1997) to prepare paired values from the measurements sorted and obtains parameter values of the gradation function and those of the indicatrix function separately from measurements in each slot. This paper applies evolutionary computation to obtain a set of parameter values for both gradation function and indcatrix function simultaneously under least squares criterion. This method enables all measurements in each slot to be used. The resulting luminance distribution function thus obtained for each sky type possesses lower bias error and lower mean square error than those of the corresponding type of SSLD when both are evaluated against measurements.
BioChemDDI: Predicting Drug–Drug Interactions by Fusing Biochemical and Structural Information through a Self-Attention Mechanism

Simple Summary Throughout history, combining drugs has been a common method in the fight against complex diseases. However, potential drug–drug interactions could give rise to unknown toxicity issues, which requires the urgent proposal of efficient methods to identify potential interactions.We use computer technology and machine learning techniques to propose a novel computational framework to calculate scores of drug–drug interaction probability for simplifying the screening process. Additionally, we built an online prescreening tool for biological researchers to further verify possible interactions in the fields of biomedicine and pharmacology. Overall, our study can provide new insights and approaches for rapidly identifying potential drug–drug interactions. Abstract During the development of drug and clinical applications, due to the co-administration of different drugs that have a high risk of interfering with each other’s mechanisms of action, correctly identifying potential drug–drug interactions (DDIs) is important to avoid a reduction in drug therapeutic activities and serious injuries to the organism. Therefore, to explore potential DDIs, we develop a computational method of integrating multi-level information. Firstly, the information of chemical sequence is fully captured by the Natural Language Processing (NLP) algorithm, and multiple biological function similarity information is fused by Similarity Network Fusion (SNF). Secondly, we extract deep network structure information through Hierarchical Representation Learning for Networks (HARP). Then, a highly representative comprehensive feature descriptor is constructed through the self-attention module that efficiently integrates biochemical and network features. Finally, a deep neural network (DNN) is employed to generate the prediction results. Contrasted with the previous supervision model, BioChemDDI innovatively introduced graph collapse for extracting a network structure and utilized the biochemical information during the pre-training process. The prediction results of the benchmark dataset indicate that BioChemDDI outperforms other existing models. Moreover, the case studies related to three cancer diseases, including breast cancer, hepatocellular carcinoma and malignancies, were analyzed using BioChemDDI. As a result, 24, 18 and 20 out of the top 30 predicted cancer-related drugs were confirmed by the databases. These experimental results demonstrate that BioChemDDI is a useful model to predict DDIs and can provide reliable candidates for biological experiments. The web server of BioChemDDI predictor is freely available to conduct further studies.
Relação entre variabilidade da frequência cardíaca e aumento no desempenho físico em jogadores de futebol The correlation between heart rate variability and improvement in soccer player's physical performance

The aim of this study was to analyze whether the heart rate variability (HRV), assessed at the beginning of a soccer preseason, reveals a correlation with the improvement of physical performance over this training period. Ten soccer players took part in the study. Their performance was evaluated by the following tests: the Yo-Yo intermittent recovery test, Level 1(Yo-Yo IR1); the 30-m sprint time, and the vertical jump; all were conducted before and after six weeks of pre-season. The HRV measurements were performed at rest in the supine position for ten minutes. The Spearman Rank Correlation test was used to investigate any possible relationship between HRV and improvements in performance, while the Student's t-test verified the changes in both performance and HRV. The Magnitude-Based Inferences approach (qualitative analysis) was applied to verify the possibilities of the observed values being positive, negative or inconclusive. There were significant improvements in Yo-Yo IR1 performance (P< 0.001) and in the 30-m sprint time (P< 0.001). The qualitative analysis revealed that the differences in Yo-Yo IR1 performance were very likely positive, were almost certainly positive for the sprint, but were inconclusive for the vertical jump. There was a strong correlation between one parasympathetic index and the change in performance (r = 0.85 P = 0.003 (IC95% = 0.49 - 0.97)). In conclusion, this study revealed a strong correlation between parasympathetic indices of HRV (analyzed before the training) with the performance improvement in Yo-Yo IR1 in football athletes during pre-season.
Steady-State Analysis of Series-Resonator Buck Converter

The series-capacitor buck converter doubles the duty ratio and equalizes the current between two phases. A series-resonator buck (SRB) converter is realized by adding a resonant tank in series with the series-capacitor Cs. All switches turn-on at zero voltage, and the low-side switches turn-off at zero current. This article focuses on modeling the steady-state operation of the SRB converter. The steady-state model calculates voltage gain, component peak voltages, and resonant inductor peak current. The understanding of the relationships between the voltage gain, regulating variable, and state variables help to optimize the power stage design. The expectations were validated by a 2-MHz converter with a peak efficiency of 98.5%, 48 V at the input and 7 V, 20 A at the output.
Blockchain‐based access control with 
k $k$ ‐times tamper resistance in cloud environment

While cloud computing services such as cloud storage are fairly mature, it remains challenging to design efficient and secure cryptographic schemes to facilitate fine‐grained access control and achieve other features. For example, existing ciphertext‐policy attribute‐based encryption schemes do not generally have in‐place limits on the number of access or access duration, which can consequently be exploited to perform economic denial of sustainability and other attacks. In addition, improving system efficiency can be challenging in large‐scale operations. Thus, in this paper, we present a blockchain‐based access control with k $k$ ‐times tamper resistance. Our proposed approach allows one to set and enforce quota/limits on the number of accesses allowed for each user, whose integrity is ensured using blockchain. In addition, our proposed approach also allows multiple attribute authorities to coexist and work together with a central authority to facilitate secret key distribution and improve system efficiency. We then evaluate the security and the efficiency of our proposed approach to demonstrate its utility.
In preparedness for an integrated infrastructure asset management system for the City of Johannesburg

A research report submitted to the Faculty of Engineering and the Built Environment, School of Civil and Environmental Engineering, University of the Witwatersrand, Johannesburg, in partial fulfilment of the requirements for the degree of Master of Science in Engineering. School of Civil & Environmental Engineering Johannesburg, September 2015
OAM Carrying Vortex Beam Mode Interconversion Using Modular Cascaded Transmitarrays

Orbital angular momentum (OAM) carrying vortex beams has been studied and realized using different methods. However, the designed devices are usually capable of generating fixed OAM modes. Here, the concept of a modular radiating system based on tightly cascaded transmitarrays (TAs) provi- ding a compact and user-accessible modular radiating system suitable for affordable detecting and imaging systems is presented. The TA is based on a novel compact unit cell. The unit cell characteristics are explained and then cascaded for the TAs’ operation. Several prototype TA modules are realized, and their interactions for OAM mode interconversion are investigated. A restricted method for determining the OAM mode purity of the generated beams is employed. The simulated and experimental results are consistent with the theory, verifying the employed strategy.
Effects of LED light spectra on lettuce growth and nutritional composition

Year-round greenhouse production in northern latitudes depends on the use of artificial lighting. Light emitting diodes provide a promising means to save energy during cultivation as well as to modify the light spectrum to regulate the growth and quality of the crop. We compared the effects of light emitting diode lighting with different spectral compositions on the growth, development and nutritional quality of lettuce (Lactuca sativa L. ‘Frillice’). We show that warm-white and warm-white supplemented with blue spectra provide equal growth and product quality compared to conventional high-pressure sodium lighting in the absence and presence of daylight. Our data indicate that for biomass accumulation, the far-red component in the light spectrum is more critical than green light or the red/blue ratio. Furthermore, we demonstrate that a red + blue spectrum increases the concentration of several vitamins in lettuce. However, biomass accumulation using this spectrum was insufficient when daylight was excluded.
Neurometabolite Levels and Relevance to Central Sensitization in Chronic Orofacial Pain Patients: A Magnetic Resonance Spectroscopy Study

Background Refractory chronic pain in the orofacial region involves central sensitization (CS). However, not all chronic pain patients exhibit CS. An objective assessment of CS may be useful for pain management. Changes in the balance of excitatory and inhibitory neural activity or excessive activity of nerves and glial cells may cause CS and contribute to pain chronification. Patients and Methods 1H-magnetic resonance spectra were acquired from the anterior cingulate cortex (ACC) and thalamus in 20 patients with chronic orofacial pain and suspected CS, and 21 healthy volunteers, using a single-voxel point-resolved spectroscopy sequence. The patients were assessed using the Central Sensitization Inventory. Results Aspartate/total creatine (tCr) and glutathione in the ACC were significantly higher in the patient group. However, no significant difference was observed between groups in the neurometabolites measured in the thalamus. Patients also exhibited a tendency for increased gamma-aminobutyric acid (GABA)/tCr in the ACC. There were positive relationships between Central Sensitization Inventory scores and glutamate + glutamine (Glx) in the thalamus, a positive trend for Glx in the ACC and a negative relationship for GABA/tCr in the ACC. Conclusion The high levels of aspartate/tCr and glutathione in the patient group suggest excitatory neuronal activity and hyperactivity of neurons and glial cells. The correlation analysis results suggest that excitatory and inhibitory neurometabolites are involved in the chronification of orofacial pain, including CS.