Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of the Frequently Used Cinacalcet for pHPT during the COVID-19 Pandemic on Perioperative Decrease in Parathyroid Hormone

Background: Cinacalcet is a calcimimetic drug that has increasingly been used as a bridging therapy for primary hyperparathyroidism (pHPT), especially during the COVID-19 pandemic. The aim of our study was to investigate if preoperative cinacalcet therapy affects intraoperative parathyroid hormone (IOPTH) monitoring during parathyroidectomy, which is an important indicator for the success of surgery. Methods: In this single-center retrospective analysis, we studied the outcomes of 72 patients who underwent surgery for pHPT. We evaluated two groups: those with cinacalcet therapy before operation—the cinacalcet group (CG)—and those without medical therapy preoperatively (non-CG). In order to perform a between-group comparison of time trends, we fit a linear mixed-effects model with PTH as the response variable and predictors PTH levels preoperatively, group (cinacalcet yes/no), time, the group-by-time interaction, and a random intercept (per subject). Results: Our cohort included 51 (71%) women and 21 (29%) men, who were operated upon for pHPT in the period from January 2018 until August 2021. All patients were diagnosed with pHPT and 54% of the cohort were symptomatic for hypercalcemia. Moreover, 30% of the patients were treated with cinacalcet as a bridging therapy preoperatively, and this increased during the COVID-19 pandemic, as 64% of this group were treated in the last two years. Calcium values were significantly different before (p < 0.001) and after (p = 0.0089) surgery, but calcium level change did not differ significantly between the CG and non-CG. Parathyroid hormone (PTH) levels dropped significantly in both groups during 10 min IOPTH monitoring (p < 0.001), but there was no significant difference between the two groups (p = 0.212). Conclusions: In the examined patient cohort, the use of cinacalcet did not affect the value of IOPTH monitoring during surgery for pHPT.
Design of Web-Based Helpdesk Ticketing System at PT DENSO Indonesia

PT DENSO Indonesia currently has a helpdesk ticket system based on IBM Lotus Notes. The system is currently not being used optimally because it has limited features, depends on the platform provider, and the source code is difficult to develop. The company has also taken a policy this year not to continue the subscription process to the platform provider. The purpose of this study is to analyze the current business processes and propose a new system model that is computerized, as needed, reliable, and does not depend on a particular platform. The system was developed based on a website using the Extreme Programming (XP) method as a Software Development Life Cycle (SDLC), Unified Model Language (UML) notation as document design, and calculation of completion rate and time-based efficiency in measuring system effectiveness and efficiency. The website-based helpdesk ticketing system was successfully implemented as a new system model that runs independently and has easy-to-develop source code. The system also has a completion rate of 86% and a time-based efficiency of 0.00924 goal/sec based on a problem reporting scenario with 15 participants.
Challenges and Opportunities of Using Telemedicine During Covid-19 Epidemic: A Systematic Review

Introduction: Covid-19 pandemic is spreading rapidly around the world. Given the high risk of transmission of the disease in order to curb the effects of the epidemic, governments and health care organizations must maximize the capacity of medical care, protective equipment and preventive measures. Therefore, in this study, the challenges and recommendations for the use and development of telemedicine technologies during the Covid-19 epidemic were examined.Material and Methods: A systematic review of English-language studies was conducted by searching for keywords in the authoritative scientific databases. Studies related to the challenges and recommendations of telemedicine at the time of the Covid-19 epidemic were considered as inclusion criteria. Titles and abstracts were screened independently based on eligibility criteria. Complete texts were then retrieved and screened independently based on eligibility criteria. The checklist was used to extract data such as study title, first author name, year of publication, country, challenges and telemedicine recommendations during the Covid-19 epidemic.Results: In this study, the existing challenges and recommendations for the use and development of telemedicine technologies were examined. Challenges studied in the studies were included three categories of human, managerial and technical challenges. Human factors, management factors and technical factorsConclusion: The results showed that today human and managerial challenges play a more fundamental and important role than technical challenges. In addition, Covid-19 emergencies are effective in addressing telemedicine challenges, and efforts are underway to expand telemedicine more rapidly as a response to managing and controlling the outbreak of Covid-19 epidemics.
14‐5L: Late‐News Paper: Retinal Imaging Laser Eyewear with Focus‐Free and Augmented Reality

Retinal Imaging Laser Eyewear has a miniature laser projector inside the frame which provides the wearer with digital image information through the pupil using the retina as a screen. This compact universal-design eyewear features focus-free and augmented-reality image independent of the wearers' visual acuity and point of focus. This paper describes the background of this technology, the principle of focus-free imaging, the device with a trademark of RETISSA®, and its laser safety analyses based on international guidelines and standards.
Trust and collaboration between patients and staff in SUD treatment: A qualitative study of patients’ reflections on inpatient SUD treatment four years after discharge

Background and aim: Substance use disorder (SUD) is multifactorial, complex, and involves the severe problematic use of alcohol, prescription drugs, and other substances. People with SUD have long histories of perceived stigma, marginalisation, exclusion, social isolation, and shame. Moreover, patients with SUD are often treated as untrustworthy and incapable of actively participating in their treatment planning, even by healthcare and social services workers. Methods: Through in-depth interviews with former patients who have undergone SUD treatment, we explored their reflections on the treatment content. Interpretative phenomenological analysis revealed that some informants experienced aspects of the treatment as valuable, whereas others considered the same treatment useless or harmful. Results: Essential aspects of treatment were developing self-esteem and optimism about the future, developing strategies for coping with stress and challenging emotions and situations, developing relationships with family and friends, and preparing for life after inpatient treatment. Relationships with staff were described as crucial in all parts of the treatment. Conclusion: In conclusion, we suggest that a transition to a more collaborative treatment philosophy would be ethically and professionally valuable for providing support and treatment and enhancing patients’ perceptions of empowerment, inclusion, participation, and well-being on their terms, in cooperation with supportive others.
Exploring modern machine learning methods to improve causal-effect estimation

This paper addresses the use of machine learning methods for causal estimation of treatment e ﬀ ects from observational data. Even though conducting randomized experimental trials is a gold standard to reveal potential causal relationships, observational study is another rich source for investigation of exposure e ﬀ ects, for example, in the research of comparative e ﬀ ectiveness and safety of treatments, where the causal e ﬀ ect can be identiﬁed if covariates contain all confounding variables. In this context, statistical regression models for the expected outcome and the probability of treatment are often imposed, which can be combined in a clever way to yield more e ﬃ cient and robust causal estimators. Recently, targeted maximum likelihood estimation and causal random forest is proposed and extensively studied for the use of data-adaptive regression in estimation of causal inference parameters. Machine learning methods are a natural choice in these settings to improve the quality of the ﬁnal estimate of the treatment e ﬀ ect. We explore how we can adapt the design and training of several machine learning algorithms for causal inference and study their ﬁnite-sample performance through simulation experiments under various scenarios. Application to the percutaneous coronary intervention (PCI) data shows that these adaptations can improve simple linear regression-based methods.
Cattaneo–Christov model for triple diffusive natural convection flows over horizontal plate with entropy analysis embedded in porous regime

This research interprets the features of triple diffusion in naturally convected viscous material flow through a horizontal plate embedded in a porous regime. The heat and mass transmission process is evaluated by the implication of Cattaneo and Christov (CC) models of energy and mass diffusions. Entropy analysis is executed with the help of the second law of thermodynamics. The homogeneous and local thermal equilibrium is assumed for porous medium. The physically modeled expressions are reframed into an ordinary differential system. This re-structured system of expressions is computed numerically by the implication of the Runge–Kutta Fehlberg method. The computed solutions are visualized graphically and in numeric forms. The validation of present solutions is reported by the comparative benchmark with already available results in a limiting sense. It is evident that the opposing and assisting flows show higher entropy generation at the convective wall. An increment in surface entropy generation rate is achieved for higher thermal and solutal relaxation times.
BETA: From Behavior Sequentializing to Task Mapping in Mobile Crowdsensing

Mobile crowdsensing (MCS) can be widely adopted for undertaking a variety of tasks for the smart city by recruiting distributed participants. With the rapid proliferation of MCS applications, tasks can be devised with different sensing scales. Hence, allocating the tasks to participants with variable spatiotemporal granularity would be urgent and challenging. To this end, we propose a behavior sequentializing and task mapping (BETA) framework, where MCS tasks can be flexibly mapped based on the moving behavior of a participant’s daily activities. A sequential behavior model is first proposed to describe the moving behavior of a participant’s daily activities. Specifically, the behavior of a daily activity is represented by the spatiotemporal trajectory distribution, and a sequential behavior graph is adopted to model the relationships among multiple activities. To extract the sequential behavior from historical trajectories, a projection-clustering (PC) algorithm and a behavior sequentializing approach are developed. Then, based on the extracted sequential behavior, a behavior-to-task mapping network is proposed to evaluate the fitness between tasks and a participant. Finally, extensive simulations are conducted to demonstrate the functionality and performance of BETA. The results show that our proposed method outperforms the state-of-the-art solutions in the mapping efficiency of tasks with different sensing scales.
Novel Solubility Prediction Models: Molecular Fingerprints and Physicochemical Features vs Graph Convolutional Neural Networks

Predicting both accurate and reliable solubility values has long been a crucial but challenging task. In this work, surrogated model-based methods were developed to accurately predict the solubility of two molecules (solute and solvent) through machine learning and deep learning. The current study employed two methods: (1) converting molecules into molecular fingerprints and adding optimal physicochemical properties as descriptors and (2) using graph convolutional network (GCN) models to convert molecules into a graph representation and deal with prediction tasks. Then, two prediction tasks were conducted with each method: (1) the solubility value (regression) and (2) the solubility class (classification). The fingerprint-based method clearly demonstrates that high performance is possible by adding simple but significant physicochemical descriptors to molecular fingerprints, while the GCN method shows that it is possible to predict various properties of chemical compounds with relatively simplified features from the graph representation. The developed methodologies provide a comprehensive understanding of constructing a proper model for predicting solubility and can be employed to find suitable solutes and solvents.
Advances in Traumatic Brain Injury Biomarkers

Traumatic brain injury (TBI) is increasingly a major cause of disability across the globe. The current methods of diagnosis are inadequate at classifying patients and prognosis. TBI is a diagnostic and therapeutic challenge. There is no Food and Drug Administration (FDA)-approved treatment for TBI yet. It took about 16 years of preclinical research to develop accurate and objective diagnostic measures for TBI. Two brain-specific protein biomarkers, namely, ubiquitin C-terminal hydrolase-L1 and glial fibrillary acidic protein, have been extensively characterized. Recently, the two biomarkers were approved by the FDA as the first blood-based biomarker, Brain Trauma Indicator™ (BTI™), via the Breakthrough Devices Program. This scoping review presents (i) TBI diagnosis challenges, (ii) the process behind the FDA approval of biomarkers, and (iii) known unknowns in TBI biomarker biology. The current lag in TBI incidence and hospitalization can be reduced if digital biomarkers such as hard fall detection are standardized and used as a mechanism to alert paramedics to an unresponsive trauma patient.
Deep Learning Applications in Solid Waste Management: A Deep Literature Review

—Solid waste management (SWM) has recently received more attention, especially in developing countries, for smart and sustainable development. SWM system encompasses various interconnected processes which contain numerous complex operations. Recently, deep learning (DL) has attained momentum in providing alternative computational techniques to determine the solution of various SWM problems. Researchers have focused on this domain; therefore, significant research has been published, especially in the last decade. The literature shows that no study evaluates the potential of DL to solve the various SWM problems. The study performs a systematic literature review (SLR) which has complied 40 studies published between 2019 and 2021 in reputed journals and conferences. The selected research studies have implemented the various DL models and analyzed the application of DL in different SWM areas, namely waste identification and segregation and prediction of waste generation. The study has defined the systematic review protocol that comprises various criteria and a quality assessment process to select the research studies for review. The review demonstrates the comprehensive analysis of different DL models and techniques implemented in SWM. It also highlights the application domains and compares the reported performance of selected studies. Based on the reviewed work, it can be concluded that DL exhibits the plausible performance to detect and classify the different types of waste. The study also explains the deep convolutional neural network with the computational requirement and determine the research gaps with future recommendations.
A Robust Reversible Data Hiding Framework for Video Steganography Applications

—Reversible Data Hiding (RDH) is a special form of data hiding approach for data integrity and confidentiality protection where the secret image bits (SI) are embedded into Cover Media (CM) by altering its intrinsic pixel attributes. However, in RDH the CM along with the secret message is recovered at the end of computing phase. However, despite of its potential use-cases for enhancing the embedding performance, when it comes to security for various network standards, the traditional RDH mechanisms cannot fully comply with the standards for different set of attacks during the bit-stream transmission scenarios. Therefore, the proposed study contributes towards a computational framework of a robust RDH framework for Video Steganography (VS) which is modeled and simulated under various attack effects and the observation outcome are produced in before and after attack situations to justify the improvement over Embedding Capacity (EC) and Peak Signal-to-Noise Ratio (PSNR) performance for both CM and secret message unlike traditional difference expansion-based methods (DE). The outcome of the study shows that the formulated RDH method not only achieves better reversibility at lower cost of computing but also ensures effective PSNR and imperceptibility outcome for both CM and secret image.
Changes in the Global Competitiveness Index 4.0 Methodology: The Improved Approach of Competitiveness Benchmarking

The Global Competitiveness Index (GCI) developed by the World Economic Forum (WEF) is used as a standard for measuring a country’s competitiveness. However, in literature, the GCI has been accused of numerous methodological flaws. Consequently, in 2018, the WEF introduced significant methodological changes. This study aims to examine whether the methodological modifications in the GCI’s structure increase its ability to capture the real competitiveness of economies. In addition, the study considers whether the selection of weights of individual elements included in the GCI is optimal or could be improved. By employing a sensitivity-based analysis, we find that the change in methodology resulted in fewer pillars of marginal importance. In the case of the GCI 2017, there were four pillars, whereas in that of the GCI 4.0, there were only two pillars: product market and labor market. Furthermore, we reveal that the WEF weights do not reflect the measured importance of the variables. In the optimization process, numerous variables (primarily opinion-based indicators) were insignificant in explaining the GCI variance and could be eliminated from the set of diagnostic variables without affecting the index’s value. For instance, in the case of the GCI 4.0, 35 out of 103 variables could be eliminated. The new rankings obtained by weight optimization and reduction of the diagnostic variables demonstrated a strong positive correlation with the original rankings. This research contributes to the literature from both a theoretical perspective (indicating the most vital indicators in the GCI) and a practical standpoint (reducing the costs and time of obtaining redundant data).
Skeleton-Based ST-GCN for Human Action Recognition With Extended Skeleton Graph and Partitioning Strategy

Skeleton-based Graph Convolutional Networks (GCN) for human action and interaction recognition have received considerable attention of researchers due to its compact and view-invariant nature of skeleton data. However, the static skeleton graph topology in conventional GCNs does not reflect the implicit relationships of non-adjacent joints, which contain vital latent information for a skeleton pose in an action sequence. Moreover, traditional tri-categorical node partitioning strategy discards much of the motion dependencies along temporal dimension for non-physically connected edges. We propose an extended skeleton graph topology along with extended partitioning strategy to extract much of the non-adjacent joint relational information in the model for robust discriminative features. Extended skeleton graph represents joints as vertices and weighted edges represent intrinsic and extrinsic relationships between physically connected and non-physically connected joints respectively. Furthermore, extended partitioning strategy divides the input graph for GCN as five-categorical fixed-length tensor to encompass maximal motion dependencies. Finally, the extended skeleton graph and partitioning strategy are realized by adopting Spatio-Temporal Graph Convolutional Network (ST-GCN). The experiments carried out over three large scale datasets NTU-RGB+D, NTU-RGB+D 120 and Kinetics-Skeleton show improved performance over conventional state-of-the-art ST-GCNs.
Is switchgrass good for carbon savings? Long‐term results in marginal land

The cultivation of perennial crops in marginal land as a feedstock for renewable energy or bio‐based products constitutes a promising alternative to mitigate greenhouse gas emissions since they can be produced without land competition issues and with potentially significant amounts of belowground C storing. The objective of this study was to assess soil C storage capacity of switchgrass on a marginal land (poor soil with limited rooting depth on a hilly area) through a spatialized analysis derived from several soil samples. A significant increase in SOC was observed after 13 years. In general terms, SOC increased by 12 Mg C ha−1. The estimated switchgrass‐derived C was 12.7 (±5.3) Mg C ha−1. A significant positive correlation was observed between the C derived from switchgrass and SOC gain. The increased SOC along the field, however, was patchy, with the highest increments registered in the center of the field, while the lowest ones in the top and bottom of the hill. Soil N stocks have also increased along with the soil C storage, although no direct relationship with C derived from switchgrass was observed. Therefore, it could be stated that growing switchgrass in marginal land can be a valuable option to mitigate the risk of agricultural land abandonment while storing a significant amount of soil C.
Pathways, parallels and pitfalls: the Scholarly Web, the ESRC and Linked Open Data*

Abstract This paper highlights key principles that the eScholarship Research Centre (ESRC) shares with the Linked Open Data (LOD) community, primarily relationship-centric contextualisation of information resources, and a commitment to producing and publishing sustainable, standards-based data outputs suitable for machine-based interchange. This paper illustrates how these principles have enabled the ESRC, without pursuing LOD as a specific end, to hold a path close to the Linked Data road. We also note that, by extension, this has positioned the ESRC ready to translate many of its resources into LOD formats in exchanges where complementary technologies and ontology mappings are available.
The impact of oil supply shocks on real economic activity: new evidence based on the proxy SVARs

ABSTRACT The existing literature on the impact of oil supply shocks on real economic activity after the mid-1980s still fails to reach an agreement. This may be partly due to different identification assumptions and model specifications corresponding to the oil supply shock, and partly due to the oil supply shock identified previously is endogenous to real economic activity. In this paper, based on the proxy structural vector autoregressions (SAVRs), we identify an oil supply shock that is exogenous to real economic activity, and then study the impact of this shock on U.S. real economic activity. We find exogenous oil supply shocks have a very large and persistent impact on U.S. real economic activity after the mid-1980s. Besides, several new possible transmission channels through which exogenous oil supply shocks can influence U.S. real GDP are detected. Specifically, unfavorable exogenous oil supply shocks can lower the consumer sentiment index (CSI) and consumer confidence index (CCI), decrease real net exports of goods and services (NEGS) through the terms of trade (TOT) effect, and reduce real government consumption expenditures and gross investment of state and local (GCEGISL), thus leading to a decline in real GDP. These newly detected transmission channels are very important for policymakers.