Miracle-dz/UniFAR-FTUs huggingface dataset:
Sustainable Circular Economy in the Wood Construction Industry: A Business Opportunity Perspective

This study aims to explore the main drivers of recognizing a circular economy business opportunity. In this multiple case study with six entrepreneurs from the wood construction industry is a relevant part of the circular economy because its essential component is a sustainable and renewable material (trees/wood) that reduces carbon emissions. This study contributes to previous research by showing that six traditional drivers influence the entrepreneur’s recognition of business opportunities: environmental conditions, social capital, prior knowledge, systematic search, cognition and entrepreneurial alertness. Moreover, they are intertwined in the complex and processual phenomenon known as business opportunity. Furthermore, for the drivers of sustainable opportunity recognition, prior knowledge on environment and altruism are critical. The recognition of opportunity is not solely based on any of the individual drivers; rather, it relies mostly on continuous interaction with the network when the entrepreneur is creating a business opportunity.
New Insights on Metabolic and Genetic Basis of Migraine: Novel Impact on Management and Therapeutical Approach

Migraine is a hereditary disease, usually one-sided, sometimes bilateral. It is characterized by moderate to severe pain, which worsens with physical activity and may be associated with nausea and vomiting, may be accompanied by photophobia and phonophobia. The disorder can occur at any time of the day and can last from 4 to 72 h, with and without aura. The pathogenic mechanism is unclear, but extensive preclinical and clinical studies are ongoing. According to electrophysiology and imaging studies, many brain areas are involved, such as cerebral cortex, thalamus, hypothalamus, and brainstem. The activation of the trigeminovascular system has a key role in the headache phase. There also appears to be a genetic basis behind the development of migraine. Numerous alterations have been identified, and in addition to the genetic cause, there is also a close association with the surrounding environment, as if on the one hand, the genetic alterations may be responsible for the onset of migraine, on the other, the environmental factors seem to be more strongly associated with exacerbations. This review is an analysis of neurophysiological mechanisms, neuropeptide activity, and genetic alterations that play a fundamental role in choosing the best therapeutic strategy. To date, the goal is to create a therapy that is as personalized as possible, and for this reason, steps forward have been made in the pharmacological field in order to identify new therapeutic strategies for both acute treatment and prophylaxis.
Associations of hyperglycemic emergency and severe hypoglycemia incidences with seasonality and ambient temperature among pregnant women with diabetes: a nested case-control study in Taiwan

Background Associations of acute glycemic complications with season and ambient temperature have been reported in general population with diabetes. However, little is known about the risks of acute glycemic complications in relation to season and ambient temperature in pregnant women, who are likely to be even more vulnerable. This work aimed to investigate the associations of season and ambient temperature with pregnancies complicated with hyperglycemia emergency or severe hypoglycemia. Methods Two separate case-control studies were nested within 150,153 pregnancies by women with type 1, type 2, or gestational diabetes between 2009 and 2014 in Taiwan. Hyperglycemia emergency (mainly diabetic ketoacidosis and hyperosmolar hyperglycemic state) and severe hypoglycemia occurred in 77 and 153 diabetic pregnancies (cases), respectively. Ten control pregnancies were randomly selected for each case by matching each case pregnancy on type of diabetes (i.e., T1DM, T2DM, or GDM), maternal age on the date of acute glycemic complication occurrence (i.e., index date), and “length of gestation at risk” (i.e., period between conception and index date). Meteorological parameters were retrieved from 542 meteorological monitoring stations across Taiwan during 2008–2014. Conditional logistic regression analysis with generalized estimation equation was separately performed to estimate the covariate adjusted odds ratios (ORs) of each of the two acute glycemic complications in association with season and ambient temperature within 30 days prior to the index date. Results Compared to summer, winter season was associated with a significantly elevated risk of severe hypoglycemia with an OR of 1.74 (95% confidence interval (CI) 1.08–2.79). The OR of hyperglycemic emergency was also elevated in winter season at OR of 1.88, but the significance is only marginal (95% CI 0.97–3.64, p = 0.0598). Subgroup analyses further noted that such seasonal variation was also observed in pregnancies with pre-pregnancy type 1 diabetes and gestational diabetes. On the other hand, ambient temperature was not significantly associated with the two acute glycemic complications. Conclusions A moderately but significantly elevated risk of severe hypoglycemia was found in pregnant women with diabetes during winter season, and such increased risk was more evident in pregnancies with T1DM. Supplementary information The online version contains supplementary material available at https://doi.org/10.1265/ehpm.22-00003.
Modeling Dynamic Traffic Flow as Visibility Graphs: A Network-Scale Prediction Framework for Lane-Level Traffic Flow Based on LPR Data

Emerging applications in real-time traffic management put forward urgent requirements for lane-level traffic flow prediction. Limited by extremely unstable traffic volumes and heterogeneous spatiotemporal dependencies in urban road networks, network-scale prediction for lane-level traffic flow is still a critical challenge. This study models the dynamic characteristics of lane-level traffic flow as complex networks and proposes a deep learning framework for network-scale prediction. Relying on the visibility graph, we transform the temporal dependence learning task into spatial correlation mining on temporal complex networks. For spatial dependency extraction in urban traffic flows, we establish three topological graphs from traffic, statistical, and semantic perspectives to investigate the static and dynamic correlations. Then, a network-scale traffic volumes prediction model, i.e., spatiotemporal multigraph gated network (STMGG), is proposed to learn spatiotemporal correlations on visibility graphs and spatial topological graphs. This model designs an attention-based gated mechanism to incorporate global features from multigraphs. Additionally, a Seq2Seq structure is integrated to enhance multistep prediction stability. We employ two license plate recognition (LPR) datasets as case studies, and STMGG expresses superiorities over various advanced deep learning models. Meanwhile, an ablation experiment is conducted to evaluate its components, and numerical tests further reveal its impressive inductive learning capability.
A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance

This paper presents a comprehensive overview of current deep-learning methods for automatic object classification of underwater sonar data for shoreline surveillance, concentrating mostly on the classification of vessels from passive sonar data and the identification of objects of interest from active sonar (such as minelike objects, human figures or debris of wrecked ships). Not only is the contribution of this work to provide a systematic description of the state of the art of this field, but also to identify five main ingredients in its current development: the application of deep-learning methods using convolutional layers alone; deep-learning methods that apply biologically inspired feature-extraction filters as a preprocessing step; classification of data from frequency and time–frequency analysis; methods using machine learning to extract features from original signals; and transfer learning methods. This paper also describes some of the most important datasets cited in the literature and discusses data-augmentation techniques. The latter are used for coping with the scarcity of annotated sonar datasets from real maritime missions.
Untangling the Adverse Effect of SNS Stressors on Academic Performance and Its Impact on Students’ Social Media Discontinuation Intention: The Moderating Role of Guilt

In recent research on information systems (IS), the advantages of social networking sites (SNS) in the education field have received widespread attention. However, excessive use of SNSs will negatively impact students academic performance, leading to the discontinuation intentions of social media. Therefore, this study aims to investigate an extended research model drawing on the stress-strain-outcome (SSO) theory and transactional model of stress to examine the adverse effect of SNS stressors on academic performance. The required data from the target population was collected through a structured questionnaire. The valid sample size of this study was n = 505 respondents. The data were analyzed using structural equation modeling (SEM) in AMOS (version 23.0) to examine the study hypotheses. The results confirmed that the SNS stressors, namely, social overload, information overload, and system feature overload, induce strain in terms of poor academic performance by generating feelings of guilt. It also unravels the effect of guilt feelings on discontinuance intention indirectly. This study offers imperative theoretical and practical implications.
Long-Term Impact of Continental and Maritime Airflow on Aerosol Environment and Rain Microstructure Near Land–Sea Boundary

The present study deals with the interplay of multiple atmospheric processes in changing the microphysical properties of precipitation during the pre-monsoon season (March–May) using the long-term experimental data of raindrop size distributions (DSDs) spanning over 15 years (2005–2019) obtained at an urban tropical location, Kolkata (22.57°N, 88.37°E), near the land–sea boundary. The changing pattern of air mass flows from the maritime and continental region, which contribute to the formation of precipitation processes, has been responsible for the varying characteristics of rain. Changes in raindrop sizes are related to aerosol properties, cloud features, temperature, and relative humidity that change mass-weighted mean drop diameter (<inline-formula> <tex-math notation="LaTeX">$D_{m}$ </tex-math></inline-formula>) differently in low and high rain rate regimes. <inline-formula> <tex-math notation="LaTeX">$D_{m}$ </tex-math></inline-formula> has shown an increasing trend over time for low rain rates (< 15 mm/h), but a decreasing trend for high rain rate regimes (≥15 mm/h). An increase (decrease) in mean temperature (relative humidity) below the atmospheric boundary layer (< 1.6 km) has enhanced the evaporation of small raindrops and altered rain microphysical features. Based on satellite observations, it has been found that the increasing aerosol optical depth (AOD) has been accompanied by an increase in cloud effective radius (CER), resulting in the anti-Twomey effect, which is due to the dominance of maritime influence over continental activities. Because of the predominant maritime activities, sea salt aerosols have a greater presence, causing an increase in CER, which consequently prevents raindrops from becoming large enough before they fall, thereby reducing <inline-formula> <tex-math notation="LaTeX">$D_{m}$ </tex-math></inline-formula> at high rainfall rates.
Improvement of nano-polycrystalline diamond anvil cells with Zr-based bulk metallic glass cylinder for higher pressures: application to Laue-TOF diffractometer

ABSTRACT Single-crystal neutron diffraction provides direct information about crystal structures such as hydrogen positions and magnetic structures. However, in-situ experiments conducted under high pressure entail technical difficulties such as attenuation correction, masking of parasitic diffraction, and limitations of sample volumes and accessible directions. For this study, we improved diamond anvil cells with a tubular frame made of Zr-based bulk metallic glass and nano-polycrystalline diamond anvils for single-crystal neutron diffraction. The thicker tubular frame was confirmed through experimentation as stably generating 4.5 GPa. Its feasibility for neutron diffraction was assessed at the Laue-TOF diffractometer at the BL18 (SENJU) beamline in the MLF J-PARC using time-resolved two-dimensional detectors covering wide solid angles. In addition to ambient-pressure measurements of NH4Cl, diffraction patterns of a high-pressure phase of ice were also collected in-situ. The obtained intensities are of refinable quality sufficient for structure analysis.
Foundational Entrepreneurial Traits among Professional Students - A Study in Assam University

Entrepreneurial Orientation (EO) is concerned with innovativeness, pro-activeness and openness towards risk-bearing capacity of individuals. The article intends to establish a theoretical conception and practicable framework that are to be used to explore actuality of EO. The present paper is an attempt to explore the level of interest in entrepreneurship of professional students of Assam University, Silchar and draws comparison with students enrolled in the Bachelor, Master and Ph.D level. The paper is empirical in nature and based on primary data. Questionnaire method of data collection and appropriate statistical tools is used for analysis of data. This study has important implication for both researchers and practitioners. It highlights the necessity of firms to develop superior EO of all their members and also to invest on better resources and consequently superior capabilities as a way of reaching higher levels of growth.
Risk Factors for Perioperative Hidden Blood Loss After Intertrochanteric Fracture Surgery in Chinese Patients: A Meta-Analysis

Study Design A meta-analysis Introduction To perform a meta-analysis to explore the risk factors of perioperative hidden blood loss (HBL) in the treatment of intertrochanteric fracture for Chinese patients. Significance An extensive search of the literature was performed in the English databases of PubMed, Embase, and Cochrane Library and the Chinese databases of CNKI and WAN FANG (up to July 2021). We collected factors including demographic data and surgical factors. Data analysis was conducted with RevMan 5.3 and STATA 12.0. Results Finally, we collected 7 studies including 1377 patients in the final analysis. In our study, female patients (P <.00001), hypertension (P =.008), time from injury to operation (<2 days, P =.02), operation time (≥60 mins, P =.001), fracture type (A2.2 to A3.3, P <.00001), and without anticoagulation (P <.00001) were found to be associated with perioperative HBL in Chinese patients receiving the treatment of the intertrochanteric fracture. However, anesthesia, ASA classification, and body mass index were not associated with perioperative HBL. Conclusions In our study, female patients, hypertension, time from injury to operation (<2 days), operation time (≥60 mins), fracture type (A2.2 to A3.3), and without anticoagulation were related to increased risks of perioperative HBL in Chinese patients receiving the treatment for the intertrochanteric fracture. We hope this article can guide for surgeons to reduce perioperative HBL as far as possible in the treatment of intertrochanteric fracture.
Video Features, Engagement, and Patterns of Collective Attention Allocation: An Open Flow Network Perspective

Network analytics has the potential to examine new behaviour patterns that are often hidden by the complexity of online interactions. One of the varied network analytics approaches and methods, the model of collective attention, takes an ecological system perspective to exploring the dynamic process of participation patterns in online and flexible learning environments. This study selected “Fundamentals of C++ programming (Spring 2019)” on XuetangX as an example through which to observe the allocation patterns of attention within MOOC videos, as well as how video features and engagement correlate with the accumulation, circulation, and dissipation pattern of collective attention. The results showed that the types of instructions in videos predicted attention allocation patterns, but they did not predict the engagement of video watching. Instead, the length and whether the full screen was used in the videos had a strong impact on engagement. Learners were more likely to reach a high level of engagement in video watching when their attention had been circulated around the videos. The results imply that understanding the patterns and dynamics of attention flow and how learners engage with videos will allow us to design cost-effective learning resources to prevent learners from becoming overloaded.
66% CW wallplug efficiency from Al-free 0.98 /spl mu/m-emitting diode lasers

0.98 /spl mu/m-emitting, broad-waveguide-type InGaAs/InGaP/GaAs diode lasers have been optimised for maximum wallplug efficiency. Optimised-face-coated, 100 /spl mu/m-widestripe, 500 /spl mu/m-long devices having InGaAsP optical-waveguide thicknesses of 0.6 and 1.2 /spl mu/m provide maximum wallplug efficiencies of 66% and 62%, respectively. Carbon-doped cap layers allow for a series resistance of 0.08 /spl Omega/ for devices with 100/spl times/500 /spl mu/m/sup 2/ contact area.
Investigating the Asymmetric Effect of Oil Price on the Economic Growth in Malaysia: Applying Augmented ARDL and Nonlinear ARDL Techniques

This paper attempts to investigate if the effect of oil price on growth is asymmetrical for Malaysia, a small-open-dynamic oil-exporting country, over a period from 1981 to 2017. The empirical method employed in this study is the augmented autoregressive distributed lag model (ARDL) bound test approach and the recent innovative nonlinear autoregressive distributed lag (NARDL) model. Results suggest that neglecting nonlinearities can lead to misleading results. More precisely, the result reveals that adjustments in the price of oil influence Malaysia’s economic growth asymmetrically. An increase and decrease in the price of oil strengthen the economic growth of Malaysia, demonstrating Malaysia’s ability to be both an oil-producing country and a trading nation. These results strongly imply that Malaysia is able to take advantage of changes in the oil price efficiently.
Multisystem Inflammatory Syndrome of a Neonate From a COVID-19-Infected Mother: A Case Report

In neonates, the prevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 - COVID-19) is lower. There is the potential for vertical transmission of SARS-CoV-2. To date, only a few reports suggest this possibility. Neonates usually have mild symptoms, but some develop multisystem involvement, which is a concern. COVID-19 infections have been reported both in pregnant women and their neonates. However, the evidence of vertical or horizontal transmission modes has not been fully established. We recorded a case study where a 33-year-old mother was tested positive for COVID-19 infection by RT-PCR during her 27th week of gestation and needed ventilator support for her respiratory distress at that time for 11 days. Subsequently, she gave birth to a female baby at the 35th week via a lower uterine segment cesarean section. The neonate manifested a severe multisystem inflammatory syndrome associated with her possible COVID-19 infection. Sharing her uncommon clinical presentation, immunological syndrome, and disease outcome are noteworthy for similar unforeseen pediatric case management to help guide future investigations and care.
EMPIRICAL ANALYSIS OF AVAILABLE TRANSFER CAPABILITY DETERMINATION METHODS: A FUZZY STATISTICAL PERSPECTIVE

Available transfer capability (ATC) is a metric in power systems that determines the maximum amount of incremental power that can be transferred between two parts (usually seller and buyer) of the underlying power system without crossing any system thresholds. It is evaluated using 3 major metrics; i.e.; total transfer capability (TTC), transmission reliability margin (TRM) and existing transmission commitments. In order to determine these parameters, various methods are proposed by researchers, some of these methods are applicable to large scale grids, while others work effectively for small-to-medium grid deployments. In order to deploy a customized grid with effective ATC calculations, it is recommended that power system designers must select an optimum combination of these methods. For instance, Single Linear Step (SL) approach uses present state system information along with sensitivities embodied in the power transfer distribution factors (PTDF) and line outage distribution factors (LODF) in order to determine ATC values. The SL method is applicable only for small-scale power distribution systems, but can be extended via Iterated Linear Step (IL) method to incorporate controller changes for large scale systems. As system complexity increases, it becomes difficult to select the best method out of the given methods for ATC calculations. In order to reduce this complexity, the underlying text reviews different ATC calculation methods, and compares their statistical performance. Using this study, power system designers can identify most suited methods for their system deployment, and thus save time and cost for testing out different method. Index Terms – Available transfer capability, grid power, linear, iterated, distribution.
Multidimension Analysis of the Prognostic Value, Immune Regulatory Function, and ceRNA Network of LY6E in Individuals with Colorectal Cancer

Background Lymphocyte antigen 6 complex, locus E (LY6E) is abnormally expressed in several cancers and is associated with poor outcomes. However, the biological role of LY6E in colorectal cancer (CRC) remains largely unknown. Hence, we aimed to evaluate the expression levels, prognostic value, biological functions, and immune effects of LY6E via pan-cancer and CRC analyses using multiple databases. Methods We analyzed the expression pattern of LY6E in various cancers. The prognostic value of LY6E expression was identified using the Kaplan–Meier analysis and the Cox regression models. We used gene set enrichment analysis (GSEA) to identify the potential functions of LY6E. Correlations between the LY6E expression and various factors, including LY6E methylation level, copy number variation (CNV), microsatellite instability (MSI), and immune checkpoint genes, were also analyzed. The levels of LY6E expression and immune infiltration were analyzed using CIBERSORT. We constructed a regulatory network that was in compliance with the competing endogenous RNA (ceRNA) hypothesis. A ceRNA expression-based nomogram was established. Real-time PCR (qRT-PCR) was applied to validate the expression of LY6E-related ceRNA in CRC cell lines. Results LY6E is overexpressed in several tumor types, including CRC, and patients with high expression levels of LY6E have a poor prognosis. The Kaplan–Meier analysis and Cox regression analysis showed that LY6E could be considered a favorable prognostic factor in TCGA and GEO cohort. The results of GSEA showed that high LY6E expression levels were associated with immune-related pathways, such as those involved in antigen processing and presentation and the intestinal immune network for IgA production. Six methylation sites of LY6E that were associated with prognostic survival were screened. Moreover, the high levels of LY6E expression were correlated with copy number gain, microsatellite instability high, and immunotherapy response. The results of CIBERSORT analysis demonstrated that the LY6E expression levels were correlated with the infiltration of multiple immune cells, especially T cells. Then, we constructed a ceRNA network (LINC00963/miR-92a-3p/LY6E) and validated it using qRT-PCR. A predictive ceRNA-based nomogram was established and validated. Conclusion The oncogenic LY6E may serve as a promising marker for the diagnosis and treatment of CRC.