bluuebunny/crossref_metadata_2025 huggingface dataset:
Plant Nutrient Contents Rather Than Physical Traits Are Coordinated Between Leaves and Roots in a Desert Shrubland
Authors: Jiang Xiaoyan , Jia Xin , Gao Shengjie , Jiang Yan , Wei Ningning , Han Cong , Zha Tianshan , Liu Peng , Tian Yun , Qin Shugao
Published: October 2021

Although leaf economics spectrum (LES) has been extensively tested with regional and global datasets, the correlation among functional traits of desert plants remains largely unclear. Moreover, examinations on whether and how leaf and root traits are coordinated have yielded mixed results. We investigated variations in leaf and fine-root traits across 48 species in a desert community of northern China to test the hypotheses that (1) the leaf-trait syndrome of plant species in desert shrublands follows the predictions of the global LES, and is paralleled by a similar root-trait syndrome, (2) functional traits related to nutrient contents and resource uptake are tightly coordinated between leaves and fine roots in desert ecosystems where plant growth is limited primarily by dry and nutrient-poor conditions, and (3) traits as well as their relationships vary among functional groups. Our results partially supported the LES theory. Specific leaf area (SLA) was correlated with leaf tissue density, phosphorus content, and carbon-to-nitrogen ratio, but not with leaf nitrogen content. Specific root length (SRL) was not correlated with other fine-root traits, and fine-root traits were largely independent of each other. Therefore, fine-root traits did not mirror the leaf-trait syndrome. Fine-root nitrogen and phosphorus contents, nitrogen-to-phosphorous ratio, and carbon-to-nitrogen ratio all increased with analogous leaf traits, whereas SRL was not correlated with SLA. After phylogenetic effects were considered, nutrient contents and their ratios still displayed stronger coordination between leaves and fine roots than did SRL and SLA. The overall pattern of trait variations and relationships suggested differentiation among functional groups. Our results suggest that despite the absence of a root-trait syndrome, fine-root functions in the studied desert community were probably coordinated with leaf functions with respect to nutrient allocation and use.
Integrated Transcriptomic and Bioinformatics Analyses Reveal the Molecular Mechanisms for the Differences in Seed Oil and Starch Content Between Glycine max and Cicer arietinum
Authors: Cheng Kun , Pan Yi-Fan , Liu Lü-Meng , Zhang Han-Qing , Zhang Yuan-Ming
Published: October 2021

The seed oil and starch content of soybean are significantly different from that of chickpea. However, there are limited studies on its molecular mechanisms. To address this issue, we conducted integrated transcriptomic and bioinformatics analyses for species-specific genes and acyl-lipid-, starch-, and carbon metabolism-related genes. Among seven expressional patterns of soybean-specific genes, four were highly expressed at the middle- and late oil accumulation stages; these genes significantly enriched fatty acid synthesis and carbon metabolism, and along with common acetyl CoA carboxylase (ACCase) highly expressed at soybean middle seed development stage, common starch-degrading enzyme beta-amylase-5 (BAM5) was highly expressed at soybean early seed development stage and oil synthesis-related genes ACCase, KAS, KAR, ACP, and long-chain acyl-CoA synthetase (LACS) were co-expressed with WRI1, which may result in high seed oil content and low seed starch content in soybean. The common ADP-glucose pyrophosphorylase (AGPase) was highly expressed at chickpea middle seed development stage, along with more starch biosynthesis genes co-expressed with four-transcription-factor homologous genes in chickpea than in soybean, and the common WRI1 was not co-expressed with oil synthesis genes in chickpea, which may result in high seed starch content and low seed oil content in chickpea. The above results may be used to improve chickpea seed oil content in two ways. One is to edit CaWRI1 to co-express with oil synthesis-related genes, which may increase carbon metabolites flowing to oil synthesis, and another is to increase the expression levels of miRNA159 and miRNA319 to inhibit the expression of MYB33, which may downregulate starch synthesis-related genes, making more carbon metabolites flow into oil synthesis. Our study will provide a basis for future breeding efforts to increase the oil content of chickpea seeds.
The Intentional Selection Assumption
Authors: Colantonio Joseph , Durkin Kelley , Caglar Leyla Roksan , Shafto Patrick , Bonawitz Elizabeth
Published: October 2021

There exists a rich literature describing how social context influences decision making. Here, we propose a novel framing of social influences, the Intentional Selection Assumption. This framework proposes that, when a person is presented with a set of options by another social agent, people may treat the set of options as intentionally selected, reflecting the chooser's inferences about the presenter and the presenter's goals. To describe our proposal, we draw analogies to the cognition literature on sampling inferences within concept learning. This is done to highlight how the Intentional Selection Assumption accounts for both normative (e.g., comparing perceived utilities) and subjective (e.g., consideration of context relevance) principles in decision making, while also highlighting how analogous findings in the concept learning literature can aid in bridging these principles by drawing attention to the importance of potential sampling assumptions within decision making paradigms. We present the two behavioral experiments that provide support to this proposal and find that social-contextual cues influence choice behavior with respect to the induction of sampling assumptions. We then discuss a theoretical framework of the Intentional Selection Assumption alongside the possibility of its potential relationships to contemporary models of choice. Overall, our results emphasize the flexibility of decision makers with respect to social-contextual factors without sacrificing systematicity regarding the preference for specific options with a higher value or utility.
Analysis of Real-World Implementation of the Biopsychosocial Approach to Healthcare: Evidence From a Combination of Qualitative and Quantitative Methods
Authors: Xiao Xiaohua , Song Haidong , Sang Tian , Wu Zhihua , Xie Ying , Yang Qian
Published: October 2021

Aims: The modern medical model has been transformed into a biopsychosocial model. The integration of the biopsychosocial approach in healthcare can help improve the effectiveness of diagnosis and treatment. This study explored the actual application of the biopsychosocial approach in healthcare and provides a basis for targeted interventions to promote the biopsychosocial approach in healthcare.

Methods: Study 1 involved one-on-one interviews with 30 medical staff and focus group interviews with 16 recent patients. Study 2 was a cross-sectional survey of 13,105 medical staff in Hangzhou, China that analyzed the status quo implementation of the biopsychosocial approach in healthcare.

Results: Study 1 found that medical staff did not welcome patients to report information unrelated to their disease, hoping patients did not express their emotions. In the treatment process, patients believed that medical staff refused to attend to or did not encourage reporting of any information other than the disease, and that patients should have reasonable expectations for medical staff. Study 2 found that medical staff had a 37.5% probability of actively paying attention to the patient's psychosocial status. Female medical staff (38.5%) were actively concerned about the patient's psychosocial status significantly more than male medical staff (34.2%) (P < 0.01). The medical staff in the psychiatric department (58.4%) paid more active attention to the patient's psychosocial status than staff in the non-psychiatric departments (37.2%). Gender, department, hospital level, and professional title were the factors associated with the medical staff's attention to the patient's psychosocial status (P < 0.05). The influence of age on the probability of medical staff actively paying attention to the psychosocial status of patients increased with the number of years of employment. Participants that were 31–40 years old, had an intermediate professional title, and 11–15 years of employment were the least likely to actively pay attention to patients' psychosocial status.

Conclusion: Although the biopsychosocial approach has been popularized for many years, it has not been widely used in medical care. Medical staff pay more attention to patients' physical symptoms and less attention to patients' psychosocial status. It is recommended that training will be provided to medical personnel on implementing a biopsychosocial approach with particular attention to the sociodemographic characteristics of medical personnel. Additionally, we propose helping patients set reasonable expectations, and formulating guidelines for implementing the biopsychosocial approach.
The Unintended Consequence of Novel Coronavirus (COVID-19) Pandemic on Racial Inequities Associated With Adverse Childhood Experiences (ACEs): Findings From a Population-Based Study
Authors: Srivastav Aditi , Richard Chelsea L. , McRell Amanda S. , Strompolis Melissa
Published: October 2021

A rising concern is the COVID-19 pandemic effect on adverse childhood experiences (ACEs) due to increased parental stress and social/physical isolation. These pandemic effects are likely to be higher in already marginalized communities. The objective of this ecological study was to examine the relationship between COVID-19 cases and deaths, race/ethnicity, and the estimated number of adults with ACEs using data from South Carolina (SC). COVID-19 reported cases and death data were obtained from the SC Department of Health and Environmental Control. ACE data was used from the 2014–2016 SC Behavioral Risk Factor Surveillance System. Census data were used to obtain county population data. To measure the relationship between these variables, the Spearman rank-order correlation test was used because the data distribution was non-normal. There was a moderate relationship between the estimated number of adults with one or more ACEs and deaths (ρ = 0.89) and race/ethnicity-specific COVID-19 case counts by county (Black: ρ = 0.76; =White: ρ = 0.96; Hispanic: ρ = 0.89). Further, the Spearman correlation test showed the strongest relationship between COVID-19 deaths and race-ethnicity-specific county populations was with the Black adult population (ρ = 0.90). Given the known link between existing health inequities and exposure to COVID-19, these results demonstrate that the current pandemic could have unintended consequences on the well-being of children and caregivers. Response efforts should consider promoting protective factors for children and families and advocating for equitable policies and systems that serve children.
ICF-Based Assessment of Functioning in Daily Clinical Practice. A Promising Direction Toward Patient-Centred Care in Patients With Low Back Pain
Authors: Ibsen Charlotte , Maribo Thomas , Nielsen Claus Vinther , Hørder Mogens , Schiøttz-Christensen Berit
Published: October 2021

Background: Patient-centred care has received increased attention in recent years. Patient-Reported Outcomes (PROs) and shared decision-making are key components of Patient-Centred care. Low back pain (LBP) is a complex symptom affected by multiple, interacting factors. Therefore, evidence strongly recommend a biopsychosocial and patient-centred approach in the assessment and management. The International Classification of Functioning, Disability and Health (ICF) provide a biopsychosocial model for describing functioning and disability. ICF is widely acknowledged, but implementation into clinical practice is lacking. To support the use of a biopsychosocial and patient-centred approach in daily clinical practice among patients with LBP we developed a practice-friendly tool based on ICF; the LBP assessment tool.

Objective: To compare an ICF-based assessment facilitated by the LBP assessment tool with standard care in terms of the use of PROs and shared decision-making in order to promote patient-centred care in patients with LBP.

Methods: A non-randomized controlled design was used. Eligible patients were allocated to one of two groups: the ICF group, assessed with the LBP assessment tool or the control group, assessed with a conventional LBP assessment. Primary outcome includes use of PROs. Secondary outcomes include use of a graphical overview displaying the patient profile and shared decision-making. A patient evaluation questionnaire was used to collect data.

Results: Seven hundred ten patients were assessed for eligibility of whom 531 were allocated to the ICF group (n = 299) or the control group (n = 232). A significantly higher use of PRO data (p < 0.00) and the patient profile (p < 0.00) was reported in favor of the ICF group. Patients in the ICF group also experienced being more involved in decision-making (p = 0.01).

Conclusions: This study showed that a functioning assessment, by means of the LBP assessment tool, increased use of PROs and shared decision-making when compared to a conventional LBP assessment. Additionally, this study demonstrated that routine use of ICF-based PRO data and shared decision-making promoted patient-centred care in patients with LBP. The LBP assessment tool may be a strong candidate for a user-friendly ICF-based tool with the potential to support health professionals in a shift toward a biopsychosocial and patient-centred approach to patients with LBP.
Predictive Value of the Serum Albumin Level on Admission in Patients With Spontaneous Subarachnoid Hemorrhage
Authors: Shang Feng , Zhao Hao , Cheng Weitao , Qi Meng , Wang Ning , Qu Xin
Published: October 2021

Objective: To determine the effect of the serum albumin level on admission in patients with spontaneous subarachnoid hemorrhage (SAH).

Methods: A total of 229 patients with SAH were divided into control and hypoalbuminemia groups. The serum albumin levels were measured. The data, including age, gender, co-existing medical conditions, risk factors, Hunt-Hess (H-H) grade on admission, Glasgow coma score (GCS) on admission, complications during hospitalizations, length of hospital stay, length of intensive care unit (ICU) stay, in-hospital mortality, survival rate, outcome at discharge, and the 6-month follow-up outcome, were compared between the two groups.

Results: Older age, an increased number of patients who consumed an excess of alcohol, and a lower GCS on admission were findings in the hypoalbuminemia group compared to the control group (p < 0.001). The ratio of patients with H-H grade I on admission in the hypoalbuminemia group was decreased compared to the control group (p < 0.05). Patients with hypoalbuminemia were more likely to be intubated, and have pneumonia and cerebral vasospasm than patients with a normal albumin level on admission (p < 0.001). Furthermore, the length of hospital and ICU stays were longer in the hypoalbuminemia group than the control group (p < 0.001). Hypoalbuminemia on admission significantly increased poor outcomes at discharge (p < 0.001). The number of patients with severe disability was increased and the recovery rate was decreased with respect to in-hospital outcomes in the hypoalbuminemia group than the control group (p < 0.001).

Conclusion: Hypoalbuminemia was shown to be associated with a poor prognosis in patients with SAH.
Efficacy of Nx4 to Reduce Plasma Cortisol and Gastrin Levels in Norwegian Sled Dogs During an Exercise Induced Stress Response: A Prospective, Randomized, Double Blinded, Placebo-Controlled Cohort Study
Authors: Keller Alexandra , Conradi Jana , Weber Corinna , Failing Klaus , Wergin Melanie
Published: October 2021

Introduction:An exercise induced stress response is commonly seen in high performance sled dogs, resulting in increased plasma cortisol. A stress induced rise of cortisol might result in increased prevalence of gastritis and gastric ulcers mediated by an increase of gastrin. Neurexan®(Nx4) is a medicinal product used for stress relief by reduction of cortisol. The aim of the study was to show that Nx4 reduces plasma cortisol and plasma gastrin in high performance sled dogs and to show tolerability of Nx4 in dogs.

Material and Methods:First, a pilot study was done to validate the increase of cortisol by performance. The data from the pilot study was used for sample size estimation via an adapted power analysis as well as the identification of important variables. These were then used in the randomization procedure of the main study. Second, a prospective randomized, double blinded, placebo-controlled cohort study was conducted. The main study included 45 sled dogs, assigning 23 dogs to the Nx4 group, and 22 dogs to the placebo group, to analyze plasma cortisol and plasma gastrin at four time points: before, directly after and 30 and 120 min after performance.

Results:For the main target variable, area under the curve (AUC) of plasma cortisol, a significantly lower adjusted mean value in the Nx4 group compared to the placebo group (p= 0.031) was found. Plasma gastrin was also significantly reduced in the Nx4 group 30 min after performance (p= 0.023), resulting in a significantly reduced plasma gastrin AUC in the Nx4 group compared to the placebo group (p= 0.049).

Discussion:Within the limitation of the study, the results carry implications for the usefulness of Nx4 to reduce exercise induced plasma cortisol and gastrin levels. The reduction of the exercise induced stress response could help to improve the welfare of high-performance sled dogs. Since activation of the hypothalamic-pituitary-adrenal axis resulting in increased cortisol is similar for exercise induced stress and psychologic stress, the same might be true independent of the stressor, making Nx4 potentially useful in any stressful situation for dogs.
Study of Forage Quality of Grasslands on the Southern Margin of the Pannonian Basin
Authors: Čarni Andraž , Ćuk Mirjana , Krstonošić Daniel , Škvorc Željko
Published: October 2021

Since grasslands provide many ecosystem services, there are often different opinions on their management (e.g., agronomy, ecology, botany). Multidisciplinary research on this topic is therefore needed. This article focuses on the impact of ecological conditions, functional groups, ecological strategies, floristic composition (through habitat preference of species), major floristic gradients (presented as first two NMDS axes), and the management on forage quality. We estimated the forage quality using indicator values. All of the available vegetation plots in the region on wet and mesic meadows, managed pastures, and tall-herb meadow/pasture fringes on deeper or shallower soils (i.e., grasslands) were collected, organized in a database, and elaborated according to standard procedure. We used a widely accepted grassland classification system that uses floristic composition to define grassland types. Based on an NMDS ordination diagram and according to functional groups, ecological strategies, and habitat preferences (behavior of species) and management, we defined three major groups: mesic meadows (mowed), wet meadows (mowed), and pastures (grazed). We correlated all groups’ functional groups, ecological strategies, habitat preferences (behavior of species), major floristic gradients, management, and forage quality. We found that forage quality mainly depends on moisture conditions and that nutrients and grazing are less important. Within the grasslands under consideration, mesic meadows and mesic pastures have the highest forage quality.
Organic Amendments Alter Long-Term Turnover and Stability of Soil Carbon: Perspectives from a Data-Model Integration
Authors: Wang Guocheng , Luo Zhongkui
Published: October 2021

Organic amendment (OA) additions may profoundly regulate the turnover behaviours of soil organic carbon (SOC). Explicit understanding of such role of OA is crucial for accurately assessing the potential of carbon sequestration in agricultural soils. To explore the effects of OA additions on the detailed SOC stabilization and destabilization processes, we collected SOC measurements from 29 trials with experimental duration ranging from 14 to 85 years across the globe. Using these datasets, we constrained a soil carbon model to analyse SOC turnover and built-up processes as impacted by OA additions. We found that OA generally decreases microbial carbon use efficiency (CUE) and the fraction of inert SOC that is resistant to decomposition (finert), but has divergent effects on the decay rate of humic SOC (khum). Across the sites, there was great variability in the effects of OA on CUE, khum, and finert, which can be largely explained by local soil and climate conditions and the quantity and quality of OA. Long-term simulations suggested that, without considering the effects of OA on CUE, khum, and finert, the effectiveness of OA additions for carbon sequestration could be largely overestimated. Our results suggest that the strong site-specific regulations of OA on SOC dynamics as demonstrated in this study must be properly considered and better constrained by observational data when assessing SOC sequestration in agricultural soils under the management of OA additions.
Ginkgo Biloba L. Residues Partially Replacing Alfalfa Hay Pellet in Pelleted Total Mixed Ration on Growth Performance, Serum Biochemical Parameters, Rumen Fermentation, Immune Function and Meat Quality in Finishing Haimen White Goats
Authors: Chen Yinyin , Gong Xiaoxiao , Yang Tianyu , Jiang Maocheng , Wang Lin , Zhan Kang , Lin Miao , Zhao Guoqi
Published: October 2021

Sixty castrated male Haimen white growing goats with an initial age of 100 days old and similar body weight (16 ± 1.5 kg) were selected and randomly allocated into five groups with three replicates in each group with four goats in each pen (5 m × 3.2 m). Goats in the control group (CG) were fed a basal pelleted total mixed ration supplemented with 30% alfalfa hay pellet, while experimental treatments (G6, G12, G18, or G24) were supplemented with four levels (6%, 12%, 18%, or 24%) of GBLR replacing alfalfa hay pellet in the diet, separately. Results showed that (1) the final body weight, average daily gain, and average feed intake of G18 was significantly higher (p < 0.05) than CG; in contrast, the feed conversion ratio of G18 was significantly lower than CG and G12 (p < 0.05); the feed cost per head per day of CG was significantly higher (p < 0.05) than that of G18 and G24, and exhibited obvious linearly decrease (p = 0.04) with increasing GBLR supplementation; and apparent total-tract digestibility of DM and NDF in GBLR treatments were significantly higher (p < 0.05) than CG; (2) alanine transaminase (ALT) concentration in the G18 group was significantly lower (p < 0.05) than those in the control group; aspartate transaminase (AST) concentration in the G24 was significantly lower (p < 0.05) than those in the control group, and an increase in dietary level of GBLR tended to result in a linear decrease (p = 0.09) in the concentration of serum AST; (3) the concentration of malondialdehyde (MDA) demonstrated a tendency to decrease (p = 0.06) linearly with increasing GBLR supplementation; however, glutathione peroxidase (GSH-PX) activity in G12 was significantly higher (p < 0.05) than CG, G6, and G24; in addition, superoxide dismutase (SOD) activity in G18 was significantly higher (p < 0.05) than CG and G6; concentration of immunoglobulin M (IgM), immunoglobulin G (IgG), and immunoglobulin A (IgA) were not affected by GBLR, but increasing dietary GBLR showed a tendency (p = 0.08) to linearly increase the IgG concentration; the content of interleukin 4 (IL4) was significantly higher (p < 0.05) in G12, G18, and G24 than that in CG and G6; (4) There were similar NH3-N, pH, TVFA, and butyrate for goats fed different levels of GBLR supplementation; the C2 (p = 0.07) and acetate: propionate (p = 0.06) demonstrated a tendency to increase linearly with increasing level of GBLR supplementation, separately; however, it was observed that concentration of propionate showed a tendency to decrease (p = 0.08) linearly in response to GBLR supplementation; and (5) Increasing dietary GBLR tended to linearly enhance the lightness (L*) (p = 0.07) and yellowness (b*) (p = 0.09) values of longissimus dorsi muscles; the redness (a*) value in G18 was significantly higher than that in CG (p < 0.05).
Illuminant Estimation Using Adaptive Neuro-Fuzzy Inference System
Authors: Luo Yunhui , Wang Xingguang , Wang Qing , Chen Yehong
Published: October 2021

Computational color constancy (CCC) is a fundamental prerequisite for many computer vision tasks. The key of CCC is to estimate illuminant color so that the image of a scene under varying illumination can be normalized to an image under the canonical illumination. As a type of solution, combination algorithms generally try to reach better illuminant estimation by weighting other unitary algorithms for a given image. However, due to the diversity of image features, applying the same weighting combination strategy to different images might result in unsound illuminant estimation. To address this problem, this study provides an effective option. A two-step strategy is first employed to cluster the training images, then for each cluster, ANFIS (adaptive neuro-network fuzzy inference system) models are effectively trained to map image features to illuminant color. While giving a test image, the fuzzy weights measuring what degrees the image belonging to each cluster are calculated, thus a reliable illuminant estimation will be obtained by weighting all ANFIS predictions. The proposed method allows illuminant estimation to be dynamic combinations of initial illumination estimates from some unitary algorithms, relying on the powerful learning and reasoning capabilities of ANFIS. Extensive experiments on typical benchmark datasets demonstrate the effectiveness of the proposed approach. In addition, although there is an initial observation that some learning-based methods outperform even the most carefully designed and tested combinations of statistical and fuzzy inference systems, the proposed method is good practice for illuminant estimation considering fuzzy inference eases to implement in imaging signal processors with if-then rules and low computation efforts.
Toward a Connected System—Understanding the Contribution of Light from Different Sources on Occupants’ Circadian Rhythms
Authors: Hu Wenye , Davis Wendy
Published: October 2021

Light that enters humans’ eyes and impacts circadian rhythms may come from various sources, including the sun, electric lighting systems, and self-luminous displays. Occupants’ activities strongly impact the light entering their eyes, which is difficult to predict and not yet well understood. This study investigated the circadian contributions of light from different sources in real building environments to better understand the variables that influence the circadian health of occupants. Spectral irradiance distributions at a position equivalent to the front of an eye of a seated occupant in various interior office spaces were collected. Daylight and electric light were measured separately, and light emitted from displays was measured when a variety of different computer tasks was performed. Circadian stimulus (CS) and α-opic irradiance, defined by CIE DIS026/E:2018, were further calculated, and the circadian effects of light from different sources were compared. The results show that daylight has the greatest circadian effect, while electric light in spaces that were predominantly designed with conventional downward lighting has a very limited impact. The circadian effect of light from screens was considerably high. The outcomes suggest that, to optimise the circadian effects of light, connected lighting systems are needed to control light from different sources.
Dynamic Stall Characteristics of the Bionic Airfoil with Different Waviness Ratios
Authors: Wu Liming , Liu Xiaomin
Published: October 2021

A dynamic stall will cause dramatic changes in the aerodynamic performance of the blade, resulting in a sharp increase in the blade vibration load. The bionic leading-edge airfoil with different waviness ratios, inspired by the humpback whales flipper, is adopted to solve this problem. In this study, based on the NACA0015 airfoil, the three-dimensional unsteady numerical simulation and sliding mesh technique are used to reveal the flow control mechanism on the dynamic stall of the bionic wavy leading edge. The effects of the waviness ratio on the dynamic stall characteristics of the airfoil are also investigated. The results show that the peak drag coefficient is dramatically reduced when a sinusoidal leading edge is applied to the airfoil. Although the peak lift coefficient is also reduced, the reduction is much smaller. When the waviness ratio R is 0.8, the peak drag coefficient of the airfoil is reduced by 17.14% and the peak lift coefficient of the airfoil is reduced by 9.20%. The dynamic hysteresis effect is improved gradually with an increasing waviness ratio. For the bionic airfoil with R = 1.0, the area of the hysteresis loop is the smallest.
Cloud-Based Analytics Module for Predictive Maintenance of the Textile Manufacturing Process
Authors: Chang Ray-I , Lee Chia-Yun , Hung Yu-Hsin
Published: October 2021

Industry 4.0 has remarkably transformed many industries. Supervisory control and data acquisition (SCADA) architecture is important to enable an intelligent and connected manufacturing factory. SCADA is extensively used in many Internet of Things (IoT) applications, including data analytics and data visualization. Product quality management is important across most manufacturing industries. In this study, we extensively used SCADA to develop a cloud-based analytics module for production quality predictive maintenance (PdM) in Industry 4.0, thus targeting textile manufacturing processes. The proposed module incorporates a complete knowledge discovery in database process. Machine learning algorithms were employed to analyze preprocessed data and provide predictive suggestions for production quality management. Equipment data were analyzed using the proposed system with an average mean-squared error of ~0.0005. The trained module was implemented as an application programming interface for use in IoT applications and third-party systems. This study provides a basis for improving production quality by predicting optimized equipment settings in manufacturing processes in the textile industry.
The Accurate Measurement of Students’ Learning in E-Learning Environments
Authors: Lee Sunbok , Choi Youn-Jeng , Kim Hyun-Song
Published: October 2021

The ultimate goal of E-learning environments is to improve students’ learning. To achieve that goal, it is crucial to accurately measure students’ learning. In the field of educational measurement, it is well known that the key issue in the measurement of learning is to place test scores on a common metric. Despite the crucial role of a common metric in the measurement of learning, however, less attention has been paid to this important issue in E-learning studies. In this study, we propose to use fixed-parameter calibration (FPC) in an item response theory (IRT) framework to set up a common metric in E-learning environments. To demonstrate FPC, we used the data from the MOOC “Introduction to Psychology as a Science” offered through Coursera collaboratively by Georgia Institute of Technology (GIT) and Carnegie Mellon University (CMU) in 2013. Our analysis showed that the students’ learning gains were substantially different with and without FPC.
Reliable Bidirectional Data Transfer Approach for the Internet of Secured Medical Things Using ZigBee Wireless Network
Authors: Rehman Amjad , Haseeb Khalid , Fati Suliman Mohamed , Lloret Jaime , Peñalver Lourdes
Published: October 2021

Nowadays, the Internet of Things (IoT) performs robust services for real-time applications in monitoring communication systems and generating meaningful information. The ZigBee devices offer low latency and manageable costs for wireless communication and support the process of physical data collection. Some biosensing systems comprise IoT-based ZigBee devices to monitor patient healthcare attributes and alert healthcare professionals for needed action. However, most of them still face unstable and frequent data interruption issues due to transmission service intrusions. Moreover, the medical data is publicly available using cloud services, and communicated through the smart devices to specialists for evaluation and disease diagnosis. Therefore, the applicable security analysis is another key factor for any medical system. This work proposed an approach for reliable network supervision with the internet of secured medical things using ZigBee networks for a smart healthcare system (RNM-SC). It aims to improve data systems with manageable congestion through load-balanced devices. Moreover, it also increases security performance in the presence of anomalies and offers data routing using the bidirectional heuristics technique. In addition, it deals with more realistic algorithm to associate only authorized devices and avoid the chances of compromising data. In the end, the communication between cloud and network applications is also protected from hostile actions, and only certified end-users can access the data. The proposed approach was tested and analyzed in Network Simulator (NS-3), and, compared to existing solutions, demonstrated significant and reliable performance improvements in terms of network throughput by 12%, energy consumption by 17%, packet drop ratio by 37%, end-to-end delay by 18%, routing complexity by 37%, and tampered packets by 37%.
Experimental Study on Lateral-Load-Resisting Capacity of Masonry-Infilled Reinforced Concrete Frames
Authors: Kim Minjae , Yu Eunjong
Published: October 2021

In this study, an experimental program was performed on masonry-infilled frame specimens with varied construction precision and masonry thickness. A total of five portal frame specimens, which consist of four masonry-infilled frames and a bare frame, were tested, and the results were analyzed to investigate the effects of construction precision and interaction between the masonry infill and the frame. The test results indicated that the gap in the masonry infill decreased strength by 75% to 80% and stiffness by 55% to 70%. A comparison between the measured and predicted peak strength using the current code shows that the code equations underestimate the strength by up to 70%. This is due to the fact that the contribution of friction in shear resistance of the masonry wall is actually ignored in the current code since no adequate method for estimating the normal force is provided. In addition, reflecting the observation that the failure mode of the columns changed to shear failure when thick masonry walls were used, a mechanical model that can explain the shear failure and enables the estimation of maximum strength was proposed. The maximum strengths of the specimens calculated using the proposed model were in good agreement with the experimental results.
Compact Single-Frequency MOPA Using a Silica Fiber Highly Doped with Yb3+
Authors: Balliu Enkeleda , Engholm Magnus , Digonnet Michel , Coetzee Riaan , Elgcrona Gunnar , Nilsson Hans-Erik
Published: October 2021

We report on a single-frequency fiber master oscillator power amplifier utilizing a polarization-maintaining step-index fiber with an Al/Ce/F core-glass composition doped with a very high Yb concentration (0.25 at.%). This design made it possible to use a very short fiber (~1 m) and to coil it in a tight radius (4 cm in the amplifier, while 2 cm gave similarly negligible bending loss) so that the packaged system is one of the most compact reported to date (~0.6 L). The use of a short fiber increased the threshold for stimulated Brillouin scattering well above 100 W while maintaining near-ideal beam quality. The fiber was pumped with a diode-pumped solid-state laser and cooled passively by spooling it on a grooved aluminum mandrel. The amplifier produced a strongly linearly polarized output at 1064 nm in the fundamental mode (M2 ≤ 1.2) with a 150 kHz linewidth and a power of 81.5 W for 107 W of launched pump power. No deleterious effects from the elevated thermal load were observed. The residual photodarkening loss resulting from the high Yb concentration, found to be small (~0.7 dB/m inferred at 1064 nm) with accelerated aging, reduced the output power by only ~20% after 150 h of operation.
Exploring the Benefits of Photovoltaic Non-Optimal Orientations in Buildings
Authors: Sánchez Esteban , Ordóñez Ángel , Sánchez Alberto , García Ovejero Raúl , Parra-Domínguez Javier
Published: October 2021

As solar photovoltaics in buildings reaches maturity, grid integration and economic yield are topics of greater interest. The traditional design of photovoltaic installations has considered the optimal orientation of photovoltaic modules to be that which yields the maximum annual energy production. The influence of the consumption patterns and the hourly variable electricity prices implies that this traditional optimal design might not be the most profitable. Using a full-year dataset for a residential installation, alternative installations using canopies and modules attached to the façades are simulated. By evaluating the energy balances for different annual consumptions, it is found that the canopy and façade installations offer better self-consumption of the PV produced energy, reflected in a 9% higher self-consumption degree using modules on façades and a 5% higher self-consumption degree using canopies. The economic evaluation under the new electricity tariffs in Spain shows a better profit for PV self-consumption, reducing the time of return on investment by more than 2 years. The analysis of different alternatives for an industrial PV allowed us to identify several benefits for these orientations which are confirmed after a full year of operation, such as an increase of up to 59% in annual energy production over the optimal-producing orientation.
Semantic IFC Data Model for Automatic Safety Risk Identification in Deep Excavation Projects
Authors: Zhang Yongcheng , Xing Xuejiao , Antwi-Afari Maxwell Fordjour
Published: October 2021

Safety risk identification throughout deep excavation construction is an information-intensive task, involving construction information scattered in project planning documentation and dynamic information obtained from different field sensors. However, inefficient information integration and exchange have been an important obstacle to the development of automatic safety risk identification in actual applications. This research aims to achieve the requirements for information integration and exchange by developing a semantic industry foundation classes (IFC) data model based on a central database of Building Information Modeling (BIM) in dynamic deep excavation process. Construction information required for risk identification in dynamic deep excavation is analyzed. The relationships among construction information are identified based on the semantic IFC data model, involved relationships (i.e., logical relationships and constraints among risk events, risk factors, construction parameters, and construction phases), and BIM elements. Furthermore, an automatic safety risk identification approach is presented based on the semantic data model, and it is tested through a construction risk identification prototype established under the BIM environment. Results illustrate the effectiveness of the BIM-based central database in accelerating automatic safety risk identification by linking BIM elements and required construction information corresponding to the dynamic construction process.
HCL Control Strategy for an Adaptive Roadway Lighting Distribution
Authors: Liu Chun-Hsi , Hsiao Chun-Yu , Gu Jyh-Cherng , Liu Kuan-Yi , Yan Shu-Fen , Chiu Chien Hua , Ho Min Che
Published: October 2021

This study aims to develop a human-centric, intelligent lighting control system using adaptive LED lights in roadway lighting, integrated with an imaging luminance meter that uses an IoT sensor driver to detect the brightness of road surfaces. AI image data are collected for luminance and vehicle conditions analyses to adjust the output of the photometric curve. Type-A lenses are designed for R3 dry roads, while Type-B lenses are designed for W1 wet roads, to solve hazards caused by slippery roads, for optimizing safety and for visual clarity for road users. Data are collected for establishing formulae to optimize road lighting. First, the research uses zonal flux analysis to design secondary optical components of LED roadway lighting. Based on the distribution of LED lights and the target photometric curve, the freeform surface calculation model and formula are established, and control points of each curved surface are calculated using an iterative method. The reflection coefficient of a roadway is used to design optical lenses that take into account the illuminance and luminance uniformity to produce photometric curves accordingly. This system monitors roadway luminance in real time, which simulates drivers’ visual experiences and uses the ZigBee protocol to transmit control commands. This optimizes the output of light according to weather and produces quality roadway lighting, providing a safer driving environment.
Effect of Nano Hydrotalcite on the Aging Resistance of a High Binder Content Stone Mastic Asphalt
Authors: Crucho João , Neves José
Published: October 2021

Hydrotalcite, a type of layered double hydroxide (LDH), reveals an interesting potential for the modification of bitumen. The LDH can induce a barrier effect that prevents the loss of volatiles, retards oxidation, and protects against ultraviolet radiation. Such properties can enhance the aging resistance of the bitumen. However, there is a gap in knowledge regarding the effects of the modification with hydrotalcite in the properties of the asphalt mixture. To contribute to fill such a gap, the current study presents a characterization of the effects of the modification with nano hydrotalcite in the surface characteristics, mechanical performance, and aging resistance of an asphalt mixture. To better explore the effects of the modification, the selected asphalt mixture was a high binder content (7.5%) Stone Mastic Asphalt (SMA). The experimental study indicates that the binder-rich SMA presented adequate performance for application in surface courses. If compared to conventional mixtures, the binder-rich SMA presented better initial mechanical performance (unaged conditions). Furthermore, it presented smaller variation in the parameters between unaged and aged conditions, indicating enhanced aging resistance. The modification with nano hydrotalcite induced smaller evolution in the fatigue resistance parameters, indicating enhanced aging resistance; however, in the remaining tests, the trends were not clear.
A Multi-Layer Security Scheme for Mitigating Smart Grid Vulnerability against Faults and Cyber-Attacks
Authors: Chen Jian , Mohamed Mohamed A. , Dampage Udaya , Rezaei Mostafa , Salmen Saleh H. , Obaid Sami Al , Annuk Andres
Published: October 2021

To comply with electric power grid automation strategies, new cyber-security protocols and protection are required. What we now experience is a new type of protection against new disturbances namely cyber-attacks. In the same vein, the impact of disturbances arising from faults or cyber-attacks should be surveyed by network vulnerability criteria alone. It is clear that the diagnosis of vulnerable points protects the power grid against disturbances that would inhibit outages such as blackouts. So, the first step is determining the network vulnerable points, and then proposing a support method to deal with these outages. This research proposes a comprehensive approach to deal with outages by determining network vulnerable points due to physical faults and cyber-attacks. The first point, the network vulnerable points against network faults are covered by microgrids. As the second one, a new cyber-security protocol named multi-layer security is proposed in order to prevent targeted cyber-attacks. The first layer is a cyber-security-based blockchain method that plays a general role. The second layer is a cyber-security-based reinforcement-learning method, which supports the vulnerable points by monitoring data. On the other hand, the trend of solving problems becomes routine when no ambiguity arises in different sections of the smart grid, while it is far from a big network’s realities. Hence, the impact of uncertainty parameters on the proposed framework needs to be considered. Accordingly, the unscented transform method is modeled in this research. The simulation results illustrate that applying such a comprehensive approach can greatly pull down the probability of blackouts.
Intelligent Detection Methods of Electrical Connection Faults in RF Circuits
Authors: Wang Ziren , Li Jiaqi , Flowers George T. , Gao Jinchun , Song Kaixuan , Yi Wei , Cheng Zhongyang
Published: October 2021

Printed circuit boards (PCBs) have a large number of electrical connection nodes. Exposure to harsh environments may lead to connection faults in these nodes. In the present work, intelligent detection methods for electrical connection faults were studied. Specifically, the fault characteristics of connectors, bonding wires and solder balls in the frequency domain were analyzed. The reflection and transmission parameters of an example filter circuit with electrical connection faults were calculated using the Simulation Program with Integrated Circuit Emphasis (SPICE). With these obtained electrical parameters, three machine learning algorithms were used to detect example electrical connection faults for the example circuit. Based upon the performance evaluations of the three algorithms, one can conclude that machine-learning-based intelligent fault detection is a promising technique in diagnosing circuit faults due to electrical connection issues with high accuracy and lower time cost as compared to current manual processes.
Data-Driven Convolutional Model for Digital Color Image Demosaicing
Authors: de Gioia Francesco , Fanucci Luca
Published: October 2021

Modern digital cameras use specific arrangement of Color Filter Array to sample light wavelength corresponding to visible colors. The most common Color Filter Array is the Bayer filter that samples only one color per pixel. To recover the full resolution image, an interpolation algorithm can be used. This process is called demosaicing and it is one of the first processing stages of a digital imaging pipeline. We introduce a novel data-driven model for demosaicing that takes into account the different requirements for reconstruction of the image Luma and Chrominance channels. The final model is a parallel composition of two reconstruction networks with individual architecture and trained with distinct loss functions. In order to solve the overfitting problem, we prepared a dataset that contains groups of patches that share common chromatic and spectral characteristics. We reported the reconstruction error on noise-free images and measured the effect of random noise and quantization noise in the demosaicing reconstruction. To test our model performance, we implemented the network on NVIDIA Jetson Nano, obtaining an end-to-end running time of less than one second for a full frame 12 MPixel image.