Miracle-dz/UniFAR-FTUs huggingface dataset:
A Multi-Parameter Integrated Sensor Based on Selectively Filled D-Shaped Photonic Crystal Fiber

We propose and numerically investigate a multi-parameter integrated sensor based on a selectively filled D-shaped photonic crystal fiber (PCF). The simple structure can be used to comprehensively detect refractive index, magnetic field, temperature, and voltage. According to the surface plasmon resonance and directional coupling effect, the PCF is coated with a gold nano-film to detect the refractive index of the external environment. In addition, magnetic fluid (water-based Fe3O4), toluene, and nematic liquid crystal (NLC E7) are selectively filled into different cladding air holes of the D-shaped PCF to realize the different sensing of the magnetic field, temperature, and voltage. The measurement of refractive index, magnetic field, temperature, and voltage are independent of each other, so these four parameters can be measured simultaneously. The sensing characteristics of the proposed structure are investigated systematically by the finite element method. The results show that the sensitivities of refractive index, magnetic field, temperature, and voltage are 4600 nm/RIU, 1.375 nm/Oe, 15.143 nm/°C, and 0.971 nm/V, respectively. The presented design based on materials selectively filled with D-shaped PCF might enable promising application in multi-parameter optical sensing.
ANALYSIS AND MITIGATION OF COMMON MODE AND DIFFERENTIAL MODE NOISE ON BOOST CONVERTERS

This paper is about the study and analysis on the effects of snubber circuits such as RLD, RCD and mixed (RLD+RCD) over the conducted ELECTROMAGNETIC INTERFERENCE on high frequency power MOSFET. In this paper for various duty cycles separation of conducted EMI is done with the help of LISN circuit. The analysis is done for various duty cycles as 0.5, 0.55 and 0.6 for a switching frequency of 50 KHz using ORCAD PSPICE software. The simulated results are then compared with respect to their duty cycle.
The softest radio

Software and digital logic are encroaching further on analogue territory in mobile radio. Cambridge Consultants built what appears to be the first true software radio. Developed from off-the-shelf programmable logic and very little else, the device is a transmitter that can send data at rates of gigabits per second at carrier frequencies up to 14 GHz, switching between different carrier frequencies almost instantaneously.
Content of smart wireless sensor network security and its network security policy

Wireless sensor network is generally composed by plenty of micro-sensors that arranged on designated area. The supervision of these sensor nodes is used to finish the collecting, disposing and uploading of vast information. However, the security of wireless sensor network has many problems, since the sensor node itself exists plenty of limitation. Aiming at the problems of smart wireless sensor network security as well as the analysis of wireless sensor network node easy been attacked, wiretapped and forged without safeguard, this paper put forward a security policy of smart wireless sensor network. Simple and useful intrusion detection policy was realized from a series of improvement of LEACH protocol of low-energy self-adaptation cluster routing protocol.
Environmental and stomatal control on evapotranspiration in a natural oak forest

Evapotranspiration (ET) is an important component in water budgets in forest ecosystems, and the associated latent heat flux has important implications for regional climate. However, biophysical control mechanisms on variations in ecosystem ET on intra‐annual scales may have year‐to‐year variations in different years with contrasting precipitation and soil water contents. Related research is lacking in natural oak forests in Central China. Based on eddy‐covariance technique, we investigated variations of ET and controlling factors in a natural oak forest in central China over three years (2017–2019) with contrasting soil water contents (SWCs) and precipitation. The ET of the oak forest was mainly affected by surface conductance (gs) in growing seasons, and the restriction from stoma on ET was aggravated in dry year. High vapour pressure deficit (VPD) promoted gs and ET in wet year but decreased gs and ET in dry year. There was a positive relationship between shallow soil water contents and gs in dry year but no relationship between the two variables in wet year. The ET were 738.1, 750.6 and 513.1 mm, respectively, for 2017–2019, with a coefficient of variation (CV) of 20%, while the corresponding precipitation (P) were 1239.4, 855.8 and 645.6 mm, with a CV of 33%. Annual ET was the lowest in the year with the lowest P, SWC and gs. Short‐term droughts (periods with relative extractable water content less than 0.1 for less than 15 days) only constrained gs and ET in short timescales (e.g., hours). Long‐term droughts (periods with relative extractable water content less than 0.1 for more than 15 days) evidently decreased the gs, thus reducing the ET and affecting energy partitioning in the corresponding period. ET/P were 0.6, 0.88 and 0.79 in 2017–2019, respectively, indicating that ET consumed most of the rain. The ratios of latent heat flux (LE) to net radiation (Rn) were 0.86, 0.88 and 0.6 during growing seasons in 2017–2019, respectively. Our study suggested that the natural oak forest has high ET rate and soil moisture affects the variations of ET.
Impact of the Feed-in Tariff Policy on Renewable Innovation: Evidence from Wind Power Industry and Photovoltaic Power Industry in China

Technological innovation is the key to develop wind power and photovoltaic power industries. The feed-in tariff (FIT) policy, as a demand-pull policy, is important to support renewable energy technological innovation. Using the “difference-in-differences” method, this paper investigates the impact of FIT policy of wind power and the impact of the FIT policy designed according to differences in the distribution of resources on wind power technological innovation. The findings show that the FIT policy can drive patenting in wind power technologies during the implementation period, but may play a relatively weak promoting role in technological innovation in the latter term, and the FIT policy designed according to differences in the distribution of resources also stimulates more patent counts. Finally, based on the fixed effect negative binomial regression model, this paper finds that the higher feed-in tariffs can increase the patent counts in photovoltaic power technologies.
A Stable Payoff Allocation Protocol for Controlling the Selfishness and Managing the Power Consumption in Mobile Ad Hoc Networks

We present a model to control the mobile nodes’ selfish behavior in Mobile Ad Hoc Networks using the cooperative game. We design the model such that a path from a source node to a destination node is a stable coalition among the nodes. We achieve this by compensating the nodes using virtual currency to take part in the coalition formation. The incentive provided to a node taking part in the coalition is determined using the Shapley value of the coalition formed. We also design the model to use minimum power to reach the one-hop node in the communication path. We achieve each node’s dynamic power control while forming the coalition among the nodes in the path from the source node to the destination node. In addition to these, we design the model where a node in the path should be truthful regarding the power requiring to reach a one-hop node. Finally, we perform a rigorous simulation to check the performance of the model.
Mid- and Later-life Community College Students: “Off-time” Education and the Significance of Intracohort Diversity

ABSTRACT Through a gerontological lens, using grounded theory methods and both qualitative and quantitative data, we investigated the “off-time” enrollment of mid- and later-life (MLL) community college students (age 40+) to explore how their enrollment decisions and academic goals are situated in the timing and intersection of life events and transitions, such as the responsibilities of work and family. We conducted twelve focus group interviews with MLL community college students (n = 68) in Ohio and conducted a quantitative analysis of the distribution of academic goals by age group in Ohio’s 23 community colleges. We conclude that the foremost characteristic of MLL students as a group is its within-group heterogeneity and argue that the greatest barrier to serving MLL students is in how we have been thinking – or failing to think – about them. We take a first slice at disaggregating “adult” students and turn away from deficit narratives to identify opportunities and assets among MLLs. Finally, we offer a theory to explain how especially wide within-group diversity of MLL academic goals is produced by cohort divergence of life events, transitions and trajectories; diverse enrollment precipitants; and individual point-of-time appraisals of lifetime elapsed and lifetime remaining. We suggest that community colleges, already leaders in adult education, are in a unique position to effectively serve MLL students by recognizing and accommodating their diversity. We present implications and strategies for colleges, policymakers and researchers based on our findings.
A New Algorithm to Derive High Performance and Low Hardware Cost DCT for HEVC

Owing to its good compression ability, discrete cosine transform (DCT) is widely used in signal processing, including high-efficiency video coding (HEVC). For efficient implementation, approximate DCTs and integer DCTs were proposed, but the challenge in the tradeoff between coding performance and implementation cost always exists. To solve this problem, this article proposes a new algorithm to derive Int-DCT with good coding performance and low hardware cost. To address the multiobjective optimization problem, we extract information from the initially shortlisted transform matrices to derive the weighting factors for the different matrices properties. Those properties having a higher impact to the performance are assigned with higher weights and vice versa. Subsequently, an efficient search algorithm is proposed to shortlist those candidates which can lead to better coding performance evaluated by the proposed measure. Last but not the least, a splitting method is proposed to search the efficient extended double-base number system (EDBNS) representation of the coefficients for hardware implementation and select the solution which encounters the lowest cost. The experimental results in ASIC demonstrate that the implementation area and power cost of designs by the new algorithm are reduced by at least 11.1% and 17.5% for 32-point transform, respectively, over other competing algorithms. Meanwhile, the transforms generated by the proposed algorithm causes negligible impact to coding performance compared to the original transforms in HEVC.
Mortality Rate of Ischemic Stroke Patients Undergoing Decompressive Hemicraniectomy With Obesity

Background Obesity has been shown to have a positive mortality benefit in patients undergoing percutaneous coronary intervention and dialysis and those with rheumatoid arthritis, chronic obstructive pulmonary disease, and various wasting diseases. Studies for this mortality benefit in ischemic stroke patients are conflicting and have not been well studied in hemicraniectomy patients. We sought to determine the impact of obesity on outcomes of hemicraniectomy patients. Methods We performed a retrospective case-control database analysis using a multi-institutional database (TriNetX) looking at obese versus non-obese patients with ischemic stroke undergoing hemicraniectomy. Our primary endpoint was mortality. Secondary endpoints included seizure, pulmonary embolism, myocardial infarction (MI), cerebral infarction, deep vein thrombosis, tracheostomy, and percutaneous endoscopic gastrostomy. Cohorts were propensity-score matched for confounders. Results After propensity score matching for basic demographics and common comorbidities, as well as indicators of stroke severity, 646 patients were identified that were obese and had an ischemic stroke with subsequent hemicraniectomy (cohort 1), and 646 patients were identified who were non-obese with ischemic stroke and hemicraniectomy (cohort 2). Thirty-day survival rate was 98.142% in the obese vs. 87.771% in the non-obese cohorts, 90-day survival was 85.15% vs. 79.35%, 180-day survival was 96.44% vs. 84.52%, 365-day survival was 94.272% vs. 81.734%, and five-year survival was 81.889% vs. 75.077%, respectively. At five years, risk difference was −7.276% (95% CI: −11.757, −2.794) and odds ratio was 0.666 (95% CI: 0.510, 0.871) (p = 0.0029). Despite a higher mortality rate, obese patients had a statistically significant increase in pulmonary embolism (11.61% vs. 5.108, p < 0.0001), deep venous thrombosis (16.873% vs. 9.133%, p < 0.0001), and MI (8.824% vs. 5.882%, p = 0.0428). There was no significant difference in intensive care unit length of stay, ventilator dependence, tracheostomy placement, percutaneous endoscopic gastrostomy placement, or intracerebral hemorrhage. Conclusions Despite the increased risk of ischemic stroke, obese patients who undergo hemicraniectomy have decreased mortality rates compared to their non-obese counterparts.
Hepatitis B Virus Genotypes and Antiviral Resistance Mutations in Romanian HIV-HBV Co-Infected Patients

Background and Objectives: Romania has one of the highest prevalence of hepatitis B virus (HBV) infection in human immunodeficiency virus (HIV) patients, mostly in those parenterally infected during childhood; nevertheless, there are scarce data on the virological profile of co-infection. The objective of this study was to assess the prevalence of HBV genotypes and antiviral resistance-associated mutations (RAMs) in these co-infected patients, in order to monitor the viral factors associated with the evolution of liver disease. Materials and Methods: HBV genotypes and RAMs were detected using nested PCR and line probe assays (INNO-LiPA HBV genotyping assay, and INNO-LiPA HBV DR v2, Innogenetics). Results: Out of 117 co-infected patients, 73.5% had detectable HBV-DNA, but only 38.5% presented an HBV viral load >1000 IU/mL. HBV genotype A was present in 66.7% of the cases and was dominant in patients parenterally infected during early childhood, who experienced multiple treatment regimens, with a mean therapy length of 15.25 years, and present numerous mutations associated with lamivudine (LAM) resistance, but very rarely active liver disease. HBV genotype D was detected in 33.3% of the cases, mostly in recently diagnosed injecting drug users who are treatment naïve, but, nevertheless, present RAMs in 63.5% of the cases, suggesting transmitted drug resistance, and display more frequently advanced liver fibrosis (36.1% vs. 12.3%; p = 0.033). The most frequently encountered RAMs are M204V/I: 48.8%, L180M: 33.3%, L80V: 28.8%, and V173L: 42.2%. There are no significant differences in the distribution of RAMs in patients infected with different HBV genotypes, except for the L80V and N236T mutations, which were more frequently found in HBV genotype A infections (p = 0.032 and p = 0.004, respectively). Conclusions: HBV genotypes A and D are the only genotypes present in HIV–HBV co-infected patients from Romania, with different distributions according to the infection route, and are frequently associated with multiple RAMs, conferring extensive resistance to LAM.
MOBILE SCHOOL CONCEPTUAL MODEL FOR SECONDARY SCHOOLS IN MALAYSIA

Incorporation of e-learning and mobile web technology in the education produces significant positive impacts to the students. These technologies are able to complement the weaknesses of current practices in teaching and learning. This paper presents Mobile School conceptual model that has been developed for secondary schools in Malaysia. This model is developed by integrating the potential concepts and theories to solve the problems related to teaching and learning in Malaysian secondary schools. Validation of this model has been conducted using questionnaire instrument where the questionnaire has been distributed to 407 respondents including school administrators, teachers, students and parents. The analysis showed that the variables that have been integrated in the model could solve the identified problems. The variables are also important to be implemented in mobile learning (m-learning) system development for secondary schools in Malaysia.
Cognitive proximity, technological regime and knowledge accumulation: an agent-based model of interfirm knowledge exchange

ABSTRACT Innovations in industrial clusters are highly dependent on a combination of internal and external knowledge among cluster firms with optimal levels of cognitive proximity. Meanwhile, all innovation activities in a cluster are affected by technological regime within that cluster, which is captured in terms of knowledge cumulativeness and knowledge distribution in this paper. Based on knowledge-based theory, this study develops an agent-based model of interfirm knowledge exchange, exploring the interplay of cognitive proximity and technological regime on a cluster’s knowledge accumulation. The results corroborate that cognitive proximity and cumulativeness condition jointly exert a significant inverted ‘U’-shaped effect on a cluster’s knowledge accumulation. The strength and shape of this effect are different for clusters with low versus high levels of cognitive proximity. Furthermore, the study extends the literature on technological regime by distinguishing the effects between knowledge cumulativeness and knowledge distribution, and the results reveal that large firm clusters (/SME clusters) perform best under a high (/low) level of knowledge cumulativeness. By simulating the sequential process of innovation dynamics, the study deepens understanding of mechanism behind how proximity affects innovation within clusters, thereby contributing to disentangling the interrelationships between cognitive proximity and technological regime.
Phenolic Acids and Flavonoids in Acetonic Extract from Quince (Cydonia oblonga Mill.): Nutraceuticals with Antioxidant and Anti-Inflammatory Potential

Quince (Cydonia oblonga Mill.) is a potential source of polyphenolic compounds related with beneficial biological processes. In this study polyphenols from quince fruit were extracted with aqueous acetone at different ratios. A polyphenol profile was identified and quantified by LC-ESI-QqQ. The antioxidant capacity (ORAC and DPPH) and anti-inflammatory effect (inhibition of COX-2 cyclooxygenase) were evaluated in vitro. The results indicated an effect of the aqueous acetone ratio on the extraction of polyphenolic compounds. The higher extraction yields of polyphenolic compounds were attained with 60–75% aqueous acetone. However, extracts obtained with 85% aqueous acetone promoted higher antioxidant and anti-inflammatory effects. Optimal scaling analysis indicated that hydroxycinnamic acids (quinic and chlorogenic), hydroxybenzoic acids (vanillic and syringic), flavonoids (quercetin and kaempferol), dihydrochalcones (neohesperidin) and flavones (acacetin) are related to the antioxidant activity of quince. While phenolic acids, flavonols (kaempferol-3-O-glucoside and rutin) and flavanols (epicatechin) generated the anti-inflammatory effect by inhibiting 52.3% of the COX-2 enzyme. Therefore, a selective extraction of phenolic mix can reduce oxidative stress or inflammatory processes. This suggests the use of quince as a natural source with significant nutraceutical potential.
Antibiotic-free antimicrobial poly (methyl methacrylate) bone cements: A state-of-the-art review

Prosthetic joint infection (PJI) is the most serious complication following total joint arthroplasty, this being because it is associated with, among other things, high morbidity and low quality of life, is difficult to prevent, and is very challenging to treat/manage. The many shortcomings of antibiotic-loaded poly (methyl methacrylate) (PMMA) bone cement (ALBC) as an agent for preventing and treating/managing PJI are well-known. One is that microorganisms responsible for most PJI cases, such as methicillin-resistant S. aureus, have developed or are developing resistance to gentamicin sulfate, which is the antibiotic in the vast majority of approved ALBC brands. This has led to many research efforts to develop cements that do not contain gentamicin (or, for that matter, any antibiotic) but demonstrate excellent antimicrobial efficacy. There is a sizeable body of literature on these so-called “antibiotic-free antimicrobial” PMMA bone cements (AFAMBCs). The present work is a comprehensive and critical review of this body. In addition to summaries of key trends in results of characterization studies of AFAMBCs, the attractive features and shortcomings of the literature are highlighted. Shortcomings provide motivation for future work, with some ideas being formulation of a new generation of AFAMBCs by, example, adding a nanostructured material and/or an extract from a natural product to the powder and/or liquid of the basis cement, respectively.
Dynamic Graph Convolutional Network for Long Short-term Traffic Flow Prediction

Traffic prediction is a critical component of intel-ligent transportation systems. However, highly non-linear and dynamical spatial-temporal correlations propose challenges for traffic prediction, especially long-term prediction. We propose a spatial-temporal channel-attention based graph convolutional network (STCAGCN) to improve the accuracy of both long-term and short-term traffic flow prediction. Firstly we design an attention mechanism to learn complex temporal and spatial correlations. Then we develop the stacked spatial-temporal convo-lution layer to model complex temporal and spatial correlations. Each spatial-temporal convolution layer is composed of a gated time convolution network and a graph convolution network. We develop a gated time convolution network to model non-linear temporal correlations, which process long sequences through stacked dilated convolution. Moreover, the graph convolution network exploits the hidden spatial correlations via learning self-adaptive adjacency matrix. Experiment results on real-world datasets demonstrate that the proposed STCAGCN model obtains improvements over the state-of-the-art, especially for long-term traffic flow prediction.
Wind turbine prognostics and maintenance management based on a hybrid approach of neural networks and a proportional hazards model

This paper proposes an approach for stress condition monitoring and maintenance assessment in wind turbines (WTs) through large amounts of collected data from the supervisory control and data acquisition (SCADA) system. The objectives of the proposed approach are to provide a stress condition model for health monitoring, to assess the WT’s maintenance strategies, and to provide recommendations on current maintenance schemes for future operations of the wind farm. At first, several statistical techniques, namely principal component analysis, Pearson, Spearman and Kendall correlations, mutual information, regressional ReliefF and decision trees are used and compared to assess the data for dimensionality reduction and parameter selection. Next, a normal behavior model is constructed by an artificial neural network which performs condition monitoring analysis. Then, a model based on the mathematical form of a proportional hazards model is developed where it represents the stress condition of the WT. Finally, those two models are jointly employed in order to analyze the overall performance of the WT over the study period. Several cases are analyzed with five-year SCADA data and maintenance information is utilized to develop and validate the proposed approach.