Miracle-dz/UniFAR-FTUs huggingface dataset:
Strategies for wideband light generation in nonlinear multimode integrated waveguides

In this paper we discuss two strategies to achieve wideband light generation through intermodal nonlinear parametric processes in multimode integrated waveguides. We outline how the interplay among intermodal interactions and high dispersion may lead to the generation of light characterized by substantial power spectral density if compared to supercontinuum sources. These results are valid independently of the waveguide material; however, our numerical simulations focus on silicon waveguides, which are nowadays at the core of integrated photonics. The long-term vision is that by exploiting an adequate number of intermodal processes, widely tunable radiation having high-power spectral density can be generated in a broad portion of the transparency window of silicon.
Cross-Domain Intelligent Fault Diagnosis Method of Rotating Machinery Using Multi-Scale Transfer Fuzzy Entropy

A novel rotating machine fault diagnosis method based on multi-scale transfer fuzzy entropy (MTFE) and support vector machine (SVM) is proposed in this paper. Compared with traditional machine learning methods, our proposed method can identify various fault types of rotating machinery with different data distribution by learning the transfer knowledge from different distribution source domains. First, multi-scale fuzzy entropy (MFE) features of all samples are extracted as input. Second, build a transfer learning model to find a projection matrix to map the MFE features of the source and target domains into a common subspace, called MTFE features. In this process, the distribution structure of training data is maintained and the distribution difference between training data and test data is reduced. Finally, the SVM classifier identifies the fault type of the test data. Two cases including gearbox and rolling bearing are used for validation. Experimental results demonstrate that our proposed MTFE method performs best in recognizing various fault types comparing with other six methods.
New Development of Physarum Routing Algorithm With Adaptive Power Control

Internet of Things (IoT) network infrastructure now days growing very significantly, along with the number of smart devices being used in everyday life. The existence of obstacles in the Internet of Things (IoT) network area is the biggest challenge in the IoT routing network. This obstacle in the IoT network can be a wall or the absence of a router in a specific area. The routing development to facing obstacles problems has been done a lot, however, there no routing algorithm that can overcome that obstacle. This research proposed a Physarum Routing Algorithm with Adaptive Power Control (PRA-APC) to solve the problem of IoT obstacles in the network. PRA-APC method provides sufficient power to the last node before the obstacle so it can be able to pass through the obstacle to the final destination node. PRA-APC routing method will compare with other routing algorithms such as Physarum inspired Routing Protocol (P-iRP) and Robust Selection Physarum-inspired Routing Protocol (RS-PRP). The PRA-APC protocol can develop the shortest routing path, 70% better than P-iRP and RS-PRP. The result of the delay more efficient, network lifetime is longer, throughput is much better than other routing algorithm protocols. The PRA-APC routing performance improvement results in efficiency and balance of resources in the network so that it can extend the life span of IoT networks that have limited resources in the local IoT network infrastructure. The new development of the PRA-APC routing protocol can provide solutions to overcome the problem of obstacles in the IoT network.
Online Sparse Temporal Difference Learning Based on Nested Optimization and Regularized Dual Averaging

In policy evaluation of reinforcement learning tasks, the temporal difference (TD) learning with value function approximation has been widely studied. However, feature representation has a decisive influence on both accuracy of value function approximation and convergence rate. Therefore, it is important to develop the feature selection theory and methods that can efficiently prevent overfitting and improve estimation accuracy in TD learning algorithms. In this article, we propose an online sparse TD learning algorithm for policy evaluation by using <inline-formula> <tex-math notation="LaTeX">$\ell _{1}$ </tex-math></inline-formula>-regualrization for feature selection. The per-step-time runtime computational complexity of the proposed algorithm is linear with respect to feature dimension. The loss function is defined as a nested optimization with <inline-formula> <tex-math notation="LaTeX">$\ell _{1}$ </tex-math></inline-formula>-regularization penalty, and the solver minimizes two suboptimization problems by running stochastic gradient descent and regularized dual averaging method, alternately. The convergence results for the fixed points are also established. The experiments on benchmarks with high-dimensional features show the abilities of learning and generalization of the proposed algorithms.
Quality of Experience (QoE) Studies: Present State and Future Prospect

SUMMARY With the spread of the broadband Internet and high-performance devices, various services have become available anytime, anywhere. As a result, attention is focused on the service quality and Quality of Experience (QoE) is emphasized as an evaluation index from the user’s viewpoint. Since QoE is a subjective evaluation metric and deeply involved with user perception and expectation, quantitative and comparative research was di ﬃ cult because the QoE study is still in its infancy. At present, after tremendous devoted e ﬀ orts have contributed to this research area, a shape of the QoE management architecture has become clear. More-over, not only for research but also for business, video streaming services are expected as a promising Internet service incorporating QoE. This paper reviews the present state of QoE studies with the above background and describes the future prospect of QoE. Firstly, the historical aspects of QoE is reviewed starting with QoS (Quality of Service). Secondly, a QoE management architecture is proposed in this paper, which consists of QoE measurement, QoE assessment, QoS-QoE mapping, QoE modeling, and QoE adaptation. Thirdly, QoE studies related with video streaming services are introduced, and ﬁnally individual QoE and physiology-based QoE measurement methodologies are explained as future prospect in the ﬁeld of QoE studies.
Analysis of Mathematics Teacher Performance in Building Resilience and Mathematical Literacy on Student Learning Outcomes

The purpose of this study was to determine the performance of mathematics teachers in building resilience and mathematical literacy towards student learning outcomes. This type of research is ex post facto with a sample of 283 students from a population of 961 students consisting of seven state and five private high schools, and obtained in clusters from twelve sub-districts in North Sumatra. The research instrument used a questionnaire and test. The data analysis used descriptive and inferential analysis. The results of the descriptive analysis conclude several things, namely (1) the teacher's performance in building mathematical resilience and literacy is in the medium category, (2) students 'mathematical resilience, students' mathematical literacy abilities, and student learning outcomes are in the medium category. The results of inferential analysis show that the teacher's performance in building mathematical resilience, (1) has a high impact on the mathematical resilience of students which is influenced by students' mathematical literacy abilities to influence student learning outcomes, and (2) has a high impact on students' mathematical literacy abilities which is influenced by students' mathematical resilience to affect student learning outcomes. The teacher's performance in building mathematical literacy, (3) has a high impact on students 'mathematical resilience which is influenced by students' mathematical literacy abilities to influence student learning outcomes, and (4) has a high impact on students' mathematical literacy abilities which is influenced by students' mathematical resilience to influence student learning outcomes in math. Thus it can be stated that the performance of teachers in building resilience and mathematical literacy has an impact on student learning outcomes.
Real-Time Autonomous Residential Demand Response Management Based on Twin Delayed Deep Deterministic Policy Gradient Learning

With the roll-out of smart meters and the increasing prevalence of distributed energy resources (DERs) at the residential level, end-users rely on home energy management systems (HEMSs) that can harness real-time data and employ artificial intelligence techniques to optimally manage the operation of different DERs, which are targeted toward minimizing the end-user’s energy bill. In this respect, the performance of the conventional model-based demand response (DR) management approach may deteriorate due to the inaccuracy of the employed DER operating models and the probabilistic modeling of uncertain parameters. To overcome the above drawbacks, this paper develops a novel real-time DR management strategy for a residential household based on the twin delayed deep deterministic policy gradient (TD3) learning approach. This approach is model-free, and thus does not rely on knowledge of the distribution of uncertainties or the operating models and parameters of the DERs. It also enables learning of neural-network-based and fine-grained DR management policies in a multi-dimensional action space by exploiting high-dimensional sensory data that encapsulate the uncertainties associated with the renewable generation, appliances’ operating states, utility prices, and outdoor temperature. The proposed method is applied to the energy management problem for a household with a portfolio of the most prominent types of DERs. Case studies involving a real-world scenario are used to validate the superior performance of the proposed method in reducing the household’s energy costs while coping with the multi-source uncertainties through comprehensive comparisons with the state-of-the-art deep reinforcement learning (DRL) methods.
Computer‐assisted demand‐side energy management in residential smart grid employing novel pooling deep learning algorithm

Demand‐side energy management increases the unpredictability and ambiguity of forecasting the load profiles of residential energy management. The energy management accuracy seems to be low by employing a traditional residential energy forecasting algorithm. This research work emphasizes on design and development of computer‐assisted residential energy management by forecasting employing a deep learning algorithm. Hankel matrix is formed using copula function to process the collected automatic metering infrastructure (AMI) load data in the smart grid. From this data processing, model optimization was obtained by the proposed novel pooling‐based deep neural network (PDNN). Moreover, this proposed PDNN avoid overfitting problem in training and testing by increasing AMI data variety and data size. The proposed PDNN is implemented in the TensorFlow platform. Based on real‐time AMI southern grid data onto Tamil Nadu Electricity Board, India testing case studies was carried. Compared to traditional residential energy management techniques the proposed deep learning model outperforms support vector machine by 9.5% and 12.7%, deep belief network by 6.5% and 9.5%, and neural network auto aggressive integral moving average by 20.5% and 8.5% in terms of accuracy of energy forecasting and mean absolute error, respectively. Overall, the obtained results proved the effectiveness of the proposed deep learning algorithm for residential short‐term load forecasting and management over other traditional methods.
Spillage Forecast Models in Hydroelectric Power Plants Using Information from Telemetry Stations and Hydraulic Control

Hydroelectric power plants’ operational decisions are associated with several factors, such as generation planning, water availability and dam safety. One major challenge is to control the water spillage from the reservoir. Although this action represents a loss of energy production, it is a powerful strategy to regulate the reservoir level, ensuring the dam’s safety. The decision to use this strategy must be made in advance based on level and demand predictions. The present work applies supervised machine learning techniques to predict the operating condition of spillage in a hydroelectric plant for 5 h ahead. The use of this method, in real time, aims to assist the operator so that he can make more assertive and safer decisions, avoiding waste of energy resources and increasing the safety of dams. The Random Forest and Multilayer Perceptron methods were used to define the architecture compared to the forecasting capacity. The proposed methodology was applied to a 902.5 MW Hydroelectric Power Plant located on the Tocantins River, Brazil. The results demonstrate effective assistance to operators in the decision-making, presenting accuracy of up to 99.15% for the spill decision.
Research on Automated Detection of Sensitive Information Based on BERT

With the booming of the Internet, Web public opinion plays an increasingly important role in the stability of the network community. Therefore, the sensitive information hidden on the Internet is likely to lead to unpredictable social impact. This paper focuses on the detection of Chinese sensitive information. First, we build a corpus to train the detection model. Secondly, we apply the Bert method to the detection problem. Then, many popular NLP methods are applied to this problem to show the progress of Bert in a sensitive information detection task. Finally, we got a sensitive information detection model based on BERT with a high F1 score of 97.31.
Nanocomposites for Enhanced Osseointegration of Dental and Orthopedic Implants Revisited: Surface Functionalization by Carbon Nanomaterial Coatings

Over the past few decades, carbon nanomaterials, including carbon nanofibers, nanocrystalline diamonds, fullerenes, carbon nanotubes, carbon nanodots, and graphene and its derivatives, have gained the attention of bioengineers and medical researchers as they possess extraordinary physicochemical, mechanical, thermal, and electrical properties. Recently, surface functionalization with carbon nanomaterials in dental and orthopedic implants has emerged as a novel strategy for reinforcement and as a bioactive cue due to their potential for osseointegration. Numerous developments in fabrication and biological studies of carbon nanostructures have provided various novel opportunities to expand their application to hard tissue regeneration and restoration. In this minireview, the recent research trends in surface functionalization of orthopedic and dental implants with coating carbon nanomaterials are summarized. In addition, some seminal methodologies for physicomechanical and electrochemical coatings are discussed. In conclusion, it is shown that further development of surface functionalization with carbon nanomaterials may provide innovative results with clinical potential for improved osseointegration after implantation.
Predicting the Use Labor Induction Intervention: A Machine Learning Approach for the Birth-Cohort Registry at A Tertiary Hospital in North Tanzania.

Introduction: Following an increased use of labor induction procedure to prevent adverse maternal and fetal outcomes in Sub-Saharan Africa, hitting the best algorithm that accurately classify subjects in need of the intervention is of paramount importance. This study aimed at comparing the potential benefits of applying machine learning (ML) algorithms over the conventional logistic regression model in predicting the use of labor induction intervention in pregnant women attending one of the tertiary hospitals in north Tanzania for delivery. Methods: We conducted a secondary data analysis of the Kilimanjaro Christian Medical Centre (KCMC) birth registry database for women with uncomplicated pregnancies from the year 2000 to 2015. We excluded observations with non-vertex presentation and those with missing information on labor induction status. Model accuracy and Area under the receiver operating characteristic curve (AUC - ROC) were used to assess the discriminative ability of the selected models. We plotted the decision curve analysis (DCA) to assess the clinical utility of the models under observation. Results: A total of 21,578 deliveries were analyzed. Among these, 8814 (41%) were induced during the study period. Among the selected machine learning models, Random forest algorithm exhibited the best performance in terms of accuracy [0.75; 95%CI (0.73 – 0.76)] and AUC-ROC [AUC-ROC: 0.75; 95% CI (0.74 – 0.76)] compared to other models including logistic regression. Among assessed maternal attributes, parity, maternal age, body mass index, gestational age and birthweight were deemed most important predictors for labor induction intervention. Conclusion: The selected machine learning methods offered better computational performance compared to the conventional logistic regression model in predicting the use of labor induction intervention. The current study lends substantial support to the use of machine learning models in predicting the use of labor induction intervention.
Lower socioeconomic status neighborhoods in Puerto Rico have more diverse mosquito communities and higher Aedes aegypti abundance

Mosquito community dynamics in urban areas are influenced by an array of both social and ecological factors. Human socioeconomic factors (SEF) can be related to mosquito abundance and diversity as urban mosquito development sites are modified by varying human activity, e.g., level of abandoned structures or amount of accumulated trash. The goal of this study was to investigate the relationships among mosquito diversity, populations of Aedes aegypti, and SEF in a tropical urban setting. Mosquitoes were collected using BG Sentinel 2 traps and CDC light traps during three periods between late 2018 and early 2019 in San Juan, Puerto Rico, and were identified to species. SEFs (i.e. median household income, population density, college-level educational attainment, unemployment, health insurance coverage, percentage of households below the poverty line, amount of trash and level of abandoned homes) were measured using foot surveys and U.S. Census data. We found 19 species with the two most abundant species being Culex quinquefasciatus (n = 10 641, 87.6%) and Ae. aegypti (n = 1558, 12.8%). We found a positive association between Ae. aegypti abundance and mosquito diversity, which were both negatively related to SES and ecological factors. Specifically, lower socioeconomic status neighborhoods had both more Ae. aegypti and more diverse communities, due to more favorable development habitat, indicating that control efforts should be focused in these areas.
The δ13C, δ18O and Δ47 records in biogenic, pedogenic and geogenic carbonate types from paleosol-loess sequence and their paleoenvironmental meaning

Abstract Paleoenvironmental reconstructions are commonly based on isotopic signatures of a variety of carbonate types, including rhizoliths and land-snail shells, present in paleosol-loess sequences. However, various carbonate types are formed through distinct biotic and abiotic processes over various periods, and therefore may record diverging environmental information in the same sedimentological layer. Here, we investigate the effects of carbonate type on δ13C, δ18O, and clumped isotope-derived paleotemperature [T(Δ47)] from the Quaternary Nussloch paleosol-loess sequence (Rhine Valley, SW Germany). δ13C, δ18O, and T(Δ47) values of co-occurring rhizoliths (-8.2‰ to -5.8‰, -6.1‰ to -5.9‰, 12–32°C, respectively), loess dolls (-7.0‰, -5.6‰, 23°C), land-snail shells (-8.1‰ to -3.2‰, -4.0‰ to -2.2‰, 12–38°C), earthworm biospheroliths (-11‰, -4.7‰, 8°C), and “bulk” carbonates (-1.9‰ to -0.5‰, -5.6‰ to -5.3‰, 78–120°C) from three sediment layers depend systematically on the carbonate type, admixture from geogenic carbonate, and the duration of formation periods. Based on these findings, we provide a comprehensive summary for the application of the three isotopic proxies of δ13C, δ18O, and Δ47 in biogenic and pedogenic carbonates present in the same sediment layer to reconstruct paleoenvironments (e.g., local vegetation, evaporative conditions, and temperature). We conclude that bulk carbonates in Nussloch loess should be excluded from paleoenvironmental reconstructions. Instead, pedogenic and biogenic carbonates should be used to provide context for interpreting the isotopic signature for detailed site- and time-specific paleoenvironmental information.
(Re)Shaping the Socialization of Scientific Labs: Understanding Women's Doctoral Experiences in STEM Lab Rotations

Abstract:Building on literature addressing doctoral student socialization and scientific research groups, we qualitatively explore the gendered nature of laboratory rotations for 54 women pursuing STEM doctorates. Using in-depth interviews and a feminist phenomenological approach, findings highlight how women (re)constructed strategies to select lab rotations, relying on social comparisons, social cues about labor practices, and support from principal investigators, peers, and departmental staff. This study documents how women were sometimes felt they had to choose between a prestigious lab aligning with their interests and a lab that would not be overtly sexist. We conclude with practical implications for enhancing equitable socialization structures in STEM.
Validation of Quaternary Ammonia and Hydrogen Peroxide Powder for Control of Listeria monocytogenes in Ready-to-Eat Meat and Poultry Plants

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Dampening the warm glow of a visible brand logo: how positive affect influences the perceived value of status goods

ABSTRACT High status is conducive to many pleasant outcomes in life, and people often desire status goods for the attention and respect that are given to those who have them. This research shows that positive affect decreases the extent to which people pursue the extrinsic consumption benefits that hinge on others’ approval, and attenuates the magnitude of value people normally place on the social signaling attributes. When target products feature socially visible status attributes (e.g., brand logo), positive affect dampens people’s normative valuation of the goods. When the status attributes are invisible to others, the dampening effect becomes insignificant. When the products are noted for intrinsic quality attributes, positive affect enhances the evaluation. Thus, positive affect has more nuanced and distinct, rather than general, effects on product evaluation. These results suggest that positive affect can naturally attenuate people’s desire for status goods that are normally pursued for their social signaling efficacy.