Miracle-dz/UniFAR-FTUs huggingface dataset:
Air microfluidic chip design for atmospheric particulate matter detection

In recent years, studies on fine particulate matter have shown that high concentrations of particulate matter seriously affect the quality of weather, creating a series of severe weather such as haze and posing a great risk to human health. The results of epidemiological studies suggest that particulate matter is associated with a higher risk of cardiopulmonary mortality and morbidity. Therefore, there is an urgent need to conduct research on particulate matter to solve the human health problems caused by particulate matter pollution. The identification of the compositional characteristics of particulate matter presupposes the separation of particulate matter with different aerodynamic diameters and provides scientific guidance for solving the problem of atmospheric particulate matter pollution. To address this problem, a virtual impactor with a cutting particle size of 1.2 μm is designed in this paper. The influence of key parameters on the performance of the virtual impactor is also discussed. The results show that the proposed virtual impactor has a cutting particle size of 1.2um and a good steepness of the collection efficiency curve. It shows that it can effectively separate atmospheric particulate matter according to particle size and provides a design basis for realizing a low-cost atmospheric particulate matter mass concentration detection instrument. Meanwhile, we design a microfluidic chip for particulate matter detection based on this virtual impactor. The hardware circuit of this microfluidic chip is also designed.
The KLC Cultures, Tacit Knowledge, and Trust Contribution to Organizational Intelligence Activation

In this paper, the authors address a new approach to three organizational, functional cultures: knowledge culture, learning culture, and collaboration culture, named together the KLC cultures. Authors claim that the KLC approach in knowledge-driven organizations must be designed and nourished to leverage knowledge and intellectual capital. It is suggested that they are necessary for simultaneous implementation because no one of these functional cultures alone is as beneficial for a company as all of them are together. Moreover, there is a risk that organizations with a learning culture developed without collaboration are stuck at the individual level of learning only; and that a knowledge culture developed without a learning culture jeopardizes the organization to be stuck in a passive way where only old, multiply verified knowledge is accepted. As a result, such companies cannot grow. That extreme situation leads to the rejection of new knowledge that is usually rationalized by the need for business safety security - that is nothing more than a ruse for intellectual laziness or personal barriers of fixed-minded managers.
Towards Sustainable and Intelligent Machining: Energy Footprint and Tool Condition Monitoring for Media-Assisted Processes

Reducing energy consumption is a necessity towards achieving the goal of net-zero manufacturing. In this paper, the overall energy footprint of machining Ti-6Al-4V using various cooling/lubrication methods is investigated taking the embodied energy of cutting tools and cutting fluids into account. Previous studies concentrated on reducing the energy consumption associated with the machine tool and cutting fluids. However, the investigations in this study show the significance of the embodied energy of cutting tool. New cooling/lubrication methods such as WS 2 -oil suspension can reduce the energy footprint of machining through extending tool life. Cutting tools are commonly replaced early before reaching their end of useful life to prevent damage to the workpiece, effectively wasting a portion of the embodied energy in cutting tools. A deep learning method is trained and validated to identify when a tool change is required based on sensor signals from a wireless sensory toolholder. The results indicated that the network is capable of classifying over 90% of the tools correctly. This enables capitalising on the entirety of a tool’s useful life before replacing the tool and thus reducing the overall energy footprint of machining processes
Mahalanobis Distance Measurement Based Locally Linear Embedding Algorithm

Euclidean distance is normally used to measure the similarity between samples in locally linear embedding algorithm(LLE). But for some high dimensional data,such as images,Euclidean distance can not accurately reflect the similarity between samples.A Mahalanobis distance metric based locally linear embedding algorithm(MLLE) is proposed.Firstly,MLLE ascertains a Mahalanobis metric from the existing samples.Then,the Mahalanobis metric is used to choose neighborhoods and to reduce the dimensionality of the existing samples and the new samples.The comparison result of MLLE algorithm and some classical manifold based algorithms on ORL and USPS databases proves that MLLE algorithm is effective in recognizing images.
Aspects of femtosecond pulse shape transfer via difference frequency mixing

We investigate the generation of shaped femtosecond pulses in the infrared spectral regime via a nonlinear process called di erence frequency mixing. First we develop a detailed model of the process, incorporating pulse propagation during di erence frequency mixing, in the slowly varying envelope approximation. Di erence frequency mixing (DFM) is numerically simulated for several wavelengths, nonlinear crystals and Type I and Type II frequency mixing. The di erent factors in uencing the shape transfer e ciency of a shaped pulse to a di erent wavelength regime, as well as the conversion e ciency, is identi ed and investigated thoroughly by doing a parametric study. The numerical modeling demonstrates that the e ciency, with which a shaped pulse in the near-infrared is transferred to another wavelength regime, depends strongly on the refractive index of the nonlinear medium for the interacting pulses and is optimal when the velocity of the generated pulse equals that of the shaped input pulse. We show that it is possible to control the temporal pulse duration of the generated pulse by using speci c input angles and so manipulating the e ective refractive index of the nonlinear material for the input and generated pulses. It was found that it is possible to temporally broaden or narrow the generated pulse relative to the input pulses. We compare the developed numerical model to experimental measurements. A liquid crystal spatial light modulator (SLM), inserted in a 4f setup, is used to generate the shaped pulses. Experimentally we demonstrate highdelity shape transfer by mixing 795 nm and 398 nm femtosecond pulses in a BBO crystal. The temporal broadening and narrowing of the generated pulse is also shown and compared to the numerical simulations showing excellent agreement with measured results. ii Stellenbosch University https://scholar.sun.ac.za
New Evidence Composition Algorithm Based on Evidence Distance

Since Dempster-Shafer(DS) theory involves counter-intuitive behaviors when evidences highly conflict,a new approach of combination of weighted belief functions based on evidence distance is proposed.The new approach still uses DS combination formula,but considers the revision means which fuses the evidence source of the system.Jousselme evidence distance is revised by making use of expectation of all evidences,and then obtains the evidence weight.The basic beliability partition function of source evidence is revised through evidence weight.The numerical examples are given to illustrate the effectiveness and the good performance of the new approach in case of high conflict between evidences.
Probabilistic Load Flow of Distribution Network Considering Correlated Photovoltaic Power Output

The power outputs of photovoltaic generators(PVs)are strongly random,and appreciably correlated in close proximity.The research on probabilistic load flow of distribution network mainly focuses on how to deal with correlated random variables.Nataf transformation is used to sample correlated non-normal random variables and the method of combining Nataf transformation with the point estimate method(PEM)is proposed to develop a new correlated probabilistic load flow method.Compared with the orthogonal transformation,Nataf transformation is able to reflect the changes of correlation coefficients in different sampling spaces,and therefore it is more accurate.The method is applied in a modified IEEE 33-bus test system with PVs integration for calculation of average relative errors in bus voltages.Compared with orthogonal transformation,the advantage of Nataf transformation is verified.Furthermore,a sensitivity analysis is made on three factors,the correlation coefficients of solar radiation,the load power fluctuation and different estimating points.
Solution Processed Active Materials for Pixel Sensor Element and Integrated Circuits

We describe an integrated planar circuit for an active pixel sensor consisting of a metal-semiconductor-metal (MSM) photodetector with hybrid perovskite as the semiconductor and organic field effect transistors (OFETs) as a readout circuitry. The adopted fabrication approach reduces the complexity of implementing these circuits over a large area, for a variety of substrates from flexible to rigid ones. The features of this circuit include low threshold voltage and a reasonable switching speed of <inline-formula> <tex-math notation="LaTeX">$\approx 100~\mu \text{s}$ </tex-math></inline-formula>. The achievable channel length of <inline-formula> <tex-math notation="LaTeX">$\approx 2.5~\mu \text{m}$ </tex-math></inline-formula> for both the photodetector element and OFETs, implies the possibility of high pixel density for imaging applications.
Efficient Error Estimation for High-Level Design Space Exploration of Approximate Computing Systems

This article presents an error estimation technique for a data-flow graph (DFG) representation of an approximate computing (AC) circuit. The technique, which may be used during the high-level design of digital circuits, estimates error metrics for the outputs of the approximate circuit. The proposed technique receives as an input, output error characterizations of arithmetic modules in a high-level library. The error modeling of a library module is accomplished by dividing the input (operand) ranges into intervals and then characterizing the output error for different combinations of these input intervals. Subsequently, the error for each combination is stored in a lookup table (LUT). The module error models are integrated into a design space exploration (DSE) framework to evaluate different combinations of exact and approximate realizations of various operations in the DFG. The DSE, which performs error calculation and propagation from inputs to outputs of the target DFG, may be used to explore trade-offs between the output error and other design metrics of the approximate circuit, e.g., energy efficiency. The efficacy of the proposed method is assessed for three image processing benchmarks. Results for these benchmarks demonstrate that the framework can efficiently generate the Pareto frontier (PF) in the trade-off space of accuracy versus energy efficiency for the targeted benchmarks. Compared to a purely simulation-based exploration, the proposed technique provides an average of $92\times $ speed improvement.
Twelve Weeks of Oral L-Serine Supplementation Improves Glucose Tolerance, Reduces Visceral Fat Pads, and Reverses the mRNA Overexpression of Renal Injury Markers KIM-1, IL-6, and TNF-α in a Mouse Model of Obesity

Comorbidities associated with obesity, including diabetes and kidney diseases, greatly increase mortality rates and healthcare costs in obese patients. Studies in animal models and clinical trials have demonstrated that L-serine supplementation is a safe and effective therapeutic approach that ameliorates the consequences of obesity. However, little is known about the effects of L-Serine supplementation following high-fat diet (HFD) consumption and its role in the mRNA expression of markers of kidney injury. We provide a descriptive action by which L-serine administration ameliorated the consequences of HFD consumption in relation to weight loss, glucose homeostasis as well as renal mRNA expression of markers of kidney injury. Our results indicated that L-Serine supplementation in drinking water (1%, ad libitum for 12 weeks) in male C57BL/6J mice promoted a significant reduction in body weight, visceral adipose mass (epididymal and retroperitoneal fat pads) as well as blood glucose levels in mice consuming a HFD. In addition, the amino acid significantly reduced the mRNA expression of the Kidney Injury Marker 1 (KIM-1), P2Y purinoceptor 1 (P2RY1), as well as pro-inflammatory cytokines (IL-6 and TNFα). L-serine administration had no effect on mice consuming a standard chow diet. Collectively, our findings suggest that L-serine is an effective compound for long-term use in animal models and that it ameliorates the metabolic consequences of HFD consumption and reduces the elevated levels of renal pro-inflammatory cytokines occurring in obesity.
Twitter sentimental analysis using machine learning

This research paper aims to explore the effectiveness of machine learning algorithms in analyzing sentiment on Twitter. The study utilizes a dataset of tweets collected from various sources, which were then preprocessed to remove noise and irrelevant data [4, 5] . To categorize the tweets as positive, negative, or neutral, a number of machine learning techniques were used, such as logistic regression and Naive Bayesian [1] . The efficiency of these algorithms is also assessed in the study using a number of criteria, including accuracy, precision, recall, and F1 score. The results indicate that machine learning algorithms are effective in analyzing sentiment on Twitter, with Naive Bayes providing the best performance [18] . The results of this study have significant ramifications for companies and organizations looking to track consumer opinion of their goods or services [7] . This paper examines the problem of analyzing sentiment in Twitter by examining the tweets' expressed sentiments—whether they be favourable, negative, or neutral. Natural language processing methods will be used to analyze the messages that
Disentangling the relationship between falls, fear of falling, physical function and walking by applying a socioecological framework to the International Mobility in Aging Study

Introduction:The relationships between falls, fear of falling, poor mobility, and PA avoidance occur in a cyclic, multi-directional fashion. Aim: This study investigates the concomitant associations of fall history, fear of falling, and physical performance (SPPB) on physical activity using a cross-national sample of community-dwelling older adults from middle and high-income countries.Methods:Linear mixed-effects models looking at the influence of individual and environmental factors were used and participants were nested within each study site.Results:Estimated walking minutes was 52% lower for those with low SPPB compared to high SPPB, 20% lower for those with medium level fear of falling compared to low levels, and 50% lower for those with high level fear of falling compared to low levels.Conclusion:An individual’s fear of falling and physical performance may be important to consider when making PA recommendations to older adults regardless of sex, age, and environment.
The Effect of Product Development, Health Consciousness on Purchase Intention and Decision

Cigarette products in Indonesia have developed from conventional cigarettes, cigars, and shisha to electric cigarettes or commonly known as vapes. Indonesia ranks as the country with the third-largest number of smokers globally, even though people know that the consumption of tobacco products can cause harm to their bodies. Warning labels are printed on each pack of conventional cigarettes and e-cigarettes with sentences such as "smoking can damage health" (or similar meaning sentences) and pictures ranging from damaged human lungs, and perforated throat to pictures of fallen teeth. This behavioral phenomenon becomes interesting for further study. This research aims to analyze the influence of product development and health consciousness on purchase intention and its impact on the purchase decision of e-cigarettes in Tangerang City, Indonesia. This study is a quantitative research using a non-probability approach with purposive sampling. The number of populations in this study is 1,378,860, with a minimum sample of 385 respondents. Data were collected by distributing online questionnaires to smokers and non-smokers above 18 years old in Tangerang and then analyzed using SPSS. The results showed that product development has a positive significant impact on purchase intention and purchase decision, and purchase intention has a significant effect on purchase decisions, while other variables have a negative significant impact on each other.
Mesh-Based Consensus Distributed Particle Filtering for Sensor Networks

Following the Bayesian inference framework, this article investigates the problem of distributed particle filtering over a sensor network to achieve consensus. The objective of the posterior-consensus strategy is to fuse the posterior probability distribution functions (PDFs) at different sensor nodes, so that an agreement of belief can be established in terms of the Kullback-Leibler average (KLA). To facilitate the consensus process and reduce the communication load, the local PDFs are approximated with weighted meshes and transmitted between neighboring nodes. The mesh representations are constructed by resorting to a grid partition of the state space, such that the PDF can be approximated by a linear combination of indicator functions. To derive a particle representation of the fused PDFs, a novel importance density function is designed to draw particles with respect to the information from all neighboring nodes. The weights of the particles are calculated via the recursive solution of the KLA. The effectiveness of the proposed filtering approach is demonstrated through two target tracking examples.
Synergizing Low Rank Representation and Deep Learning for Automatic Pavement Crack Detection

Due to the critical role of pavement crack detection for road maintenance and eventually ensuring safety, remarkable efforts have been devoted to this research area, and such a trend is further intensified for the coming unmanned vehicle era. However, such crack detection task still remains unexpectedly challenging in practice since the appearance of both cracks and the background are diverse and complex in real scenarios. In this work, we propose an automatic pavement crack detection method via synergizing low rank representation (LRR) and deep learning techniques. First, leveraging LRR which facilitates anomaly detection without making any specific assumption, we can easily discriminate most of the frames with cracks from the long sequence with a consistent pavement base, followed by a straightforward algorithm to localize the cracks. In order to achieve the intelligence of detecting cracks with different pavement basis under unconstrained imaging conditions, we resort to deep learning techniques and propose a deep convolutional neural network for crack detection leveraging on multi-level features and atrous spatial pyramid pooling (ASPP). We train this network based on the training data obtained in the previous stage in an end-to-end manner. Extensive experiments on a wide range of pavements demonstrate the high performance in terms of both accuracy and automaticity. Moreover, the dataset generated by us is much more extensive and challenging than public ones. We put it online at https://gaozhinuswhu.com to benefit the community.
Quadrolltor: A Reconfigurable Quadrotor With Controlled Rolling and Turning

This letter reports an aerial robot–Quadrolltor, with the ability to roll and turn. Existing bimodal quadrotors feature cylindrical rolling cages that are rotationally decoupled from the robot's main rigid body. In contrast, the proposed robot employs passively reconfigurable structures to enable the second mode of locomotion, tightly coupling the attitude of the robot to the rolling cage. The benefits are precise rolling and turning control as well as improved rolling efficiency. Experiments were conducted to comprehensively validate the hybrid locomotion. The robot leveraged the superior maneuverability in the rolling mode to take photos of the surroundings at different tilting and panning angles to construct a panoramic image. Besides, the results of the power measurements show a significant reduction in the cost of transport brought by at low speed, equating to a 15-fold extension in the operational range.
Flexible and High-Gain DOFS Deconvolution Based on Data-Driven Denoising Prior

Spatial resolution is an essential parameter for distributed optical fiber sensing (DOFS). Deconvolution is a promising solution to improve spatial resolution because of its good universality and ability to restore infinitely high spatial resolution in theory. However, conventional iterative deconvolution algorithms require manually designed priors, which makes it difficult to restore the signal accurately. Although end-to-end methods based on artificial neural networks provide more accurate results based on data-driven priors, a neural network can only be applied to data with similar characteristics to its training data. For systems with different parameters, the neural network needs to be retrained or fine-tuned, which is time and computational resource consuming. Here, we employ a new deconvolution method for DOFS based on half-quadratic splitting and denoising neural networks that exploits the advantages of both iterative and machine learning methods. Besides, we propose a method to calculate the deconvolution gain to evaluate the deconvolution performance quantitatively. In an experiment with 10 deconvolution ratios, the deconvolution gain of the employed method is 15.8 dB, while that of a conventional iterative method and an end-to-end machine learning method are 7.9 dB and 15 dB, respectively. Moreover, this new deconvolution method can be adapted to data with arbitrary system parameters, making it more flexible than end-to-end neural networks in practical applications.