Miracle-dz/UniFAR-FTUs huggingface dataset:
Urbanization impacts water quality and the use of microhabitats by fish in subtropical agricultural streams

Summary Land-use changes have negative effects on stream conditions and fish assemblages, but their effects on water quality and the use of microhabitats by fish in subtropical lowland streams are unclear. We evaluated the effects of urban patches (two urban streams) in an agricultural matrix (two ‘agricultural’ streams) on water quality, microhabitat diversity and fish assemblages, as well as the selection of microhabitat types by fish in the Arroyo Colorado basin (Uruguay). Physicochemical water parameters were measured, the fish sampled using electrofishing, microhabitat diversity, occupancy of microhabitats by fish and the most important microhabitat types for fish species were analysed. Agricultural streams presented higher water quality and microhabitat diversity, and most of the microhabitats were occupied by fish. Microhabitats with medium substrate sizes and running water prevailed in urban streams, while the presence of macrophytes, shallow waters and different substrate sizes were common in agricultural streams. The most important microhabitats used by fish species were not the most abundant, highlighting the fragility of streams resulting from the loss of appropriate conditions. Understanding how different degrees of deterioration in streams affect water quality and microhabitat–fish interactions is essential for the designing of effective monitoring and restoration programmes.
Children’s Use of Uptalk in Narratives

Uptalk refers to the use of rising intonation on declarative utterances. Previous research has shown that, at age 6 years, children use rising contours with declaratives more frequently than adults, and this pattern appears to persist until 14 years of age. However, it is unclear why such a trend persists. To gain a clearer developmental picture of uptalk, the present study analyzed the form and function of uptalk produced by children aged 6 to 7 and 10 to 11 years from the American Midwest, using a storytelling task. Contrary to previous findings, the results indicate that children of both age groups use uptalk in an adult-like way: they overwhelmingly favor L-H% over H-H% boundary tones, and most strongly associate the contour with continuation. The lack of age differences suggests that children’s use of uptalk is comparable to that of adults by the age of 6, at least in certain narrative contexts. The use of a familiar storytelling task in the current study may explain the greater success observed for children than in previous studies, suggesting the relative importance of the elicitation task in the investigation of child speech.
Prosodic realisation of enjambment in recitations of German poetry

A salient feature of poetry is the organisation in lines and stanzas. Versiﬁcation thus serves as a structural (and aesthetic) layer that either conforms to syntactic units or breaks them up. If line breaks disrupt syntactic units, we speak of enjambment - the line suggests a pause while the syntactic information continues. Since prosodic boundaries typically reﬂect syntactic boundaries, the question is which information is marked - the line or the syntactic unit. In a preliminary study with one professional speaker of German, we investigate how line breaks are prosodically realised in recitations of twenty poems by Friedrich Hölderlin. We compare cases of enjambment to line breaks without enjambment and look at lengthening of the line-ﬁnal segment, frequency and duration of pauses and F 0 reset across the line break, which are typical cues used for the prosodic marking of phrase boundaries in speech. We found that while pauses and F 0 reset are tuned down in cases of enjambment, the line break is still prosodically marked by means of ﬁnal lengthening. This preliminary result supports the idea that a speaker can convey both the syntactic continuity as well as the versiﬁcation of the poem by strengthening different cues.
Impact of Population Aging and Renewable Energy Consumption on Agricultural Green Total Factor Productivity in Rural China: Evidence from Panel VAR Approach

China is moving toward the important goal of being a green and low-carbon country, and the current severity level of population aging is of particular concern to the government. Aging, renewable energy consumption, and technological progress are closely linked. In this research, a panel vector autoregressive (PVAR) model is employed to investigate the long-run equilibrium relationship between population aging, renewable energy consumption and agricultural green total factor productivity using panel data for 30 Chinese provinces (cities) from 2000 to 2019. The findings reveal that, in the long run, both population aging and renewable energy use have considerable positive impacts on agricultural green total factor productivity. In addition, in order to more intuitively understand the impact of population aging and renewable energy consumption on agricultural green total factor productivity, the analysis adopts the impulse response function and variance decomposition. The contributions of population aging and renewable energy consumption to agricultural green total factor productivity are 2.23% and 0.56%, respectively, when the lag period is chosen to be 15, which implies that population aging and renewable energy use will continuously contribute to agricultural green total factor productivity. The study results have significant theoretical implications for understanding China’s aging population structure and current renewable energy use. Given the above results, this study puts forward countermeasures and suggestions from four aspects: improving agricultural infrastructure, increasing agricultural technology investment, increasing the stock of agricultural human capital and strengthening international cooperation.
NCA‐based hybrid convolutional neural network model for classification of cervical cancer on gauss‐enhanced pap‐smear images

Cervical cancer is a very serious disease that deeply affects women's lives, often resulting in death. This type of cancer, which is very common in women, is diagnosed at an early stage and is of vital importance for the success of the treatment. Pap‐smear tests are used by physicians as the primary diagnostic tool to diagnose the disease. In this study, a hybrid deep model is proposed to classify pap‐smear images to detect cervical cancer. In addition, the Gaussian method was applied to improve the images in the original dataset. Feature maps were taken from both the original dataset and the Gaussian‐enhanced dataset in the built hybrid architecture, which used Darknet53 and Mobilenetv2 models as the base. After these obtained feature maps were combined, useless features were extracted and the number of features was reduced by using the Neighborhood Component Analysis (NCA) dimension reduction method. Finally, this optimized feature map was classified into different classifiers. As a result of the experimental studies, it was determined that the proposed hybrid model performed better when compared to other studies in the literature and the accuracy rate was 98.90% in the Support Vector Machines (SVM) classifier.
Social and situational factors of speaker variability in collaborative dialogues

The acoustic features of the speaker’s voice in dialogues are li-able to change due to various situational factors, such as success of communication, social distance between the interlocutors, conversational roles etc. This paper presents an analysis of variation in the basic prosodic features—pitch, intensity, and speech tempo—across speakers’ gender, conversational role (informa-tion leader vs. follower), and social distance. The research is based on the SibLing speech corpus where ﬁve degrees of social distance between the interlocutors are presented: there are dialogues between same-gender siblings, same-gender friends, same-gender and opposite-gender strangers, strangers of different age and social status. Each pair of interlocutors played a card-matching game and performed a classical map task. The factor of conversational role revealed a signiﬁcant inﬂuence on all the analysed speech features: pitch, intensity, and speech tempo. Gender was not found to inﬂuence speech tempo, unlike pitch and loudness. Social distance was shown to play a signiﬁcant role for speech tempo (e.g., it tends to be lower in dialogues with strangers of different age and social sta-tus), and also, in interaction with other factors, for pitch and loudness. There was also a signiﬁcant inﬂuence of the type of task: card-matching game vs. map task.
ROAD CONDITION ASSESSMENT FROM AERIAL IMAGERY USING DEEP LEARNING

Abstract. Terrestrial sensors are commonly used to inspect and document the condition of roads at regular intervals and according to defined rules. For example in Germany, extensive data and information is obtained, which is stored in the Federal Road Information System and made available in particular for deriving necessary decisions. Transverse and longitudinal evenness, for example, are recorded by vehicles using laser techniques. To detect damage to the road surface, images are captured and recorded using area or line scan cameras. All these methods provide very accurate information about the condition of the road, but are time-consuming and costly. Aerial imagery (e.g. multi- or hyperspectral, SAR) provide an additional possibility for the acquisition of the specific parameters describing the condition of roads, yet a direct transfer from objects extractable from aerial imagery to the required objects or parameters, which determine the condition of the road is difficult and in some cases impossible. In this work, we investigate the transferability of objects commonly used for the terrestrial-based assessment of road surfaces to an aerial image-based assessment. In addition, we generated a suitable dataset and developed a deep learning based image segmentation method capable of extracting two relevant road condition parameters from high-resolution multispectral aerial imagery, namely cracks and working seams. The obtained results show that our models are able to extraction these thin features from aerial images, indicating the possibility of using more automated approaches for road surface condition assessment in the future.
Chitin nanocrystal based aqueous inks for 3D printing via direct ink writing

Chitin nanocrystal (ChiNC), a kind of rod-like nanoparticle, exhibits a high elastic modulus, high mechanical strength, good biocompatibility, and biodegradability. Its aqueous dispersion behaves with a prominent shear thinning and concentration-related sol-gel transition characteristics, which has feasibility of extrusion-based 3D printing, that is, direct ink writing (DIW). Based on this, herein, ChiNC was chosen as matrix material to explore its printability via DIW. A kind of waterborne polycitrate prepolymer (pre-POFC) was introduced into the ChiNC aqueous dispersion inks. On the one hand, pre-POFC was as rheology modifier to tune the flow behavior of the inks; on the other hand, the cured POFC component was as adhesive agent to glue ChiNC nanoparticles, aiming to improve water-stability and remove brittleness of the ChiNC-based materials. The results showed the optimal loading ratio of POFC component was 30–50 wt% determined via water-stability tests. The rheological analyses showed that the ChiNC/pre-POFC aqueous inks exhibited a prominent shear thinning behavior, thixotropy, modulus recovery ability, and higher dynamical yield stress (σy), meaning such systems had a good DIW printability theoretically. Actually, DIW results showed that only ChiNC/pre-POFC-6/4–30wt% and ChiNC/pre-POFC-5/5–30wt% inks exhibited optimal printability, in consideration of minimal contraction and superior shape retention after dehydration and thermocuring. However, the mechanical properties of printed films were weaker than that of cast films due to the limited printing precision and lower density.
Understanding the Use of Bio-Inspired Design Tools by Industry Professionals

Bio-inspired design (BID) has the potential to evolve the way engineers and designers solve problems. Several tools have been developed to assist one or multiple phases of the BID process. These tools, typically studied individually and through the performance of college students, have yielded interesting results for increasing the novelty of solutions. However, not much is known about the likelihood of the tools being integrated into the design and development process of established companies. The mixed-methods study presented in this paper seeks to address this gap by providing industry engineers and designers hands-on training with the BID process and four BID tools. Understanding which tools are valued and could be adopted in an industry context is the goal. The results indicate multiple encouraging outcomes including that industry practitioners highly valued the process framework tool (BID canvas) as it allows for flexibility in tool use, as well as valued learning with a suite of BID tools rather than a single one to accommodate different workflows and ways of thinking.
Wayside Acoustic Fault Diagnosis of Train Bearing Based on Improved Linear Discriminant Analysis

Train bearing is one of the key parts of a train wheel, and its fault diagnosis is crucial for train safety. This paper proposes an Improved Linear Discriminant Analysis (ILDA) algorithm to diagnose train bearing faults. A new distance function is employed to adjust the between-class divergence matrix of LDA. The adjusted between-class divergence matrix is adopted for selecting the projection direction with the best clustering performance and reducing the ambiguity or overlap among different classes. The sample clustering evaluation index(SI) is adopted to evaluate the clustering performance of fusion features obtained by ILDA. First, twenty-four original features are extracted from wayside acoustic signals after reducing the Doppler effect. Then, ILDA is adopted to extract fusion features. Finally, the train bearing faults are identified by extreme learning machine(ELM). The experimental results show that the fusion features extracted by the proposed method can be effectively identified, which have good clustering performance and high identification rate. Furthermore, the identification results of two datasets provided by different universities show that the fusion features obtained by ILDA also have good clustering performance and high identification rate, which further verify the validity of the proposed method.
Decentralized Moving Target Defense for Microgrid Protection Against False-Data Injection Attacks

This paper proposes the Decentralized Moving Target Defense via Data Replication (DMTDR) framework, which increases the security of microgrids by adding two layers of uncertainty that limit the success of false-data injection attacks. The DMTDR framework exploits the scalability and low cost of IoT devices to replicate relevant sensory and control signals, which are randomly transmitted through independent communication channels. Then, one of the transmitted replicas is randomly selected to reach its intended destinations. As opposed to most moving target defense (MTD) approaches found in the literature, the proposed DMTDR does not require central coordination but instead it can be applied to any monitoring or control device that utilizes a communication channel. Furthermore, the proposed approach does not deteriorate the system performance in normal operation. The theoretical foundations for the optimal allocation of replicas per signal are developed, and fundamental limits of uncertainties introduced by the framework are calculated. Moreover, taking advantage of the replicated information, a decentralized attack detection strategy is introduced, which is able to detect the presence of malicious data by only using local information without requiring the computation of any estimation model. The proposed framework is demonstrated on a test microgrid, where the data replication and random transmission are implemented on a WiFi network with IoT devices. The results show that the proposed framework considerably improves: i) security by limiting the impact of false-data injection attacks; ii) detectability by enabling the detection of stealthy attacks.
economic ordering policy to control deteriorating medicinal products of uncertain demand with trade credit for healthcare industries

In Healthcare industries, the most challenging problem for a decision maker is to develop an optimal inventory policy to estimate medicinal products with an uncertain scenario. The proposed model has been developed in such a way that it could provide an optimal policy for all relevant factors in the healthcare industry, such as uncertainty in demand, deterioration of products, preservation technology costs, shortages of products, inflation, and trade credit. The price and supply levels of some medicinal products have a substantial impact on the rate of consumption. Therefore, in this study, the demand function is dependent on price as well as stock, and the retailer invests in preservation technologies to decrease the deterioration of products. This leads to increased product demand, which benefits both the retailer and the supplier. In an attempt to bring the model closer to a real world problem, the variation in cost parameters over time is included in the fuzzy. The cost minimization technique is defuzzified using the signed distance method. Furthermore, the model has been demonstrated and validated with suitable numerical examples, and a sensitivity analysis has been provided.
The Physical Properties and Starspot Activity of the Triple System KIC 6525196

We present the results of photometric and spectroscopic analyses for the triple-lined system KIC 6525196, an eclipsing binary accompanied by a third star. By modeling the Kepler light curves and radial velocities from LAMOST and HIDES observations, absolute parameters of the system are determined. Both components of the eclipsing binary are found to be solar-like stars with masses and radii of M 1 = 1.0286 ± 0.0026 M ⊙, R 1 = 1.127 ± 0.008 R ⊙, and M 2 = 0.9667 ± 0.0024 M ⊙, R 2 = 0.963 ± 0.007 R ⊙. The mass of the third star is determined to be M 3 = 0.772 ± 0.010 M ⊙. With the out-of-eclipse light residuals, we measure rotation period and decay timescale of an active region by using the autocorrelation function. In comparison to the Sun, the activity level of the system is significantly stronger. In addition, a possible short photometric activity cycle of ∼244 days is detected.
Genetic testing of allelic variants of PIZ (GLU342Lys, RS28929474) and PIS (GLU264Val, RS17580) of SERPINA1 gene in children with bronchial asthma

According to world publications, mutations in the SERPINA1 gene may be a genetic risk factor for severe chronic obstructive pulmonary disease and, consequently, rapid progression of respiratory dysfunction. This disease leads to a decrease in the level of alpha-1-antitrypsin protein. It is inherited by autosomal recessive type, but there are registered cases of codominance. In the absence of treatment, diseases of the respiratory system become chronic and lead to disability in adulthood. Early diagnosis of AAT deficiency is important to prevent complications and reduce mortality among people with this pathology. Due to these factors, genetic testing of SERPINA1 gene mutations in children with chronic lung diseases is appropriate to detect and prevent severe complications, associated with AATD. The aim of this work is to improve the effectiveness of early diagnosis of AAT deficiency in children with bronchial asthma and recurrent obstructive bronchitis by identifying different genotypes and phenotypes of A1AT deficiency, studying their relationship with the clinical course of respiratory diseases in children
Historical biogeography of the gingers and its implications for shifts in tropical rain forest habitats

The relationships between biome shifts and global environmental changes in temperate zone habitats have been extensively explored; yet, the historical dynamics of taxa found in the tropical rain forest (TRF) remain poorly known. This study aims to reconstruct the relationships between tropical rain forest shifts and global environmental changes through the patterns of historical biogeography of a pantropical family of monocots, the Zingiberaceae.
A comprehensive approach for the empirical investigation of success factors in product development

The outcome of product development projects depends on a multitude of different factors, deriving not only from technical challenges, but also from aspects like human behavior or complex organizational environments. Empirical research studies have become increasingly important in verifying which of these Potential Success Factors contribute most to successful product development. Many studies have focused on certain aspects of the design process, such as the individual designer or design teams. These have contributed greatly to a better understanding in Engineering Design Research. Comprehensive studies, viewing the product development process holistically with all its influencing factors, are rare. However, additional information can be obtained from comprehensive investigations. In addition to understanding which of the Potential Success Factors show an actual correlation to Design Success then considered Success Factors it can also be investigated how these Success Factors depend on each other. Such dependencies provide information about causalities and subsequently lead to a set of Dominant Success Factors. These not only influence the outcome of product development projects significantly, but also cause other Success Factors to change, which is why they need to be given the first and most attention by engineering management. This dissertation illustrates the framework of an empirical approach, which supports the comprehensive investigation of Success Factors in product development. The approach was applied to 44 gas turbine component development projects of varying complexity. The collected data was analyzed using statistical methods, which provided information about the reliability of the results. Following the proposed approach with its two analysis steps lead to the identification of the following four Dominant Success Factors, which distinguished successful from less successful development projects: Priority of Project; Skills and Experience of Project Lead; Complexity and amount of Innovation of Project; Awareness of Lessons Learned and State of the Art Technology Knowledge. The remaining factors, not identified as dominant, are considered Supplementary Success Factors, as they can help to leverage Design Success. However, the research work clearly shows that the Dominant Success Factors contribute most and have to be seen as the foundation for successful product development. Engineering management can use the results of this work for an improved project risk management, especially in the early stages of product development.