Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of the Field-Stress State on the Subgrade Resilient Modulus for Pavement Rutting and IRI

The new Mechanistic-Empirical Pavement Design Guide (MEPDG) uses the subgrade resilient modulus (MR) as the key input parameter to represent the subgrade soil behavior for pavement design. The resilient modulus increases with an increase in confining pressure, whereas, for an increase in deviatoric stress, it increases for granular soils and decreases for fine-grained soils. The value of MR is highly stress dependent, with the stress state (i.e., bulk stress) a function of the position of the materials in the pavement structure and applied traffic loading. Applying excessive vertical stress at the top of the subgrade without knowing the appropriate stress state can result in permanent deformation. In situ stress must be calculated so the correct resilient modulus can be determined. To facilitate the implementation of MEPDG, this study develops a methodology to select the appropriate subgrade resilient modulus for predicting rutting and IRI. A comprehensive research methodology was undertaken to study the effect of in situ or undisturbed subgrade MR on pavement performance using the MEPDG. Results show that MR obtained from in situ stress is approximately 1.4 times higher than the MR estimate from NCHRP-285. Thus, the in situ stress significantly affects the calculation of subgrade MR and, subsequently, the use of MR in the predicted rutting, with IRI using the AASHTOWare pavement mechanistic-empirical design. Results also show that the pavement sections were classified as in “Good” and “Fair” conditions for rutting and IRI, respectively, considering in situ MR.
Development of a multi-layer cylindrical rotating electret generator with increased power densit

Electrostatic electret generator has attracted a lot of attention in recent years, but their low power density cannot be ignored. Based on the existing research, this paper increases its power density from two aspects, including in-plane and in-space aspects. In the plane aspect, through simulation analysis, we can draw a conclusion that the output power can be increased effectively by using the bipolar charging method. From the perspective of space, the power density can be increased several times by connecting the output ends of the multi-layer generation structure. By integrating the method into the electrostatic electret generator, the power output can be effectively increased, which is suitable to the application of higher energy requirements in the specific field and a certain application prospect.
Open-Metric for Unknown Signal Inference

The closed-set assumption that only known classes can be classified during testing is usually adopted in traditional signal recognition missions. It has been thoroughly explored and made huge progress in the past decade. However, it cannot cope with the unknown categories coming from the changing environment. As waveform diversity technology evolves by leaps and bounds, the existence of novel unknown classes is inevitable in the open-world environment, imposing a huge challenge to the noncooperative electronic support system. To further satisfy the demands of modern electronic intelligence systems and sense new threats, it is urgent to find an approach that can tackle unknown signals. An open-metric strategy is proposed in this article to deduce the unknown classes from the intercepted signals. By virtue of metric learning methods, a plain backbone can be trained to offer more discriminative representations and a consistent proxy for each class. According to extreme value analysis, information about known classes can be converted into an estimated score of unknown classes, creating an open decision layer. The investigated open-metric approach demonstrates its superiority in unknown signal inference tasks, facilitating improvements in the intelligence and smartness of the electronic reconnaissance agents to cope with future cognitive electronic warfare.
Alternative expressions of 'want' complements

The complements of “want” predicate may vary depending on the identity of the main and the dependent subjects. When identical, five main tendencies are found cross-linguistically: (i) the dependent subject is omitted, (ii) the dependent subject is overtly expressed, (iii) “want” is a desiderative affix or (iv) an uninflected particle, and (v) there are alternative choices (Haspelmath 2005). Using data from the Uto-Aztecan family, this article focuses on one of the less common means, the co-existence of alternative expressions. The analysis provides an account for this phenomenon in light of the Interclausal Relations Hierarchy (Van Valin 2005): whenever there are available structures in a language, the tightest linkage encodes intention, and the less tight expresses different mental states.
Implementation of Bayesian parameter estimation in adaptive control and signal processing

Severe constraints are put on Bayesian estimation in adaptive control and signal processing. The sequential character of computations together with finite memory boundaries often require using a reduced rather than sufficient statistic. A fixed sampling period firmly limits the computational time. Under these conditions, the textbook scheme of Bayesian estimation may be impracticable. This paper asks: 'Does there exist a looser scheme that would meet the practical constraints and still be consistent in a certain sense with the Bayesian paradigm? ' The key point appears to be the construction of a reduced data statistic. When the equivalence induced by the used statistic on the set of possible posteriors is compatible with the Bayes rule action, the true Bayesian inference can be performed recursively on the equivalence classes of posteriors. This fact opens a way towards a better-justified real-time implementation of Bayesian parameter estimation.
Toric Mirror Constructions and Derived Equivalence

We unify the various mirrors in the Batyrev-Borisov and Berglund-Hubsch constructions by proving equivalences of derived categories. In particular, we prove a conjecture of Batyrev and Nill and a derived analogue to the results of Borisov, Clarke, Kelly, and Shoemaker on the birationality of Berglund-Hubsch-Krawitz mirrors. Heavily bootstrapping off work of Seidel and Sheridan, we obtain Homological Mirror Symmetry for Berglund-Hubsch-Krawitz mirror pencils to hypersurfaces in projective space. Our results extend to Fano hypersurfaces in projective space where they provide a categorical explanation of mirrors to rigid Calabi-Yaus appearing as Fano Calabi-Yau manifolds.
On weak convergence within a family of life distributions

It is well known that the weak convergence and moment convergence of distributions are not equivalent in general. However, for distributions within a suitable family, these two modes of convergence can be equivalent. For example, Basu and Bhattacharjee (1984) obtained the equivalence of weak convergence and moment convergence for life distributions within the harmonic new better than used in expectation family (HNBUE family) which is related to the exponential distribution, and Lin (1994, 1995) extended their result from the HNBUE family to some larger families. In this letter we further prove that the same equivalence property holds for the following larger family ~cx which is related to the gamma distribution. Let X be a general non-negative random variable with distribution F, and denote IlF=EX. Also, let Ycx,p be a random variable obeying the gamma distribution Gcx,p with density function 1 g",p(x) = r(a.)f3« X«-le-xIP
Study on image threshold segmentation based upon improved fuzzy entropy

Image threshold method based on fuzzy entropy combines the conception of entropy in information theory with image processing,thus threshold segmentation method takes on new improvement.But,its application is restricted within narrow limits by reason of big calculation,difficult adaptive selection of membership function window size and difficulty of obtaining threshold searching zone,etc.To solve his problem,a new method of adaptive window size selection and obtaining threshold zone based on the statistic characteristic of gray distribution is presented.It can obtain better segmentation result whether image gray histogram is double peak or not.In addition,in order to satisfy the requirement of real-time segmentation,adaptive genetic algorithm is used to search the optimum,which improves the speed of segmentation to a great extent.Comparison with other algorithms indicates the elegance,validity and robustness of this method.
Intramammary infections and risk factors in freshly calved heifers in dairy herds.

Subclinical mastitis can be common among freshly calved heifers (FCH), but the prevalence differs between herds possibly due to variation in risk factors. The aims of this observational study were to identify differences in occurrence of IMI in FCH between herds with documented good or not so good first parity udder health based on cow SCC (CSCC) in early lactation, and to study herd differences in animal factors important for udder health such as udder and hock skin lesions and animal cleanliness. Three groups of herds were included, those with high proportion of FCH with low (≤75,000 cells/ml) CSCC at the first 2 milk recordings after calving (LL), herds with high proportion of FCH with high (>100,000 cells/ml) CSCC at the first and low CSCC at the second recording (HL), and herds with high proportion of FCH with high CSCC at both recordings (HH). Thirty-one herds (13 LL, 11 HL, 15 HH) were visited 3 times during a 12-mo period for observation of cleanliness and hock lesions, and sampling of udder/teat skin using swab cloths of milk-fed calves, early pregnant heifers and late pregnant heifers. In 25 (9 LL, 9 HL, 7 HH) udder quarter samples from colostrum and milk on d 3-4 after calving were taken by the farmers from FCH during one year. The farmers also provided information on calving (individual or group), use of restraint and oxytocin at milking, and presence of teat and udder skin lesions. Bacterial growth in swab samples and quarter samples was investigated by culturing, and a selection of isolates was genotyped using whole genome sequencing (WGS). Cleanliness, hock and udder skin lesions other than udder-thigh dermatitis, and growth of bacteria in swab samples did not differ between herd groups. It was more common that FCH from LL herds, compared with FCH in HH and HL herds, calved in a group of animals. Use of restraint at milking was more common in LL herds than in HH herds while presence of udder-thigh dermatitis was lowest in LL herds. Specific infection was found in 14% of 5,593 quarter samples from 722 FCH. The most common IMI was S. chromogenes. Growth of S. simulans was more common in HH than in LL and HL herds. In colostrum samples, S. haemolyticus was more common in HL and HH than in LL herds. The proportion of quarters with the same specific infection at both samplings was higher in HH than in LL herds and tended to be higher in HH than in HL herds. The proportion of quarters with S. chromogenes IMI at both samplings tended to differ between herd groups and was highest in HH herds. WGS found the same sequence type of S. chromogenes and S. aureus in both samples in almost all quarters with the same infection at both samplings. The differences in IMI between herd groups were in line with the higher SCC in HH herds. The reasons for the predominance of S. chromogenes IMI in FCH need further studies.
HealthFaaS: AI-Based Smart Healthcare System for Heart Patients Using Serverless Computing

Heart disease is one of the leading causes of death worldwide, and with early detection, mortality rates can be reduced. Well-known studies have shown that the latest artificial intelligence (AI) can be used to determine the risk of heart disease. However, existing studies did not consider dynamic scalability to get the best performance from these AI models in case of an increasing number of users. To solve this problem, we proposed an AI-powered smart healthcare framework called HealthFaaS, using the Internet of Things (IoT) and a Serverless Computing environment to reduce heart disease-related deaths and prevent financial losses by reducing misdiagnoses. HealthFaaS framework collects health data from users via IoT devices and sends it to AI models deployed on a Google Cloud Platform (GCP)-based serverless computing environment due to its advantages, such as dynamic scalability, less operational complexity, and a pay-as-you-go pricing model. The performance of five different AI models for heart disease risk detection is evaluated and compared based on key parameters, such as accuracy, precision, recall, $F$ -Score, and AUC. Experimental results demonstrate that the light gradient boosting machine model gives the highest success in detecting heart diseases with an accuracy rate of 91.80%. Further, we have tested the performance of the HealthFaaS framework in terms of Quality-of-Service (QoS) parameters, such as throughput and latency against the increasing number of users and compared it with a non-serverless platform. In addition, we have also evaluated the cold start latency using a serverless platform which determined that the amount of memory and the software language makes a direct impact on the cold start latency.
An Insight of Parasitic Weeds in Africa and Scientific Developments: A Review

Parasitic weeds are a major threat to food security in Africa and control measures mostly done by smallholder farmers are not effective in eradicating the parasites. This results in a yield loss up to 100%. Parasitic weeds comprise Alectra vogelii, Striga spp. , Orobanche spp. , Rafflesia spp. , and Phoradendron spp. Parasitic attachment is successful when three necessary conditions have been fulfilled namely the compatible host, suitable environment, and parasitic weed. These species parasite plant species through special attachment features such as modified leaves, suckers, haustoria, or modified roots. In Africa, the variability of parasitic weeds is largely driven by environmental factors such as temperature, rainfall, soil type, and crop husbandry practices. Warmer temperatures create more hospitable conditions for certain parasitic weeds, and allowing them to spread to new areas. Parasitic weed control is vital for effective crop production and the control strategies can be achieved through integrated weed control method that embraces mechanical, cultural, chemical, and biological methods. However, the most effective and crucial method is the cultivation of resistant varieties that provide long-term protection against parasitic weeds. Studies have been done on host-parasite attachment where dodder can send out new roots to infected neighbouring plants and spread their parasitic behaviour. More insight and knowledge should offer new goals for control within the life cycle of the parasitic weeds and their metabolic activities. Lastly, disciplines such as agronomy, plant breeding, nutrition, economics, and IT should play their roles effectively in combating parasitic weeds.
AN ALGORITHMIC APPROACH BY LINEAR PROGRAMMING PROBLEMS IN GENERALIZED SEMI-INFINITE OPTIMIZATION ¤

Здесь I — конечно, а возможно бесконечное множество Y задано конечным набором равенств uk и неравенств v`. Дополнительно предполагается, что множество MSI компактно и объединение множеств Y (x) может быть задано конечным набором ограничений. При этих условиях в окрестности каждого элемента множества MSI задача PSI может быть с любой точностью аппроксимирована линейной задачей оптимизации P lin F . Такие линейные аппроксимации с конечным числом ограничений порождают итерационную проследовательность, содержащую подпоследовательность, сходящуюся к решению глобальной проблемы PSI . В будущем на основе этой процедуры может быть разработан алгоритм.
Geometric Primitive-Guided UAV Path Planning for High-Quality Image-Based Reconstruction

Image-based refined 3D reconstruction relies on high-resolution and multi-angle images of scenes. The assistance of multi-rotor drones and gimbal provides great convenience for image acquisition. However, capturing images with manual control generally takes a long time. It could easily lead to redundant or insufficient local area coverage, resulting in poor quality of the reconstructed model. We propose a surface geometric primitive-guided UAV path planning method (SGP-G) that aims to automatically and quickly plan a collision-free path to capture fewer images, based on which high-quality models can be obtained. The geometric primitives are extracted by plane segmentation on the proxy, which performs three main functions. First, a more representative evaluation of the reconstructability of the whole scene is realized. Second, two optimization strategies for different geometric primitives are executed to quickly generate a near-global optimized set of viewpoints. Third, regularly arranged viewpoints are generated to improve the efficiency of image acquisition. Experiments on virtual and real scenes demonstrate the remarkable performance of our method. Compared with the state of the art, we accomplish the planning of the photographic path with higher efficiency in a relatively simple way, achieving equivalent and even higher quality of the reconstructed model with fewer images.
STRUCTURE OF ALGEBRAIC COMPETENCIES

Ideally, algebra lessons lead students to develop a profound understanding of algebraic concepts and the ability to see algebra as a central and connected branch of mathematics and the ability to apply algebra to a wide range of topics. If this happens, then students can be said to have a high algebraic competency. Even with this aim in mind, it is not clear how to design algebra courses. There are many approaches to the teaching of algebra (see e.g. Bednarz et al. 1996) and they obviously differ in the algebraic concepts that are given priority. The field of algebraic concepts is very broad, e.g. mastering the concept of an equation is a long process in which various aspects of the equation concepts are learned and they all interact with other algebraic concepts. To help in planning the algebraic learning process, it would thus be useful to gain more insight into the inner structure and dependencies of these algebraic concepts.
OptEF-BCI: An Optimization-Based Hybrid EEG and fNIRS–Brain Computer Interface

Multimodal data fusion (electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS)) has been developed as an important neuroimaging research field in order to circumvent the inherent limitations of individual modalities by combining complementary information from other modalities. This study employed an optimization-based feature selection algorithm to systematically investigate the complementary nature of multimodal fused features. After preprocessing the acquired data of both modalities (i.e., EEG and fNIRS), the temporal statistical features were computed separately with a 10 s interval for each modality. The computed features were fused to create a training vector. A wrapper-based binary enhanced whale optimization algorithm (E-WOA) was used to select the optimal/efficient fused feature subset using the support-vector-machine-based cost function. An online dataset of 29 healthy individuals was used to evaluate the performance of the proposed methodology. The findings suggest that the proposed approach enhances the classification performance by evaluating the degree of complementarity between characteristics and selecting the most efficient fused subset. The binary E-WOA feature selection approach showed a high classification rate (94.22 ± 5.39%). The classification performance exhibited a 3.85% increase compared with the conventional whale optimization algorithm. The proposed hybrid classification framework outperformed both the individual modalities and traditional feature selection classification (p < 0.01). These findings indicate the potential efficacy of the proposed framework for several neuroclinical applications.
Adult ethnic minorities’ mainstream language proficiency: cultural knowledge, cultural identification, and language use attitudes

ABSTRACT This study examines how immigrants' enculturation/acculturation, language use, and parenting were linked to their oral and written language proficiencies in multilingual Hong Kong (speak English, speak Cantonese, read and write Chinese). Participants in this study included 655 immigrant parents (from Pakistan, Nepal, India, Philippines, Indonesia, Kenya, Bangladesh, Malaysia, and others). Structural equation modelling of survey responses showed that parents with greater ethnic enculturation or host acculturations reported better mainstream language skills (speak English, speak Cantonese, read and write Chinese). More heritage cultural knowledge was linked to better spoken Cantonese. Greater cultural identification with their own ethnic community was linked to better spoken English. Those with more local Chinese cultural knowledge reported better Chinese reading and writing and permissive parenting, which contributed to better English speaking. Compared to immigrant fathers, immigrant mothers reported lower Cantonese and English speaking proficiencies. Together, these results show that greater cultural identification with the ethnic community was linked to greater English-speaking skills.
Shared Micro-Mobility Services for University Communities: A Multivariate Ordered Probit Approach

The market penetration of shared micro-mobility services (shared bikes, e-bikes, and e-scooters) suggest that young and highly educated people constitute a group of people that is more eager to use such services. However, little is known about the factors influencing their behavioral intention as a system. The analysis may provide insights on the adoption of shared micro-mobility services by modeling them as a system using multivariate ordered probit. This paper contributes to the existing literature by evaluating similarities and differences to these services focusing on assessing the behavioral intentions of a large sample of young and highly educated people that traditionally show a higher tendency of adopting technologically innovative services. It is hypothesized that the mobility patterns can affect people's adoption of these shared services in a car-dependent empirical setting. The analysis suggested that a variety of variables affect the behavioral intention to use shared micro-mobility services, such as employment situation, household size, travel patterns, level of awareness, and attitudinal variables in the form of perceptions on incentives and infrastructure. This study can enhance the current understanding of the factors affecting the behavioral intention, facilitating the development of policies toward such means of transportation. The results could convincingly answer key questions on the market adoption of these shared services, and characteristics of distinct market segments; thereby providing a deployment pathway for adoption. Additionally, the results could be useful to original equipment manufacturers for marketing and pricing-scheme decisions, planning agencies for policy-making decisions, and to transportation planners for provisions in infrastructure.