Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimating the burst pressure of a filament wound composite pressure vessel using two-scale and multi-scale analyses

Abstract The influence of inescapable imperfections associated with fiber arrangement in the filament winding process on the burst pressure of a composite vessel is analyzed. Firstly, the influence of fiber packing, fiber spacing and fiber contiguity is analyzed through a two-scale analysis. Mechanical properties of the layers are estimated considering aforementioned parameters at the scale of micro and the burst pressure is estimated at the scale of macro. As the second approach, fiber-bundle crossovers and undulation arisen from filament winding process are also considered at the scale of meso. Thus, a multi-scale modeling is performed capturing micro, meso and macro scales.
Robust Optimization of the Hub Location Problem for Fresh Agricultural Products With Uncertain Demand

Traditional hub location problems are usually based on deterministic circumstances. However, many uncertain factors can cause demand to vary in the long run, which increases the difficulty of strategic hub location planning. The hub location problem for fresh agricultural products is studied considering the perishability of the products and the uncertainty of customer demands. An uncertain demand variable is described by an affine function of the nominal mean and several independent uncertainty sources and is further adjusted by the deterioration rate. An uncapacitated robust hub location model for fresh agricultural products with uncertain demand is first established and solved using the Lagrangian relaxation approach. Then, a robust optimization model for the corresponding capacitated hub location problem is given. A numerical study based on the Australian Post data set (AP20) shows that the deterioration rate of fresh agricultural products, the uncertainty of demand and the degree of conservatism of decision makers all have significant impacts on the total transportation profit. Furthermore, the capacitated model yields more profit than the uncapacitated model because it allows the effects of the deterioration rate and uncertainty to be moderated through flow reallocation. The proposed models are useful for helping decision makers determine the locations and capacities of hubs for fresh agricultural products in accordance with different risk preferences.
Artificially Intelligent, Interactive, and Assistive Machines: A Definitional Framework for Intelligent Assistants

Abstract Intelligent assistants have proliferated across various settings spurring research and ideas. However, there is a lack of clarity on how they can be defined and identified. This definitional and terminological incoherence manifests in narrow and misinformed understanding of technology and thus, poses threats to theoretical and empirical endeavors in AI. To overcome these challenges, this paper presents a definitional framework that defines intelligent assistants and distinguishes them from other technologies. This framework is informed by tracing the historical evolution of this technology in the past 70 years which saw the rise of three constitutive features in machines: AI, interaction, and assistance. A review of technology’s definitions published since 2010 and associated terminologies was also conducted to unpack their conceptual limitations and support the framework. A new definition is then presented which contends that a technology can only be characterized as an intelligent assistant if it is AI-enabled, interactive, and assistive.
High temporal resolution estimates of Arctic snowfall rates emphasizing gauge and radar-based retrievals from the MOSAiC expedition

This article presents the results of snowfall rate and accumulation estimates from a vertically pointing 35-GHz radar and other sensors deployed during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. The radar-based retrievals are the most consistent in terms of data availability and are largely immune to blowing snow. The total liquid-equivalent accumulation during the snow accumulation season is around 110 mm, with more abundant precipitation during spring months. About half of the total accumulation came from weak snowfall with rates less than approximately 0.2 mmh–1. The total snowfall estimates from a Vaisala optical sensor aboard the icebreaker are similar to those from radar retrievals, though their daily and monthly accumulations and instantaneous rates varied significantly. Compared to radar retrievals and the icebreaker optical sensor data, measurements from an identical optical sensor at an ice camp are biased high. Blowing snow effects, in part, explain differences. Weighing gauge measurements significantly overestimate snowfall during February–April 2020 as compared to other sensors and are not well suited for estimating instantaneous snowfall rates. The icebreaker optical disdrometer estimates of snowfall rates are, on average, relatively little biased compared to radar retrievals when raw particle counts are available and appropriate snowflake mass-size relations are used. These counts, however, are not available during periods that produced more than a third of the total snowfall. While there are uncertainties in the radar-based retrievals due to the choice of reflectivity-snowfall rate relations, the major error contributor is the uncertainty in the radar absolute calibration. The MOSAiC radar calibration is evaluated using comparisons with other radars and liquid water cloud–drizzle processes observed during summer. Overall, this study describes a consistent, radar-based snowfall rate product for MOSAiC that provides significant insight into Central Arctic snowfall and can be used for many other purposes.
Quasi-Negative Binomial: Properties, Parametric Estimation, Regression Model and Application to RNA-SEQ Data

Background: The Poisson and the Negative Binomial distributions are com-monly used to model count data. The Poisson is characterized by the equality of mean and variance whereas the Negative Binomial has a variance larger than the mean and therefore both models are appropriate to model over-dispersed count data. Objectives: A new two-parameter probability distribution called the Quasi-Negative Binomial Distribution (QNBD) is being studied in this paper, generalizing the well-known negative binomial distribution. This model turns out to be quite flexible for analyzing count data. Our main objectives are to estimate the parameters of the proposed distribution and to discuss its applicability to genetics data. As an application, we demonstrate that the QNBD regression representation is utilized to model genomics data sets. Results: The new distribution is shown to provide a good fit with respect to the “Akaike Information Criterion”, AIC, considered a measure of model goodness of fit. The proposed distribution may serve as a viable alternative to other distributions available in the literature for modeling count data exhibiting overdispersion, arising in various fields of scientific investigation such as genomics and biomedicine.
Small obstacles detection on roads scenes using semantic segmentation for the safe navigation of autonomous vehicles

Abstract. Currently, autonomous vehicles and intelligent robots are used in an extensive set of industrial applications. However, in the recent past, the frequency with which small obstacles arise on highways has risen dramatically, which will lead to severe incidents on highways. Hence, small obstacle detection is critical for increasing the efficiency of autonomous vehicles to enable their safe navigation to avoid road accidents. On the other hand, these small-size obstacles are of varying sizes, shapes, and colors and are difficult to be detected under low lighting and illumination conditions. Existing research studies suggest deep-learning-assisted semantic segmentation models; however, an optimal model along with improved performance is of greater necessity. From this perspective, we have suggested transfer learning-based approaches using state-of-the-art semantic segmentation models namely UNet++, PSPNet, PANNet, LinkNet, and DeepLabV3+ for the detection of small-size obstacles under strict lighting and illumination conditions. Furthermore, we used images of road scenes with extremely small-size obstacles, which were neglected by past research studies, since these obstacles were not included in the databases that they were using in their findings and may have failed to effectively address the problem of obstacle detection. Moreover, for faster and better convergence, we have modified the backbone architectures of these models with Residual-Network (ResNet)-18 and ResNet-34 trained on ImageNet weights. It is observed that DeepLabV3 + ResNet-18 as backbone architecture shows the highest results with a mean intersection-over-union score of 64% along with a 95% value of accuracy.
The emotional journey of the beginning teacher: Phases and coping strategies

ABSTRACT Research on the transition from teacher education to beginning to teach have focused on the ability to teach, as well as on classroom practices, and how complicated socialisation processes impede developing skills when starting to teach. The aim of the study was to investigate emotionally challenging situations during teacher education and when starting to teach, with a focus on how the participants’ perspectives and coping strategies changed over time. In this study, 20 participants were followed during their final year of teacher education and into their first year of teaching. Data was collected through interviews and written self-reports. A constructivist grounded theory methodology was adopted. We found that new teachers experience three main emotional phases as they move from teacher education and into teaching, namely (1) opposite positions, (2) enthusiasm mingled with fear, and (3) a rollercoaster of emotions. Emotions and coping strategies linked with the phases are illustrated, and practical implications are discussed.
Mechanical Characterization and Finite Element Analysis of Hierarchical Sandwich Structures with PLA 3D-Printed Core and Composite Maize Starch Biodegradable Skins

The objective of this research is the fabrication of biodegradable starch-based sandwich materials. The investigated sandwich structures consist of maize starch-based films as skins and biodegradable 3D-printed polylactic filaments (PLA) as the core. To investigate the tensile properties of the skins, conventional and nanocomposite films were prepared by a solution mixing procedure with maize starch and glycerol as the plasticizer, and they were reinforced with sodium montmorillonite clay, cellulose fibers and fiberglass fabric, with different combinations. Test results indicated a significant improvement in the mechanical and morphological properties of composite films prepared with sodium montmorillonite clay in addition with cellulose fibers and fiberglass fabric, with 20 wt% of glycerol. The morphology of the skins was also examined by scanning electron microscopy (SEM). Three orders of hierarchical honeycombs were designed for the 3D-printed core. To investigate how the skin material and the design of the core affect the mechanical properties of the starch-based sandwich, specimens were tested under a three-point bending regime. The test results have shown that the flexural strength of the biodegradable sandwich structure increased with the use of a second order hierarchy core and starch-based skins improved the strength and stiffness of the neat PLA-based honeycomb core. The bending behavior of the hierarchical honeycombs was also assessed with finite element analysis (FEA) in combination with experimental findings. Flexural properties demonstrated that the use of starch-based films and a PLA honeycomb core is a suitable solution for biodegradable sandwich structures.
Multi-Fidelity Surrogate-Based Process Mapping with Uncertainty Quantification in Laser Directed Energy Deposition

A multi-fidelity (MF) surrogate involving Gaussian processes (GPs) is used for designing temporal process maps in laser directed energy deposition (L-DED) additive manufacturing (AM). Process maps are used to establish relationships between the melt pool properties (e.g., melt pool depth) and process parameters (e.g., laser power and scan velocity). The MFGP surrogate involves a high-fidelity (HF) and a low-fidelity (LF) model. The Autodesk Netfabb® finite element model (FEM) is selected as the HF model, while an analytical model developed by Eagar-Tsai is chosen as the LF one. The results show that the MFGP surrogate is capable of successfully blending the information present in different fidelity models for designing the temporal forward process maps (e.g., given a set of process parameters for which the true depth is not known, what would be the melt pool depth? ). To expand the newly-developed formulation for establishing the temporal inverse process maps (e.g., to achieve the desired melt pool depth for which the true process parameters are not known, what would be the optimal prediction of the process parameters as a function of time? ), a case study is performed by coupling the MFGP surrogate with Bayesian Optimization (BO) under computational budget constraints. The results demonstrate that MFGP-BO can significantly improve the optimization solution quality compared to the single-fidelity (SF) GP-BO, along with incurring a lower computational budget. As opposed to the existing methods that are limited to developing steady-state forward process maps, the current work successfully demonstrates the realization of temporal forward and inverse process maps in L-DED incorporating uncertainty quantification (UQ).
Impact of hand lymphedema on pain and grip strength of the hand in women with breast cancer

Background: It is not clear to what extent signs and symptoms other than arm swelling, including pain, grip strength differ between women with hand lymphedema and without hand lymphedema in breast cancer–related lymphedema (BCRL)Aim of Study: To estimate the extent to which the impairments associated with hand lymphedema on pain and grip strength in women with breast cancer related lymphedema (BCRL) and to see the association between severity of hand edema and each of independent variables ( pain and hand grip strength )Methods: sixty women experiencing lymphedema after breast surgery participated in this study. They were recruited from Kasr Al-ainy , between 40: 60 years old were assigned into two equal groups, group (A) women with hand edema (HE+, n = 30) and without hand edema (HE−, n = 30) after breast cancer treatment . Pain and grip strength were evaluated Results: inter group difference showed that there is a there was significant difference in pain difference between the group A and group B (p = 0.04)And there was a significant increase in median value of strength difference of the group A compared with that of group B (p = 0.001).
Digital Assistive Technology to Support Everyday Living in Community-Dwelling Older Adults with Mild Cognitive Impairment and Dementia

Objective The objective of this review was to explore whether knowledge about and practice of technology for older adults with mild cognitive impairment (MCI) and dementia (D) had developed since our 2017 review. Furthermore, we wanted to explore the usability and acceptability of technology in the newer trials, and how these may impact quality of life, occupational performance and human dignity. Materials and Methods We searched for primary studies published between 2017 and 2020 reusing medical subject heading (MeSH) terms in five databases – Medline, PsycINFO, Embase, Amed and Cinahl – and obtained 1452 titles. The titles were divided in six piles, two for each of the three authors. The titles were sorted utilizing the Rayyan web tool. Fourteen studies were included in this review. The Mixed Method Appraisal Tool (MMAT) was used to assess the quality of the studies examined. Results This review included almost twice as many participants as in the 2007–2017 review. Since 2017, a shift seems to have occurred toward technologies that can be worn on the body to monitor body functions and report states, or imbalances. Moreover, research interest is now focused on mobile phone apps and wearables providing reminders and timely support, rather than on separate devices at home. Conclusion The studies conducted since 2017 report on wearable and environmental digital assistive technologies and often with multiple purposes. Three strategies for support seem evident: prompting and reminding people with dementia, monitoring people with dementia at home using environmental sensors and biosensors and providing safety outdoors. Thus, there is still a need for further research on the impact of technologies promoting occupational performance, quality of life, and human dignity for independent living.
Decision Tree-Based Weather Prediction

Weather is formed from a group of weather elements and can be changed fast. For example, in the morning, afternoon, or evening, the weather can be different for each place and every hour. Weather is the condition of the air that occurs in a narrow place and lasts for a short time. Weather conditions in a place can be determined by many factors, such as air pressure, humidity, wind, sunlight, and so on. Therefore, by looking at these factors it can be estimated the weather that will occur the next day. Fishermen and farmers are fields of work that are closely related to weather forecasting, accurate and fast weather predictions are needed by these fields to carry out various activities. The amount of rainfall that occurs cannot be determined exactly but can be predicted or estimated. The application of determining weather information is needed, hence, the prediction can be utilized optimally by the community. The design of a system that will classify automatically can be developed by applying machine learning methods, one of them is used in this study, i.e., C.4. 5 Algorithm using weather data that a reference in determining the weather conditions whether not rainy, rainy, light rain, or heavy rain.
Emergency Radio Identification by Supervised Learning based Automatic Modulation Recognition

Post-disaster scenario requires quick and effective rescue efforts. Lack of collaboration among the rescue teams creates severe interference among the emergency emitters. A sensor network in terms of architecture and technology has been proposed in this study in order to create a collaborative environment in such cases. Possible architecture and technology of an emergency sensor network to collect the emitter information is discussed in the first segment of this study. Remaining part discussed a supervised learning based automatic modulation classification system to indentify the emitters. Modulation classification is necessary to recognize the identity of the emergency emitters. To detect the modulation schemes of emergency radios automatically in a disaster scenario, the algorithms are needed to be quick and precise. Processing speed of the recognition system can be controlled by appropriate selection of parameters with proper signal processing techniques. This study investigated some well known techniques and proposed a supervised learning based scheme for automatic modulation classification. The performance of the proposed algorithm is quite satisfactory on analog modulations whereas there are still some provisions of improvement on digital modulations classification.
A Low-Profile Dual-Band Elliptical Patch Antenna Suitable For WIFI With Bandwidth

In this paper, a low profile broadband truncated Elliptic patch microstrip antenna (EPMA) is proposed with dual polarization and dual frequency band based on odd-mode eTM11 and even-mode oTM21. The working principle of the elliptic patch antenna working in eTM11 mode and oTM21 mode is introduced, and by truncating the patch antenna, the oTM21 mode of radiation is transformed from two ends to the broadside. Coupling the stub and slot radiator on the ground plane improved the defect of insufficient impedance bandwidth of the patch antenna. The measured results show that the fabricated antennas obtained the bandwidth of low frequency 2.38GHz-2.7GHz (fractional bandwidth 12.6%) and high frequency 5.1Ghz-5.56ghz (fractional bandwidth 8.6%) respectively, the presented antenna is a good candidate for IEEE802.11n (2.4GHZ - 2.48GHz and 5.15GHz - 5.35GHz). In low frequency band, radiation pattern is wide beam, which is suitable for WIFI communication with wide coverage indoor. In high frequency band, the proposed antenna has high gain, which is suitable for WIFI in narrow and long rooms.
The Expression of PPAR Pathway-Related Genes Can Better Predict the Prognosis of Patients with Colon Adenocarcinoma

The postoperative survival time and quality of life of patients with colon adenocarcinoma (COAD) varies widely. In order to make accurate decisions after surgery, clinicians need to distinguish patients with different prognostic trends. However, we still lack effective methods to predict the prognosis of COAD patients. Accumulated evidences indicated that the inhibition of peroxisome proliferator-activated receptors (PPARs) and a portion of their target genes were associated with the development of COAD. Our study found that the expression of several PPAR pathway-related genes were linked to the prognosis of COAD patients. Therefore, we developed a scoring system (named PPAR-Riskscore) that can predict patients' outcomes. PPAR-Riskscore was constructed by univariate Cox regression based on the expression of 4 genes (NR1D1, ILK, TNFRSF1A, and REN) in tumor tissues. Compared to typical TNM grading systems, PPAR-Riskscore has better predictive accuracy and sensitivity. The reliability of the system was tested on six external validation datasets. Furthermore, PPAR-Riskscore was able to evaluate the immune cell infiltration and chemotherapy sensitivity of each tumor sample. We also combined PPAR-Riskscore and clinical features to create a nomogram with greater clinical utility. The nomogram can help clinicians make precise treatment decisions regarding the possible long-term survival of patients after surgery.
Linear quadratic optimal control of on-ball balancing mobile robot

A dynamic analysis is made on a kind of on-ball balancing wheeled mobile robot system. A Lagrange equation method is used to establish its dynamic model,then a linear quadratic optimal control method used in robot control is presented based on the characteristics of the system. Simulation results show that the design of linear quadratic optimal controller can effectively achieve balance on-ball balancing mobile robot control,positioning control and tracking control,and the entire control process has fast response,small overshoot,disturbance rejection ability and other characteristics.