Miracle-dz/UniFAR-FTUs huggingface dataset:
Dual Stiffness Tensegrity Platform for Resilient Robotics

Collision resilience is an important feature of robots deployed in unstructured and partially unpredictable environments. Herein, a novel dual stiffness (DS) tensegrity platform to integrate collision resilience into a robot body is proposed. The proposed DS tensegrity platform is rigid during normal robot operation, but softens upon collision to withstand the impact. The DS behavior is achieved by means of a novel DS strut that is rigid, but can buckle without breaking under high loads, thus preventing damage to the robot. Compression tests and finite element method simulations show that both the DS struts and DS tensegrities undergo substantial stiffness change with maximum load‐bearing ratios up to 10.5 and 5.74, respectively, before and after buckling. These DS tensegrity structures are integrated into two types of robots, a drone and a rover, that are shown to withstand falls from 2 and 5 m, respectively. The mechanical tunability of the proposed DS tensegrity system makes it suitable for impact attenuation in a wide range of situations and robot types.
Gaussian Process-Based Personalized Adaptive Cruise Control

Advanced driver-assistance systems (ADAS) have matured over the past few decades with the dedication to enhance user experience and gain a wider market penetration. However, personalization features, as an approach to make the current technologies more acceptable and trustworthy for users, have been gaining momentum only very recently. In this work, we aim to learn personalized longitudinal driving behaviors via a Gaussian Process (GP) model. The proposed method learns from individual driver’s naturalistic car-following behavior, and outputs a desired acceleration profile that suits the driver’s preference. The learned model, together with a predictive safety filter that prevents rear-end collision, is used as a personalized adaptive cruise control (PACC) system. Numerical experiments show that GP-based PACC (GP-PACC) can almost exactly reproduce the driving styles of an intelligent driver model. Additionally, GP-PACC is further validated by human-in-the-loop experiments on the Unity game engine-based driving simulator. Trips driven by GP-PACC and two other baseline ACC algorithms with driver override rates are recorded and compared. Results show that on average, GP-PACC reduces the human override duration by 60% and 85% as compared to two widely-used ACC models, respectively, which shows the great potential of GP-PACC in improving driving comfort and overall user experience.
The Role of Histopathology as a Complementary Diagnostic Tool in the Monitoring of Bovine Tuberculosis

The diagnosis of bovine tuberculosis (bTB) is based on the single intradermal tuberculin test (SIT), interferon gamma, and compulsory slaughter of reactor animals. Culture and PCR from fresh tissue are regarded as gold standard techniques for post-mortem confirmation, with the former being time-consuming and presenting moderate to low sensitivity and the latter presenting promising results. Histopathology has the advantage to identify and categorize lesions in both reactor and non-reactor animals. Therefore, this study aims to highlight the role of histopathology in the systematic diagnosis of bTB to shorten the time to disclose positive animals. Blood (212) and lymph node (681) samples were collected for serological, bacteriological, and histopathological analyses from a total of 230 cattle subjected to the Spanish bTB eradication program. Seventy-one lymph nodes and 59 cattle yielded a positive result to bacteriology, with 59 lymph nodes and 48 cattle presenting a positive result in real-time PCR from fresh tissue. Roughly 19% (40/212) of sera samples gave a positive result to ELISA. Tuberculosis-like lesions (TBLs) were observed in 11.9% (81/681) of the lymph nodes and 30.9% (71/230) of cattle. Noteworthy, TBLs were evidenced in 18 out of 83 SIT− and real-time PCR and bacteriology negative animals, with 11/18 disclosing a positive result to Ziehl-Neelsen technique and two of them to ddPCR from paraffin blocks targeting IS6110. Six out of these 11 ZN+ corresponded with mesenteric LN and were confirmed positive to paratuberculosis. Histopathology yielded a sensitivity of 91.3% (CI95 83.2–99.4%) and a specificity of 84.4% (CI95 78.6–89.3%) with good agreement (κ = 0.626) when compared with real-time PCR. Our results confirm that histopathology allows a rapid confirmation of real-time PCR and bacteriology, emphasizing its contribution to bTB control and monitoring.
Variability of ENSO Forecast Skill in 2‐Year Global Reforecasts Over the 20th Century

In order to explore temporal changes of predictability of El Niño Southern Oscillation (ENSO), a novel set of global biennial climate reforecasts for the historical period 1901–2010 has been generated using a modern initialized coupled forecasting system. We find distinct periods of enhanced long‐range skill at the beginning and at the end of the twentieth century, and an extended multi‐decadal epoch of reduced skill during the 1930s–1950s. Once the forecast skill extends beyond the first spring barrier, the predictability limit is much enhanced and our results provide support for the feasibility of skillful ENSO forecasts up to 18 months. Changes in the mean state, variability (amplitude), persistence, seasonal cycle and predictability suggest that multi‐decadal variations in the dynamical characteristics of ENSO rather than the data coverage and quality of the observations have primarily driven the reported non‐monotonic skill modulations.
A Model for Dynamic Cyber Security Risk Assessment in the Industrial IOT Environment

: This paper considers cyber security risk assessment, as a vital part of the risk management process, in the industrial Internet of Things (IIoT) systems. A general model for IIoT dynamic risk assessment (DRA) is proposed, starting from the definition of IIoT context. Several risk calculation algorithms are surveyed, with an emphasis on artificial intelligence and machine learning-based methods. The model is illustrated in the example of IIoT-based super-visory control and data acquisition (SCADA) system in a hydropower plant.
Vinculación profesional para el monitoreo de servicios de TICS utilizando Big Data

: The articulation between university and professional training is one of many targets from our educational work. The interest of this work is to show the relevant skills of the students in a concrete process of linking between the academic and the productive environment. The research is based on the development of tools for the monitoring of services based on ICT systems (information and communication technology). In this university-company articulation, Big Data technology is used to process a large volume of data contained in the files generated by the communication systems of a mass service organization that serves more than two million clients. It is intended to build a control panel based on an open source ELK (Elasticsearch-Logstash-Kibana) technology, which will be supplied with the call detail records CDRs (Call Detail Records) generated by the communications platform. The validate if the domain of technical skills is enough condition or if soft skills are also necessary to achieve efficient performance in a project of these characteristics.
Effect on the Properties of Edible Starch-Based Films by the Incorporation of Additives: A Review

At present, people more actively pursuing biodegradable-based food packaging to lower the environmental problems of plastic-based packaging. Starch could become a promising alternative to plastic because of its properties (easily available, nontoxic, tasteless, biodegradable, ecofriendly, and edible). This review article is focused mainly on the impact of the properties of starch-based biodegradable films, such as their thickness, morphology, and optical, water-barrier, mechanical, oxygen-barrier, antioxidant, and antimicrobial properties, after the incorporation of additives, and how such films fulfill the demands of the manufacturing of biodegradable and edible food-based film with preferable performance. The incorporation of additives in starch-based films is largely explained by its functioning as a filler, as shown via a reduction in water and oxygen permeability, increased thickness, and better mechanical properties. Additives also showed antimicrobial and antioxidant properties in the films/coatings, which would positively impact the shelf life of coated or wrapped food material.
Metrological Validation of Pixhawk Autopilot Magnetometers in Helmholtz Cage

The use of drones is very extended for multiple applications. Some of them impose attitude and navigation requirements that need appropriate measurements. Pixhawk is an open-source autopilot used on board drones that includes a magnetometer as part of its inertial measurement unit. A testing facility based on a Helmholtz cage is used to evaluate the metrological features of different magnetometer units. The first test induces a magnetic field rotation around six different Pixhawk magnetometers, simulating changes in attitude and course for a drone. The data show a standard deviation higher than 250 mG, in comparison with the standard deviation of 30 mG in the standard gaussmeter model HMR2300. The second test is focused on the stability of the magnetometer, where the Helmholtz cage simulates a certain flight altitude and course. Values obtained from Pixhawk magnetometers range between 25 mG and 116 mG for the highest peak-to-peak magnetic field amplitude, while the standard gaussmeter range is only between 1.3 mG to 5.4 mG. Considering these results, it is possible to determine the limitations of the Pixhawk autopilot magnetometer, both in terms of manufacturing tolerances and measuring stability. Therefore, users should be very careful when using this type of autopilot in professional applications that require precision and safety.
Frequent Articulation Disorders in Children

Millions of children worldwide suffer from articulation disorders. An essential part of their treatment requires performing home exercises prescribed by their Speech Language Pathologist.Hence, academic institutions and companies are developing algorithms to address the correct classification of good versus poor phoneme articulation. As of today, the efforts to cover all the phonemes in the English language provide less than 90% accuracy. TIK TALK to Me Ltd., an Israeli company that develops methods and devices for treating speech disorders, conducted a large-scale study on children's most frequent articulation disorders. Over more than 24 months, the company accumulated records from some 250 children, ages 3 to 12, treated by 45 Speech Language Pathologists (SLPs) in the US. The metadata analysis obtained from the above records shows that 80% of the children required treatment on one or more of just 6 out of the 44 phonemes in the English language. The significance of the above findings is not just of academic interest to the community of speech-language pathologists. Companies and researchers should prioritize reaching top performance in the six most frequent articulation problems.
Inter-continental Data Centre Power Load Balancing for Renewable Energy Maximisation

The ever increasing popularity of Cloud and similar services pushes the demand for data centres, which have a high power consumption. In an attempt to increase the sustainability of the power generation, data centres have been fed by microgrids which include renewable generation—so-called `green data centres’. However, the peak load of data centres often does not coincide with solar generation, because demand mostly peaks in the evening. Shifting power to data centres incurs transmission losses; shifting the data transmission has no such drawback. We demonstrate the effectivity of computational load shifting between data centres located in different time zones using a case study that balances demands between three data centres on three continents. This study contributes a method that exploits the opportunities provided by the varied timing of peak solar generation across the globe, transferring computation load to data centres that have sufficient renewable energy whenever possible. Our study shows that balancing computation loads between three green data centres on three continents can improve the use of renewables by up to 22%. Assuming the grid energy does not include renewables, this amounts to a 13% reduction in CO2 emissions.
Availability of NGA Market Potential Indicators in the Western Balkans

: Broadband residential access to next-generation networks (NGA) with bit-rates greater than 100 Mbps is critical in terms of data volume growth and approach to the gigabit networks implementation. Reliable demand forecasting of the required bandwidth for the residential market is a necessary input for the successful planning of the communication network resources. This paper investigates and compares the availability of the data suitable to be used as market indicators for several Western Balkans countries related to the application categories. This study could be useful for the market potential estimation and development of the NGA demand forecasting model.
Physical and Chemical Characterization of Biomineralized Collagen with Different Microstructures

Mineralized collagen is the basic unit in hierarchically organized natural bone with different structures. Polyacrylic acid (PAA) and periodic fluid shear stress (FSS) are the most common chemical and physical means to induce intrafibrillar mineralization. In the present study, non-mineralized collagen, extrafibrillar mineralized (EM) collagen, intrafibrillar mineralized (IM) collagen, and hierarchical intrafibrillar mineralized (HIM) collagen induced by PAA and FSS were prepared, respectively. The physical and chemical properties of these mineralized collagens with different microstructures were systematically investigated afterwards. Transmission electron microscopy (TEM) and scanning electron microscopy (SEM) showed that mineralized collagen with different microstructures was prepared successfully. The pore density of the mineralized collagen scaffold is higher under the action of periodic FSS. Fourier transform infrared spectroscopy (FTIR) analysis showed the formation of the hydroxyapatite (HA) crystal. A significant improvement in the pore density, hydrophilicity, enzymatic stability, and thermal stability of the mineralized collagen indicated that the IM collagen under the action of periodic FSS was beneficial for maintaining collagen activity. HIM collagen fibers, which are prepared under the co-action of periodic FSS and sodium tripolyphosphate (TPP), may pave the way for new bone substitute material applications.
Independent Learning Method Based on Virtual Reality to Improve Foreign Language Skills

Understanding the meaning of a word needs imagination from students, especially the meaning of the word in the right situation and right place. This research creates media from Virtual Reality (VR) to give enough imagination of the meaning of a word or sentence in a foreign language. The purpose of this study is to improve the skills of foreign language that students can learn with a model: self-regulated learning with 3 factors (goal setting and planning, rehearsing and memorizing, and self-evaluating). A test case of media was used involving 10 undergraduate students at the Department of Mandarin Language at the Universitas Negeri Malang. The result of the test indicated a significant growth in the skill of foreign language, reaching 81.5%.
ICN Based Efficient Content Caching Scheme for Vehicular Networks

The Information Centric Networking (ICN) is a future internet architecture to support efficient content distribution in a vehicular environment. In-network caching in ICN provides a realistic solution for vehicular communication due to storage of content replicas inside network vehicles. However, the challenge still exists while caching content replicas in resource constraint vehicles (such as limited power and cache capacity) to minimize the communication latency. To address the above mentioned challenge, this paper proposes EPC - an ICN based Energy efficient Placement of Content chunk that fits well in a vehicular environment. The proposed resource management strategy mainly aims to reduce the content fetching delay by caching content replicas towards the network edge router. The EPC strategy decides on placement of content chunks on each vehicle by jointly considering residual power of current vehicle, local popularity of content, and caching gain. The EPC supports efficient utilization of network available resources by allowing only vehicles with their residual power greater than threshold to perform chunk caching and hence, further offers reduced content duplication in the whole network. The effectiveness of the proposed scheme is evaluated in Icarus- an ICN simulator for analyzing the performance of ICN caching and routing strategies. The EPC outperforms various state of the art caching strategies approximately by 30% when gets evaluated in terms of offered cache hit ratio, content retrieval delay, and the average number of hops utilized for fetching the requested content.
Dynamics of Soil Carbon Fractions and Carbon Stability in Relation to Grassland Degradation in Xinjiang, Northwest China

Grassland degradation usually results in significant shifts in vegetation species composition and plant biomass, thus altering the soil organic carbon (SOC) content and stability. Dynamics of labile carbon fractions after grassland degradation were well addressed; however, the changes in stable carbon fractions were poorly quantified. Soil samples at 0–10 cm and 10–20 cm depth were collected from a native grassland (NA), a lightly degraded grassland (LD), a moderately degraded grassland (MD), and a severely degraded grassland (SD) in northwest China to assess the influence of grassland degradation on the total SOC content, four SOC fractions (very labile carbon, CF1; labile carbon, CF2; less labile carbon, CF3; non-labile carbon, CF4), and SOC stability. Compared with the NA, the contents under LD, MD, and SD at 0–20 cm depth reduced by 20.58%, 29.22%, and 64.58% for total SOC, 21.38%, 23.00%, and 63.66% for CF1, 13.81%, 20.58%, and 62.26% for CF2, 24.30%, 35.05%, and 68.63% for CF3, and 22.17%, 38.80%, and 63.82% for CF4, respectively. The linear relationships between the total SOC and the four fractions of CF1, CF2, CF3, and CF4 were significant in this study. The lability index of SOC under the NA, LD, MD, and SD was 1.57, 1.59, 1.67, and 1.57, respectively, and no significant difference was found among the four grasslands. To conclude, grassland degradation changes the contents of total SOC and its labile and stable fractions but did not change the SOC stability in northwest China.
Molecular Characterization of Outer Capsid Proteins VP5 and VP7 of Grass Carp Reovirus

Aquareovirus, which is a member of the Reoviridae family, was isolated from aquatic animals. A close molecular evolutionary relationship between aquareoviruses and mammalian orthoreoviruses was revealed. However, the functions of the aquareovirus genome-encoded proteins are poorly understood. We investigated the molecular characteristics of the outer capsid proteins, namely, VP5 and VP7, of grass carp reovirus (GCRV). The peptides VP5 and VP7 were determined using in-gel tryptic digestion and mass spectrometry. Recovered peptides represented 76% and 66% of the full-length VP5 and VP7 sequences, respectively. Significantly, two-lysine acetylation, as well as two-serine and two-threonine phosphorylation modifications, were first revealed in VP5. We found that the initial amino acid in VP5 was Pro43, suggesting that a lower amount of VP5 remained uncleaved in virions at the autocleavage site (Asn42-Pro43). Further biochemical evidence showed that the cleaved VP5N/VP5C conformation was the major constituent of the particles. Moreover, early cleavage fragments of VP7 and enhanced infectivity were detected after limited tryptic digestion of GCRV, indicating that stepwise VP7 cleavage is essential for VP5 conformational rearrangement. Our results provide insights into the roles of posttranslational modifications in VP5 and its association with VP7 in the viral life cycle.
Supervised and Weakly Supervised Deep Learning for Segmentation and Counting of Cotton Bolls Using Proximal Imagery

The total boll count from a plant is one of the most important phenotypic traits for cotton breeding and is also an important factor for growers to estimate the final yield. With the recent advances in deep learning, many supervised learning approaches have been implemented to perform phenotypic trait measurement from images for various crops, but few studies have been conducted to count cotton bolls from field images. Supervised learning models require a vast number of annotated images for training, which has become a bottleneck for machine learning model development. The goal of this study is to develop both fully supervised and weakly supervised deep learning models to segment and count cotton bolls from proximal imagery. A total of 290 RGB images of cotton plants from both potted (indoor and outdoor) and in-field settings were taken by consumer-grade cameras and the raw images were divided into 4350 image tiles for further model training and testing. Two supervised models (Mask R-CNN and S-Count) and two weakly supervised approaches (WS-Count and CountSeg) were compared in terms of boll count accuracy and annotation costs. The results revealed that the weakly supervised counting approaches performed well with RMSE values of 1.826 and 1.284 for WS-Count and CountSeg, respectively, whereas the fully supervised models achieve RMSE values of 1.181 and 1.175 for S-Count and Mask R-CNN, respectively, when the number of bolls in an image patch is less than 10. In terms of data annotation costs, the weakly supervised approaches were at least 10 times more cost efficient than the supervised approach for boll counting. In the future, the deep learning models developed in this study can be extended to other plant organs, such as main stalks, nodes, and primary and secondary branches. Both the supervised and weakly supervised deep learning models for boll counting with low-cost RGB images can be used by cotton breeders, physiologists, and growers alike to improve crop breeding and yield estimation.