Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance optimization of banana vibrating screens based on PSO-SVR under DEM simulations

This paper carried out the numerical simulation about the movement of non-spherical particles on banana vibrating screen using direct element method (DEM) considering the complexity of particle collision and avoiding obtaining motion information with difficulty. Experimental prototype of banana vibrating screen under variable parameters was manufactured to verify the feasibility of simulations. Because the complex non-linear mathematical model is the basis of optimization. Based on the simulation data this paper applied the least squares support vector machines (LS-SVM) to establish relationships between vibrating parameters of banana screen and screening performance. LS-SVM based on statistical theory can effectively solve the mapping problem of small sample. At same time, in order to improving the quality of modeling, the kernel parameters of SVM were optimized by particle swarm optimization (PSO). Considering multi-extremum, large-scale, and non-differentiable of this computational model, the artificial fish-swarm algorithm (AFSA) with strong robustness and global convergence was applied to vibration parameters optimization. Finally, the optimal vibration parameters were: vibration amplitude 2.4 mm, vibration frequency 21 Hz, vibration direction angle 40 degrees.
Object representation enhancement for self‐supervised colocalization

Self‐supervised colocalization is to localize common objects in the data set containing only one superclass without using human‐annotated labels. Existing methods achieve impressive results by employing self‐supervised pretext learning. However, a common limitation still exists. They either tend to overextend activations to the background, or they tend to activate the most discriminative object part. To alleviate this problem, we propose an object representation enhancement model to weaken background distraction and to mine complementary object regions during the object representation learning. Specifically, we first propose an Object‐aware Representation Enhancement (ORE) module to estimate an object mask for each input image, guiding the model to disregard the background content and focus on the foreground object. The ORE module and the subsequent self‐supervised learning can mutually reinforce each other. Then we propose a Masked Self‐supervised Learning branch and design a masked attention consistency objective to induce the model to activate complementary parts of the object effectively. Extensive experiments on four fine‐grained data sets demonstrate the superiority of the proposed model.
Ray‐tracing assisted fingerprinting for localization in IoT Health 4.0

The Internet of Things (IoT) can help healthcare companies establish Ambient Assisted Living (AAL). It permits innovative solutions to a well‐known problem, notably the real‐time localization and monitoring of patients and clinicians. Yet, there is no method for indoor localization in the healthcare context that delivers centimeter‐level precision. Although radio‐based localization has intriguing potential, the primary limitations of such a method are non‐line‐of‐sight radio signal transmission and multipath reflection, both of which lead to the lowest accuracy. The advanced 5G communication technology allows for the simultaneous utilization of temporal and angular features of a broadcast signal, as well as its multipath property, to accomplish precise target localization. This article presents a method for multipath channel‐assisted localization in the AAL system by utilizing the temporal and angular measurement of the signal concurrently. We simulated and tested the localization approach in an AAL system and achieved centimeter‐level accuracy over 90% of the time in all four cases.
In Vitro High-Throughput Toxicological Assessment of Nanoplastics

Sub-micrometer particles derived from the fragmentation of plastics in the environment can enter the food chain and reach humans, posing significant health risks. To date, there is a lack of adequate toxicological assessment of the effects of nanoplastics (NPs) in mammalian systems, particularly in humans. In this work, we evaluated the potential toxic effects of three different NPs in vitro: two NPs obtained by laser ablation (polycarbonate (PC) and polyethylene terephthalate (PET1)) and one (PET2) produced by nanoprecipitation. The physicochemical characterization of the NPs showed a smaller size, a larger size distribution, and a higher degree of surface oxidation for the particles produced by laser ablation. Toxicological evaluation performed on human cell line models (HePG2 and Caco-2) showed a higher toxic effect for the particles synthesized by laser ablation, with PC more toxic than PET. Interestingly, on differentiated Caco-2 cells, a conventional intestinal barrier model, none of the NPs produced toxic effects. This work wants to contribute to increase knowledge on the potential risks posed by NPs.
Topic-model based Estimation of Passive Twitter-User's Interests from Followed Users' Tweets

User modeling based on the contents of social network services has been developed to recommend information related to the preference of each user. Most of the previous studies have analyzed active users' tweets and estimated their interests. Meanwhile, although there are more than a certain number of passive users, who do not tweet but only gather information, little research has been conducted on interest estimation for them due to the lack of clues for estimating their interests. In this study, we developed an interest estimation method for passive Twitter users from the tweets of followed users by applying an interest topic extraction method for active users. In our evaluation, we confirmed the effectiveness of the proposed method by comparison with simple topic extraction methods based on data with interest topic evaluation of 12 users.
Antitrypanosomal and Anthelminthic Properties of Ethanol Extracts of Carica papaya Linn. and Ceiba pentandra (L) Gaertn.

The most common diseases that affect low-income countries are helminthosis and trypanosomosis. In Ghana, and in many other African countries, herbal treatment of various diseases is still common. In the present study, we sought to determine the antitrypanosomal and anthelminthic activities of Carica papaya and Ceiba pentandra. The ethanol extracts of Carica papaya stem bark (PPSe) and leaves (PPLe) and ethanol extracts of Ceiba pentandra stem bark (CPSe) and leaves (CPLe) were screened against Trypanosoma brucei brucei and Pheretima posthuma worms in vitro. CPSe exhibited strong antitrypanosomal activity, while the other extracts exhibited moderate activity against T. b. brucei. All the extracts showed weak selective indices (SI) when tested on Jurkat cell lines, which is indicative of a potential toxic effect. When the extracts were screened against P. posthuma worms, only PPSe and CPSe were able to kill the worms after the exposure time at concentrations of 2.5, 5, and 10 mg/mL. PPSe was again the only extract that potentiated the anthelminthic activity of albendazole against P. posthuma worms. Preliminary phytochemical screening and GC-MS analysis revealed the presence of compounds with antitrypanosomal and anthelminthic properties. The results confirmed the potential of C. papaya and C. pentandra as remedies for trypanosomosis and helminthosis and also gives credence to their folkloric use.
A Novel Electrochemical Sensor Modified with a Computer-Simulative Magnetic Ion-Imprinted Membrane for Identification of Uranyl Ion

In this paper, a novel ion-imprinted electrochemical sensor modified with magnetic nanomaterial Fe3O4@SiO2 was established for the high sensitivity and selectivity determination of UO22+ in the environment. Density functional theory (DFT) was employed to investigate the interaction between templates and binding ligands to screen out suitable functional binding ligand for the reasonable design of the ion imprinted sensors. The MIIP/MCPE (magnetic ion imprinted membrane/magnetic carbon paste electrode) modified with Fe3O4@SiO2 exhibited a strong response current and high sensitivity toward uranyl ion comparison with the bare carbon paste electrodes. Meanwhile, the MCPE was fabricated simultaneously under the action of strong magnetic adsorption, and the ion imprinted membrane can be adsorbed stably on the electrode surface, handling the problem that the imprinted membrane was easy to fall off during the process of experimental determination and elution. Based on the uranyl ion imprinting network, differential pulse voltammetry (DPV) was adopted for the detection technology to realize the electrochemical reduction of uranyl ions, which improved the selectivity of the sensor. Thereafter, uranyl ions were detected in the linear concentration range of 1.0 × 10−9 mol L−1 to 2.0 × 10−7 mol L−1, with the detection and quantification limit of 1.08 × 10−9 and 3.23 × 10−10 mol L−1, respectively. In addition, the sensor was successfully demonstrated for the determination of uranyl ions in uranium tailings soil samples and water samples with a recovery of 95% to 104%.
Moving to the Edge-Cloud-of-Things: Recent Advances and Future Research Directions

Cloud computing has significantly enhanced the growth of the Internet of Things (IoT) by ensuring and supporting the Quality of Service (QoS) of IoT applications. However, cloud services are still far from IoT devices. Notably, the transmission of IoT data experiences network issues, such as high latency. In this case, the cloud platforms cannot satisfy the IoT applications that require real-time response. Yet, the location of cloud services is one of the challenges encountered in the evolution of the IoT paradigm. Recently, edge cloud computing has been proposed to bring cloud services closer to the IoT end-users, becoming a promising paradigm whose pitfalls and challenges are not yet well understood. This paper aims at presenting the leading-edge computing concerning the movement of services from centralized cloud platforms to decentralized platforms, and examines the issues and challenges introduced by these highly distributed environments, to support engineers and researchers who might benefit from this transition.
Comparison of Physical Adsorption and Covalent Coupling Methods for Surface Density-Dependent Orientation of Antibody on Silicon

The orientation of antibodies, employed as capture molecules on biosensors, determines biorecognition efficiency and bioassay performance. In a previous publication we demonstrated for antibodies attached covalently to silicon that an increase in their surface amount Γ, evaluated with ellipsometry, induces changes in their orientation, which is traced directly using Time-of-Flight Secondary Ion Mass Spectroscopy combined with Principal Component Analysis. Here, we extend the above studies to antibodies adsorbed physically on a 3-aminopropyltriethoxysilane (APTES) monolayer. Antibodies physisorbed on APTES (0 ≤ Γ ≤ 3.5 mg/m2) reveal the Γ ranges for flat-on, side-on, and vertical orientation consistent with random molecular packing. The relation between orientation and Γ is juxtaposed for silicon functionalized with APTES, APTES modified with glutaraldehyde (APTES/GA) and N-hydroxysuccinimide-silane (NHS-silane). Antibody reorientation occurs at lower Γ values when physisorption (APTES) is involved rather than chemisorption (APTES/GA, NHS-silane). At high Γ values, comparable proportions of molecules adapting head-on and tail-on vertical alignment are concluded for APTES and the NHS-silane monolayer, and they are related to intermolecular dipole–dipole interactions. Intermolecular forces seem to be less decisive than covalent binding for antibodies on the APTES/GA surface, with dominant head-on orientation. Independently, the impact of glutaraldehyde activation of APTES on vertical orientation is confirmed by separate TOF-SIMS measurements.
Calibration and validation of cellular automaton traffic flow model with empirical and experimental data

For traffic flow models, calibration and validation are essential. Cellular automaton (CA) models are a special class of models, describing the movement of vehicles in discretised space and time. However, the previous work on calibration and validation does not discuss CA models systematically. This study calibrates and validates a stochastic CA model. The authors use a simple CA model, which only has two important parameters to be calibrated. The methodology for optimisation is to minimise the relative root mean square error between two properties: the averaged velocity and the variation of velocities in a platoon at a given density. Three different sites are used as cases to show the methodology, for which different types of data (video trajectories or GPS data) are available. The authors find that the best model parameters vary for the different locations. This may result from various driving strategies and potential tendencies. Thus, it is concluded that for CA models, various traffic flow phenomena need to be simulated by various parameters.
In Rust We Trust – A Transpiler from Unsafe C to Safer Rust

Rust is a type-safe system programming language with a compiler checking memory and concurrency safety. For a smooth transition from existing C projects, a source-to-source transpiler can auto-transform C programs into Rust using program transformation. However, existing C-to-Rust transformation tools (e.g. the open-source C2Rust transpiler1 project) have the drawback of preserving the unsafe semantics of C, while rewriting them in Rust’s syntax. The work by Emre et el. [2] acknowledged these drawbacks, and used rustc compiler feedback to refactor one certain type of raw pointers to Rust references to improve overall safety and idiomaticness of C2Rust output. Focusing on improving API-safeness (i.e. lowering unsafe keyword usage in function signatures), we apply source-to-source transformation technique to auto-refactor C2Rust output using code structure pattern matching and transformation, which does not rely on rustc compiler feedback. And by relaxing the semantics-preserving constraints of transformations, we present CRustS2 a fully-automated source-to-source transformation approach that increases the ratio of the transformed code passing the safety checks of the rustc compiler. Our method uses 220 new TXL [1] source-to-source transformation rules, of which 198 are strictly semantics-preserving and 22 are semantics-approximating, thus reducing the scope of unsafe expressions and exposing more opportunities for safe Rust refactoring. Our method has been evaluated on both open-source and commercial C projects, and demonstrates significantly higher safe code ratios after the transformations, with function-level safe code ratios comparable to the average level of idiomatic Rust projects.
Novel fast safety assessment method for the buffer section of maintenance work zone

Safety assessment of the buffer section of maintenance work zone is crucial for the safety of maintenance staff and travelling vehicles. To make up for the limitations of current methods to do timely safety assessment, this study proposes a fast method based on naturalistic driving, automatic segmentation technology, and spatial clustering analysis. Firstly, collect vehicle behaviour data by naturalistic driving method; secondly, extract fragments in the data containing traffic conflict information using the automatic segmentation technology; thirdly, cluster these fragments by kernel density analysis to find the location of the high-density point of traffic conflict (O tc); finally, calculate the stopping sight distance (SSD) that vehicles need to stop completely from O tc, measure the distance Lꞌ between O tc and the work area, and compare SSD, Lꞌ with the buffer section length L, then the safety of the buffer section of maintenance work zone can be assessed. Practice has proved that this novel method can collect the behaviour data of vehicles passing through quickly and flexibly, and do a timely safety assessment for the zone, thus can better meet the actual needs of maintenance.
Neuropeltis acuminata (P. Beauv.): Investigation of the Chemical Variability and In Vitro Anti-inflammatory Activity of the Leaf Essential Oil from the Ivorian Species

The variability of chemical composition of the leaf essential oil (EO) from Neuropeltis acuminata, a climbing liana growing wild in Ivory Coast, was investigated for the first time. The in vitro anti-inflammatory activity was also evaluated. Thirty oil samples were isolated from leaves collected in three forests of the country and analyzed using a combination of Column Chromatography (CC), Gas Chromatography with Retention Indices (GC(FID)), Gas Chromatography-Mass Spectrometry (GC-MS), and 13Carbon-Nuclear Magnetic Resonance (13C-NMR). Fractionation by CC led to the first-time isolation from natural source of δ-cadinen-11-ol, whose structural elucidation by one dimension (1D) and 2D-NMR spectroscopy is reported here. Finally, 103 constituents accounting for 95.7 to 99.6% of the samples’ compositions were identified. As significant variations of the major constituents were observed, the 30 oil compositions were submitted to hierarchical cluster and principal components analyses. Five distinct groups were evidenced: Group I, dominated by (E)-β-caryophyllene, kessane, and δ-cadinene, while the main constituents of Group II were germacrene B, ledol, α-humulene, (E)-γ-bisabolen-12-ol, and γ-elemene. Group III exhibited guaiol, germacrene D, atractylone, (E)-γ-bisabolen-12-ol, δ-cadinene and bulnesol as main compounds. Group IV was dominated by (E)-nerolidol, guaiol, selina-4(15),7(11)-diene and bulnesol, whereas (E)-β-caryophyllene, α-humulene and α-muurolene were the prevalent compounds of Group V. As the harvest took place in the same dry season in the three forests, the observed chemical variability could be related to harvest sites, which includes climatic and pedologic factors, although genetic factors could not be excluded. The leaf oil sample S24 behaved as a high inhibitor of LipOXygenase (LOX) activity (half maximum Inhibitory Concentration, IC50: 0.059 ± 0.001 mg mL−1), suggesting an anti-inflammatory potential.
Pair of grounded L strips loaded broadband circularly polarised square slot antenna with enhanced axial ratio bandwidth

Broadband circularly polarised square slot antenna excited by a coplanar waveguide feed with enhanced axial ratio bandwidth (ARBW) is proposed, designed and experimentally studied. Larger than 21% 3 dB ARBW can be attained by using asymmetric feed and implementing a T-shaped grounded stub from the left side of the square slot in orthogonal direction towards the slot centre. To further enhance the 3 dB ARBW of the proposed slot antenna, a pair of equal grounded L-shaped strips with different sizes is embedded at the top right corner of the square slot. Small L-shaped grounded strip plays an important role for merging two closely spaced multi-resonant modes, generated by the combined effect of asymmetric feed with T-shaped grounded stub and with large L-shaped strip. Overall size of the proposed antenna is 40 × 40 × 1.6 mm3. Measured value shows that the impedance bandwidth and 3 dB ARBW of the designed antenna are 73.39% (5.02–10.84 GHz) and 58.08% (5.07–9.22 GHz), respectively. Good agreement is achieved between simulated and measured results.
A Novel Detection Refinement Technique for Accurate Identification of Nephrops norvegicus Burrows in Underwater Imagery

With the evolution of the convolutional neural network (CNN), object detection in the underwater environment has gained a lot of attention. However, due to the complex nature of the underwater environment, generic CNN-based object detectors still face challenges in underwater object detection. These challenges include image blurring, texture distortion, color shift, and scale variation, which result in low precision and recall rates. To tackle this challenge, we propose a detection refinement algorithm based on spatial–temporal analysis to improve the performance of generic detectors by suppressing the false positives and recovering the missed detections in underwater videos. In the proposed work, we use state-of-the-art deep neural networks such as Inception, ResNet50, and ResNet101 to automatically classify and detect the Norway lobster Nephrops norvegicus burrows from underwater videos. Nephrops is one of the most important commercial species in Northeast Atlantic waters, and it lives in burrow systems that it builds itself on muddy bottoms. To evaluate the performance of proposed framework, we collected the data from the Gulf of Cadiz. From experiment results, we demonstrate that the proposed framework effectively suppresses false positives and recovers missed detections obtained from generic detectors. The mean average precision (mAP) gained a 10% increase with the proposed refinement technique.
Towards facilitating software engineering for production systems in Industry 4.0 with behavior models

With the growing adoption of Industry 4.0 concepts in production systems, new challenges arise in engineering control software. Highly distributed control with tight real-time constraints and safety regulations results in increasingly complex software. Current research focuses on increasing the abstraction with new architectures and modularization of software. The presented PhD research addresses modeling of the interactions between control software components, and of the emergent behavior of these compositions. Such behavior models can support the initial implementation, and facilitate (semi-)automated testing and monitoring of control software. Finally, visualizing behavior in a model can enhance understandability of existing control software, when software developers need not access abstracted hierarchy levels to deduct their functionality. This work aims at optimizing the benefit of behavior models in developing control software: Modeling the expected behavior directly for new software will allow using them throughout the software life-cycle. For legacy software, the initial development effort of behavior models will be minimized by automatically capturing behavior models from the implementation. The approach is evaluated in case studies and user studies to integrate experiences from the industrial domain into this software engineering research.
A 5-Month Comparative Study of Japanese Input Speed by Keyboard of Elementary School Children Learning with 1:1 Devices for the First Time

This study involved a 5-month investigation of Japanese input skills by keyboard of elementary school children learning with 1:1 devices for the first time. Participants were from two classes of elementary schools. The Class X was 4 th grade, and the Class Y was 6 th grade. The input speed improved in 3 rd , 4 th , and 5 th months compared to 2 nd month after the start of utilization. The input speed while copying by looking at the text increased as the months went by. However, the results suggest that the speed of input while thinking by reading the text does not necessarily improve in the same way. We divided the survey results into two groups based on input speed by keyboard. In the upper group, 6 th graders were higher than 4 th graders, although there was no significant difference. In the lower group, 6 th graders were significantly lower than 4 th graders in 3 rd , 4 th , and 5 th months. Therefore, it is necessary to pay particular attention to supporting the acquisition of Japanese input skills by keyboard, especially for children in the lower group.