Miracle-dz/UniFAR-FTUs huggingface dataset:
Digital Capabilities, Their Role in Business Model Innovativeness, and the Internationalization of SMEs

The issues of fostering internationalization and innovating business models through digital capabilities appear neglected in the research on small and medium enterprises (SMEs). The literature on internationalization theory can be enriched by understanding how SMEs innovate their business models to promote their internationalization in the digital era. We specifically examined the influence of digital capabilities in the form of digital knowledge sharing capability, digital business capability (DBC), and using a digital platform for the internationalization process as part of the mediating role of business model innovativeness (BMI). Based on the empirical data of 213 manufacturing SMEs, we concluded that the digital capabilities we studied do not directly foster internationalization. Instead, digital capabilities (digital knowledge sharing capability, DBC, and digital platform capability) indirectly boost the internationalization of SMEs through BMI. Based on these insights, managers should embrace digitalization activities to innovate their business model, which in turn can help enter more stable and advanced markets in the global economy to maximize profitability.
Debris discs at centimetre wavelengths: planetesimal populations in young extrasolar Kuiper belts

Two luminous debris discs around 100 Myr old have been searched for 1 cm dust emission, to a depth three times greater than in any such previous study. Very low limits were set towards both HD 377 and HD 104860 (noise levels of 12–14 Jy), extending the spectral range from 70 m to 3 mm where cool dust has previously been sought. The almost-identical fluxes of the two systems were merged into an average spectral energy distribution, which was then fitted using a distribution of grain sizes. The canonical infinite collisional cascade, with numbers of particles n(D) ∝ D−3.5 for diameter D, overpredicts the 1-cm flux, which should have been detected at over 3σ for the merged system. Preferred solutions have truncated grain populations with largest particles around 1.5–4 mm in diameter, and slightly flatter distributions, up to n(D) ∝ D−3.1. The lack of cm-sized and larger particles is reminiscent of the similar deficit inferred around comets from spacecraft encounters. Such departures from simple power-law distributions of particles have been predicted in recent models where some size regimes are more readily broken up than others. A deficit of cm-scale particles can explain the fits we obtain, and reduces the total masses inferred for the comet belts of these stars.
TP Transformation Based Observer and Controller Design of 2DoF Aeroelastic Wing Section Model

This paper presents a Tensor Product (TP) model based observer and controller design via Linear Matrix In-equality (LMI) method of 2DoF aeroelastic wing section model. The transformation is based on the Higher Order Singular Value Decomposition HOSVD methodology and the generated linear time-invariant (LTI) vertex systems describe the nonlinear model. The aim of the paper is to investigate that selecting and varying the transformation space and weighting functions have influence on the feasibility regions of LMI based observer and controller. The paper gives a comparison between the controller and observer performance. The proposed method results globally asymptotically stable system, and the LMI gives feasible solution for the observer and controller design if the transformation space and weighting functions are selected adequately.
Reconstruction of 3D objects from multi-frequency experimental data with a fast DBIM-BCGS method

The objective of this work is to perform image reconstruction of 3D dielectric targets from multi-frequency experimental data by using a fast DBIM-BCGS method that combines the distorted Born iterative method (DBIM) and the stabilized biconjugate-gradient fast Fourier transform (BCGS-FFT) method. In this reconstruction technique, the BCGS-FFT method is used as a forward scattering method for solving the volume integral equations governing the 3D scattering problem; it provides both the predicted scattered fields due to 3D heterogeneous objects and the Fréchet derivatives in the inverse scattering problem. The plane-wave source model and the point receiver model are used in the inversion procedure to invert the calibrated scattering data obtained from Institut Fresnel's measurements. The multi-frequency experimental data are processed with the frequency-hopping approach to obtain high-resolution 3D images. The reconstruction of five different targets from the measured scattered fields verifies the capability and the effectiveness of the DBIM-BCGS method.
Mechanically Programmable Jamming Based on Articulated Mesh Structures for Variable Stiffness Robots

Soft robots are capable of effortlessly adapting to their environment using elastic materials that impart structural compliance into their designs, allowing them to execute complex tasks with minimal sensing and control. However, soft robots cannot exert high forces and can only handle low deformation forces. These characteristics typically limit their applicabil-ity to tasks that require delicate interactions. In this work, we present a mechanically programmable, variable stiffness, jamming actuator based on an articulated mesh structure. The proposed actuator can elastically bend when it is not activated but compresses to attain a pre-programmed shape that is determined by the mesh geometry of the multi-layer jamming architecture when pressure is applied to the silicone pouch containing it. Unlike traditional jamming structures the utilisation of the articulated mesh structure facilitates elastic deformations past the yield point when jammed. The actuator can become >27 times stiffer than its relaxed configuration when exposed to only 90 kPa pressure. We demonstrate the efficiency of this actuator by developing variable stiffness joints that can be used to create: i) underactuated, tendon driven robotic grippers and soft, disposable robotic grippers that exhibit increased dexterity and ii) wearable, affordable, lightweight elbow exoskeleton systems that can assist humans in holding heavy objects with minimal effort.
The rheology of dilute solutions of flexible polymers: Progress and problems

Recent progress toward understanding the rheology of dilute solutions of flexible polymers is reviewed, emphasizing experimental results from flows imaging single deoxyribonucleic acid (DNA) molecules and filament-stretching rheometry of dilute polystyrene Boger fluids, as well as Brownian dynamics (BD) simulations of these flows. The bead-spring and bead-rod models are presented, the range of their applicability discussed, and methods presented for inclusion of hydrodynamics interactions, excluded volume, and other physical effects within BD simulations. After reviewing and updating work in the linear viscoelastic regime, the primary focus shifts to the more complex nonlinear regime. While BD predictions of the conformations of 20 to 100 micron long DNA molecules in strong shear and extensional flows has been in good to excellent agreement with the corresponding experiments, predictions of the polystyrene dilute solution rheometry data have been hit or miss, with poorer results obtained for the higher mo.
Comprehensive Analysis of Nature-Inspired Algorithms for Parkinson’s Disease Diagnosis

Parkinson’s disease (PD) is a prominent neurodegenerative disease that damages the neurons of the substantia nigra, causing irreversible impairments leading to involuntary movements. As this disease disrupts patients’ daily activities in a mature stage, early detection of the disease is crucial. Several methods based on nature-inspired (NI) algorithms have been proposed for PD detection and patient management. As there are several NI algorithms for feature selection, a mapping with an individual machine learning (ML) classifier is necessary to obtain optimal performance of the detection pipeline. To fill this gap, in this work, 13 NI algorithms and 11 ML classifiers were selected, and critical comparisons were performed regarding their combined performance in detecting PD. Each NI algorithm was employed to select an optimal feature set which was then classified by the 11 ML classifiers keeping the same parameters. This generated 143 NI-ML pairs, which were carefully compared to find the best-performing pairs considering several assessment criteria such as accuracy, cross-validation mean score, precision, recall and F1-score. The results of the extensive comparative analysis allowed the ranking of the algorithms in the 50th, 75th and 95th percentile to identify the best-performing pairs. The analyses revealed that 12 NI-ML models obtained a testing accuracy of over 91%, which is above the 95th percentile value. The Flower Pollination Algorithm and Extreme Gradient Boost Algorithm pair obtained the highest testing accuracy of 93%. This study revealed the remarkable performance of the boosting algorithms promoting explainable machine learning in PD detection.
A Torque Controlled Approach for Virtual Remote Centre of Motion Implementation

In this paper, we propose a novel torque controller for the implementation virtual remote center of motion. The controller allows the system to implement the required behavior and guarantees the satisfaction of the remote center of motion constraint. Exploiting the Udwadia-Kalaba equation for constrained dynamic systems, the controller is synthesized considering the dynamic effect the constraint produces on the manipulator, achieving more effective control with respect to kinematic strategies, and allowing the implementation of compliance behaviors. Simulations and experimental validation with a KUKA LWR 4+ with 7 degrees of freedom has been performed to check the performances of the proposed controller. Results show the effectiveness of the proposed controller with different control action, and the capability to interact with the environment by implementing compliant motion control.
SanitizerBot: How Human-in-the-Loop Social Robots Can Playfully Support Humans

This paper evaluates a robot that distributed hand-sanitizer over an eight month period (October 2020-June 2021) in public places on the Oregon State University campus. During COVID times, many robots have been deployed in public places as social distancing enforcers, food delivery robots, UV-sanitation robots and more, but few studies have assessed the social situations of these robots. Using the context of robot distributing hand sanitizer, this work explores the benefits that social robots may provide to encouraging healthy human activities, as well as ways in which street-performance inspired approaches and a bit of humor might improve the quality and experience of functional human-robot interactions. After gaining human-in-the-loop deployment experience with a customized interface to enable both planned and improvized responses to human bystanders, we run two sub-studies. In the first, we compare the performance of the robot (moving or still) relative to a traditional hand sanitizer dispenser stick ($\mathrm{N}=2048$, 3 week data collection period). In the second, we evaluate how varied utterance strategies further impact the interaction results ($\mathrm{N}=185$, 2 week data collection period). The robot dramatically outperforms the stick dispenser across all tracked behavioral variables, cuing high levels of positive social engagement. This work finds the utterance design is more complex socially, and offer insights to future robot designers about how to integrate helpful and playful speech into service robot interactions. Finally, across both sub-studies, the work shows that people in groups are more likely to engage with the robot and each other, as well as sanitize their hands.
Model Parameter Self-Correcting Fuzzy Proportional–Integral–Derivative Adaptive Control of the Integration Time of Raman Spectrometers

The control of the integration time of Raman spectrometer is a crucial factor for the acquisition quality of Raman spectrum. However, due to the coupling effect of linear and nonlinear factors such as the intensity of Raman scattering effect, acquisition noise, fluorescence effect, and spectrometer optical path, it is rather difficult to fast control the integration time, which influences the acquisition efficiency. In this article, aiming at the nonlinear automatic optimal control of the integration time, a model parameter self-correcting fuzzy proportional–integral–derivative adaptive (MPSC-FPIDA) control method is proposed. By constructing an initial parameter prediction model and the fuzzy PID controller, two processing stages, namely, Raman spectrum intensity detection and fuzzy PID iterative control, are carried out, with which the automatic calculation and optimal control of the integration time of the Raman spectrometer are achieved according to the set point of spectrum intensity or signal-to-noise ratio (SNR). By introducing a parameter self-correcting factor of the initial model, the adaptive correction of the parameters of the initial prediction model of Raman spectrometers with different optical laser wavelengths is realized, which ensures the universality of this method to spectrometers of different types. The experiment demonstrates that when it comes to acquisition of Raman spectrum for various samples, the method proposed in this article can accurately calculate the optimal integration time within 2–3 iterations and automatically adapt to the performance parameters of different Raman spectrometers, of which the control performance is as good as that of modeling spectrometers. This shows that the method features fast convergence speed, strong robustness, and versatility. In addition, compared with commonly used linear control methods, it can effectively suppress the coupling effect of linear and nonlinear factors, which improves the efficiency of spectrum acquisition and enhances the control performance by over 50% as well.
InCOpt: Incremental Constrained Optimization using the Bayes Tree

In this work, we investigate the problem of incre-mentally solving constrained non-linear optimization problems formulated as factor graphs. Prior incremental solvers were either restricted to the unconstrained case or required periodic batch relinearizations of the objective and constraints which are expensive and detract from the online nature of the algorithm. We present InCOpt, an Augmented Lagrangian-based incremental constrained optimizer that views matrix operations as message passing over the Bayes tree. We first show how the linear system, resulting from linearizing the constrained objective, can be represented as a Bayes tree. We then propose an algorithm that views forward and back substitutions, which naturally arise from solving the Lagrangian, as upward and downward passes on the tree. Using this formulation, In-COpt can exploit properties such as fluid/online relinearization leading to increased accuracy without a sacrifice in runtime. We evaluate our solver on different applications (navigation and manipulation) and provide an extensive evaluation against existing constrained and unconstrained solvers.
Cartilage Extracellular Matrix-derived Matrikines in Osteoarthritis.

Osteoarthritis (OA) is among the most frequent diseases of the musculoskeletal system. Degradation of cartilage extracellular matrix (ECM) is a hallmark of OA. During the degradation process intact/full-length proteins as well as proteolytic fragments are released which then might induce different downstream responses via diverse receptors, therefore leading to different biological consequences. Collagen type II and the proteoglycan aggrecan are the most abundant components of the cartilage ECM. However, over the last decades, a large number of minor components have been identified and for some of those a role in the manifold processes associated with OA has already been demonstrated. To date, there is still no therapy able to halt or cure OA. A better understanding of the matrikine landscape occurring with or even preceding obvious degenerative changes in joint tissues is needed and might help to identify molecules that could serve as biomarkers, druggable targets, or even be blueprints for DMOADs. For this narrative review, we screened PubMed for relevant literature in English language and summarized the current knowledge regarding the function of selected ECM molecules and the derived matrikines in the context of cartilage and OA.
5G network slicing with unmanned aerial vehicles: Taxonomy, survey, and future directions

With the ever‐expanding rise of network demands and user expectations, the fifth generation (5G) of cellular networks was envisioned to support a plethora of use cases and conflicting user demands. Next to providing traditional connectivity like its previous generations, 5G also promises to be a heterogeneous network connecting humans, vehicles, unmanned aerial vehicles (UAVs), smart devices, and more. These challenging expectations proved to be overwhelming for traditional network infrastructures to handle. Network slicing has emerged as a promising solution that can achieve such diverse, taxing, and sometimes conflicting requirements in a dynamic and programmable way. There is no denying that UAVs have attained significant focus and research in recent years, and with 5G already being deployed, UAVs can now exploit the capabilities of the new networks. Extensive research is being taken to integrate UAVs into networks, assisting and improving aspects like latency, coverage, and capacity. Motivated by these facts, this survey distinguishes itself from other works by jointly exploring 5G, network slicing, and UAVs. The main contributions of this article are to showcase how UAVs can assist networks, provide a taxonomy of UAVs in the context of network slicing, and survey works that contribute to network slicing with UAVs. In this article, we present a comprehensive survey on UAVs in the context of network slicing, covering contributions, and state‐of‐the‐art literature. We discuss network slicing in‐depth, focusing especially on the three major slices: enhanced Mobile BroadBand, massive machine type communications, and ultra‐reliable low‐latency communications. We provide an overview of 5G enablers, including software‐defined networking and network function virtualization. We cover UAVs and identify their roles in networks as both users and assistants. Furthermore, this survey provides insight into open issues and future research directions related to network slicing and UAVs before concluding.
Fast and Safe Exploration via Adaptive Semantic Perception in Outdoor Environments

Autonomous exploration in unknown environments is a fundamental task for robots. Existing approaches mostly were concentrated on the efficiency of the exploration with the assumption of perfect state estimation, but the drift of pose estimation in visual SLAM occurs frequently and is detrimental to robot's localization and exploration performance. In this paper, a perception-aware exploration(PAE) method is proposed for rapidly and safely autonomous exploration in outdoor environments. The adaptive semantic information is proposed to improve the robustness of perception. Based on the perception module, both the selection of exploration goal on a novel weighted information gain and path planning can avoid the areas with high localization uncertainty. In addition, thanks to the proposed pipeline, including scan-based frontier detection, kd-tree based map prediction and suboptimal frontier buffer strategy, the PAE planner can explore the environment with high success rate and high efficiency. Several simulations are performed to verify the effectiveness of our methods.
DIST: Deterministic In-System Test with X-masking

In-field and in-system deterministic tests begin to play a pivotal role in safety-critical applications (compliant with regulations such as ISO 26262), in large data centers, or in monitoring silicon aging. All of them require periodic, high-quality tests to assure required test coverage and short test time in designs that must test themselves during system operations. In order for deterministic tests to be in-system applicable, they should compact multi-million-bit test responses with unknowns (X) to small signatures. This, in turn, allows for a much faster input-only streaming and reduction of the stored test data volume, a system memory, and test time. Typically, the unknown states, whose sources vary from uninitialized memories to unpredictable last-minute timing violations, render signatures unusable. Hence, test response compaction requires some form of protection. This paper presents a user-tunable X-masking scheme that employs compressed data to completely filter out unknown values that otherwise might reach a test response compactor such as a MISR or test result sticky-bits used by the on-chip compare framework. Experimental results obtained for several industrial cores show feasibility and efficiency of the proposed scheme altogether with actual impact of X-mask-in2 on various test-related statistics.
Evaluation of the potential corrosivity of groundwater using an Analytic Hierarchy Process-based index

ABSTRACT In this study, a new index based on the Analytical Hierarchy Process, and a corrosion classification system have been proposed to evaluate the potential corrosivity of groundwater. The hierarchical structure of the index has been designed using 7 different criteria affecting the potential corrosivity of groundwater and the water analysis results. The highest weight values in the hierarchical system were calculated as 35%, 23.7% and 15.8% for SO4 2-, CO2, and pH, respectively. The index has been tested using the analysis results of water samples taken from 120 groundwater wells in Aksaray (Turkey). 15.8% of the groundwater samples have been classified as “strongly aggressive’. The areas classified as ‘strongly aggressive’ are located in the northwest, east, and southeast of the region. The results obtained from this study showed that the index developed to predict the potential corrosivity of groundwater gives successful and reliable results.
Leveraging Publicly Available Textual Object Descriptions for Anthropomorphic Robotic Grasp Predictions

Robotic systems using anthropomorphic end-effectors face tremendous challenges choosing a suitable pose for grasping an object. The fact that the choice of a grasp is influenced by the physical properties of an object, the intended task, and the environment results in a considerable amount of variables. The majority of models targeted towards enabling such robots to determine a suitable grasping pose rely on computer vision techniques, sometimes complemented by textual data. This paper investigates the potential of publicly available textual descriptions to predict a suitable grasping pose for anthropomorphic end-effectors. To this end, we have retrieved textual descriptions from Wikipedia, Wiktionary, and WordNet as well as a number of well-known dictionaries for 100 everyday objects. We analyze and compare the prediction quality of multiple learning methods while showing that a support vector machine-based approach can utilize this data for achieving a prediction accuracy above 0.75. Finally, we make our collected data available to the research community.