Miracle-dz/UniFAR-FTUs huggingface dataset:
Graphene Oxide versus Carbon Nanofibers in Poly(3-hydroxybutyrate-co-3-hydroxyvalerate) Films: Degradation in Simulated Intestinal Environments

Poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) is a microbial biodegradable polymer with a broad range of promising industrial applications. The effect of incorporation of low amounts (1% w/w) of carbon nanomaterials (CBNs) such as 1D carbon nanofibers (CNFs) or 2D graphene oxide (GO) nanosheets into the PHBV polymer matrix affects its degradation properties, as it is reported here for the first time. The study was performed in simulated gut conditions using two different media: an acidic aqueous medium (pH 6) and Gifu anaerobic medium. The results of this study showed that the incorporation of low amounts of filamentous 1D hydrophobic CNFs significantly increased the degradability of the hydrophobic PHBV after 3 months in simulated intestinal conditions as confirmed by weight loss (~20.5% w/w in acidic medium) and electron microscopy. We can attribute these results to the fact that the long hydrophobic carbon nanochannels created in the PHBV matrix with the incorporation of the CNFs allowed the degradation medium to penetrate at ultrafast diffusion speed increasing the area exposed to degradation. However, the hydrogen bonds formed between the 2D hydrophilic GO nanosheets and the hydrophobic PHBV polymer chains produced a homogeneous composite structure that exhibits lower degradation (weight loss of ~4.5% w/w after three months in acidic aqueous medium). Moreover, the water molecules present in both degradation media can be linked to the hydroxyl (-OH) and carboxyl (-COOH) groups present on the basal planes and at the edges of the GO nanosheets, reducing their degradation potential.
Analysis of Thermal Characteristics in Cooling System of Spindle on the XK717 CNC Milling Machine

Thermal characteristics of spindle system on the XK717 CNC milling machine were analyzed by combining finite element method (FEM) with cooling model between spindle system and water tank.Effect rule was obtained to describe the effective relationship between the cooling water flux and size of water tank.This research provides the theoretical fundamentals for design of CNC milling machine.
Towards InAs/InP Quantum-dash Laser-based Ultra-high capacity Heterogeneous Optical Networks: A Review

The unprecedented increase in internet traffic witnessed in the last few years has pushed the community to explore heterogeneous optical networks, including free-space-optics (FSO) communication, radio-over-fiber (RoF), wireless (WL), and their convergent hybrid technologies. This stems from the possibility of seamless integration with the existing fiber-optic backhaul networks while providing much-needed flexibility and scalability. Moreover, efficient optical transceivers, particularly light sources in the form of semiconductor laser diodes, are key in meeting the future demands of the next generation green optical access networks. In this respect, InAs/InP Quantum-dash (Qdash) nanostructure-based lasers have shown tremendous progress in the past few years with applications spanning from a wavelength division multiplexed (WDM) optical communication to recently, millimeter-wave (MMW) over fiber networks. This waveguide-based laser diode (LD) has demonstrated a rule-changing broad multi-wavelength lasing emission, thanks to the inherent and wavelength-tunable wide gain profile offered by the Qdash active-gain region covering S- to U-band regions. Moreover, exploiting mode-locking and optical injection locking assisting techniques, highly coherent InAs/InP Qdash comb laser source at 1550 nm and 1610 nm have been respectively realized, thus opening a new paradigm for their potential applications in green optical and 5G access networks. In this work, progress on utilizing InAs/InP Qdash LD, supporting several sub-carriers, in energy-efficient ~1550 nm and ~1610 nm WDM heterogeneous optical networks with single-mode-fibers (SMF) has been highlighted, exhibiting aggregate data rates of 11 – 12 Tb/s by employing a single device, and accommodating PAM4 and higher-order 32-QAM modulation schemes. Moreover, the recent deployment of this LD in the RoF domain, targeting MMW frequency band, for convergent fiber-wireless networks, is also summarized, with demonstrated 25 to > 100 GHz MMW beat-tone frequency generates and transmission of up to 24 Gb/s.
A Person-Centered, Theoretically-Informed Analysis of Gender Differences in Aggression

The “gender gap” in offending is largest for violent crime types, and many theoretical perspectives have attempted to account for this gap. The current study provides unique insight into these issues by using a sample of men and women in prison to examine and explain intentions to be aggressive in response to a hypothetical scenario. Results show that similar factors (e.g., low self-control, prior aggression) predict aggressive responses to a hypothetical scenario for both men and women, but some evidence also emerged that different factors account for men’s and women’s behavior (e.g., education, family attachment).
Aboveground Biomass, Carbon Sequestration, and Yield of Pyrus pyrifolia under the Management of Organic Residues in the Subtropical Ecosystem of Southern Brazil

Organic residues management (ORM) alter plant traits and soil properties by changing nutrient and carbon cycling. It is unclear how ORM (mulching, compost, and their combination) applied for 18 months creates a mechanism to promote changes in a P. pyrifolia field. Our aim was to evaluate the influence of ORM on P. pyrifolia nutritional status, plant traits, yield, and carbon sequestration in a 16-year subtropical P. pyrifolia field. For this purpose, we performed an experiment in a randomized block design, using a factorial scheme 2 × 2, with the use of Compost and Mulching (e.g., presence and absence). The highest values of leaves N content, plant height, stem biomass, root biomass, total biomass, yield, and above- and belowground carbon (C) density were found on plots that received compost as the ORM. For soil organic C stock, the highest values were found on plots where mulching was applied. Finally, the highest values of total C density were found on plots that received the combination of Mulching and Compost. Our findings suggest that: (i) the use of Compost is the best alternative to promote leaves N content, plant height, stem dry biomass, root dry biomass, and total dry biomass, plant yield, and above- and belowground C density into a 16-year P. pyrifolia field into subtropical conditions; and (ii) the soil organic C stocks were improved using just the mulching treatment. The results highlight the importance of considering just one organic residue practice based on a sustainable way to improve both plant production and carbon sequestration, no differences were found between the use of compost and the combination of compost and mulching.
Qualitative studies on mechanisms of stress transfer across cracks in concrete.

Mechanisms of concrete stress transfer were examined and thoroughly understood using the contact density model developed by B. Li and K. Maekawa. Qualitative studies were done on high strength concrete and on structural-deformation-like loading paths involving crack closing or opening with shear slip happening at the same time. The studies indicated that stress transfer mechanisms in high strength concrete crack were different from that of normal concrete due to difference in crack surface configuration. They also showed that the mechanisms in the structural-deformatin-like paths much differed from those in classical crack width constant loading paths due to significantly induced effect of friction and due to unconfined nature of the paths which gave rise to anisotropic property and contact fracturing of a crack surface.
Temperature Effects on the Peak and Large-Displacement Shear Strength of Needle-Punched Reinforced GCLs

The objective of this study was to evaluate the effect of temperature on the peak and largedisplacement shear strength of needle-punched reinforced geosynthetic clay liners (NP GCLs). A series of displacement-controlled internal shear tests were performed at room temperature (20 °C) and elevated temperature (80 °C). Experiments were performed on two non-heat-treated NP GCLs with different peel strength. An increase in temperature changed the failure mode from reinforcement fiber disentanglement to fiber rupture at low normal stresses. The peak shear strength of both NP GCLs reduced at 80 °C, whereby the peak shear strength reduction was 36% to 42% for the lower peel strength NP GCL and 19% to 25% for the higher peel strength NP GCL. The large-displacement shear strength of both NP GCLs was comparable, and negligible change in large displacement shear strength was observed for shear tests at 20 °C and 80°C.
Diagnostic Performance of a Deep Learning Model Deployed at a National COVID-19 Screening Facility for Detection of Pneumonia on Frontal Chest Radiographs

(1) Background: Chest radiographs are the mainstay of initial radiological investigation in this COVID-19 pandemic. A reliable and readily deployable artificial intelligence (AI) algorithm that detects pneumonia in COVID-19 suspects can be useful for screening or triage in a hospital setting. This study has a few objectives: first, to develop a model that accurately detects pneumonia in COVID-19 suspects; second, to assess its performance in a real-world clinical setting; and third, by integrating the model with the daily clinical workflow, to measure its impact on report turn-around time. (2) Methods: The model was developed from the NIH Chest-14 open-source dataset and fine-tuned using an internal dataset comprising more than 4000 CXRs acquired in our institution. Input from two senior radiologists provided the reference standard. The model was integrated into daily clinical workflow, prioritising abnormal CXRs for expedited reporting. Area under the receiver operating characteristic curve (AUC), F1 score, sensitivity, and specificity were calculated to characterise diagnostic performance. The average time taken by radiologists in reporting the CXRs was compared against the mean baseline time taken prior to implementation of the AI model. (3) Results: 9431 unique CXRs were included in the datasets, of which 1232 were ground truth-labelled positive for pneumonia. On the “live” dataset, the model achieved an AUC of 0.95 (95% confidence interval (CI): 0.92, 0.96) corresponding to a specificity of 97% (95% CI: 0.97, 0.98) and sensitivity of 79% (95% CI: 0.72, 0.84). No statistically significant degradation of diagnostic performance was encountered during clinical deployment, and report turn-around time was reduced by 22%. (4) Conclusion: In real-world clinical deployment, our model expedites reporting of pneumonia in COVID-19 suspects while preserving diagnostic performance without significant model drift.
Effects of Direction-Specific Training Interventions on Physical Performance and Inter-Limb Asymmetries

This study analyzed the effects of two different training programs on functional performance and inter-limb asymmetries in basketball players. Twenty-four elite youth basketball players were randomly assigned to a training program including variable unilateral horizontal movements (VUH, n = 12) or unilateral lateral movements (VUL, n = 12). Eccentric-overload training (EOT) was performed twice a week for a six-week period. Functional performance assessment included a countermovement jump test, unilateral multidirectional jumping tests (i.e., lateral, horizontal, and vertical), a rebound jump test, a limb symmetry index, a 25 m linear sprint test, and several change of direction (COD) tests. Within-group analysis showed substantial improvements in almost all functional tests in both groups (ES = 0.35–0.89). Furthermore, almost all jumping asymmetries were improved in both groups (ES = 0.38–0.69) except for vertical jumping asymmetry in VUL (ES = −0.04). Between-group analyses showed a substantial and possibly better performance in vertical jumping asymmetry and 5 m in VUH compared to that of VUL, respectively. In contrast, lateral jumping with left (ES = 1.22) and right leg (ES = 0.49) were substantially greater in VUL than in VUH. Specific force-vector training programs induced substantial improvements in both functional performance tests and inter-limb asymmetries, although greater improvements of lateral and horizontal variables may depend on the specific force vector targeted.
Machine Learning-based Optimal Framework for Internet of Things Networks

: Deep neural networks (DNN) are widely employed in a wide range of intelligent applications, including image and video recognition. However, due to the enormous amount of computations required by DNN. There-fore, performing DNN inference tasks locally is problematic for resource-constrained Internet of Things (IoT) devices. Existing cloud approaches are sensitive to problems like erratic communication delays and unreliable remote server performance. The utilization of IoT device collaboration to create distributed and scalable DNN task inference is a very promising strategy. The existing research, on the other hand, exclusively looks at the static split method in the scenario of homogeneous IoT devices. As a result, there is a pressing need to investigate how to divide DNN tasks adaptively among IoT devices with varying capabilities and resource constraints, and execute the task inference cooperatively. Two major obstacles confront the aforementioned research problems: 1) In a heterogeneous dynamic multi-device environment, it is difficult to estimate the multi-layer inference delay of DNN tasks; 2) It is difficult to intelligently adapt the collaborative inference approach in real time. As a result, a multi-layer delay prediction model with fine-grained interpretability is proposed initially. Furthermore, for DNN inference tasks, evolutionary reinforcement learning (ERL) is employed to adaptively discover the approximate best split strategy. Experiments show that, in a heterogeneous dynamic environment, the proposed framework can provide considerable DNN inference acceleration. When the number of devices is 2, 3, and 4, the delay acceleration of the proposed algorithm is 1.81 times, 1.98 times and 5.28 times that of the EE algorithm, respectively.
Upgrading the Nutritional Value of PKC Using a Bacillus subtilis Derived Monocomponent β-Mannanase

Palm kernel cake (PKC) is an abundant side stream that can only be added to non-ruminant feed in small concentrations due to its content of antinutritional factors, mainly galactomannan, which cannot be digested by non-ruminants. β-mannanases can be added to partially hydrolyze galactomannan to form mannose oligosaccharides, which are known to be prebiotic. We here investigate the action of a β-mannanase from B. subtilis on PKC by colorimetry, NMR and fluorescence microscopy. The amount of mannan oligosaccharides in solution was significantly increased by the β-mannanase and their degree of polymerization (DP) was significantly reduced. There was a dose-response behavior in that larger β-mannanase concentrations increased the amount of soluble mannose oligosaccharides while reducing their average DP. Using confocal immunofluorescence microscopy, solubilization of galactomannan in PKC was clearly visualized. Images show a clear disruption of the cellulose and galactomannan structures of the PKC cell walls. We thus show in this study that using commercial dosages of β-mannanase on PKC can lead to formation of prebiotic compounds. Thus, this study suggests that utilization of PKC in poultry feed formulation might be increased by addition of a β-mannanase and would improve the return on investment.
Haemodynamic Analysis of Branched Endografts for Complex Aortic Arch Repair

This study aims to investigate the haemodynamic response induced by implantation of a double-branched endograft used in thoracic endovascular aortic repair (TEVAR) of the aortic arch. Anatomically realistic models were reconstructed from CT images obtained from patients who underwent TEVAR using the RelayPlus double-branched endograft implanted in the aortic arch. Two cases (Patient 1, Patient 2) were included here, both patients presented with type A aortic dissection before TEVAR. To examine the influence of inner tunnel branch diameters on localised flow patterns, three tunnel branch diameters were tested using the geometric model reconstructed for Patient 1. Pulsatile blood flow through the models was simulated by numerically solving the Navier–Stokes equations along with a transitional flow model. The physiological boundary conditions were imposed at the model inlet and outlets, while the wall was assumed to be rigid. Our simulation results showed that the double-branched endograft allowed for the sufficient perfusion of blood to the supra-aortic branches and restored flow patterns expected in normal aortas. The diameter of tunnel branches in the device plays a crucial role in the development of flow downstream of the branches and thus must be selected carefully based on the overall geometry of the vessel. Given the importance of wall shear stress in vascular remodelling and thrombus formation, longitudinal studies should be performed in the future in order to elucidate the role of tunnel branch diameters in long-term patency of the supra-aortic branches following TEVAR with the double-branched endograft.
Criticality in the Healthy Brain

The excellence of the brain is its robustness under various types of noise and its flexibility under various environments. However, how the brain works is still a mystery. The critical brain hypothesis proposes a possible mechanism and states that criticality plays an important role in the healthy brain. Herein, using an electroencephalography dataset obtained from patients with psychotic disorders (PDs), ultra-high risk (UHR) individuals and healthy controls (HCs), and its dynamical network analysis, we show that the brain of HCs remains around a critical state, whereas that of patients with PD falls into more stable states. Meanwhile, the brain of UHR individuals is similar to that of PD in terms of entropy but is analogous to that of HCs in causality patterns. These results not only provide evidence for the criticality of the normal brain but also highlight the practicability of using an analytic biophysical tool to study the dynamical properties of mental diseases.
Computational Models in Neurosciences Between Mechanistic and Phenomenological Characterizations

Computational neuroscience combines mathematics, computer science models, and neurosciences for theorizing, investigating, and simulating neural systems involved in the development, structure, physiology, and cognitive abilities of the brain. Computational models constitute a major stake in translational neuroscience: the analytical understanding of these models seems fundamental to consider a translation towards clinical applications. Method: We propose a minimal typology of computational models, which allows distinguishing between more realistic models (e.g., mechanistic models) and pragmatic models (e.g., phenomenological models). Result: Understanding the translational aspects of computational models goes far beyond the intrinsic characteristics of models. First, we assume that a computational model is rarely uniquely mechanistic or phenomenological. Idealization seems necessary because of i) the researcher’s perspectives on the phenomena and the purposes of the study (i.e., by the relativity of the model); ii) The complexity of reality across different levels and therefore the nature and number of dimensions required to consider a phenomenon. Especially, the use of models goes far beyond their function, and requires considering external characteristics rooted in path dependence, interdisciplinarity, and pluralism in neurosciences. Conclusion: The unreasonable use of computational models, which are highly complex and subject to a shift in their initial function, could be limited by bringing to light such factors.
An EFSM-Based Test Data Generation Approach in Model-Based Testing

: Testing is an integral part of software development. Current fast-paced system developments have rendered traditional testing techniques obso-lete. Therefore, automated testing techniques are needed to adapt to such system developments speed. Model-based testing (MBT) is a technique that uses system models to generate and execute test cases automatically. It was identified that the test data generation (TDG) in many existing model-based test case generation (MB-TCG) approaches were still manual. An automatic and effective TDG can further reduce testing cost while detecting more faults. This study proposes an automated TDG approach in MB-TCG using the extended finite state machine model (EFSM). The proposed approach integrates MBT with combinatorial testing. The information available in an EFSM model and the boundary value analysis strategy are used to automate the domain input classifications which were done manually by the existing approach. The results showed that the proposed approach was able to detect 6.62 percent more faults than the conventional MB-TCG but at the same time generated 43 more tests. The proposed approach effectively detects faults, but a further treatment to the generated tests such as test case prioritization should be done to increase the effectiveness and efficiency of testing.
Preparation of Veterinary Premix with Dual Active Ingredients in Granular Form

The aim of the study was to investigate the preparation of granular veterinary premix containing two different antibiotics by fluid bed granulation process. The particle size and density of the active ingredients were investigated for the proper selection of the filler material. The used antibiotics were tylosin tartarate and colistin sulfate, while isomalt sugar alcohol and cellulose materials were selected as filler and binder, respectively. The colistin sulfate was fed together with the binder solution because of its low density, fine particle size and relatively low (1.2%) concentration in the product. The type and concentration of the binder in its solution, the feeding rate and the total amount of added binder solution were optimized for obtaining dust free granules with desirable abrasion resistance and good flowability. The active ingredient content for both antibiotics was preserved even at the elevated temperature applied during the fluid granulation process.
Copper nanowire with enriched high‐index facets for highly selective CO2 reduction

Electroreduction of carbon dioxide into fuels and feedstocks with renewable energy is an attractive route to mitigate carbon emission and solve energy crisis. However, how to improve the selectivity of high‐value multicarbon products is still challenging. Here, we demonstrate that the high‐index crystalline surface of copper could be designed and obtained through a simple square‐wave potential treatment on copper nanowires, which is beneficial to improve the selectivity of multi‐carbon products, especially the reaction route towards ethylene. The Faradaic efficiency of C2+ products can reach nearly 60%, and hydrogen can be suppressed to below 20%. Density functional theory (DFT) calculations reveal that (311) high‐index facet can activate CO2 effectively and promote adsorption of the *COCOH intermediate on copper for ethylene formation, therefore improves the selectivity of ethylene and inhibits the competing hydrogen evolution reaction. This method can be extended to the design of other catalytic systems and has inspirations for other electrochemical catalytic reactions.