Miracle-dz/UniFAR-FTUs huggingface dataset:
in the invasive pest Drosophila suzuki : useful clues for control strategies

: 20 Successful management of invasive pests, such as Drosophila suzukii, relies on a fine understanding 21 of their biology. Genomic and physiological studies have suggested that the invasive success of D. 22 suzukii is strongly associated to its ability to overwinter in a reproductive diapause state. Here, we 23 coupled field surveys with comparative morphology and genetics to increase our understanding of 24 D. suzukii overwintering behavior, and provide useful indications for its management. 25 The results of a four-year long field trapping in an Italian mountain region indicate that D. suzukii is 26 continuously captured during winter months and that the number of captures is correlated with 27 temperature. In addition, during winter females were consistently trapped at higher numbers than 28 males. We also found that overwintering not only occurs in anthropic shelters but also in natural 29 environments such as woods. Comparative morphological and genetic studies indicate that D. 30 suzukii spermathecae are larger in size, more pigmented, extend more after mating, and overexpress 31 the spermathecae-related Cyp4d20 cytochrome: this suggests that females are able to collect and 32 protect for UV more sperm than closely related species, a possible adaptation to the dormant 33 reproductive state, when males are less likely to survive. We hence propose that early season 34 population size can be better forecasted by taking into account the captures of the previous winter. 35 We also recommend that control methods should be diapause-aware, therefore done in late 36 winter/early spring and close to natural environments, and not only in fruit ripening season and 37 close to orchards. 38
TWENTY ANSWERS TO ONE QUESTION: WHAT IS THE CUBIC B-SPLINE FOR THE KNOTS -2,-1,0,1,2?

In this composition an attempt is made to answer one simple question only: What is the cubic B-spline for the knots -2,-1,0,1,2? The note will take you on a most interesting trip through various ﬁelds of Mathematics and ﬁnally convince you on how little we know.
An Interaction-Aware predictive motion Planner for Unmanned Ground Vehicles in Dynamic Street Scenarios

An interaction-aware predictive motion planning method for unmanned ground vehicles is presented in dynamic street scenarios. Although trajectory prediction in motion planners is widely covered in the past few years, most of them only consider the physical model of the vehicles and ignore the interaction among vehicles. Our motion planner predicts the future trajectories of surrounding participant vehicles taking the traﬃc interaction and manoeuvres into consideration. Furthermore, the motion planner exploits an improved trajectory generation method. The kinematically feasible trajectories are generated, which prevents a long-term collision using the predicted results in a probabilistic manner. The results show that our motion planner improves the safety and smoothness of driving trajectories in interactive scenarios.
Complex Product Architecture Analysis using an Integrated Approach

Product design decomposition and synthesis is a constant challenge with its continuously increasing complexity at each level of abstraction. Currently, design decomposition and synthesis analytical tasks are mostly accomplished via functional and structural methods. These methods are useful in different phases of design process for product definition and architecture but limited in a way that they tend to focus more on 'what' and less on 'how' and vice versa. This paper combines a functional representation tool known as System State Flow Diagram (a solution independent approach), a solution search tool referred as Morphology Table, and Design Structure Matrix (mainly a solution dependent tool). The proposed approach incorporates Multiple Domain Matrix (MDM) to integrate the knowledge of both solution independent and dependent analyses. The approach is illustrated with a case study of solar robot toy, followed by its limitations, future work and discussion.
An Analytical Tuning of MPC Control Horizon Using the Hessian Condition Number

—Model Predictive Control (MPC) is based on the concept of receding horizon, the future output prediction, and the minimization of a cost function to provide the optimal control sequence. MPC controller contains three parameters: a control horizon N c , a prediction horizon N p and a weighting factor λ . A successeful implementation of MPC requires an appropriate setting of these parameters. In this paper, an analytical approach for tuning the control horizon is presented while taking into account constraints. The idea of our novel approach consists on computing the value of the optimal control horizon in such a way it ensures the numerical stability. The interest of our approach is to be applicable to a wide set of linear controllable Single-Input Single-Output (SISO) processes whatever their orders. The issues of numerical condition and closed-loop stability are addressed in this paper. The proposed approach is tested via a simulated pH neutralization process. Results are compared to emphasize the effectiveness of the proposed approach.
Multi‐task deep learning with optical flow features for self‐driving cars

: The control of self-driving cars has received growing attention recently. While existing research shows promising results in vehicle control using video from a monocular dash camera, there has been very limited work on directly learning vehicle control from motion-based cues. Such cues are powerful features for visual representations, as they encode the per-pixel movement between two consecutive images, allowing a system to effectively map the features into the control signal. We propose a new framework that exploits the use of a motion-based feature known as optical ﬂow extracted from the dash camera, and demonstrates that such a feature is effective in signiﬁcantly improving the accuracy of the control signals. Our proposed framework involves two main components. The ﬂow predictor, as a self-supervised deep network, models the underlying scene structure from consecutive frames and generates the optical ﬂow. The controller, as a supervised multi-task deep network, predicts both steer angle and speed. We demonstrate that the proposed framework using the optical ﬂow features can effectively predict control signals from a dash camera video. Using the Cityscapes dataset, we validate that the system prediction has errors as low as 0.0130 rad/s on steer angle and 0.0615 m/s on speed, outperforming existing research.
E ﬀ ects of Hybrid Exchange Correlation Functional (Vwdf3) on the Structural, Elastic, and Electronic Properties of Transition Metal Dichalogenides

In this research, the e ﬀ ects of Van der Waals forces on the structural, elastic, electronic, and optical properties of bulk transition metals dichalco-genides (TMDs) were studied using a novel exchange-correlation functional, vdW-DF3. This new functional tries to correct the hidden Van der Waals problems which are not reported by the previous exchange functionals. Molybdenum dichalcogenide, MoX 2 (X = S, Se, Te) was chosen as a representative transition metal dichalcogenide to compare the performance of the newly designed functional with the other two popular exchange-correlation functional; PBE and rVV10. From the results so far obtained, the analysis of the structural properties generally revealed better performance by vdW-DF3 via the provision of information on lattice parameters very closer to the experimental value. For example, the lattice constant obtained by vdW-DF3 was 3.161 Å which is very close to 3.163 Å and 3.160 Å experimental and theoretical values respectively. Calculations of the electronic properties revealed good performance by vdW-DF3 functional. Furthermore, new electronic features were revealed for MoX2 (x = S, Se, Te). In terms of optical properties, PBE functional demonstrates lower absorption than vdW-DF3, as such it can be reported that vdW-DF3 improves photon absorption by TMDs. However, our results also revealed that vdW-DF3 performed well for MoS2 than for MoSe2 and MoTe2 because of the lower density observed for the S atom in MoS2.
Design and Optimization of a Robotic Finger with a Sliding, Rotating, and Soft-Bending Mechanism

In this article, a new soft finger with a pneumatic-actuated movable joint is introduced, optimized and characterized in terms of degrees of freedom, workspace and fingertip force. The finger consists of one soft link as the body and the bending pneumatic joint as the actuator. Due to the additional translation and rotation movement capabilities of the joint carried out by two stepper motors, the finger can bend in any direction while having different lengths, thanks to a configurable bending point. This results in more dexterity of the finger dealing with a target inside its 3D workspace by increasing the number of configurations in which the finger can reach the target and exert force. The finite element method (FEM) and NSGA-II algorithm are applied to optimize the joint geometry to maximize the bending angle and minimize the joint dimensions. Furthermore, the variations of each design parameter and the consequent effects on the optimization objectives are analyzed. The optimal geometrical parameters are used to fabricate a prototype with silicone rubber. Tests on bending angle and tip force variability are conducted on the prototype to validate the numerical modeling. The experimental results show that the finger exerts force up to 650 mN with a response time of less than three seconds. The stiffness of the finger can be changed by applying the pneumatic pressure in the hollow space inside the link. This consequently varies the amount of applied force at the tipping point of the finger up to two times.
Information-theoretic secret key agreement

• A submitted manuscript is the author's version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers.
Spectral response of MEMS plate resonators exhibiting non-conventional vibrational modes in fluids

Understanding of the fluid-structure interaction between a MEMS resonator and a surrounding fluid is key for understanding and predicting the dynamics of MEMS resonators in fluids. Here, we present a numerical method for modelling the fluid-structure interaction between a viscous fluid and vibrational eigenmodes of a plate-like MEMS resonator. The dynamics of the MEMS resonator is determined using a finite element method while the fluid flow is obtained from a boundary integral formulation. With this method we compute the spectral response of MEMS plate resonators in fluids.
A CRISPRi screen of essential genes reveals that proteasome regulation dictates acetic acid tolerance in Saccharomyces cerevisiae

CRISPR interference (CRISPRi) is a powerful tool to study cellular physiology under different growth conditions and this technology provides a means for screening changed expression of essential genes. In this study, a Saccharomyces cerevisiae CRISPRi library was screened for growth in medium supplemented with acetic acid. Acetic acid is a growth inhibitor challenging the use of yeast for industrial conversion of lignocellulosic biomasses. Tolerance towards acetic acid that is released during biomass hydrolysis is crucial for cell factories to be used in biorefineries. The CRISPRi library screened consists of >9,000 strains, where >98% of all essential and respiratory growth-essential genes were targeted with multiple gRNAs. The screen was performed using the high-throughput, high-resolution Scan-o-matic platform, where each strain is analyzed separately. Our study identified that CRISPRi targeting of genes involved in vesicle formation or organelle transport processes led to severe growth inhibition during acetic acid stress, emphasizing the importance of these intracellular membrane structures in maintaining cell vitality. In contrast, strains in which genes encoding subunits of the 19S regulatory particle of the 26S proteasome were downregulated had increased tolerance to acetic acid, which we hypothesize is due to ATP-salvage through an increased abundance of the 20S core particle that performs ATP-independent protein degradation. This is the first study where a high-resolution CRISPRi library screening paves the way to understand and bioengineer the robustness of yeast against acetic acid stress. IMPORTANCE Acetic acid is inhibitory to the growth of the yeast Saccharomyces cerevisiae, causing ATP starvation and oxidative stress, which leads to sub-optimal production of fuels and chemicals from lignocellulosic biomass. In this study, where each strain of a CRISPRi library was characterized individually, many essential and respiratory growth essential genes that regulate tolerance to acetic acid were identified, providing new understanding on the stress response of yeast and new targets for the bioengineering of industrial yeast. Our findings on the fine-tuning of the expression of proteasomal genes leading to increased tolerance to acetic acid suggests that this could be a novel strategy for increasing stress tolerance, leading to improved strains for production of biobased chemicals.
Traffic Modeling of Transit Oriented Development: Evaluation of Transit Friendly Strategies and Innovative Intersection Designs in West Valley City, UT

Street networks designed to support Transit Oriented Development (TOD) increase accessibility for non-motorized traffic. However, the implications of TOD supportive networks for still dominant vehicular traffic are rarely addressed. Due to this lack of research, decision making in favor of TOD supportive street networks is often a difficult process. The goal of this project is to quantify the traffic impacts of TOD using a study network in West Valley City, Utah. In the methodology, the test network is modified using not only designs typical for TODs, but also some network designs that enhance traffic operations. Proposed network designs represent the alternatives to traditional street widening approaches that should increase traffic efficiency while not discouraging non-motorized modes. This approach would increase the potential of the test network to become a TOD in the future, with two Bus Rapid Transit (BRT) lines already in place. The results indicate that network designs that could be beneficial for TOD, such as enhanced street connectivity, innovative intersection designs, traffic calming measures and Transit Friendly Designs (TFD), do not necessarily decrease the efficiency of vehicular traffic for the most critical travel demand conditions. The major contributions of this study are the indications that TOD-supportive network designs are not necessarily associated with negative effects for vehicular traffic, even in conditions where mode shift does not occur and auto-mode travel demand remains the same. This is a significant finding that could be useful for metropolitan regions looking to retrofit the suburban neighborhoods into multimodal developments.
Neural mechanisms of transitive inference and fast knowledge reassembly in learning neural networks

How do we learn structured relations from partial information? A striking instance of animal and human relational learning is transitive inference (learning A > B and B > C , and inferring A > C ), which can be quickly and globally rearranged upon learning a new item (learning A > B > C and D > E > F , then C > D , and inferring B > E ). Despite multiple modelling proposals, the neural mechanisms of transitive inference and fast reassembly of existing knowledge remain an open question. Here we adopt a meta-learning (“learning-to-learn”) approach. We train artificial neural networks, endowed with synaptic plasticity and neuromodulation, to be able to autonomously learn any novel ordered series of arbitrary stimuli, through repeated presentation of stimulus pairs. The resulting learning networks exhibit transitive inference and can quickly assemble separately learned lists after learning their linking pair. We obtain a complete mechanistic understanding of this discovered neural learning algorithm. Surpris-ingly, this learning involves active cognition: previously seen items are selectively reinstated in working memory, allowing transfer of information and explaining fast reassembly of existing knowledge. The discovered mechanism offers possible suggestions for biological experiments. More generally, this complete elucidation of a cognitive learning behavior illustrates the potential of trained plastic networks as an approach to discovering biologically plausible learning mechanisms.
Figures of merit and optimization of a VO 2 microbolometer with strong electrothermal feedback

The inﬂuence of electrothermal feedback and hysteresis on the operation conditions, noise, and performance of a VO 2 transition-edge microbolometer has been evaluated. The material undergoes a ﬁrst-order semiconductor-to-metal phase transition (cid:1) SMT (cid:2) within the temperature range 40 (cid:1) T (cid:1) 70 °C. Due to electrothermal feedback, all device parameters, including the required heat-sink temperature, output voltage and current response, response time, linear dynamic range, re-sponsivity, noise, and detectivity, display complex and nonlinear variations with temperature, electrical biasing conditions, input radiation levels, and hysteresis width. In the constant-current mode, the device responsivity extends over a broad temperature range, but under constant-voltage operation it is sharply localized and restricted to the SMT center. Film quality, as represented by the transition and the hysteresis width and the ﬂicker noise magnitude, crucially affects device performance. In the weak hysteretic case and at low 1/ f noise levels, the device detectivity improves substantially in both operation modes. The spectral range of the device is largely determined by the optical absorp-tivity of the VO 2 ﬁlm. For operation within the SMT, it extends well into the far IR wavelength region of the atmospheric window, but is substantially smaller for operation in the semiconducting region.
ON QUASI  -STATISTICAL CONVERGENCE OF TRIPLE SEQUENCES IN CONE METRIC SPACES

Fast [12] is credited with pioneering the field of statistical convergence. This topic has been researched in many spaces such as topological spaces, cone metric spaces, and so on (see, for example [19, 21]). A cone metric space was proposed by Huang and Zhang [17]. The primary distinction between a cone metric and a metric is that a cone metric is valued in an ordered Banach space. Li et al. [21] investigated the definitions of statistical convergence and statistical boundedness of a sequence in a cone metric space. Recently, Sakaoğlu and Yurdakadim [29] have introduced the concepts of quasi-statistical convergence. The notion of quasi  -statistical convergence for triple and multiple index sequences in cone metric spaces on topological vector spaces is introduced in this study, and we also examine certain theorems connected to quasi  -statistically convergent multiple sequences. Finally, we will provide some findings based on these theorems.
Study of a Cooling Device of Confined Impacting Air Jets on a Heating Cylinder

Future aircraft technology will increasingly rely on electrical power. The substitution of mechanical energy by electrical energy will lead to an increasing amount of heat power that has to be evacuated. Thus innovative cooling processes have to be set up according to constraints imposed by the technological design. The present study is conducted within the framework of onboard aircraft. We aim at studying a new cooling system of a small turbo engine stator. The cooling techniques, fully designed and built at Pau University, consists in air jets impacting around a heated circular cylinder. As the inlet velocity magnitude given by the experience is relatively low (V in =4,37m/s - Mach in =0.0125), the use of a compressible solver presents a number of difficulties. In this paper we study the performance of two different compressible solvers, one based on the finite volume approach, and the other on a discontinuous
TRAIL IMPACTS IN A TROPICAL RAINFOREST NATIONAL PARK

Generally considered a sustainable economic activity, tourism can generate environmental deterioration due to a lack of planning. In this case, the edge effect is utilized to assess the degree of human interference in the environment. Although the Atlantic Forest is known as a hotspot because of its high species richness and endemism, it is also a threatened biome. In this context of anthropogenic pressure, our work assesses the edge-interior gradient in the regenerating forest based on the physical characteristics of vegetation and floristic composition, in addition to providing overall guidelines to effectively assist in its management. For this study, 30 plots of 25 m² (10 m x 2.5 m) equally distributed among the edges of trails and locations 20 and 40 m away from them were established, with the greatest length measured parallel to the edge of the tracks. Overall, 443 individuals of 122 species were investigated. More than 60 of them were endemic to Brazil, 13.3% were threatened and 9.1% were widely distributed species. Some species were present at all distances from the trails, others co-occurred, but the majority were exclusive to a single plot category. Differences in species diversity were also observed with an increasing trend in dominance at the edges of trails along with a decreased richness at the same distance.
High-definition tDCS improves delayed memory in Alzheimer’s disease patients: A pilot study using computational modeling to optimize electrode

Background: There is a lack of consensus regarding the optimal stimulation parameters when using transcranial direct current stimulation (tDCS) to improve memory performance in patients with Alzheimer’s disease (AD). In healthy individuals, inter-individual differences in brain anatomy significantly influence current distribution during tDCS, an effect that might be aggravated by variations in cortical atrophy in AD patients. Methods: Nineteen AD patients were randomly assigned to receive either real high-definition tDCS (HD-tDCS) or sham HD-tDCS. Computational modeling of the HD-tDCS-induced electric field in each patient’s brain was analyzed based on magnetic resonance imaging (MRI) scans. The chosen montage provided the highest net anodal electric field in the left dorsolateral prefrontal cortex (DLPFC). An accelerated HD-tDCS design was conducted, stimulating with 2mA for 3 x 20 minutes on two separate days. Pre-and post-intervention cognitive tests and T1 and T2-weighted MRI and diffusion tensor imaging data at baseline were obtained and analyzed for each participant. Results: Different montages were optimal for individual patients in terms of the electric field distribution in the brain. The active HD-tDCS group improved significantly in delayed memory and Mini Mental Status Examination (MMSE) performance compared to the sham group. Five participants in the active group had higher scores on delayed memory post HD-tDCS, four remained stable and one declined. There was a significant positive correlation between fractional anisotropy in the HD-tDCS group in the anterior thalamic radiation and the score change in delayed memory. Conclusions : HD-tDCS targeting the left DLPFC significantly improved delayed memory in AD. Our study can be regarded as a proof-of-concept attempt to increase the efficacy of tDCS. Due to the small sample size, the present findings should be confirmed in larger samples.