Miracle-dz/UniFAR-FTUs huggingface dataset:
Genetic Programming for Symbolic Regression: A Study on Fish Weight Prediction

The fish weight is a very important factor in fisheries science and management since it explains the growth and living conditions of fish populations. A power regression model has been commonly used to explain the relationship between the fish length and the weight. In this work, Genetic Programming (GP) for symbolic regression is used to build a new model for predicting the fish weight, which allows us to include more features into the model to discover any hidden relationship, and the GP based symbolic regression makes the model interpretable comparing with other machine learning methods. A publicly available dataset is taken with four species of fish which includes more features than just the fish length that is commonly used in existing models. The proposed GP based symbolic regression method has been examined on those four species. The results are compared with the weight prediction baseline methods including Linear Regression, Power Regression model, k-Nearest Neighbour, Ridge Regression, Decision Tree, Random Forest, Gradient Boosting, and Multilayer Perceptron. GP performs better, or at least as good as the baseline methods on the test set. Furthermore, the generated GP models also can select different features for different species to improve the prediction performance due to GP’s explicit feature selection ability. Some models are interpretable with relatively simple expression. The GP method is also able to find models that are similar to the power regression model, but more features are included rather than a single length feature to gain improved prediction performance.
A Knowledge Transfer-Based Evolutionary Algorithm for Multimodal Optimization

There are some natural similarities among fitness landscapes of different modals involved in a multimodal optimization problem (MMOP). However, existing multimodal evolutionary algorithms (MMEAs) tend to handle these modals separately, which limits their performance to a great extent. As the first attempt, this study proposes a knowledge transfer-based MMEA by mining and exploiting modal similarities. To this end, a center-aligned normalization strategy (CANS) is first designed to map all the subpopulations corresponding to identified modals into a unified 0-1 space, and then a distribution similarity-based transfer strategy (DSTS) is proposed to guide knowledge transfer among subpopulations. DSTS explicitly quantifies the similarity between two subpopulations and probabilistically transfers elite individuals in the subpopulation of the largest similarity to the target subpopulation. By integrating CANS and DSTS into a well-known MMEA, i.e., NEA2, the final knowledge transfer-based NEA named KTNEA is developed. Experimental results on CEC’2013 niching benchmark suite indicate that KTNEA performs competitively in comparison with state-of-the-art MMEAs.
Design of Miniaturized Luzzatto Power Divider with Second and Third Harmonic Suppression Using Open Stubs

A miniaturized three-way Luzzatto power divider with second and third harmonic suppression is presented in this paper. In this proposed structure, the quarter-wavelength microstrip lines are replaced with T-shaped structures for harmonic suppression and miniaturization, while the half-wavelength microstrip lines are shortened by adding several open stubs. The proposed structure achieves suppression of more than 14.2 dB at second harmonic and 24.4 dB at third harmonic within the 1 dB bandwidth, while the circuit size is reduced to 1/4 compared with conventional Luzzatto divider. Measurement and simulation results are in good agreements under the fundamental frequency of 2.4 GHz.
Research on Performance of Power Saving Technology for 5G Base Station

Compared with the fourth generation (4G) technology, the fifth generation (5G) network possesses higher transmission rate, larger system capacity and lower transmission delay, which requires lager radio frequency (RF) bandwidth and more transport and receive channels to cater for the target requirement proposed by International Telecommunication Union (ITU). The power consumption of the base station has increased quickly and the operators worldwide are in sore need of corresponding energy saving solutions. In order to avoid the bad influences on network coverage or user experience, it is necessary to look for the balance between power saving effects and network performance when implementing any schemes. This article analyzes the power consumption model of 5G base stations firstly, and then introduces four basic power consumption strategies. Based on the field trial results, power conservation performances of each scheme are shown clearly. Finally, the technology development trends and challenges of 5G power conservation from the perspective of network operations are analysed.
The Power-Sharing System of DFIG-Based Shaft Generator Connected to a Grid of the Ship

This article proposes a control system for a ship power station using a doubly-fed-induction generator (DFIG). Firstly, the author analyzes the characteristics of the power generation system using DFIG based on the rotor sync signal technique and then proposes a control system for adjusting the active and reactive power of the generator supplied to the grid. The advantage of the rotor sync technique is that two control channels of active and reactive power are independent. It is favorable for the author proposes the Fuzzy-PID controller for each control channel. The result is that active and reactive power always follow the desired values in a fast response time. Thus, the author applies this proposed system for automatic load division in a parallel power grid on the ship. The entire generator system has well ensured the load distribution between the shaft-generator and the grid in the case of changes in load consumption and rotor speed of the generator.
Proof of Concept: Glass-Membrane Based Differential Pressure Sensor

As an alternative to silicon membranes, a pressure sensor concept based on glass-membranes was developed, mono-lithically manufactured, and evaluated. Pt strain gauge structures of two geometries were fabricated on circular and square glass membranes, realized using the Laser Induced Deep Etching (LIDE) technology. The basal resistivity, the offset voltage, and pressure and temperature dependencies were measured. The designs exhibited linear behavior in the tested regimes and high sensitivity. A discussion of the results and classification regarding the use of glass as a substrate draws the conclusion that the LIDE technology is well suited to manufacture mechanically loaded, monolithic glass-based MEMS without process-related substrate damage.
A Four-Element Antenna Array System With 15 Reconfigurable Radiation Patterns

This paper describes the design of a planar antenna array system with pattern reconfiguration in the azimuthal plane. The antenna array system is designed based on four antenna units where each unit is composed of two printed Yagi-Uda elements that radiate in directional beams over a dedicated sector, thus ensuring maximum isolation with neighboring units. The various units are fed on demand through a reconfigurable structure that is composed of four reconfigurable feeding networks and a one-to-four power divider. The feeding structure distributes the required power to the various radiating units through the activation of the two integrated PIN diodes along each of the four reconfigurable feeding networks. Such activation results in 15 reconfigurable radiation patterns that cover four orthogonal sectors over a fixed operating frequency, at 5.8 GHz. Each antenna unit is designed to exhibit a gain of 9 dBi with a half-power beamwidth of 44° and a sidelobe level of −16 dB. The antenna array is fabricated and tested, where the measured results validate the predicted simulated data.
First demonstration of sub-12 nm Lg gate last IGZO-TFTs with oxygen tunnel architecture for front gate devices

The first amorphous IGZO-based transistors integrated in a BEOL compatible gate-last integration scheme with buried oxygen tunnel under the channel and self-aligned contacts are demonstrated. This architecture reduces the number of critical process steps containing hydrogen and mitigates the Rseries increase during oxygen anneal for defect passivation. Higher Ion is achieved without compromising on Ioff. We also report the shortest ever IGZO-TFTs with Lg < 12 nm. Further, normally-off devices with Ion > 6 µA/µm (without defect passivation anneal) were achieved through gate-oxide and IGZO thickness scaling.
Network Function Decomposition and Offloading on Heterogeneous Networks With Programmable Data Planes

Programmable network hardware is emerging as a viable option for offloading and thus accelerating network functions. However, the heterogeneous resources available in the network calls for a disaggregated deployment approach. Programmable switches, programmable Network Interface Cards (NICs), and in-network compute nodes exposes different peculiar resources and capabilities that can be maximally exploited only if the network functions are decomposed into multiple smaller network functions. This work presents a framework for the automatic deployment of disaggregated and decomposed network functions. The framework comprises an orchestrator capable of deploying the decomposed network functions on programmable network hardware and software switches running in containers. The orchestrator exploits an optimization model for choosing the best decomposition according to the traffic demands, the network topology, and other constraints. An improved seamless deployment model and heuristic also accounts for the necessity of rerouting traffic when hardware nodes need to be re-programmed. Furthermore, the framework provides a tool to combine multiple functions into a single P4 program via a template pipeline that can be deployed to a programmable switch. Numerical results highlight the advantages of offloading decomposed network functions to programmable network hardware. Furthermore, we show how the seamless deployment model and heuristic have negligible effects on the allocation time and accepted traffic requests while guaranteeing the rerouting of traffic when switches are put in maintenance mode.
Assessing the impact of novel polymers and thermal management in a power electronics module using machine learning approaches

We highlight the predictive utility of machine learning (ML) techniques in estimating thermal performance benefits in power electronics modules, resulting from the use of high thermal conductivity polymers and thermal management techniques. The thermal performance of a commercial 1.2kV/444A SiC half-bridge module is quantified via high fidelity numerical simulations. Parametric simulations are conducted by considering the thermal conductivity of i) encapsulant (anisotropic), ii) baseplate and iii) heat sink as variable parameters, along with the coolant temperature and convection heat transfer coefficient. These simulations generate a data set of more than 2500 data points, which is used to train and evaluate the performance of machine learning algorithms to estimate the maximum junction temperature (Tj) of the package. Parameters are varied to represent a broad spectrum of possibilities ranging from high thermal conductivity polymer-based heat sinks to copper heat sinks; and air to two-phase liquid cooling technologies. The performance of three different statistical machine learning models is evaluated: polynomial regression, random forest, and support vector machines in predicting Tj. While polynomial regression does not predict Tj with a reasonable accuracy, random forest and support vector machines demonstrate excellent prediction accuracies w ith overall R2 of 99.6 and 99.98%, respectively. To estimate the relative contribution of the underlying thermal parameters, we use SHAP (Shapley Additive exPlanations) dependence plots in combination with random forest algorithm to identify parameters which strongly influence Tj. We observe that the thermal conductivity of heat sink material and heat transfer coefficient have the maximum impact on Tj reduction, whereas the thermal conductivity of the polymeric encapsulant has the least influence on Tj. The presently used approach of simulations-based training of ML algorithms can be adapted for the thermal design and parameter optimization in other electronics packages.
External I/O interfaces in sub-5nm GAA NS Technology and STCO Scaling Options

In this paper, the challenges of the I/O development roadmap are discussed. The impact of I/O application in FEOL scaling and 3D integration are evaluated. A cost-efficient circuit solution to the I/O implementation in a gate-all-around (GAA) nanosheet (NS) technology is proposed. The functionality verification and the relevant reliability concerns are assessed in a mature industrial CMOS process. Finally, to foresee the design strategies in future STCO scaling era, the impact of the fully back-side (BS) connections on I/O performance is investigated. Capacitance is doubled compared to front-side (FS) I/O due to the contributions from BS connection layers. The layout techniques can mitigate ~30% of the extra capacitance and the technology option of the deep trench isolation (DTI) is considered to reduce the extra capacitance to only ~8%.
Chinese Relation Extraction Using Extend Softword

In recent years, many scholars have chosen to use word lexicons to incorporate word information into a model based on character input to improve the performance of Chinese relation extraction (RE). For example, Li et al. proposed the MG-Lattice model in 2019 and achieved state-of-the-art (SOTA) results. However, MG-Lattice still has the problem of information loss due to its model structure, which affects the performance of Chinese RE. This paper proposes an adaptive method to include word information at the embedding layer using a word lexicon to merge all words that match each character into a character input-based model to solve the information loss problem of MG-Lattice. The method can be combined with other general neural system networks and has transferability. Experimental studies on two benchmark Chinese RE datasets show that our method achieves an inference speed up to 12.9 times faster than the SOTA model, along with a better performance. The experimental results also show that this method combined with the BERT pretrained model can effectively supplement the information obtained from the pretrained model, further improving the performance of Chinese RE.
A Hybrid Storage Systems for All Electric Aircraft

A hybrid energy storage system specifically designed for a fully electric aircraft is presented in the paper. The analysis of the time evolution of the power demand of the electric propulsion system during a test mission of Maxwell X-57, an all-electric aircraft developed by NASA, has pointed out the presence of significant peak power during take-off and air tack. Considered the issues related to weight and the volume of the energy storage systems (ESSs) in all-electric aircraft, a hybridization of aircraft ESS with a Supercapacitors (SCs) bank, devoted to smooth peak power demand, has been investigated. A comparison between the simulation results of an electrochemical battery and hybrid ESSs, designed on the test mission of Maxwell X-57 power demand, has been developed. The advantage of hybrid configuration with respect to battery-based one in term of volume and weight reduction is finally presented.
Genetic Programming with Algebraic Simplification for Dynamic Job Shop Scheduling

Genetic Programming (GP) has been successfully applied to evolve effective dispatching rules for Dynamic Job Shop Scheduling (DJSS). However, the GP-evolved rules are usually too complex and hard to interpret. In this paper, we aim to address this issue by evolving simpler rules without losing effectiveness. To this end, we develop a set of algebraic simplification operators based on our domain knowledge about dynamic scheduling, which can recursively convert a rule into a mathematically equivalent but simpler one. The algebraic simplification operators can guarantee that the individual stays equivalent before and after the simplification. Then, we develop a GP algorithm with these simplification operators. We compared the GP with the simplification operators with the baseline GP without simplification on a range of scheduling instances, and the results showed that using the algebraic simplification can slightly reduce the program size without sacrificing the test performance of the evolved dispatching rules. Furthermore, through deep analysis, we have also discovered the limitations of the pure algebraic simplification for GP to evolve DJSS dispatching rules, which can hardly simplify the individuals after the first generation.
Induced Resistance by Ascorbate Oxidation Involves Potentiating of the Phenylpropanoid Pathway and Improved Rice Tolerance to Parasitic Nematodes

Anticipating an increased ecological awareness, scientists have been exploring new strategies to reduce the use of chemical pesticides to control pests and diseases. Triggering the intrinsic plant defense system is one of the promising strategies to reduce yield loss by pathogenic organisms, such as nematodes. Ascorbate oxidase (AO) enzyme plays an important role in plant defense by regulating the apoplastic ascorbate/dehydroascorbate (DHA) ratio via the ascorbate oxidation process. Ascorbate oxidation is known to induce systemic resistance in rice against parasitic root-knot nematodes (RKN). Here, we sought to evaluate if AO- or DHA-induced resistance (IR) against RKN M. graminicola involves activation of the phenylpropanoid pathway and whether this IR phenotype has potential effects on growth of rice seedlings under stressed and unstressed conditions. Our results show that AO/DHA-IR against these parasitic nematodes is dependent on activation of phenylalanine ammonia lyase (PAL). However, application of reduced ascorbic acid (AA) did not induce this response. Gene expression analysis via qRT-PCR showed that OsPAL2 and OsPAL4 are highly expressed in AO/DHA-sprayed nematode-infected roots and PAL-activity measurements confirmed that AO/DHA spraying triggers the plants for primed activation of this enzyme upon nematode infection. AO/DHA-IR is not effective in plants sprayed with a chemical PAL inhibitor confirming that AO/DHA-induced resistance is dependent on PAL activity. Improved plant growth and low nematode infection in AO/DHA-sprayed plants was found to be correlated with an increase in shoot chlorophyll fluorescence (Fv/Fm), chlorophyll index (ChlIdx), and modified anthocyanin reflection index which were proven to be good above-ground parameters for nematode infestation. A detailed growth analysis confirmed the improved growth of AO/DHA-treated plants under nematode-infected conditions. Taken together, our results indicate that ascorbate oxidation enhances the phenylpropanoid-based response to nematode infection and leads to a tolerance phenotype in treated rice plants.
Applications of Recurrent Neural Network for Biometric Authentication & Anomaly Detection

Recurrent Neural Networks are powerful machine learning frameworks that allow for data to be saved and referenced in a temporal sequence. This opens many new possibilities in fields such as handwriting analysis and speech recognition. This paper seeks to explore current research being conducted on RNNs in four very important areas, being biometric authentication, expression recognition, anomaly detection, and applications to aircraft. This paper reviews the methodologies, purpose, results, and the benefits and drawbacks of each proposed method below. These various methodologies all focus on how they can leverage distinct RNN architectures such as the popular Long Short-Term Memory (LSTM) RNN or a Deep-Residual RNN. This paper also examines which frameworks work best in certain situations, and the advantages and disadvantages of each proposed model.
Mitofusin 2 Deficiency Causes Pro-Inflammatory Effects in Human Primary Macrophages

Mitofusin 2 (MFN2) is a mitochondrial outer membrane GTPase, which modulates mitochondrial fusion and affects the interaction between endoplasmic reticulum and mitochondria. Here, we explored how MFN2 influences mitochondrial functions and inflammatory responses towards zymosan in primary human macrophages. A knockdown of MFN2 by small interfering RNA decreased mitochondrial respiration without attenuating mitochondrial membrane potential and reduced interactions between endoplasmic reticulum and mitochondria. A MFN2 deficiency potentiated zymosan-elicited inflammatory responses of human primary macrophages, such as expression and secretion of pro-inflammatory cytokines interleukin-1β, -6, -8 and tumor necrosis factor α, as well as induction of cyclooxygenase 2 and prostaglandin E2 synthesis. MFN2 silencing also increased zymosan-induced nuclear factor kappa-light-chain-enhancer of activated B cells and mitogen-activated protein kinases inflammatory signal transduction, without affecting mitochondrial reactive oxygen species production. Mechanistic studies revealed that MFN2 deficiency enhanced the toll-like receptor 2-dependent branch of zymosan-triggered responses upstream of inhibitor of κB kinase. This was associated with elevated, cytosolic expression of interleukin-1 receptor-associated kinase 4 in MFN2-deficient cells. Our data suggest pro-inflammatory effects of MFN2 deficiency in human macrophages.
To What Extent Do Musculoskeletal Ultrasound Biomarkers Relate to Pain, Flexibility, Strength, and Function in Individuals With Chronic Symptomatic Achilles Tendinopathy?

Introduction: Achilles tendinopathy (AT) is a chronic musculoskeletal pathology best evaluated by ultrasound imaging. This cross-sectional study aimed at better understanding the relationship between musculoskeletal ultrasound biomarkers (MUBs) of Achilles tendon and localized pain, ankle flexibility, ankle strength, and functional abilities. Method: Forty-one participants with unilateral midportion chronic AT had their tendon images analyzed bilaterally in the longitudinal and transverse planes. The Victorian Institute of Sport Assessment-Achilles questionnaire (VISA-A) and Lower Extremity Functional Scale (LEFS) assessed pain and function, respectively, during standing and walking-related activities. Ankle flexibility was evaluated by weight-bearing lunge tests, while ankle isometric peak strength was measured using an instrumented dynamometer. Achilles tendon ultrasonographic images were analyzed using geometric (thickness), composition (echogenicity), and texture (homogeneity) MUBs. Discriminative validity was evaluated using paired Student's t-tests to compare MUBs between symptomatic and asymptomatic sides. Predictive validity was evaluated by computing the Pearson product-moment correlations coefficient between MUBs and pain, ankle flexibility, ankle strength, and function. Results: Significant differences were found in MUBs between the symptomatic and asymptomatic sides, confirming the discriminative validity of the selected MUBs. On the symptomatic side, thickness was found 29.9% higher (p < 0.001), echogenicity 9.6% lower (p < 0.001), and homogeneity 3.8% higher (p = 0.001) when compared with the asymptomatic side. However, predictive validity was scarcely confirmed, as most of the correlation coefficients were found negligible for the associations investigated between MUBs with localized pain, ankle flexibility, strength, and function. Only 14 statistically significant low to moderate associations were found, with negative and positive correlations ranging between −0.31 and −0.55 and between 0.34 and 0.54, respectively. Discussion: Musculoskeletal ultrasound biomarkers have a clinical utility in visualizing in vivo tendon integrity and diagnosing AT. MUBs should be valued as part of a comprehensive neuro-musculoskeletal assessment as they complement pain, flexibility, strength, and function measures. Altogether, they may inform the development and monitoring of a personalized rehabilitation treatment plan.