Binary Ones Research Articles

BYDSEX: Binary Young's double-slit experiment optimizer with adaptive crossover for feature selection: Investigating performance issues of network intrusion detection

Contemporary advancements in technology provide vast quantities of data with large dimensions, leading to high computing burdens. These big data quantities suffer from irrelevant, redundant, and noisy features. Hence, Feature Selection (FS) has become a crucial task to identify the optimal subsets of features. This research proposes a Binary version of Young's Double-Slit Experiment optimizer (BYDSE) with crossover operation (BYDSEX) for tackling FS issues. Furthermore, the proposed algorithm employs the V-shaped transfer function to convert continuous solutions generated by the standard YDSE into binary ones. To assess the new solutions, we employ a well-known wrapper approach, K-Nearest Neighbors (KNN), which uses the Euclidean distance metric. We integrate an adaptive crossover with a bitwise AND operation into the suggested algorithm to enhance its exploration and population diversity. Moreover, the bitwise AND operation transfers the most informative and beneficial features to the new solutions. We compared BYDSEX with nine of the most recent and powerful algorithms using 31 large-scale datasets to demonstrate its efficacy. Moreover, our BYDSEX optimizer is utilized to detect the DDoS attacks faced by most IoT devices and contemporary technologies, using six datasets extracted from CIC-DDoS2019 and NSL-KDD. Various performance metrics are utilized to assess the algorithms, such as the accuracy, the selected feature size the fitness values, the fitness values, and the time. Two statistical tests are carried out, like paired-samples T and the Wilcoxon signed-rank. BYDSEX achieved superior results compared to its competitors for most of the datasets. Furthermore, BYDSEX obtains average accuracy values of 99.78%, 99.89%, 99.69% and 99.48% for LDAP and MSSQL, NETBIOS and NSL-KDD, respectively.

High performance ternary organic photomultiplication detectors based on non-fullerene acceptor ITIC

To extend response spectrum to near infrared region, one non-fullerene acceptor ITIC was doped into P3HT:PC61BM blends, and the organic photomultiplication detectors (OPMDs) with structure of ITO/PEDOT:PSS/P3HT:ITIC:PC61BM(100:x:1, wt/wt/wt)/Al were prepared. The absorption and photoluminescence spectra of the active layer films and the current density-voltage characteristics of the devices were measured and analyzed, and the photomultiplication principle of the devices was studied. The results showed that a wide spectral response of 400–850 nm is realized in the ternary P3HT:ITIC:PC61BM active layer, and the external quantum efficiency, responsivity and specific detectivity of the ternary OPMDs based on P3HT:ITIC:PC61BM are all larger than those of the binary ones based on P3HT:PC61BM, and a maximum external quantum efficiency of 1113.71 %, specific detectivity of 6.42 × 1013 Jones and photoresponsivity of 762.99 A/W are obtained at 850 nm and under −16 V bias when the ITIC mass ratio in the ternary active layer is 4 % (i.e., x = 4). Such a considerable performance can be due to the fact that doping ITIC into P3HT:PC61BM can widen absorption spectrum of active layer to near infrared area, and increase electron trap density and exciton dissociation interfaces in active layer and create cascade energy levels for better carrier transport.

A facile and effective strategy for modifying combustion of Zr/KClO4 via adding Si

As a commonly applied ignition composition, Zr/KClO4 has attracted various interests and plenty of efforts have been made to improve the combustion performance. In this study, Si nanoparticles were incorporated into Zr/KClO4 by sonication. Around 2.5× peak pressure and 9.2× reactivity were achieved by Si/50%Zr/KClO4 when compared to Zr/KClO4. In addition, the peak pressure and pressurization rate of Si/Zr/KClO4 ternary systems are all superior to ones of both Zr/KClO4 and Si/KClO4 binary systems. The synergetic effect of Si/Zr/KClO4 is a combination of shorter ignition delay of Zr/KClO4 and more gas production of Si/KClO4, which is also reflected on the higher flame propagation rate of Si/Zr-based thermites than composites with pure Si or Zr as the fuel. Meanwhile, the heat release during the redox reaction has been measured. The results show ∼700–1000 J/g higher energy release than Zr/KClO4, and higher combustion efficiency can be attained by ternary systems than binary ones. Thus, adding Si powders is an effective way to improve the energetic behavior of Zr/KClO4 ignition composition, and Si/Zr/KClO4 ternary composites can be a promising candidate for application in pyrotechnics.

Fabrication and characterisation of stable ternary complexes based on bovine lactoferrin, pectin and chlorogenic acid

Stable ternary complexes of lactoferrin (LF), high methoxyl pectin (HMP) and chlorogenic acid (CGA) were synthesised and characterised. Soluble LF–HMP complexes were prepared at pH 5, 6 and 7 using established phase diagram. CGA bound to ternary complexes at moderate binding strength (Ka: 103–105 L/M). LF, HMP and CGA interacted via noncovalent interactions like hydrophobic interaction. Ternary complexes showed particle size of 135–462 nm (except for 0.05% CGA at pH 5) and zeta‐potential from −17 to −22 mV. Ternary complexes had superior antioxidant capacity over binary ones (P < 0.05). The ternary complexes may find application in functional beverages.

Influence of Feed Composition on the Separation Factor during Nanofiltration of Organic Acids.

In this study, nanofiltration experiments using synthetic solutions containing acetate, butyrate, and lactate are carried out to assess the impact of the feed composition, i.e., feed concentration and feed proportions, on the separation factor of couples of solutes in binary and ternary solutions. In binary solutions, no influence of the solute proportions in the feed was pointed out, whatever the couple of solutes. The separation factor of acetate/butyrate and acetate/lactate was found to decrease with increasing feed concentration, while that of lactate/butyrate remained constant. The separation factors of acetate/lactate and lactate/butyrate were identical in ternary solutions compared to binary ones, showing no impact of the addition of the third solute. In ternary solutions, the presence of lactate decreased the separation factor of acetate/butyrate, but this decrease was not influenced by the proportion of lactate.

MXene quantum dots decorated g-C3N4/BiOI heterojunction photocatalyst for efficient NO deep oxidation and CO2 reduction

The photocatalytic performance of g-C3N4 is greatly limited by the severe charge recombination due to its s-triazine unit structure. Hence, enhancing the separation of photogenerated carriers is the pivotal factor for high-efficiency photocatalysis of g-C3N4. In this work, we construct a MXene quantum dots (MQDs) decorated BiOI/g-C3N4p-n heterojunction photocatalyst. The interfacial charge separation and transfer are substantially promoted, due to the synergistic effect of the internal electric field (IEF) of BiOI/g-C3N4 heterojunction and strong electron-withdrawing capability of MQDs. Furthermore, the introduction of BiOI with a low band gap and MQDs with large specific areas and rich surface terminals lead to enhanced light absorption and active sites. As a result, the ternary g-C3N4/MQDs/BiOI photocatalyst achieves a much higher NO removal rate of 42.23 % and discharges less NO2 intermediate than the individual and binary ones. Meanwhile, the g-C3N4/MQDs/BiOI photocatalyst also exhibits the most effective CO2 photoreduction, with a CO production rate of 57.8 μmol·g−1·h−1 and a CH4 production rate of 3.6 μmol·g−1·h−1, surpassing all other photocatalysts in this study. In addition, the designed composite photocatalyst shows remarkable stability. The photocatalytic mechanisms are studied by the trapping experiment and in-situ Fourier Transform Infrared (FTIR) Spectra. This work paves a new avenue for enhancing charge separation and thus improving performances of g-C3N4-based photocatalysts by integrating a p-n heterojunction and a co-catalyst, which would accelerate commercial deployment of emerging photocatalysts.

An image processing approach for fatigue crack identification in cellulose acetate replicas

The cellulose acetate replication technique is an important method for studying material fatigue. However, extracting accurate information from pictures of cellulose replicas poses challenges because of distortions and numerous artifacts. This paper presents an image processing procedure for effective fatigue crack identification in plastic replicas. The approach employs thresholding, adaptive Gaussian thresholding, and Otsu binarization to convert gray-scale images into binary ones, enhancing crack visibility. Morphological operations refine object shapes, and Connected Components Analysis facilitates crack identification. Despite limited data, the handcrafted feature extraction algorithm proves robust, addressing challenges. The algorithm shows efficacy in detecting cracks as small as 30 μm, even in the presence of cellulose replication artifacts. The results highlight ability to capture significant cracks’ orientation, length, and growth stages, essential for understanding fatigue mechanisms. Analysis of results, especially evaluation metrics encompassing false positives and false negatives, provides a comprehensive understanding of the algorithm’s strengths and limitations. The proposed tool is available on GitHub.

Enhancement of non-covalent interaction between soy protein isolate and quercetin by sodium alginate

Effects of sodium alginate (SA) on the non-covalent interaction between soybean protein isolate (SPI) and quercetin (Que) were investigated by multispectral technology, molecular docking and dynamics simulation technology. Structural alterations of the binary complexes were observed after SA addition, characterized by a red shift of maximum fluorescence emission wavelength. The introduction of 0.1% (w/v) SA led to a reduction of 12.3% in the α-helix and β-sheet structures, accompanied by 12.6% increase in the β-turn and random coil conformations. The binding of SA to SPI provided electrostatic interactions and facilitated the subsequent binding of SPI to Que. Molecular docking confirmed that hydrophobic interactions and electrostatic interactions were also the main driving force. Molecular dynamics simulation emphasized that the ternary complexes with SA exhibited greater stability compared to the binary ones. The foaming and emulsifying properties of SPI-Que complexes were enhanced by 33.76% and 68.28%, respectively, due to the addition of SA.

Natural polymers and electrolytes enhanced coal dust suppression and explosion prevention: New insight from experimental and molecular dynamic simulations

This paper expressed a new insight including experimental and molecular dynamic simulations to illuminate multi-scale mechanisms of coal dust suppression and explosion prevention when applying natural polymers and electrolyte. Results reveals that the addition of natural polymers and electrolyte achieve a maximum 11.85% improvement on dedusting rate and 36.36% improvement on suppression speed when compares with the individual application of SDBS, the MIE and MITC also receives a maximum 1.5 times and 23.58% improvement as well. This achievement is jointly affected by the wetting and agglomerating behaviour between suppressant and coal particles. The chain-like ionic structure and two benzene rings offer LS-Na exhibits good planarity in space and can vertically embed into the surface of coal particles. The lower diffusion coefficient and larger wetting area brings LS-Na stronger ability to help SDBS adsorbing on the coal particles and therefore exhibit better wettability. However, higher electrostatic potential difference endows CMC-Na stronger ability to absorb H2O molecules to form more viscous liquid environment. More hydrogen bond and higher bond energy of CMC-Na could promote the agglomeration of coal particles. The kinetic analysis reveals that the activation energy of coal particles treated by ternary suppressant is higher than that of binary ones. With the help of neutralization reactions between Na· and Cl· generated by molten NaCl and H· and OH· generated by the combustion, the reactivity of coal particles has been diminished and the combustion chain has been interrupted.

Coordinating Qualitative Predictor Variables in an Applied Linear Model: Analysis and Application for Applied Sciences.

Background In applied sciences, statistical models are pivotal for uncovering relationships in complex datasets. The applied linear model establishes associative links between variables. While qualitative predictors are essential, their integration into linear models poses challenges. The dummy variable approach transforms qualitative variables into binary ones for regression analysis. Multilayer Feedforward Neural Networks (MLFFNN) offer validation of regression models, and fuzzy regression offers alternative methods to address the ambiguity of qualitative predictors. This study aims to enhance the integration of qualitative predictors in applied linear models through statistical methodologies. Material and methods This study design involves the transformation of qualitative predictors into dummy variables, the bootstrapping technique to improve the parameter estimates, theMultilayer Feedforward Neural Network, and fuzzy regression. This study uses the programming language Ras an analysis tool. Results The multiple linear regression model demonstrates precision and a significant fit (p<0.05), with an R-squared value of 0.95 and mean square error (MSE) of 9.97. Comparing actual and predicted values, fuzzy regression exhibits superior predictability over linear regression. The MLFFNN yields a reduced MSE net of 0.362, indicating enhanced prediction precision for derived models. Conclusion This study presents a precise methodology for integrating qualitative variables into linear regression, supported by the combination of specific statistical methodologies to enhance predictive modeling. By integrating fuzzy linear regression, MLFF neural networks, and bootstrapping, the proposed technique emerges as the most effective approach for modeling and prediction. These findings underscore the efficacy of this method in seamlessly integrating qualitative variables into linear models, ultimately enhancing accuracy and prediction capabilities.

Robustness Verification of Multi-Class Tree Ensembles

Tree ensembles are one of the most widely used model classes. However, these models are susceptible to adversarial examples, which are slightly perturbed examples that elicit a misprediction. There has been significant research on designing approaches to verify the robustness of tree ensembles to such attacks. However, existing verification algorithms for tree ensembles are only able to analyze binary classifiers and hence address multiclass problems by reducing them to binary ones using a one-versus-other strategy. In this paper, we show that naively applying this strategy can yield incorrect results in certain situations. We address this shortcoming by proposing a novel approximate heuristic approach to verification for multiclass tree ensembles. Our approach is based on a novel generalization of the verification task, which we show emits other relevant verification queries.

Role of the plasticizers on the crystallization of PLA and its composites with mesoporous MCM-41

Several materials were prepared by melt extrusion, based on an L-rich polylactide (PLA), as polymeric matrix, acetyl tri-n-butyl citrate (ATBC) or trioctyl trimellitate (TOTM), as plasticizer, and mesoporous MCM-41 particles, playing a nucleant role. Influence of the addition of these nucleating particles, together with the comparative effect of the two plasticizers, has been thoroughly evaluated on the features of the several phase transitions existing in the PLA polymeric matrix (glass transition, cold crystallization, α′ to α transition, melting processes and melt crystallization) as well as on the rate of PLA crystallization. The window where this ordering process can take place is shifted down to lower temperatures for these binary and ternary systems and the range of temperatures at which the α′ and α polymorphs crystallize under isothermal crystallization from the glassy state significantly changes in the materials containing ATBC as plasticizer, either the binary or ternary ones. Type of the major crystalline lattice developed at 85 and 110 °C has been confirmed, additionally to the DSC experiments, by FTIR spectroscopy.Graphical abstract

Synergistic roles of Ru, CeO2, and HZSM-5 in a ternary-active center catalyst for catalytic destruction of dichloromethane

Developing well-qualified industrial catalysts with excellent oxidation ability, selectivity, and resistance is still a challenge for chloroalkane destruction. Herein, we optimized the composition of multi-component catalysts with diverse active sites to adjust their acidic and redox properties for reasonable catalytic performance. The synergy of redox center Ru, activation center CeO2, and acid center HZSM-5 endowed the ternary catalyst Ru/CeO2/HZSM-5 with superior low-temperature activity (30 ∼ 60 °C lower for 50 % conversion), promoted stability and chlorine tolerance (100 % conversion for at least 30 h), and less toxic by-products for dichloromethane (DCM) destruction compared with the binary ones, promising for industrial application under tough conditions (e.g., thermal shock, HCl, H2O). The strong interaction of Ru and CeO2 enhanced the redox property of Ru/CeO2/HZSM-5 and thus promoted the deep oxidation of DCM and intermediates, thereby generating more CO2 and less dechlorinated by-products (e.g., CH3Cl). The synergistic effects of Ru, CeO2, and HZSM-5 facilitated chlorine removal and suppressed the generation of polychlorinated by-products (e.g., CHCl3) over the Ru/CeO2/HZSM-5 catalyst by improving redox capacity and inhibiting reactivity of chlorine. Notably, the introduction of CeO2 weakened the hydrophilicity of Ru and HZSM-5, improving the water resistance of the catalyst. The corresponding monolithic catalyst could achieve complete oxidation of DCM at 400 °C with the total concentration of dioxins measured at 0.026 ng I-TEQ kg−1 after continuous operation at 250 °C for 30 h.

Synchronization of Tree Parity Machines Using Nonbinary Input Vectors.

Neural cryptography is the application of artificial neural networks (ANNs) in the subject of cryptography. The functionality of this solution is based on a tree parity machine (TPM). It uses ANNs to perform secure key exchange between network entities. This brief proposes improvements to the synchronization of two TPMs. The improvement is based on learning ANN using input vectors that have a wider range of values than binary ones. As a result, the duration of the synchronization process is reduced. Therefore, TPMs achieve common weights in a shorter time due to the reduction of necessary bit exchanges. This approach improves the security of neural cryptography.

Comparing Unbalanced and Balanced CNTFET Ternary Adders and Multipliers with the Corresponding Binary Ones

This paper compares the performance of the ternary adders and multipliers using balanced and unbalanced set of values. We use the 1-trit adders to evaluate the two versions of a 4-trit propagate adder, which are comparedwith a 6-bit corresponding propagate adder. Similarly, we compare the two types of 2*2 trit multipliers with a3*3 bit multiplier. The simulations with a 32-nm Carbon Nanotuble Field-Effect Transistor (CNTFET) technology show that the binary adders and multipliers are more efficient than the ternary ones that compute the same amount of information

Ternary superconducting hydrides in the La–Mg–H system

Ternary or more complex hydrogen-rich hydrides are the main hope of reaching room-temperature superconductivity at high pressures. Their chemical space is vast and its exploration is challenging. Here we report the investigation of the La–Mg–H ternary system using the evolutionary algorithm USPEX at pressures in the range 150–300 GPa. Several ternary superconducting hydrides were found, including thermodynamically stable P6/mmm-LaMg3H28 with TC = 164 K at 200 GPa, P/2m-LaMgH8, C2/m-La2MgH12 and P2/m-La3MgH16. In addition, novel binary hydrides were predicted to be stable at various pressures, such as Cm–Mg6H11, P1–MgH26, Fmm2-MgH30, P1–MgH38 and R3̄m-LaH13. We also report several novel low-enthalpy metastable phases, both ternary and binary ones. Finally, we demonstrate important methods of exploring very large chemical spaces and show how they can improve crystal structure prediction.

BLogic: A Bayesian Model Combination Approach in Logic Regression

With the increasing complexity and dimensionality of datasets in statistical research, traditional methods of identifying interactions are often more challenging to apply due to the limitations of model assumptions. Logic regression has emerged as an effective tool, leveraging Boolean combinations of binary explanatory variables. However, the prevalent simulated annealing approach in logic regression sometimes faces stability issues. This study introduces the BLogic algorithm, a novel approach that amalgamates multiple runs of simulated annealing on a dataset and synthesizes the results via the Bayesian model combination technique. This algorithm not only facilitates predicting response variables using binary explanatory ones but also offers a score computation for prime implicants, elucidating key variables and their interactions within the data. In simulations with identical parameters, conventional logic regression, when executed with a single instance of simulated annealing, exhibits reduced predictive and interpretative capabilities as soon as the ratio of explanatory variables to sample size surpasses 10. In contrast, the BLogic algorithm maintains its effectiveness until this ratio approaches 50. This underscores its heightened resilience against challenges in high-dimensional settings, especially the large p, small n problem. Moreover, employing real-world data from the UK10K Project, we also showcase the practical performance of the BLogic algorithm.

Roles of Blend Morphology and Energy Level Alignment in Determining Open‐Circuit Voltage and Fill Factor of Ternary All‐Polymer Solar Cells

AbstractTuning the open‐circuit voltage (VOC) and fill factor (FF) without sacrificing the short‐circuit current density is crucial for achieving performance advantages in ternary blend organic solar cells as an alternative to the binary ones. This study investigates ternary all‐polymer solar cells comprised of two polymer donors (D1 and D2) and one polymer acceptor (A), using two types of D2 polymers with different compatibilities with D1. As demonstrated by photoelectron yield spectroscopy, the ionization energy (IE) of the ternary blends continuously shifts between the IEs of D1:A and D2:A binary blends when D2 forms a single indistinguishable domain with D1. In contrast, the IE of the blends does not exhibit any noticeable change when D2 induces distinct phase separation with D1. Models describing the blend composition dependence of the VOC and FF are constructed by correlating the effects of the blend morphology and the shift in the energy level of the hole‐transporting state in the ternary blends. This study demonstrates that a pseudo‐binary blend morphology with a mixed D1 and D2 donor domain and an A acceptor domain is necessary to obtain a broad tunability of VOC and tolerance of FF to the blend composition in ternary all‐polymer solar cells.

IntSat: integer linear programming by conflict-driven constraint learning

State-of-the-art SAT solvers are nowadays able to handle huge real-world instances. The key to this success is the Conflict-Driven Clause-Learning (CDCL) scheme, which encompasses a number of techniques that exploit the conflicts that are encountered during the search for a solution. In this article, we extend these techniques to Integer Linear Programming (ILP), where variables may take general integer values instead of purely binary ones, constraints are more expressive than just propositional clauses, and there may be an objective function to optimize. We explain how these methods can be implemented efficiently and discuss possible improvements. Our work is backed with a basic implementation showing that, even in this far less mature stage, our techniques are already a useful complement to the state of the art in ILP.

A robust and lightweight voice activity detection algorithm for speech enhancement at low signal-to-noise ratio

Voice Activity Detection (VAD) is a crucial component of Speech Enhancement (SE) for accurately estimating noise, which directly affects the SE effectiveness in improving speech quality. However, conventional non-data-driven VADs often suffer from decreased accuracy at a low signal-to-noise ratio (SNR). To address this issue, a multi-feature and cosine similarity-based multi-observation VAD algorithm (mVAD) are proposed in this study. This algorithm selects noise-robust features, with Mel-frequency Cepstral Coefficients (MFCCs) as the main features, and utilizes several optimization techniques and an adaptive threshold for background noise updating. Furthermore, the soft VAD results are smoothed with an improved exponential moving average (EMA) algorithm. Besides, a shifting window is utilized to track the mean value and obtain an adaptive threshold for converting the soft results to binary ones. Experimental results indicate that mVAD can maintain high classification accuracy down to -10 dB with an increment of approximately 28% while also being computationally efficient for the CPU time (about 1/3 of statistical model-based methods). It also maintained high robustness at SNRs less than 0 dB (Δ≤2.1%). Moreover, sometimes mVAD even achieved higher accuracy levels than deep learning-based VADs. To further demonstrate the effectiveness of the proposed method, the VAD results are used as an additional feature to train and test a neural network (NN)-based SE model, enhancing the SE performance. This study proves that mVAD does not rely on prior noise knowledge, reaching the dual effect of complexity reduction and accuracy improvement for speech enhancement, making it a promising approach for robust VAD in low SNR environments.