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The electromagnetic acoustic transducer (EMAT) consisting of racetrack coils presents directionality, and hence, waves propagating in sidelobe directions experience significant energy attenuation, resulting vulnerability to noise interference. To address the challenge of weak signal denoising, a novel denoising method is proposed based on a combination of the Butterworth bandpass filtering, an improved continuous wavelet transform (CWT) incorporating high-order statistical (HOS) and block threshold (BT), and Wiener filtering. The proposed method is verified by means of simulations and experiments. In the simulations, a periodic permanent magnet EMAT (PPM-EMAT) model was established to illustrate the directivity of PPM-EMAT and generate mimic shear horizontal (SH) waves to demonstrate the effectiveness of the proposed denoising method. In the experiments, the actual receiving signals from different transmitting angles were extracted based on the fabricated PPM-EMAT. Experimental results showed that the proposed method can significantly improve signal-to-noise ratios (SNRs) of the signals received at both the main-lobe direction and sidelobe direction while maintaining the signal characteristics compared with other denoising methods, especially presenting SNRs increase from 3.67 to 13.74 dB within a 60° beam angle of the radiation pattern. The proposed denoising method will provide a foundation for high-resolution imaging and weak signal denoising below 20 dB based on PPM-EMAT.

Atomically precise Ag4Ni2nanoclusters with 2,4-dimethylbenzenethiol as the ligands were synthesized and characterized as a cocatalyst of SiC for the selective hydrogenation of nitroarenes to arylamine in the presence of NaBH4. The obtained Ag4Ni2/SiC samples exhibited extraordinary catalytic activity, and a self-accelerated catalytic process was observed with the reduction of nitrophenol to aminophenol as the model reaction. Experimental comparison between the Ag4Ni2/SiC samples before and after the catalysis showed that the transformation of Ag4Ni2clusters to polydisperse Ag particles as well as amorphous NiOxon the surface of SiC in the catalysis was the key to their high activity. AIMD calculations revealed that the transformation of Ag4Ni2was driven by the presence of multiple hydrides on the cluster, which induced the detachment of the thiol ligand of the nanoclusters.

Research in medical visual question answering (MVQA) can contribute to the development of computer-aided diagnosis. MVQA is a task that aims to predict accurate and convincing answers based on given medical images and associated natural language questions. This task requires extracting medical knowledge-rich feature content and making fine-grained understandings of them. Therefore, constructing an effective feature extraction and understanding scheme are keys to modeling. Existing MVQA question extraction schemes mainly focus on word information, ignoring medical information in the text, such as medical concepts and domain-specific terms. Meanwhile, some visual and textual feature understanding schemes cannot effectively capture the correlation between regions and keywords for reasonable visual reasoning. In this study, a dual-attention learning network with word and sentence embedding (DALNet-WSE) is proposed. We design a module, transformer with sentence embedding (TSE), to extract a double embedding representation of questions containing keywords and medical information. A dual-attention learning (DAL) module consisting of self-attention and guided attention is proposed to model intensive intramodal and intermodal interactions. With multiple DAL modules (DALs), learning visual and textual co-attention can increase the granularity of understanding and improve visual reasoning. Experimental results on the ImageCLEF 2019 VQA-MED (VQA-MED 2019) and VQA-RAD datasets demonstrate that our proposed method outperforms previous state-of-the-art methods. According to the ablation studies and Grad-CAM maps, DALNet-WSE can extract rich textual information and has strong visual reasoning ability.

Ultrasensitive atomic comagnetometers have proven to be valuable tools for precise measurement and search for new physics. Besides, comagnetometers have potential for inertial navigation and quantum information. Despite their great potential, sensitivity improvement has been limited by different types of noise. To address this issue, the sources and magnitudes of noises are comprehensively analyzed and a suppression method is proposed for the most critical noise source, optical nonorthogonality. The optical nonorthogonality leads to critical transverse pumping effect, thus transverse light shift, and transverse effective magnetic noise. A direct correlation between the optical nonorthogonality and the electronic spin transverse relaxation rate is found, and a method for suppression of the optical nonorthogonality is proposed accordingly. Finally, the optical nonorthogonal noise is suppressed by 3.1 dB. Besides, an increase of 5.2 dB in the scale factor is achieved. With the combined effect of noise suppression and scale factor enhancement, the resulting inertial rotation sensitivity of $2.4 \times 10^{-8}$ rad/s/Hz $^{1/2}$ in the frequency range from 1 to 3 Hz is among the most sensitive comagnetometers, which opens up promising opportunities for new physics measurements, such as the search for ultralight dark matter and the fifth force.

The Transformer has been successfully used in medical image segmentation due to its excellent long-range modeling capabilities. However, patch segmentation is necessary when building a Transformer class model. This process ignores the tissue structure features within patch, resulting in the loss of shallow representation information. In this study, we propose a Heterogeneous Swin Transformer with Multi-Receptive Field (HST-MRF) model that fuses patch information from different receptive fields to solve the problem of loss of feature information caused by patch segmentation. The heterogeneous Swin Transformer (HST) is the core module, which achieves the interaction of multi-receptive field patch information through heterogeneous attention and passes it to the next stage for progressive learning, thus complementing the patch structure information. We also designed a two-stage fusion module, multimodal bilinear pooling (MBP), to assist HST in further fusing multi-receptive field information and combining low-level and high-level semantic information for accurate localization of lesion regions. In addition, we developed adaptive patch embedding (APE) and soft channel attention (SCA) modules to retain more valuable information when acquiring patch embedding and filtering channel features, respectively, thereby improving model segmentation quality. We evaluated HST-MRF on multiple datasets for polyp, skin lesion and breast ultrasound segmentation tasks. Experimental results show that our proposed method outperforms state-of-the-art models and can achieve superior performance. Furthermore, we verified the effectiveness of each module and the benefits of multi-receptive field segmentation in reducing the loss of structural information through ablation experiments and qualitative analysis.

In this article, we propose a method using $T$ (0,1) guided waves combined with coil coding technique to detect defects in buried liquid-filled pipes implemented by an electromagnetic acoustic transducer (EMAT). Due to its non-dispersive properties and the fact that there is no energy loss in non-viscoelastic fluids, the $T$ (0,1) mode is selected for pipe defects detection. The electromagnetic device that generates the circumferential magnetic field is optimized to excite the pure $T$ (0,1) mode. To realize energy enhancement and defect location identification, the electromagnetic acoustic coil is spatially encoded by 11-bit Barker code and the receiver coil is multiplexed consisting of a spatial coded coil and a unit coil. The defect detection is accomplished through time-of-flight (TOF) time-frequency analysis, and the defect location identification is achieved by digital signal processing methods (cross correlation and convolution). The feasibility of this method is verified by the finite element (FE) model and experimental analysis, indicating the defect locating error in a liquid-filled pipes is less than 1%. Overall, the proposed method achieves a high-precision flaw detection and location identification.

Optical rotation detection system (ORDS) utilizing quantum nondemolition (QND) measurement methods is widely applied in the field of quantum metrology and quantum information. However, the sensitivity of ORDS is limited by the uncertainty from optical-couple noise during the measurement of the atomic spin ensemble. In this study, we specifically analyze the mechanism of optical-couple noise caused by the fluctuations of probe light’s polarization in the modulated ORDS with a new model established to describe atomic spin precession in this particular condition. It is discovered that transverse electron-spin polarization errors are generated by the residual probe photon spin polarization in the ORDS, which results in extra coupling magnetic noise. In order to suppress this noise, a novel in situ method is proposed that the resultant electron-spin errors are reduced by a specifically designed closed-loop system. The results are verified through the ORDS in a co-magnetometer. After zeroing the extra probe photon spin polarization, an angular sensitivity better than $1 \times 10 ^{\mathrm {-8}}$ rad/Hz $^{\mathrm {1/2}}$ is achieved for frequencies higher than 5 Hz, demonstrating a probe background noise of 0.26 fT/Hz $^{\mathrm {1/2}}$ @14.5 Hz, approaching electronic noise and photon shot noise (PSN). With closed-loop control, the optical rotation bias instability is promoted by 4.2 times (from 107.9 to $25.7 \mu $ rad/h), and the angular noise of the ORDS is reduced by 2.4 times at 1–100 Hz. The measurement uncertainty of the realized ORDS nears the standard quantum limit, paving the road for long-term ultrasensitive measurements for new physics explorations.

Computer vision field has achieved great success in interpreting semantic meanings from images, yet its algorithms can be brittle for tasks with adverse vision conditions and the ones suffering from data/label pair limitation. Among these tasks is in-bed human pose monitoring with significant value in many healthcare applications. In-bed pose monitoring in natural settings involves pose estimation in complete darkness or full occlusion. The lack of publicly available in-bed pose datasets hinders the applicability of many successful human pose estimation algorithms for this task. In this paper, we introduce our Simultaneously-collected multimodal Lying Pose (SLP) dataset, which includes in-bed pose images from 109 participants captured using multiple imaging modalities including RGB, long wave infrared (LWIR), depth, and pressure map. We also present a physical hyper parameter tuning strategy for ground truth pose label generation under adverse vision conditions. The SLP design is compatible with the mainstream human pose datasets; therefore, the state-of-the-art 2D pose estimation models can be trained effectively with the SLP data with promising performance as high as 95% at PCKh@0.5 on a single modality. The pose estimation performance of these models can be further improved by including additional modalities through the proposed collaborative scheme.

Ultrasensitive spin exchange relaxation-free (SERF) comagnetometers have been widely applied in various new physics experiments and show great potential for inertial navigation. However, the spatially nonuniform spin polarization of alkali metal (AM) and noble gas (NG) atoms results in an effective magnetic field gradient on the order of 100 nT/cm which is much larger than that is produced by the coils and remanence on the magnitude of 1 nT/cm, thereby reducing the suppression ability of the magnetic noise and limiting the scale factor and noise floor of the comagnetometers. In this study, we establish a model of the effective magnetic field gradient caused by uneven spin polarization and analyze the influencing factors. Our results indicate that optimizing the density ratio of hybrid AM atoms can suppress the effective magnetic field gradient. Nevertheless, the available pump light power imposes a limitation on the density ratio. To further address this issue, we propose a balanced light-assisted hybrid spin exchange optical pumping (HSEOP) method. Our experimental results confirm the effectiveness of the mentioned approach, with 67% suppression of the nuclear transverse relaxation rate and a nearly threefold increase in the scale factor. Furthermore, the optimal density ratio in experimental applications can be reduced from 200 to below 80, thereby overcoming the limitation of laser power in SERF comagnetometers and providing the possibility for wider applications. Finally, the suppression factor of magnetic noise is enhanced by 89% by optimizing the density ratio and the balanced light-assisted HSEOP method. This research sheds light on the fundamental physics experiments and potential for inertial navigation.

The classic Dodd and Deeds model has been applied to estimate the electromagnetic properties of samples in eddy current testing (ECT). In fact, the model is only suitable for the condition that sample is much larger than the radius of coil (generally speaking, three–five times larger). However, the dimension of the samples cannot meet the condition in some practical applications. This article first investigated the modified analytical solution for square metallic plates with finite dimension based on Dodd and Deeds, of which the lower limit of integral changes from 0 to a value related with the side length of square sample. Furthermore, the modified truncated region eigenfunction expansion (TREE) analytical is also established for finite square plate by truncating the range of eigenvalues. Based on the modified analytical solution, the conductivity and side length measurement of square metallic plates with finite dimension has been presented. The experiments have been carried out to verify the conductivity and side length measurement method, and the results show the average errors are only 2.3% and 0.9%, respectively.

BackgroundSilent MR angiography (silent MRA) is a new generation of non-contrast enhanced angiography with outstanding advantages in visualizing cerebrovascular lesions and the follow-up after endovascular treatment for intracranial aneurysms (IAs). This study aims to investigate the reliability of silent MRA-based three-dimensional (3D) geometric description and hemodynamic calculation of IAs.Methods19 patients with 23 unruptured IAs, who underwent both silent MRA and 3D rotational angiography (3DRA), were included in this study. Computational fluid dynamics simulations were performed on all patient-specific 3D reconstruction images to compare the morphology and hemodynamics of the two different imaging models for IAs.ResultsSilent MRA models had smaller maximum and perpendicular height (mm), aneurysmal surface area (mm2), and aneurysmal volume (mm3) than 3DRA (p<0.05); the differences of the above parameters between the two models were 9.0±6.2%, 7.7±7.4%, 15.9±13.0%, and 21.4±17.5%, respectively. However, correlation analysis of morphological parameters in various dimensions and model comparison showed good overall consistency in geometrical characteristics between the two models. Moderate coherence was observed between models in time-averaged wall shear stress of aneurysm and parent vessel (TAWSS, PAWSS), aneurysm velocity (AV), parent vessel velocity, and oscillatory shear index (OSI). However, strong correlations were observed among normalized aneurysm wall shear stress (NWSS), low shear area (LSA), inflow concentration index (ICI), and normalized aneurysm velocity (NAV).ConclusionBoth morphological and hemodynamic assessments of IAs for silent MRA are comparable to 3DRA. Additionally, normalized indicators such as NWSS, LSA, ICI, and NAV were better than TAWSS, AV, and OSI in silent MRA-related hemodynamic evaluation.

The utilization of optimal orthodontic force is crucial to prevent undesirable side effects and ensure efficient tooth movement during orthodontic treatment. However, the sensitivity of existing detection techniques is not sufficient, and the criteria for evaluating optimal force have not been yet established. Here, by employing 3D finite element analysis methodology, we found that the apical distal region (A-D region) of mesial roots is particularly sensitive to orthodontic force in rats. Tartrate-resistant acidic phosphatase (TRAP)-positive osteoclasts began accumulating in the A-D region under the force of 40 grams (g), leading to alveolar bone resorption and tooth movement. When the force reached 80 g, TRAP-positive osteoclasts started appearing on the root surface in the A-D region. Additionally, micro-computed tomography revealed a significant root resorption at 80 g. Notably, the A-D region was identified as a major contributor to whole root resorption. It was determined that 40 g is the minimum effective force for tooth movement with minimal side effects according to the analysis of tooth movement, inclination, and hyalinization. These findings suggest that the A-D region with its changes on the root surface is an important consideration and sensitive indicator when evaluating orthodontic forces for a rat model. Collectively, our investigations into this region would aid in offering valuable implications for preventing and minimizing root resorption during patients’ orthodontic treatment.

A recent innovation in seawater desalination is the use of multi-stage and multi-pass combinations of nanofiltration (NF) and reverse osmosis (RO) membranes. One example of this approach is the “Long Beach method,” in which seawater passes through two different types of NF membranes to produce potable water. After several years of pilot studies comparing the performance of two-pass NF and single-pass RO systems, a number of membrane elements were sacrificed for autopsy analyses. The selected membranes represent different stages of operation including (1) new, (2) fouled, and (3) cleaned membranes. Used NF and RO spiral wound elements were removed from the first and last positions of the demonstration plant. Although operating data suggested no outward signs of membrane fouling – inorganic, organic, and bacterial accumulation were identified on all membranes. First pass RO and NF membranes contained similar amounts of deposited solids, while significantly fewer solids were found on second pass NF membranes. Viable, culturable marine bacteria were observed on all fouled and cleaned membranes, indicating that bacterial colonization of seawater NF/RO membranes was not (a) detected by plant performance monitoring devices, (b) prevented by microfiltration and chlorination, or (c) removed by chemical cleaning. Chemical cleaning recovered the measurable performance of both first pass RO and second pass NF membranes, but was relatively ineffective at removing deposited solids from first-pass NF membranes. Therefore, chemical-cleaning methods may need to be tailored and optimized more specifically for NF membranes used in seawater desalination.

Elucidating the correlations between the core structure of atomically precise nanoclusters and their catalytic activities is fundamentalfor exploring highly efficient nanocatalysts. Herein, a series of Ag-based nanoclusters protected by 2,4-dimethylphenylthiophenol (specifically Ag4Pd2(SPhMe2)8and Ag24M(SPhMe2)18where M = Ag, Pd, and Pt) were synthesized and deposited on TiO2supports as heterogeneous catalysts for the selective hydrogenation of nitroarenes with NaBH4as the reductant. It was found that Ag4Pd2(SPhMe2)8could spontaneously lose its ligands during catalysis, leading to the formation of polydispersed AgPd nanoparticles. This transformation endows the system with extraordinary activity for driving the hydrogenation of nitroarenes. However, the Ag24M (M = Ag, Pd, and Pt) systems, maintain their core structures during catalysis. They follow the generally reported ligand-mediated hydride-involved process, with catalytic activities depending on the central atom (Pt > Pd > Ag), which affects the hydride transferred from the nanoclusters to the reactant to regulate the catalysis.

Mechanical properties of ageing dams and rock foundations can differ significantly from those design values used during construction. It is necessary to conduct the inverse analysis of mechanical parameters of dam and rock foundation. However, it is inconsistent with the actual situation by utilising the conventional inversion method for structural analysis, as the conventional method can only inverse the integrated elastic modulus. Therefore, a new inversion method is proposed in this paper, which utilises adjustment coefficient to inverse initial elastic modulus, and introduces the adjoint method combined with DFP quasi Newton method in order to inverse zoning elastic modulus of dam and rock foundation. The adjoint method calculates the gradients in a much more efficient way, as it can avoid the repeated computation of large stiffness matrix. Therefore, the time consumption is reduced greatly and the computational efficiency increases significantly. Combined with project case, the feasibility and validity of the proposed method are verified.

Using ligand-protected metallic nanoclusters with atomic precision as catalysts and elucidating its ligand effect in the catalysis are the prerequisites to deepen the structure–catalysis relationship of nanoclusters at the molecular level. Herein, a series of Ag33nanoclusters protected with different thiolate ligands (2-phenylethanethiol, 4-chlorobenzyl mercaptan, and 4-methoxybenzyl mercaptan as precursors) were synthesized and used as heterogeneous catalysts for the conversion of nitroarenes to arylamine with NaBH4as reductant. The obtained nanoclusters exhibited ligand-dependent catalytic activity, with benzyl thiolate ligands distinctly superior to the phenethyl thiolate ligands. DFT calculations revealed that the ligand regulated catalytic activity of the nanoclusters was ascribed to the H−π and π–π interactions between the ligands and the substrates, owing to the presence of phenyl rings in these structures. This work highlighted the importance of the ligands on the metallic nanoclusters in catalysis and provides a strategy to regulate the catalytic activity by utilizing weak interactions between the catalysts and the substrates.

Bone regeneration remains a great clinical challenge. Low intensity near-infrared (NIR) light showed strong potential to promote tissue regeneration, offering a promising strategy for bone defect regeneration. However, the effect and underlying mechanism of NIR on bone regeneration remain unclear. We demonstrated that bone regeneration in the rat skull defect model was significantly accelerated with low-intensity NIR stimulation. In vitro studies showed that NIR stimulation could promote the osteoblast differentiation in bone mesenchymal stem cells (BMSCs) and MC3T3-E1 cells, which was associated with increased ubiquitination of the core circadian clock protein Cryptochrome 1 (CRY1) in the nucleus. We found that the reduction of CRY1 induced by NIR light activated the bone morphogenetic protein (BMP) signaling pathways, promoting SMAD1/5/9 phosphorylation and increasing the expression levels of Runx2and Osterix. NIR light treatment may act through sodium voltage-gated channel Scn4a, which may be a potential responder of NIR light to accelerate bone regeneration. Together, these findings suggest that low-intensity NIR light may promote in situ bone regeneration in a CRY1-dependent manner, providing a novel, efficient and non-invasive strategy to promote bone regeneration for clinical bone defects.

AbstractThe Acinetobactersp. HITLi 7 was isolated from the Songhua River and shown to be capable of heterotrophic nitrification ability at 2°C. To predict the ammonium reduction performance in drinking water at low temperature, the kinetics of strain HITLi 7 were investigated using Monod kinetic models. The results of calculations showed that the substrate half saturation constant Kswas 9.9 mg/L of total ammonium, and the maximum specific rate μmaxwas 7.9 × 10−4 h−1at 8°C, C/N 2, pH 6.0 while shaking at 100 r/min. The effects of temperature (2, 5 and 8°C), C/N (2, 4, and 10), and pH (6.0, 7.0, and 7.5) on kinetic parameters were also evaluated. Ksand μmaxincreased consistently with an increase in temperature and decreased as C/N ratio increased. The specific affinity a0(a0 = μmax/Ks) for ammonium was the highest for a C/N of 10. This value was 2.1-fold higher than the affinity observed for a C/N of 2. The results demonstrated that the affinity of HITLi 7 for ammonium was higher when a sufficient carbon source was present. The maximum ammonium reduction rate was 0.18 mg -N/L/h at a C/N of 10. These results suggest that HITLi 7 may be used for ammonium removal in drinking water at low temperatures.

The Acinetobactersp. HITLi 7 was isolated from the Songhua River and shown to be capable of heterotrophic nitrification ability at 2°C. To predict the ammonium reduction performance in drinking water at low temperature, the kinetics of strain HITLi 7 were investigated using Monod kinetic models. The results of calculations showed that the substrate half saturation constant Kswas 9.9 mg/L of total ammonium, and the maximum specific rate μmaxwas 7.9 × 10−4 h−1at 8°C, C/N 2, pH 6.0 while shaking at 100 r/min. The effects of temperature (2, 5 and 8°C), C/N (2, 4, and 10), and pH (6.0, 7.0, and 7.5) on kinetic parameters were also evaluated. Ksand μmaxincreased consistently with an increase in temperature and decreased as C/N ratio increased. The specific affinity a0(a0= μmax/Ks) for ammonium was the highest for a C/N of 10. This value was 2.1-fold higher than the affinity observed for a C/N of 2. The results demonstrated that the affinity of HITLi 7 for ammonium was higher when a sufficient carbon source was present. The maximum ammonium reduction rate was 0.18 mg NH4+-N/L/h at a C/N of 10. These results suggest that HITLi 7 may be used for ammonium removal in drinking water at low temperatures.

A study of the decolorization of reactive brilliant blue in an aqueous solution using Fe-Mn-sepiolite as a heterogeneous Fenton-like catalyst has been performed. The Fourier transform infrared (FTIR) spectra of the catalyst showed bending vibrations of the Fe-O. The Xray diffraction (XRD) patterns of the catalyst showed characteristic diffraction peaks of α-Fe2O3, γ-Fe2O3and MnO. A four factor central composite design (CCD) coupled with response surface methodology (RSM) was applied to evaluate and optimize the important variables (catalyst addition, hydrogen peroxide dosage, initial pH value and initial dye concentration). When the reaction conditions were catalyst dosage = 0.4 g, [H2O2]= 0.3 mL, pH = 2.5, [reactive brilliant blue]o= 50mg·L−1, and volume of solution = 500 mL at room temperature, the decolorization efficiency of reactive brilliant blue was 91.98% within 60 min. Moreover, the Fe-Mn-sepiolite catalyst had good stability for the degradation of reactive brilliant blue even after six cycles. Leaching of iron ions (<0.4 mg·L−1) was observed. The decoloring process was reactive brilliant blue specific via a redox reaction. The benzene ring and naphthalene ring were first oxidized to open ring; these were then oxidized to the alcohol and carboxylic acid. The reactive brilliant blue was decomposed mainly by the attack of ·OH radicals including surface-bound ·OH radicals generated on the catalyst surface.

Low-temperature ammonia monooxygenase (AMO) was purified from a heterotrophic nitrifier Acinetobactersp. Y16 by anion-exchange and gel-filtration chromatography. The purified enzyme was a membrane-bound monomer with a molecular mass of approximately 31 kDa. It could catalyze the oxidation of ammonium without stabilizing agents in vitroat low temperature. Addition of CuCl2could stimulate AMO activity in vitro. The enzyme was stable in the temperature range of 4–15°C with less than 9% change in its activity. The optimal activity temperature was 15°C. Above 20°C, the enzyme completely lost its activity. The enzyme activity was stable when stored at 4°C for five days, at 10°C for two days, and at 15°C for one day. This study purified a highly pure AMO from a heterotrophic nitrifier Acinetobactersp. for the first time.

Constraint-based optimization, which is NP-hard, describes a large class of problems in many fields, especially in formulating early perception tasks. For example, Shape from shading of a polyhedron [12, 11] in computer vision can be formulated under such a paradigm. Some of the traditional problem-solving methods, like the exhaustive search, guarantee finding the global solution, but are too expensive in practice. Local Search, Simulated Annealing, Tabu Search, and the evolutionary algorithms have no general conditions to stop searching when the global optimum is what we are looking for. In this paper, we will present a polynomial-time algorithm [5 ]for solving the practical problems. The algorithm has many interesting computational properties not possessed by the classic ones. It is guaranteed to converge linearly, insensitive to the perturbations to its initial conditions and intermediate solutions. The closeness of the solutions to the optimal ones in cost is always provided by the algorithm so that we can stop search when the gap is closed or is small enough. It has a number of sufficient conditions for identifying optimal solutions, and a number of necessary conditions for reducing the search spaces. With 1,000 test problems randomly generated by computer, the Local Search algorithm with multiple restarts has a successful rate of only 0.2% after 25 iterations, and the Simulated Annealing algorithm [7, 2 ] with a linear cooling scheme has a successful rate of 1.5% after 250,000 steps. In contrast, the new algorithm achieved a successful rate of over 98% after only 7.27 iterations on average.