Analisis Kualitas Pelayanan Di Balai Penempatan Dan Perlindungan Tenaga Kerja Indonesia (Bp3tki) Semarang Provinsi Jawa Tengah

BP3TKI Semarang is one Unit located in Central Java. KTKLN manufacture services have established procedures to obtain KTKLN, satisfaction CTKI / TKI will service KTKLN manufacture can be fulfilled. KTKLN-making services are still not as expected CTKI / workers. This can be seen from several things, among others: the online system used often trobel, queue numbers available does not guarantee CTKI / TKI directly dilayanani. For the waiting room available in BP3TKI also inadequate. Employees are less vigilant attitude, and competent in providing services. This study aims to: 1) How is the quality of service manufacture BP3TKI KTKLN in Semarang? 2) what are the factors that cause the quality of service to less well in the service BP3TKI KTKLN in Semarang? The study states: Quality of Service contained in Semarang BP3TKI less maximal, it is seen in: 1) The procedure was appropriate service standards of service, but the procedure is too long and should be simplified. 2) Suitability period given to the products CTKI / migrant workers in the service KTKLN are in accordance with the needs. However, the card can not be extended, making it less effective and efficient. 3) online monitoring tool can make a solution for the repair or continuous improvement, but because it has not applied to make less good. 4) product in the form of smart card services are free of damage but it is not free from errors in typing, but can be said is good because it rarely happens. 5) The need CTKI / migrant workers in terms of information contained in BP3TKI Semarang has been good, but there is no other information provided there, such as the cost of insurance information, or information about CTKI / other migrant workers. 6) Design services available has been good, but the facilities need to be supported by good physical infrastructure that makes CTKI / TKI feel comfortable. Dimensions cause menajadi poor service quality can be seen below: 1) Facility located in BP3TKI less good, because inadequate. 2) skills possessed by each officer has been good, but needs to be balanced with extensive knowledge. 3) Responsiveness of employees in giving attention to the applicant has been good because in accordance with what is expected by the applicant. 4) Warranty service is good, timeliness of service, and also manufacture KTKLN is free of charge. 5) Empathy care workers is not good, because there are still complaints coming from CTKI / TKI

UniMAP: Universal SMILES-Graph Representation Learning

Molecular representation learning is fundamental for many drug related applications. Most existing molecular pre-training models are limited in using single molecular modality, either SMILES or graph representation. To effectively leverage both modalities, we argue that it is critical to capture the fine-grained 'semantics' between SMILES and graph, because subtle sequence/graph differences may lead to contrary molecular properties. In this paper, we propose a universal SMILE-graph representation learning model, namely UniMAP. Firstly, an embedding layer is employed to obtain the token and node/edge representation in SMILES and graph, respectively. A multi-layer Transformer is then utilized to conduct deep cross-modality fusion. Specially, four kinds of pre-training tasks are designed for UniMAP, including Multi-Level Cross-Modality Masking (CMM), SMILES-Graph Matching (SGM), Fragment-Level Alignment (FLA), and Domain Knowledge Learning (DKL). In this way, both global (i.e. SGM and DKL) and local (i.e. CMM and FLA) alignments are integrated to achieve comprehensive cross-modality fusion. We evaluate UniMAP on various downstream tasks, i.e. molecular property prediction, drug-target affinity prediction and drug-drug interaction. Experimental results show that UniMAP outperforms current state-of-the-art pre-training methods.We also visualize the learned representations to demonstrate the effect of multi-modality integration

Effect of Na Doping on the Nanostructures and Electrical Properties of ZnO Nanorod Arrays

The p-type ZnO nanorod arrays were prepared by doping Na with hydrothermal method. The structural, electrical, and optical properties were explored by XRD, Hall-effect, PL, and Raman spectra. The carrier concentrations and the mobility of Na-doped ZnO nanorod arrays are arranged from 1.4×1016 cm−3 to 1.7×1017 cm−3 and 0.45 cm2 v−1 s−1 to 106 cm2 v−1 s−1, respectively

Fractional Denoising for 3D Molecular Pre-training

Coordinate denoising is a promising 3D molecular pre-training method, which has achieved remarkable performance in various downstream drug discovery tasks. Theoretically, the objective is equivalent to learning the force field, which is revealed helpful for downstream tasks. Nevertheless, there are two challenges for coordinate denoising to learn an effective force field, i.e. low coverage samples and isotropic force field. The underlying reason is that molecular distributions assumed by existing denoising methods fail to capture the anisotropic characteristic of molecules. To tackle these challenges, we propose a novel hybrid noise strategy, including noises on both dihedral angel and coordinate. However, denoising such hybrid noise in a traditional way is no more equivalent to learning the force field. Through theoretical deductions, we find that the problem is caused by the dependency of the input conformation for covariance. To this end, we propose to decouple the two types of noise and design a novel fractional denoising method (Frad), which only denoises the latter coordinate part. In this way, Frad enjoys both the merits of sampling more low-energy structures and the force field equivalence. Extensive experiments show the effectiveness of Frad in molecular representation, with a new state-of-the-art on 9 out of 12 tasks of QM9 and on 7 out of 8 targets of MD17

Effect of moxifloxacin on oxidative stress, paraoxonase-1 (PON1) activity and efficacy of treatment in patients with multiple drug-resistant tuberculosis

Purpose: To investigate the effect of moxifloxacin on paraoxonase-1 (PON1)  activity, and serum oxidative stress in patients with multiple drug-resistant tuberculosis (MDR-TB).Methods: A total ofof 130 MDR-TB patients who were treated with moxifloxacin from October 2014 to October 2010 in Eastern Medical District of Linyi People's Hospital of Shandong Province, China were randomly divided into an observation group (65 cases, moxifloxacin group) and control group (65 cases, non-moxifloxacin group). Total oxidant status (TOS), total antioxidant status (TAS), oxidative stress index (OSI), PON1 levels and treatment efficacy for groups were determined.Results: Compared with pre-treatment levels, TOS (23.3 ± 4.7 vs 13.9 ± 3.3 umol H2O2 Eq/L, t = 13.20, p = 0.00) and OSI (17.4 ± 4.8 vs 5.7 ± 1.4 U, t = 18.87, p = 0.00) of the observation group significantly decreased, while TAS (1.4 ± 0.5 vs 3.5 ± 0.7 umol Trolox Eq/L, t = 19.68, p = 0.00) and PON1 (15.5 ± 6.9 vs 31.1 ± 8.3 U/L, t = 11.65, p = 0.00) significantly increased. TOS (23.3 ± 4.7 vs 13.9 ± 3.3 umol H2O2 Eq/L, t = 7.73, p < 0.05) and OSI (16.9 ± 5.5 vs 7.4 ± 3.2U, t = 12.04, p = 0.05) reduced significantly in the control group. Moxifloxacin correlated positively with △TAS (r = 0.697, p = 0. 04) and △PON1 (r = 0.785, p = 0.01), but correlation with △TOS (r = -0.625, p = 0.01) was negative. Efficacy was significantly higher in the observation group than that in the control group (81.54 % vs 56.92 %, p =0.00).Conclusion: Oxidative stress injury in MDR-TB patients may be effectively managed by combination of moxifloxacin with anti-TB treatmentKeywords: Multiple drug-resistant TB, Moxifloxacin, Paraoxonase, Oxidative stres

Protein-ligand binding representation learning from fine-grained interactions

The binding between proteins and ligands plays a crucial role in the realm of drug discovery. Previous deep learning approaches have shown promising results over traditional computationally intensive methods, but resulting in poor generalization due to limited supervised data. In this paper, we propose to learn protein-ligand binding representation in a self-supervised learning manner. Different from existing pre-training approaches which treat proteins and ligands individually, we emphasize to discern the intricate binding patterns from fine-grained interactions. Specifically, this self-supervised learning problem is formulated as a prediction of the conclusive binding complex structure given a pocket and ligand with a Transformer based interaction module, which naturally emulates the binding process. To ensure the representation of rich binding information, we introduce two pre-training tasks, i.e.~atomic pairwise distance map prediction and mask ligand reconstruction, which comprehensively model the fine-grained interactions from both structure and feature space. Extensive experiments have demonstrated the superiority of our method across various binding tasks, including protein-ligand affinity prediction, virtual screening and protein-ligand docking

Probability weighted four-point arc imaging algorithm for time-reversed lamb wave damage detection

Damage imaging based on scattering signals of ultrasonic Lamb waves in plate structure is considered as one of the most effective ways for structural health monitoring area. To improve location accuracy and reduce the impact of artifacts, a probability weighted four-point arc imaging algorithm for time reversal Lamb wave damage detection is proposed in this paper. By taking the defect as a secondary wave source, the four-point arc positioning method is used to calculate the propagation time of the signal from transducer to defect. And the amplitude of damage signal corresponding to the time of flight is used for imaging. In order to eliminate the artifacts, a damage probability weighting is combined with four-point circular arc imaging algorithm. The effectiveness of the proposed method is experimentally verified in aluminum plate. Experimental results indicate that damage location accuracy and imaging quality has been improved in both single-flaw and double-flaw samples compared with conventional delay-and-sum method

A modified damage index probability imaging algorithm based on delay-and-sum imaging for synthesizing time-reversed Lamb waves

Imaging for damage in plate structure by Lamb waves is one of the most effective methods in the field of structural health monitoring. In order to improve the accuracy of damage localization, a novel method is proposed to modify damage exponent probability imaging algorithm based on delay-and-sum imaging by using time reversal Lamb waves. A new probability distribution function is introduced to improve the damage index probability method and is combined with delay-and-sum method for damage localization. Experimental results on aluminum plate show that the hybrid algorithm achieves better accuracy of damage location and imaging quality than the conventional delay-and-sum method