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The acceptance and usefulness of simulation models are often limited by the efficiency, transparency, reproducibility, and reliability of the modelling process. We address these issues by suggesting that modellers (1) "trace" the iterative modelling process by keeping a modelling notebook corresponding to the laboratory notebooks used by empirical researchers, (2) use a standardized notebook structure and terminology based on the existing TRACE documentation framework, and (3) use their notebooks to compile TRACE documents that supplement publications and reports. These practices have benefits for model developers, users, and stakeholders: improved and efficient model design, analysis, testing, and application; increased model acceptance and reuse; and replicability and reproducibility of the model and the simulation experiments. Using TRACE terminology and structure in modelling notebooks facilitates production of TRACE documents. We explain the

Collecting and maintaining radio fingerprint for wireless indoor positioning systems involves considerable time and labor. We have proposed the quick radio fingerprint collection (QRFC) algorithm which employed the built-in accelerometer of Android smartphones to implement step detection in order to assist in collecting radio fingerprints. In the present study, we divided the algorithm into moving sampling (MS) and stepped MS (SMS), and describe the implementation of both algorithms and their comparison. Technical details and common errors concerning the use of Android smartphones to collect Wi-Fi radio beacons were surveyed and discussed. The results of signal sampling experiments performed in a hallway measuring 54 m in length showed that in terms of the amount of time required to complete collection of access point (AP) signals, static sampling (SS; a traditional procedure for collecting Wi-Fi signals) took at least 2 h, whereas MS and SMS took approximately 150 and 300 s, respectively. Notably, AP signals obtained through MS and SMS were comparable to those obtained through SS in terms of the distribution of received signal strength indicator (RSSI) and positioning accuracy. Therefore, MS and SMS are recommended instead of SS as signal sampling procedures for indoor positioning algorithms.

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Quantum calculations on the VSD of Kv1.2 (3Lut pdb coordinates) show several water molecules move into the VSD when the sign of the electric field goes from positive intracellularly to negative (closed). The protein backbone remains essentially immobile; S4 does not move vertically with respect to the other transmembrane segments, but may have minimal horizontal motion (parallel to the membrane surface, were the membrane included in the calculation); side chain rearrangements, however, change some intramolecular distances. We have calculated the dipole moments of the optimized structures for several cases, as well as the structures of the water clusters (these calculations:18 water molecules, 373 atoms from the protein, from the 2nd to the 4th arginine in S4, and the complementary sections of S1, S2, and S3). Rotating two water molecules in the cluster (closed conformation) sufficed for a significant change in dipole (in most calculations, counting the dipole for the entire system; dipole changes with state as well, %5 D for the open configuration, approximately an order of magnitude more when closed, suggesting dipole shift is part of the sensing mechanism). Several energy minima were determined; the closed configurations were several kT lower in energy than open configurations. The water behavior resembled a phase change, with finite DV (volume) in its overall shift in structure with changes in electric field; there is more than one energy minimum, but the change in water density is unambiguous, although the water is not ordered in either case. Gating is coupled to water via a proton shift in the lower section of the VSD (see our other abstract).

Urban soil pollution is evaluated utilizing an efficient and simple algorithmic model referred to as the entropy method-based Topsis (EMBT) model. The model focuses on pollution source position to enhance the ability to analyze sources of pollution accurately. Initial application of EMBT to urban soil pollution analysis is actually implied. The pollution degree of sampling point can be efficiently calculated by the model with the pollution degree coefficient, which is efficiently attained by first utilizing the Topsis method to determine evaluation value and then by dividing the evaluation value of the sample point by background value. The Kriging interpolation method combines coordinates of sampling points with the corresponding coefficients and facilitates the formation of heavy metal distribution profile. A case study is completed with modeling results in accordance with actual heavy metal pollution, proving accuracy and practicality of the EMBT model.

Automatic ship detection, recognition, and counting are crucial for intelligent maritime surveillance, timely ocean rescue, and computer-aided decision-making. YOLOv3 pretraining model is used for model training with sample images for ship detection. The ship detection model is built by adjusting and optimizing parameters. Combining the target HSV color histogram features and LBP local features’ target, object recognition and selection are realized by using the deep learning model due to its efficiency in extracting object characteristics. Since tracking targets are subject to drift and jitter, a self-correction network that composites both direction judgment based on regression and target counting method with variable time windows is designed, which better realizes automatic detection, tracking, and self-correction of moving object numbers in water. The method in this paper shows stability and robustness, applicable to the automatic analysis of waterway videos and statistics extraction.