Extracellular neural recordings obtained from chronically implanted microelectrode arrays are widely used in behavioral neurophysiology and invasive brain-machine interfaces. After the raw recordings are band-pass fil...
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ISBN:
(纸本)9781665436632
Extracellular neural recordings obtained from chronically implanted microelectrode arrays are widely used in behavioral neurophysiology and invasive brain-machine interfaces. After the raw recordings are band-pass filtered within a frequency band suitable for spike detection, spikes are often detected by amplitude thresholding. Developing principled methods for computing amplitude thresholds is an active research area. `Truncation thresholds' are a pair of amplitude thresholds that are computed using a recently proposed algorithm. As part of an effort that aims to integrate this algorithm into a real-time data acquisition and spike detection system, here we present a python code for maximum likelihood estimation of the location and scale parameters of the truncated Normal distribution, which is one of the steps involved in the computation of truncation thresholds.
As dynamically-typed languages grow in popularity, especially among beginning programmers, novices have an increased need for scalable, helpful feedback for fixing their *** and repair can be ambiguous: not all repair...
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As dynamically-typed languages grow in popularity, especially among beginning programmers, novices have an increased need for scalable, helpful feedback for fixing their *** and repair can be ambiguous: not all repairs which prevent the program from crashing are equally useful for beginners. We propose scalable approaches for fault localizationand repair for dynamic languages that are helpful for debugging and generalize to handle a wide variety of errors commonly faced by novice programmers. We base our approach on acombination of static, dynamic, and contextual features, guided by machine learning. We evaluate on over 980,000 diverse real user interactions across four years from the popular *** website, which is used both in classes and by non-traditional learners. We find that our approach isscalable, general, and quite accurate: up to 77% of these historical novice users would have been helped by our top-three localization responses, compared to 45% for the default interpreter, and we successfully synthesize repairs to 76% of our historical buggy programs. We also conducted two human studies. Participants preferred our localization approach to the baseline (p = 0.018), and found it additionally useful for bugs meriting multiple edits. Participants found our repairs to contain helpful information beyond the baseline in 45% of programs.
The availability of data is the driving force behind most of the state-of-the-art techniques for machine translation tasks. Understandably, this availability of data motivates researchers to propose new techniques and...
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The availability of data is the driving force behind most of the state-of-the-art techniques for machine translation tasks. Understandably, this availability of data motivates researchers to propose new techniques and claim about the superiority of their techniques over the existing ones by using suitable evaluation measures. However, the performance of underlying learning algorithms can be greatly influenced by the correctness and the consistency of the corpus. We present our investigations for the relevance of a publicly available python to pseudo-code parallel corpus for automated documentation task, and the studies performed using this corpus. We found that the corpus had many visible issues like overlapping of instances, inconsistency in translation styles, incompleteness, and misspelled words. We show that these discrepancies can significantly influence the performance of the learning algorithms to the extent that they could have caused previous studies to draw incorrect conclusions. We performed our experimental study using statistical machine translation and neural machine translation models. We have recorded a significant difference (similar to 10% on BLEU score) in the models' performance after removing the issues from the corpus.
Electrical load forecasting is an important topic within the electrical market which has been done by a machine learning methodology: Support Vector Machines (SVM). Load forecasting with SVM will form the non-linear r...
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ISBN:
(纸本)9781509053841
Electrical load forecasting is an important topic within the electrical market which has been done by a machine learning methodology: Support Vector Machines (SVM). Load forecasting with SVM will form the non-linear relations with the parameters that have an effect on the load;additionally to the correct modeling of the load curve on weekends and holidays. The past information is used as a sample for the applying and therefore holidays associated demand as an important factor inprediction. The LibSVM package and python codeis usedfor modeling the SVM. Resultsare obtainedand comparison is made for the two methods.
python has become one of the most popular programming languages nowadays but has not received enough attention from the software engineering community. Many errors, either fixed or not yet, have been scattered in the ...
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python has become one of the most popular programming languages nowadays but has not received enough attention from the software engineering community. Many errors, either fixed or not yet, have been scattered in the lifetime of python projects, including popular python libraries that have already been reused. NameError is among one of those errors that are widespread in the python community, as confirmed in our empirical study. Yet, our community has not put effort into helping developers mitigate its introductions. To fill this gap, we propose in this work a static analysis-based approach called DENE (short for D etecting and E xplaining N ame E rrors) to automatically detect and explain name errors in python projects. To this end, DENE builds control-flow graphs for python projects and leverages a scope-aware reaching definition analysis to locate identifiers that may cause name errors at runtime and report their locations. Experimental results on carefully crafted ground truth demonstrate that DENE is effective in detecting name errors in real-world python projects. The results also confirm that unknown name errors are still widely presented in popular python projects and libraries, and the outputs of DENE can indeed help developers understand why the name errors are flagged as such.
Topological indices are essential for evaluating a compound's physicochemical characteristics. Computing the various topological indices of any molecular structure helps to comprehend their physical characteristic...
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Topological indices are essential for evaluating a compound's physicochemical characteristics. Computing the various topological indices of any molecular structure helps to comprehend their physical characteristics and they are also extremely useful for predicting the properties of chemical and biological compounds in QSPR and QSAR ***, Shanon entropy idea a bit different but provide the structural properties of a molecular graph. The atom in the molecular graph of PAMO are considered as a nodes and bonds are weighted edges. For computing the topological indices and edge weight based entropy for phenylacetone monooxygenase enzyme python has been utilized.
This research evaluates the environmental and health risks linked to potentially toxic elements (PTEs) and PAHs along the western coast of the Gulf of Suez, Egypt. This study investigated the concentration of 16 PAH c...
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This research evaluates the environmental and health risks linked to potentially toxic elements (PTEs) and PAHs along the western coast of the Gulf of Suez, Egypt. This study investigated the concentration of 16 PAH compounds in the Suez Gulf, revealing significantly higher levels than the EU (0.20 mu g/L) and US (0.030 mu g/L) standards. The average total PAH concentration across eight locations was significantly higher, with the Suez area having the highest concentration at 479 mu g/L. Pyrene (Pyr) was the dominant PAH with a concentration of 443 mu g/L in Suez, while acenaphthylene (Ace) had the lowest concentration at 0.120 mu g/L in Northern Zaafarana. Carcinogenic PAHs (CAR) ranged from 8.67 mu g/L at Ras Gharib to 29.62 mu g/L at Suez, highlighting the urgent need for regulatory measures. Confirmatory ratios pointed to industrial and shipping influences as petrogenic sources. Elevated total organic carbon (TOC) levels in Suez Bay indicated aggravated organic pollution, exacerbated by oil rigs and refineries. The ecological risk assessment highlighted substantial risks, particularly in Suez, necessitating immediate interventions to combat PAH contamination and preserve the environmental balance of the Red Sea. The dominant metals in water samples were arranged in descending order as follows: Pb > Fe > Cr > Cu > Zn > Mn > Cd > Ni. The study evaluated environmental and human health risks using a multifaceted approach, including cluster analysis, principal component analysis, and various indices (HPI, RI, MI, HQ, HI, and CR). Most water samples exhibited high pollution risks, surpassing permissible limits for HPI (> 100) and MI (> 6). Notably, HI oral values indicated significant non-carcinogenic risks for adults and children. While HI values for adults suggested low-risk dermal contact, those for children showed a substantial proportion in the high-risk category. Most water samples displayed CR values exceeding 1 x 10(-4) for Cd, Cr, and Pb, indicating vulnerabi
The Red Sea coastal environment of Halayeb and Shalateen area is renowned for its abundant marine flora and fauna. It also holds significant economic and cultural importance for local communities. However, this region...
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The Red Sea coastal environment of Halayeb and Shalateen area is renowned for its abundant marine flora and fauna. It also holds significant economic and cultural importance for local communities. However, this region is currently confronted with various challenges, including climate change and habitat destruction. To effectively address and mitigate these issues, advanced technologies that offer a holistic understanding of the area's environmental conditions are required. This paper applies the integration of Geospatial Artificial Intelligence (GeoAI) and ChatGPT to study the Red Sea Coastal water quality dynamics of Halayeb and Shalateen Area. Landsat imagery and Copernicus Marine Service were used to retrieve area boundaries and monitor the physicochemical characteristics of the coastal water respectively. ChatGPT was utilized to generate python code that facilitates the creation of optimal distribution maps for each physical and chemical property criterion. The python codes were incorporated into the python program within the ArcGIS 10.7.1 and executed to generate the desired maps representing the dynamics of physical and chemical properties. It was found an observed fluctuation in chemical properties next to the coastline around the mouth of two main wadies;Wadi Hudain, and Wadi Da'eb. The degree of stability increased away from the coast toward the deep water. That proved the effect of the runoff on the seawater, as the runoff plays an essential role in the water state, especially in such semi-closed water bodies like the Red Sea where the flashfloods are the main source that can enrich water with sediment and nutrients. The state of seawater in terms of physical properties was not characterized by a specific pattern. The distribution of physical parameters in the Red Sea is influenced by factors such as regional climate variations, monsoonal winds, and local topography. This paper serves as a stepping stone for future research endeavors, exploring the full po
PyDDT is a free python package of computer codes for exploiting X-ray dynamic multiple diffraction in single crystals. A wide range of tools are available for evaluating the usefulness of the method, planning feasible...
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PyDDT is a free python package of computer codes for exploiting X-ray dynamic multiple diffraction in single crystals. A wide range of tools are available for evaluating the usefulness of the method, planning feasible experiments, extracting phase information from experimental data and further improving model structures of known materials. Graphical tools are also useful in analytical methodologies related to the three-dimensional aspect of multiple diffraction. For general X-ray users, the PyDDT tutorials provide the insight needed to understand the principles of phase measurements and other related methodologies. Key points behind structure refinement using the current approach are presented, and the main features of PyDDT are illustrated for amino acid and filled skutterudite single crystals.
Design for additive manufacturing (DfAM) enables the design and fabrication of intricate but application-based functionally optimized geometries by reducing the manufacturing time. It also gave unlimited design freedo...
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Design for additive manufacturing (DfAM) enables the design and fabrication of intricate but application-based functionally optimized geometries by reducing the manufacturing time. It also gave unlimited design freedom to alter any specific parameter and regenerate the design with improved mechanical properties. However, designing a complex and application-specific component needs comprehensive knowledge of drawing, intended usage, high expertise, and command of designing software with ample time. Mechanical springs, e.g., wave springs of uniform/complex shaped designs, consume a significant amount of manual hard work. A new design tool, WSdesign, is developed for constructing wave springs of different morphologies with uniform or varying design parameters or a combination of both. A graphical user interface (GUI) was developed in which the user can select the type of wave spring, which can be either uniform, functional gradient, or hybrid with parametric variation defined through python code. The code is directly run in Autodesk Fusion 360 software which is used to transform that code into a 3D model with all defined features and can be saved in different formats or can be directly printed. Two designs, i.e., rectangular and variable thickness wave springs, were designed each using WSdesign and SolidWorks (manual method), manufactured, and analyzed by performing uniaxial compression testing. The results were compared with each other which were further validated by finite element analysis and found that both design strategies have negligible variations. Furthermore, several designs of complex-shaped wave springs were successfully designed and manufactured using fused deposition modeling (FDM), stereolithography (SLA), and powder bed fusion (MJF) technology with different materials, resulting in a good surface finish, smooth printability, and less dimensional variation, which proves the versatility of WSdesign. In addition, this methodology also enables to design of
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