Output Map Research Articles

Prediction of Enterprise Human Resource Demand Based on Iot and Data Mining Technology

This paper analyzes the prediction model of enterprise human resource demand based on Internet of Things (IoT) technology and data mining technology. It also analyzes the impact of the company’s growth scale and other key factors on the demand for human resources, tries to establish a coupling factor model of enterprise development and economic benefits, and then analyzes and forecasts the enterprise’s personnel structure and quality structure. The experimental results show that the optimized human resource demand forecasting model integrates the advantages of the grey system model in data processing, can mine the inherent laws of unorganized data information, and provides a certain convenience for forecasting. Through the linear mapping and processing of sample data, the input and output reflect a kind of correlation, thus changing the fault tolerance of information, making the prediction in the calculation process more accurate, and its comprehensive accuracy can exceed 92.5%.

SUITABILITY ANALYSIS FOR URBAN BICYCLE TOURISM OF ILOILO CITY USING GIS

Abstract. Urban bicycle tourism refers to the recreational and enjoyable use of bicycles in navigating urban cities like Iloilo City. The city has already established an 11-kilometer cycling infrastructure within its boundaries. It is imperative to maximize its use to combat the negative impacts of rapid urbanization and promote safe and sustainable transportation. This paper shows the use of Geographic Information Systems (GIS) to assess the bike lanes network's suitability and accessibility for building a bike-sharing system that also caters to drop-off and ride-on points at each station. Deletion of duplicates and cleaning of road network data and tourist attraction point data was performed prior to any analysis. Service areas were generated to determine the number of tourist attractions catered by each potential bike share station – defined by main road intersections. Point distance analysis was performed to reduce the number of road intersections to just thirty-nine (39). Bicycle route maps and itineraries were produced to visualize the optimal routes tourists can use for exploring the city based on preferred sites category. From here, Route Analysis was conducted to create four (4) itineraries based on tourism categories: Top 10 Most Rated Tourist Attractions, Food and Dining, Museums and Churches, and Shopping and Leisure. The output maps and itineraries can be presented to the City Tourism Office of Iloilo City as a decision support tool for its programs and policies regarding bike lanes.

Bayesian hybrid-kernel machine-learning-assisted sensitivity analysis and sensitivity-relevant inverse modeling for groundwater DNAPL contamination

Accurate source characterization and transport parameter estimation is important when seeking to predict the spatiotemporal distribution of dense non-aqueous phase liquid (DNAPL) contaminants in groundwater. However, this is a complex multimodal search problem prone to equifinality and premature convergence, which leads to considerable error. To address this, a sensitivity-relevant dynamic swarm intelligence (SRD-SI) algorithm embedded in a homotopy-variation mechanism is proposed in the present study to rationally balance the inversion processes of sensitivity-varied source characteristics and DNAPL transport parameters. In this approach, global optima are progressively approached in conjunction with the homotopy variation of the search space. Furthermore, to avoid computationally expensive numerical model repetition during the sensitivity analysis and inverse iterations, a Bayesian-based optimization framework that combines multiple kernel functions in a kernel extreme learning machine (KELM) model is designed considering the complex site conditions and statistical characteristics of the input variables, thus creating the Bayesian hybrid KELM (BHK-ELM) model for the reliable surrogate modeling of numerical DNAPL-transport simulations. The results show that the BHK-ELM model recognizes and effectively reconstructs the complex input − output mapping of the numerical model by increasing the determination coefficient R2 to 0.9988 while improving the computational efficiency approximately 4500-fold. Because source characteristics and boundary conditions are far more sensitive than transport parameters to the contaminant distribution, conventional inverse modeling methods struggle to accurately identify these transport parameters. In contrast, the proposed inverse modeling system combining sensitivity analysis, swarm intelligence, and homotopy variation is more stable and provides significantly more accurate estimations for all unknown variables. Compared with the traditional SI algorithm, the homotopy-variation SRD-SI reduced the maximum inversion relative error from 46.22 % to 9.53 %, while the mean inversion relative error was reduced from 11.09 % to 3.90 %.

A comparison of two statistical postprocessing methods for heavy‐precipitation forecasts over India during the summer monsoon

AbstractAccurate ensemble forecasts of heavy precipitation in India are vital for many applications and essential for early warning of damaging flood events, especially during the monsoon season. In this study we investigate to what extent Quantile Mapping (QM) and Ensemble Model Output Statistics (EMOS) statistical postprocessing reduce errors in precipitation ensemble forecasts over India, in particular for heavy precipitation. Both methods are applied to day‐1 forecasts at 12‐km resolution from the 23‐member National Centre for Medium Range Weather Forecasting (NCMRWF) global ensemble prediction system (NEPS‐G). By construction, QM leads to distributions close to the observed ones, while EMOS optimizes the ensemble spread, and it is not a priori clear which is better suited for practical applications. The methods are therefore compared with respect to several key aspects of the forecasts: local distributions, ensemble spread, and skill for forecasting precipitation amounts and the exceedance of heavy precipitation thresholds. The evaluation includes rank histograms, Continuous Ranked Probability Skill Scores (CRPSS), Brier Skill Scores (BSS), reliability diagrams, and receiver operating characteristic. EMOS performs best not only with respect to correcting under‐ or overdispersive ensembles, but also in terms of forecast skill for precipitation amounts and heavy precipitation events, with positive CRPSS and BSS in most regions (both up to about 0.4 in some areas), while QM in many regions performs worse than the raw forecast. QM performs best with respect to the overall local precipitation distributions. Which aspects of the forecasts are most relevant depends to some extent on how the forecasts are used. If the main criteria are the correction of under‐ or overdispersion, forecast reliability, match between the forecasted distribution for individual days and observations (CRPSS), and the skill in forecasting heavy‐precipitation events (BSS), then EMOS is the better choice for postprocessing NEPS‐G forecasts for short lead times.

Data-driven, cross-disciplinary collaboration: lessons learned at the largest academic health center in Latin America during the COVID-19 pandemic.

The COVID-19 pandemic has prompted global research efforts to reduce infection impact, highlighting the potential of cross-disciplinary collaboration to enhance research quality and efficiency. At the FMUSP-HC academic health system, we implemented innovative flow management routines for collecting, organizing and analyzing demographic data, COVID-related data and biological materials from over 4,500 patients with confirmed SARS-CoV-2 infection hospitalized from 2020 to 2022. This strategy was mainly planned in three areas: organizing a database with data from the hospitalizations; setting-up a multidisciplinary taskforce to conduct follow-up assessments after discharge; and organizing a biobank. Additionally, a COVID-19 curated collection was created within the institutional digital library of academic papers to map the research output. Over the course of the experience, the possible benefits and challenges of this type of research support approach were identified and discussed, leading to a set of recommended strategies to enhance collaboration within the research institution. Demographic and clinical data from COVID-19 hospitalizations were compiled in a database including adults and a minority of children and adolescents with laboratory confirmed COVID-19, covering 2020-2022, with approximately 350 fields per patient. To date, this database has been used in 16 published studies. Additionally, we assessed 700 adults 6 to 11 months after hospitalization through comprehensive, multidisciplinary in-person evaluations; this database, comprising around 2000 fields per subject, was used in 15 publications. Furthermore, thousands of blood samples collected during the acute phase and follow-up assessments remain stored for future investigations. To date, more than 3,700 aliquots have been used in ongoing research investigating various aspects of COVID-19. Lastly, the mapping of the overall research output revealed that between 2020 and 2022 our academic system produced 1,394 scientific articles on COVID-19. Research is a crucial component of an effective epidemic response, and the preparation process should include a well-defined plan for organizing and sharing resources. The initiatives described in the present paper were successful in our aim to foster large-scale research in our institution. Although a single model may not be appropriate for all contexts, cross-disciplinary collaboration and open data sharing should make health research systems more efficient to generate the best evidence.

Using Bibliometric Analysis to Map the Impact of a Simulation Program's Published Scholarly Works.

Bibliometrics quantitatively evaluates the targeted literature sources and can help define research and scholarly publications' impact and demonstrate connections for authors, departments, or universities. This article presents a methodology for simulation programs to evaluate their influence in terms of both impact and scope of their published simulation-based healthcare scholarly output. Using the authors' home university and healthcare system as an example, the article outlines a methodology to map research and scholarly works networks within the systems, identify and map connections outside the system, and quantifiably score the overall impact of the simulation program's scholarly output using a common scoring metric, the h-index. This generates an objective measure of impact, rather than a subjective opinion of an organization's research and scholarly impact. The combination of an institutional h-index with mapping of simulation-based healthcare scholarly output provides a full, objective description of the institution's output and provides a benchmark for other simulation programs for comparison.

Shift is derived

Shift is an input–output mapping where a feature or autosegment loses its underlying associations and surfaces with different associations. In Harmonic Serialism, shift can either be analyzed as a multi-step process or a single-step process. While Gietz et al. (2023) argue for the latter, this paper refutes their arguments and provides evidence supporting a multi-step analysis of shift. Specifically, it demonstrates that shift in Kibondei and Halkomelem, the languages analyzed by Gietz et al. (2023), does not require a single-step shift operation and that the analyses they present are empirically inadequate. Typological modeling not only reinforces the result that a single-step shift operation is superfluous but demonstrates that grammars with such an operation undergenerate with respect to the attested typology.

Lightweight YOLOv7 Algorithm for Multi-Object Recognition on Contrabands in Terahertz Images

With the strengthening of worldwide counter-terrorism initiatives, it is increasingly important to detect contrabands such as controlled knives and flammable materials hidden in clothes and bags. Terahertz (THz) imaging technology is widely used in the field of contraband detection due to its advantages of high imaging speed and strong penetration. However, the terahertz images are of poor qualities and lack texture details. Traditional target detection methods suffer from low detection speeds, misdetection, and omission of contraband. This work pre-processes the original dataset using a variety of image processing methods and validates the effect of these methods on the detection results of YOLOv7. Meanwhile, the lightweight and multi-object detection YOLOv7 (LWMD-YOLOv7) algorithm is proposed. Firstly, to meet the demand of real-time for multi-target detection, we propose the space-to-depth mobile (SPD_Mobile) network as the lightweight feature extraction network. Secondly, the selective attention module large selective kernel (LSK) network is integrated into the output of the multi-scale feature map of the LWMD-YOLOv7 network, which enhances the effect of feature fusion and strengthens the network’s attention to salient features. Finally, Distance Intersection over Union (DIOU) is used as the loss function to accelerate the convergence of the model and to have a better localisation effect for small targets. The experimental results show that the YOLOv7 algorithm achieves the best detection results on the terahertz image dataset after the non-local mean filtering process. The LWMD-YOLOv7 algorithm achieves a detection accuracy P of 98.5%, a recall R of 97.5%, and a detection speed of 112.4 FPS, which is 26.9 FPS higher than that of the YOLOv7 base network. The LWMD-YOLOv7 achieves a better balance between detection accuracy and detection speed. It provides a technological reference for the automated detection of contraband in terahertz images.

Studying the theoretical basis and advantages and disadvantages of Logistic methods of the total horizontal Gradient

Edge detection is one of the most important steps in interpretation of magnetic and gravity data. In magnetic and gravity maps, it is difficult to distinguish adjacent sources due to their field superposition. Many different techniques have been used to determine the edges of sources. These techniques are based on vertical or horizontal gradients of magnetic and gravity data or combinations of them, and the edges of the geological structures are determined by maximum, minimum, or zero values in the output maps. One of the most popular techniques is the total horizontal gradient which is based on horizontal gradients of magnetic and gravity data. The capability of the total horizontal gradient technique in mapping the boundaries of deep bodies is very limited when competing with large-amplitude shallow bodies. Some enhanced modifications of the total horizontal gradient technique have been introduced to improve the boundary estimation results. These techniques are based on logistic functions and derivatives of the total horizontal gradient. In this study, we aim to estimate the effectiveness of the logistic filters of the total horizontal gradient. To obtain optimum results, these filters were tested on synthetic gravity and magnetic data and real magnetic data from the Zhurihe region (China). The findings show that the logistic filters can provide more accurate and sharper boundaries without false source edges than the total horizontal gradient. These techniques can determine the edges of shallow and deep structures at the same time. These results demonstrate that the logistic filters are useful tools for the qualitative interpretation of potential field data.

Path planning of substation inspection robot based on high-precision positioning and navigation technology

Abstract Outdoor substation is an important part of power system. Substation inspection robot based on intelligent autonomous inspection system has become the research focus of substation unmanned inspection. In order to improve the positioning accuracy and speed of the system, a high-precision positioning algorithm of transformer detection robot is proposed in this paper. Tikhonov regularization is used to correct the pathological problem of the localization algorithm model. The observation amount of the receiver is increased by using four signals of a single base station with double frequency and double antenna, and the position is solved by using single difference carrier phase observation and the integer ambiguity is fixed. The input–output mapping of the neural network is designed according to the information acquisition and two-wheel angular velocity control of the detection robot. Using the hyperbolic tangent function as the activation function of MLP neural network, the MLP neural network with 32 neurons in each of the three hidden layers is determined. By optimizing reinforcement learning reward function, adding scoring rules, and reward parameters, this paper carries out the following simulation exploration work. The high-precision positioning algorithm of transformer inspection robot is compared with the existing algorithm, and the superiority of the algorithm is verified. The basic motion ability of the robot installed with the system was tested.

Causality and tractable probabilistic models

Causal assertions stem from an asymmetric relation between some variable's causes and effects, i.e., they imply the existence of a function decomposition of a model where the effects are a function of the causes without implying that the causes are functions of the effects. In structural causal models, information is encoded in the compositions of functions that define variables because that information is used to constraint how an intervention that changes the definition of a variable influences the rest of the variables. Current probabilistic models with tractable marginalization also imply a function decomposition but with the purpose of allowing easy marginalization of variables. In this article, structural causal models are extended so that the information implicitly stored in their structure is made explicit in an input–output mapping in higher dimensional representation where we get to define the cause–effect relationships as constraints over a function space. Using the cause–effect relationships as constraints over a space of functions, the existing methodologies for handling causality with tractable probabilistic models are unified under a single framework and generalized.

Enhancement of the balanced total horizontal derivative of gravity data using the power law approach

Mapping the boundaries of subsurface geological structures constitutes a fundamental aspect of interpreting gravity data. Various methods relying on gravity derivatives have been introduced to delineate these boundaries. However, these approaches are not without their limitations, often leading to the generation of false edges or resulting in divergent outcomes. This article introduces a novel method that utilizes the power law approach and balanced total horizontal derivative for mapping gravity data edges. To assess the robustness of the proposed technique, we conducted tests on model examples and applied it to a real case study in northwest Vietnam. The results demonstrate that our method can accurately and clearly map edges in comparison to existing methodologies, while also preventing the generation of spurious edges in the output maps.

A Principal Bundle Framework for Generalised Notions of Formation and Rigidity

In this work, the notions of formation and rigidity (first developed for formation control in Euclidean spaces) are recast in a general framework of principal fiber bundles and a control law is derived for second order systems for driving the system to a given formation. The Euclidean inter-agent distance measurements and congruence transformations are generalised as output maps and Lie group actions respectively, to capture more generic scenarios.

Accuracy Improvement With Weight Mapping Strategy and Output Transformation for STT-MRAM-Based Computing-in-Memory

Accuracy Improvement With Weight Mapping Strategy and Output Transformation for STT-MRAM-Based Computing-in-Memory

Mapping release and propagation areas of permafrost-related rock slope failures in the French Alps: A new methodological approach at regional scale

Rock slope failure (RSF) from permafrost-affected rockwall is an emerging threat for human lives and infrastructure which raises a need for RSF hazard assessments in the alpine environment. This study proposes the first mapping approach to RSF release and propagation areas in the French Alps.By analysing a unique RSF database (1389 recent events in the Mont Blanc massif), we determine three levels of RSF Susceptibility Indexes (RSI) to map the potential release areas. We highlight a strong correlation between RSF and permafrost: 99 % of RSFs occurred from rockwalls with a Mean Annual Rock Surface Temperature (MARST) ≤ 3 °C and 35 % with MARST from −2 to 0 °C.The release area maps (34 km2 to 284 km2 depending on the RSI) are entered into a simple propagation model (RockavELA), using a dimensionless area-based energy line principle (i.e. reach susceptibility) to map propagation areas of potential RSFs. Three propagation limits are proposed, fitting the propagation characteristics of another RSF database containing 3497 events observed across the European Alps with heterogeneous propagation substrates, and merged with 48 additional events from high mountains in the French Alps. RockavELA reproduced the runout extent of 20 high mountain RSFs with <50 % of frontal and lateral error. Output maps show little sensitivity to the propagation limits (<25 % difference between the high and low propagation limits).This work is a preliminary step to identify potential hazard hot spots in the French Alps, and proposes a novel approach that could be applied in other mountain ranges.

Research on Working Principle of Rotating Arc Stability Sensor and its Input and Output Mapping Relationship

Research on Working Principle of Rotating Arc Stability Sensor and its Input and Output Mapping Relationship

Uncertainty quantification of CO[formula omitted] plume migration in highly channelized aquifers using probabilistic convolutional neural networks

Characterization of the CO2 plume evolution in deep aquifers under geological uncertainties is crucial for designing geological carbon storage (GCS) projects. In this study, we are concerned with uncertainty quantification (UQ) of CO2 plume migration in uncertain permeability fields following non-Gaussian distributions. Specifically, we are concerned with the effect of intra-facies heterogeneity on plume migration in a multi-facies sedimentary setting. To alleviate the huge computational burden required for UQ, machine learning (ML) methods are broadly used to develop surrogate models. However, the quality of ML surrogate models is often not qualified. Here we present a probabilistic convolutional neural network (pCNN) method for fast surrogate modeling of the CO2 plume evolution. The dense and residual connections are adopted in the pCNN’s network architecture to enhance its performance in approximating the highly complex input–output mapping of the GCS model induced by non-Gaussian permeability heterogeneity. In addition, a Bayesian training strategy is utilized to train the pCNN surrogate model, enabling it to give predictions together with an estimate of the surrogate uncertainty. As a result, uncertainties associated with the pCNN surrogate can be quantified in addition to the model parameter uncertainty. The proposed method is demonstrated using a 3-D GCS model with an uncertain, non-Gaussian distributed, multi-facies permeability field. Results show that pCNN is able to provide a reliable and fast approximation of the highly complex GCS model. The pCNN-derived UQ estimates, obtained with negligible additional computational costs, show a good agreement with those derived from the computationally intensive GCS model. The generalization performance of pCNN is further investigated using “out-of-distribution” permeability samples. For such permeability fields, pCNN can still provide relatively accurate approximations, only showing slightly increased predictive errors and uncertainties relative to those having similar distributions to the training samples.

Unsupervised domain adaptation for brain structure segmentation via mutual information maximization alignment

Brain structure segmentation in Magnetic Resonance image (MRI) plays an important role in the diagnosis of various neuropsychiatric diseases. Recently, the high cost of manual annotation has motivated research in medical image analysis to focus on unsupervised learning methods. As a promising solution, unsupervised domain adaptation (UDA) has received increasing attention in various medical image segmentation tasks. In this work, we propose a novel UDA framework for brain structure segmentation from MRI volumes. Specifically, we design a self-training-based network architecture to learn cross-domain representations, which is achieved by transferring essential knowledge from a well-trained cumbersome teacher network to a non-trained compact student network using distillation learning technology. The pseudo-labels generated by the pre-trained student model are used to supervise the domain adaptation process of the teacher model. Furthermore, we develop a mutual information maximization alignment module to learn domain-invariant features and cross-domain segmentation knowledge, in which contrastive learning techniques are utilized to maximize the correlation between samples. Finally, we build a unified distillation framework by integrating mutual information distillation and output map distillation, correcting pseudo-labels during the self-training stage, and continuously refining the final predictions of compact networks. Extensive experiments conducted on two datasets with vastly different styles and settings demonstrate that our method is effective in improving segmentation performance on unlabeled target images, and outperforms the state-of-the-art domain adaptation approaches by a large margin. The source code is available at https://github.com/huqian999/UDA-MIMA.

Gravity Data Enhancement Using the Exponential Transform of the Tilt Angle of the Horizontal Gradient

Detecting the boundaries of geologic structures is one of the main tasks in interpreting gravity anomalies. Many methods based on the derivatives of gravity anomalies have been introduced to map the source boundaries. The drawbacks of traditional methods are that the estimated boundaries are divergent or false boundaries appear in the output map. Here, we use the exponential transform of the tilt angle of the horizontal gradient to improve the edge detection results. The robustness of the presented method is illustrated using synthetic data and real examples from the Voisey’s Bay Ni-Cu-Co deposit (Canada) and the Tuan Giao (Vietnam). The findings show that the presented technique can produce more precise and clear boundaries.

ANALISIS DISTRIBUSI SPASIAL PEMBUANGAN SAMPAH ILEGAL DI JALAN PROF. DR. HERMAN YOHANES PENFUI

The increasing population of Kupang City has resulted in an increasing volume of waste being managed so that additional landfills will always be needed. An increase in the number of piles of uncontrolled waste will eventually make the capacity of landfills reach the maximum limit or overload. Dr. Herman Yohanes Penfui, 2) to find out the strategy for determining the location of legal waste disposal sites on Jalan Prof. Dr. Herman Yohanes Penfui. The data analysis technique used in this study is descriptive analysis and is presented in the form of output maps, nearest neighbor analysis to find out the distribution pattern and analysis of the suitability of TPS presence to determine the feasibility of TPS. In this study, the research sample consisted of 2 Penfui Village officials, and 5 people from the community, so the total number of respondents consisted of 7 people. Data collection was carried out by means of observation, interviews and documentation. The results showed that 1) there were 8 illegal TPS locations which were very close together, so this greatly affected the environmental conditions in the research location area, andthe pattern of distribution of TPS in the research location was clustered, 2) Based on the results carried out by buffering analysis using the ArcGIS 10.4 application related to the strategic location of temporary waste disposal sites in accordance with predetermined conditions and directions from several sources' opinions as a reference for determining the legal TPS location strategy in the study there were 30 TPS PS recommendations needed in the research location area.