Optimizing Construction Project Plan Management Using Parameter-Adaptive Improved Genetic Algorithm

This study proposes an optimization method for construction project plan management using a parameter-adaptive improved genetic algorithm (IGA) combined with differential evolution (DE). The method addresses the challenges of inflexibility, inefficient resource allocation, and unscientific scheduling in traditional project management approaches. A multi-objective optimization model that balances project duration and resource utilization is developed. A case study demonstrated that the optimized methods reduced the project duration by up to 22.7% and the resource variance by up to 29.9% compared to the original plan. The proposed method enhances the flexibility and efficiency of construction project planning, contributing to both theoretical advancement in optimization algorithms and practical improvements in project management

Overexpression of Peptide-Encoding OsCEP6.1 Results in Pleiotropic Effects on Growth in Rice (O. sativa)

Plant peptide hormone plays an important role in regulating plant developmental programs via cell-to-cell communication in a non-cell autonomous manner. To characterize the biological relevance of C-TERMINALLY ENCODED PEPTIDE (CEP) genes in rice, we performed a genome-wide search against public databases using bioinformatics approach and identified six additional CEP members. Expression analysis revealed a spatial-temporal pattern of OsCEP6.1 gene in different tissues and at different developmental stages of panicle. Interestingly, the expression level of the OsCEP6.1 was also significantly up-regulated by exogenous cytokinin. Application of a chemically synthesized 15-amino-acid OsCEP6.1 peptide showed that OsCEP6.1 had a negative role in regulating root and seedling growth, which was further confirmed by transgenic lines. Furthermore, the constitutive expression of OsCEP6.1 was sufficient to lead to panicle architecture and grain size variations. Scanning electron microscopy analysis revealed that the phenotypic variation of OsCEP6.1 overexpression lines resulted from decreased cell size but not reduced cell number. Moreover, starch accumulation was not significantly affected. Taken together, these data collectively suggest that the OsCEP6.1 peptide might be involved in regulating the development of panicles and grains in rice

Big Data Analytics for Network Level Short-Term Travel Time Prediction with Hierarchical LSTM and Attention

The travel time data collected from widespread traffic monitoring sensors necessitate big data analytic tools for querying, visualization, and identifying meaningful traffic patterns. This paper utilizes a large-scale travel time dataset from Caltrans Performance Measurement System (PeMS) system that is an overflow for traditional data processing and modeling tools. To overcome the challenges of the massive amount of data, the big data analytic engines Apache Spark and Apache MXNet are applied for data wrangling and modeling. Seasonality and autocorrelation were performed to explore and visualize the trend of time-varying data. Inspired by the success of the hierarchical architecture for many Artificial Intelligent (AI) tasks, we consolidate the cell and hidden states passed from low-level to the high-level LSTM with an attention pooling similar to how the human perception system operates. The designed hierarchical LSTM model can consider the dependencies at different time scales to capture the spatial-temporal correlations of network-level travel time. Another self-attention module is then devised to connect LSTM extracted features to the fully connected layers, predicting travel time for all corridors instead of a single link/route. The comparison results show that the Hierarchical LSTM with Attention (HierLSTMat) model gives the best prediction results at 30-minute and 45-min horizons and can successfully forecast unusual congestion. The efficiency gained from big data analytic tools was evaluated by comparing them with popular data science and deep learning frameworks

Network level spatial temporal traffic forecasting with Hierarchical-Attention-LSTM

Traffic state data, such as speed, density, volume, and travel time collected from ubiquitous roadway detectors require advanced network level analytics for forecasting and identifying significant traffic patterns. This paper leverages diverse traffic state datasets from the Caltrans Performance Measurement System (PeMS) hosted on the open benchmark and achieved promising performance compared to well-recognized spatial-temporal prediction models. Drawing inspiration from the success of hierarchical architectures in various Artificial Intelligence (AI) tasks, cell and hidden states were integrated from low-level to high-level Long Short-Term Memory (LSTM) networks with the attention pooling mechanism, similar to human perception systems. The developed hierarchical structure is designed to account for dependencies across different time scales, capturing the spatial-temporal correlations of network-level traffic states, and enabling the prediction of traffic states for all corridors rather than a single link or route. The efficiency of the designed hierarchical LSTM is analyzed by ablation study, demonstrating that the attention-pooling mechanism in both cell and hidden states not only provides higher prediction accuracy but also effectively forecasts unusual congestion patterns. Data and code are made publicly available to support reproducible scientific research

Soziale Charakteristiken und ihre Beziehungen zur Erkrankungsschwere, Verweildauer auf Intensivstation, beatmungsfreie Tage und Besuchsdichte bei chirurgischen Intensivpatienten

Ziel dieser klinisch prospektiven Studie war es, anhand persönlicher Interviews das Patientenkollektiv einer operativen Intensivstation hinsichtlich ihrer sozialen Charakteristiken zu beschreiben und Zusammenhänge zwischen soziale Charakteristiken und Erkrankungsschwere, Verweildauer auf Intensivstation, Dauer der künstlichen Beatmung sowie Besuchsdichte zu untersuchen. Zu den ausgewählten sozialen Charakteristiken gehören Familienstand, Anzahl der Personen im Haushalt, Staatsangehörigkeit, Wohnortgröße, Krankenversichertenstatus und Religionszugehörigkeit. Aus einer Vielzahl von Studien ist bekannt, dass Morbidität und Mortalität mit zahlreichen sozialen Faktoren in Zusammenhang stehen. Die meisten Untersuchungen dazu sind allerdings auf die Allgemeinbevölkerung fokussiert. In der Intensivmedizin wurde diese Fragestellung bislang vernachlässigt. In unserem Patientenkollektiv untersuchten wir Intensivpatienten auf soziale Merkmale und verglichen sie mit der Verteilung in der deutschen Allgemeinbevölkerung. Desweiteren beschäftigten wir uns mit der Fragestellung, ob und in wie weit soziale Charakteristiken die Erkrankungsschwere, Verweildauer auf Intensivstation, Dauer der künstlichen Beatmung sowie Besuchsdichte bei Intensivpatienten beeinflussen mit Hilfe multipler logistischer Regressionsanalysen

Organizational culture & employee behavior

Organizations are among the key units of the society. During their establishment and development, a specific kind of organizational culture eventually appears. The purpose of organizational culture is to improve solidarity and cohesion, and to stimulate employees' enthusiasm and creativity to improve the organization’s economic efficiency. In addition, organizational culture greatly influences employee behavior. The aim of this study is to find out how organizational culture affects employee behavior. It is important to understand that in order to improve the organization’s business management and let the organizational culture have the right impact on employees. The results of the study indicate that organizational culture mainly impacts motivation, promotes individual learning, affects communication, and improves organizational values, group decision making and solving conflicts

Weighted Bayesian Gaussian Mixture Model for Roadside LiDAR Object Detection

Background modeling is widely used for intelligent surveillance systems to detect moving targets by subtracting the static background components. Most roadside LiDAR object detection methods filter out foreground points by comparing new data points to pre-trained background references based on descriptive statistics over many frames (e.g., voxel density, number of neighbors, maximum distance). However, these solutions are inefficient under heavy traffic, and parameter values are hard to transfer from one scenario to another. In early studies, the probabilistic background modeling methods widely used for the video-based system were considered unsuitable for roadside LiDAR surveillance systems due to the sparse and unstructured point cloud data. In this paper, the raw LiDAR data were transformed into a structured representation based on the elevation and azimuth value of each LiDAR point. With this high-order tensor representation, we break the barrier to allow efficient high-dimensional multivariate analysis for roadside LiDAR background modeling. The Bayesian Nonparametric (BNP) approach integrates the intensity value and 3D measurements to exploit the measurement data using 3D and intensity info entirely. The proposed method was compared against two state-of-the-art roadside LiDAR background models, computer vision benchmark, and deep learning baselines, evaluated at point, object, and path levels under heavy traffic and challenging weather. This multimodal Weighted Bayesian Gaussian Mixture Model (GMM) can handle dynamic backgrounds with noisy measurements and substantially enhances the infrastructure-based LiDAR object detection, whereby various 3D modeling for smart city applications could be created

On the high-energy behavior of massive QCD amplitudes

In this note, we propose a factorization formula for gauge-theory scattering amplitudes up to two loops in the high-energy boosted limit. Our formula extends existing results in the literature by incorporating the contributions from massive loops. We derive the new ingredients in our formula using the method of regions with analytic regulators for the rapidity divergences. We verify our results with various form factors and the scattering amplitudes for top-quark pair production. Our results can be used to obtain approximate expressions for complicated two-loop massive amplitudes from simpler massless ones, and can be used to resum the mass logarithms to all orders in the coupling constant.Comment: 19 pages, 2 figure

A Review on Longitudinal Car-Following Model

The car-following (CF) model is the core component for traffic simulations and has been built-in in many production vehicles with Advanced Driving Assistance Systems (ADAS). Research of CF behavior allows us to identify the sources of different macro phenomena induced by the basic process of pairwise vehicle interaction. The CF behavior and control model encompasses various fields, such as traffic engineering, physics, cognitive science, machine learning, and reinforcement learning. This paper provides a comprehensive survey highlighting differences, complementarities, and overlaps among various CF models according to their underlying logic and principles. We reviewed representative algorithms, ranging from the theory-based kinematic models, stimulus-response models, and cruise control models to data-driven Behavior Cloning (BC) and Imitation Learning (IL) and outlined their strengths and limitations. This review categorizes CF models that are conceptualized in varying principles and summarize the vast literature with a holistic framework