Research on Security Risk Assessment Mechanism of Important Event Based on Multi-Source Data

In order to effectively assess all types of security risks in the important event, the important decision-making basis for security risk warning and emergency management of important event is provided by analyzing the coupling relationship and evolution mechanism between various risks. The criminal causes, management defects, security and emergency system construction are analyzed from the possibility of accidents and risks. The multi-source data risk assessment system based on five subsystems and its index set of human factors, management factors, site factors, event factors and audit factors are proposed. The weight of each index in the assessment system is determined by the method of information entropy, and then the risk grade of important event is determined according to the weight calculation function and the improved fuzzy matter element model. The verification with an example shows that: the risk assessment model is optimized by combining entropy weight with fuzzy matter-element model，the influence of weight data extreme value was weakened，the qualitative description and quantitative analysis of multi-source data could be combined，and the subjective error was reduced. The risk grade of important event is reasonably evaluated, and the assessment effect is basically consistent with the expert inspection analysis, which shows that the method had certain application value.

A deep learning-based multisite neuroimage harmonization framework established with traveling-subject dataset

The accumulation of multisite large-sample MRI datasets collected by large brain research projects in the last decade has provided a critical resource for understanding the neurobiological mechanisms underlying cognitive functions and brain disorders. However, the significant site effects, observed in the imaging data and their derived structural and functional features, has prevented the derivation of consistent findings across different studies. The development of harmonization methods that can effectively eliminate complex site effects while maintaining biological characteristics in neuroimaging data has become a vital and urgent requirement for multisite imaging studies. Here, we proposed a deep learning-based framework to harmonize imaging data from pairs of sites, in which site factors and brain features can be disentangled and encoded. We trained the proposed framework with a publicly available traveling-subject dataset from SRPBS and harmonized the gray matter volume maps from eight source sites to a target site. The proposed framework significantly eliminated inter-site differences in gray matter volume. The embedded encoders successfully captured both the abstract texture of site factors and the concrete brain features. Moreover, the proposed framework exhibited outstanding performance relative to conventional statistical harmonization methods in site effect removal, data distribution homogenization, and intra-subject similarity improvement. Together, the proposed method offers a powerful and interpretable deep learning-based harmonization framework for multisite neuroimaging data that could enhance reliability and reproducibility in multisite studies for brain development and brain disorders.

City and Industry: How to Cross Borders? Learning From Innovative Company Site Transformations

While working and living coexisted in the historical city, the functions are separated in the Modernist city. Recently, the idea of connected urban districts with short distances and attractive work spaces have received renewed attention from companies and planners alike, as soft site factors, tacit knowledge, and local production are gaining importance. In this article we focus on the development of multi-national company sites and the economic and spatial conditions that encourage them to transform existing sites, improve placemaking, and cross borders. We also have a look at their interactive influence on the neighbourhood. We talked to the real estate managers of BASF, BMW, Bosch, Siemens, and Trumpf about site development strategies and approaches for connecting and mixing functions, and therefore crossing borders and, where it is necessary, separating. The professional discourse on “productive cities” and “urban manufacturing” is concerned with reintegrating production into the city. Reurbanisation is especially instrumental in overcoming a major guiding principle or dogma of the Modernist city: the separation of functions. Nevertheless, reurbanisation results in price rises and increases the competition for land. Therefore, planning has to pay attention to industrial areas, as well as housing or the inner-city. An important thesis of the article is that multi-national companies are pioneers in transforming their priority sites to suit future development. For cities, it is an upcoming communal task to ensure that all existing industrial areas develop into “just, green and productive cities,” as pointed out in the New Leipzig Charter. To a certain extent, it is possible to adapt the urban planning and design strategies of multi-national companies for existing industrial areas. This is especially true regarding the question of how borders and transition zones between industrial areas of companies and the surrounding neighbourhood can be designed to be spatially and functionally sustainable or how they can be transformed to suit future urban needs. However, urban planning has to balance many concerns and therefore the article concludes with a synopsis of the importance of strategic planning for transforming existing industrial areas.

An Assessment of Labour Productivity Influencing Factors in the Construction Industry: A Case study of Zaria, Nigeria

In this research, factors affecting construction labour productivity in Zaria, Kaduna state was assessed. The research seeks to identify the factors affecting labour productivity in the research area. It is believed that this information will aid site managers and the construction professionals on decisions to take in-order to limit these controlling factors thereby leading to an improved level of efficiency in labour force, increase product labour productivity and reduce cost and time over runs on construction projects. The research adopted a quantitative research approach with the use of questionnaires as an instrument for data collection from site managers at construction sites in Zaria. Sixty-seven questionnaires were administered on construction sites within Zaria and Forty-one were returned giving a sixty-one percent response. The Questionnaire sought to assess the perception of site managers on factors affecting construction labour productivity. Data was analysed using descriptive statistics analysis to obtain frequencies, mean and Relative Importance Index (RII). RII was used to rank the factors. Thirty-Nine out of the Forty-One factors researched indicated high severity with the RII ranging between 0.60 RII < 0.80. The research revealed that external forces tend to affect construction labour productivity more than Site factors and Human Labour Factors. This can be attributed to the fact that site factors and Human Labour factors can be controlled by the site engineers while the external factors cannot be really controlled by the site engineers. The survey also revealed that Rain, Conflict with project stakeholders, Skill of labour, and Financial Crisis had a very high severity in affecting construction labour productivity on the construction sites in Zaria, Kaduna State.

Frequency-dependent site factors for the Icelandic strong-motion array from a Bayesian hierarchical model of the spatial distribution of spectral accelerations

The earthquake ground motions of over 1700 earthquakes recorded on a small-aperture strong-motion array in south Iceland (ICEARRAY I) that is situated on a relatively uniform site condition characterized as rock, exhibit a statistically significant spatial variation of ground-motion amplitudes across the array. Both earthquake and microseismic horizontal-to-vertical spectral ratios (HVSR) have been shown to exhibit distinct and in some cases, bimodal peaks in amplification, indicating site resonance at periods of 0.1–0.3 s, a phenomenon that has been attributed to a surface layer of lava rock lying above a sedimentary layer, a structure that is then repeated with depth under the array. In this study, we implement a Bayesian hierarchical model (BHM) of the seismic ground motions that partitions the model residuals into earthquake event term, station term, and event–station term. We analyzed and compared peak ground acceleration (PGA) with the 5% damped pseudo-acceleration response spectrum (PSA) at oscillator periods of T = 0.05–1.0 s. The results show that the event terms, dominate the total variability of the ground-motion amplitudes over the array. However, the station terms are shown to increase in the period range of 0.1–0.3 s on most stations and to different extents, leading to an increase in the overall variability of ground motions at those periods, captured by a larger inter-station standard deviation. As the station terms are a measure of how much the ground motions at those stations deviate from the array average, they act as proxies for localized site effects and amplification factors. These results, improve our understanding of the key factors that affect the variation of seismic ground motions across the relatively small area of ICEARRAY I. This approach can help to improve the accuracy of earthquake hazard assessments on local scales, which in turn could contribute to more refined seismic risk assessments and engineering decision-making.

Predictive Modelling for Blasting-Induced Vibrations from Open-Pit Excavations

Reliable estimates of peak particle velocity (PPV) from blasting-induced vibrations at a construction site play a crucial role in minimizing damage to nearby structures and maximizing blasting efficiency. However, reliably estimating PPV can be challenging due to complex connections between PPV and influential factors such as ground conditions. While many efforts have been made to estimate PPV reliably, discrepancies remain between measured and predicted PPVs. Here, we analyzed various methods for assessing PPV with several key relevant factors and 1,191 monitored field blasting records at 50 different open-pit sites across South Korea to minimize the discrepancies. Eight prediction models are used based on artificial neural network, conventional empirical formulas, and multivariable regression analyses. Seven influential factors were selected to develop the prediction models, including three newly included and four already formulated in empirical formulas. The three newly included factors were identified to have a significant influence on PPV, as well as the four existing factors, through a sensitivity analysis. The measured and predicted PPVs were compared to evaluate the performances of prediction models. The assessment of PPVs by an artificial neural network yielded the lowest errors, and site factors, K and m were proposed for preliminary open-pit blasting designs.

A Novel Plant Resistance Inducer for the Protection of European Ash (Fraxinus excelsior L.) against Hymenoscyphus fraxineus—Preliminary Studies

Ash tree disease is caused by an ascomycete fungus Hymenoscyphus fraxineus, which first emerged in 1992, eastern Poland. Site factors, genetic predispositions, and resistance to the pathogen have not been fully described yet. The general aim of the study undertaken was to check the effect of using a new active substance representing benzothiadiazoles, a BTH derivative, namely, N-methyl-N-methoxyamide-7-carboxybenzo(1.2.3)thiadiazole (BTHWA), on ash saplings. A total of 41 ash saplings, aged three to five years, were subjected to this experiment in six variants of treatment. The results of the inoculation with H. fraxineus indicated that the treatment with BTHWA resulted in the limitation of the size of necrotic phloem lesions. Although the lesions were detectable in the cross section, the plants showed no visible signs of infection. The results suggest that H. fraxineus development in ash saplings can be slowed down or even completely stopped through triggering plant resistance by BTHWA.

Factors Influencing the Natural Forest Regeneration at Khadimnagar National Park, Bangladesh

Natural regeneration is an indicator of a healthy forest, hence, understanding the influence of site factors on natural regeneration is a significant concern for ecologists. This work aimed to assess the impact of site factors on natural tree regeneration at Khadimnagar National Park (KNP). Biotic factors (tree density, tree species richness, and basal area), physical factors (elevation, canopy openness), and soil properties (bulk density, moisture content, soil pH, organic matter, sand, silt, and clay) data were investigated from 71 sample plots to examine their effects on natural regeneration density and richness in KNP. Stepwise multiple linear regression analysis was done to predict both regeneration density and regeneration richness. The results showed that soil pH (p<0.001), canopy openness (p<0.001), tree species richness (p<0.01), and bulk density (p<0.01) had a significant effect on regeneration density, explaining 42% of the total variation. Regeneration richness was driven by four factors: tree species richness (p<0.01), soil pH (p<0.001), elevation (p<0.01), and canopy openness (p<0.01) with a model that explained 60% of the total variation. This study observed that soil pH, tree species richness, and canopy openness are the main controlling factors that influenced both the density and richness of regenerating species in KNP. Therefore, these findings have implications for natural resource management, especially in selecting suitable silvicultural systems in a tropical forest under protected area management where enhanced tree cover and conservation of biodiversity are needed.