Local Scope Research Articles

Bio-inspired robot swarm path formation with local sensor scope

Bio-inspired robot swarm path formation with local sensor scope

Using 14C of tree rings to study CO2 emissions from fossil sources at the sampling point of the Meteorological Tower in Beijing

Atmospheric fossil fuel CO2 information is an important reference for local government to formulate energy-saving and emission reduction in Beijing. We present a reconstruction of the fossil fuel CO2 mole fraction from 2007 to 2014 near the Beijing meteorological tower using AMS to measure 14C of cedar tree rings. The results show that the mole fraction of fossil fuel CO2 at the sampling point first decreased from 22.3 ± 0.7 ppm (2007) to 21.0 ± 0.9 ppm (2008), then increased to 22.5 ± 0.9 ppm (2010), and finally decreased to 18.7 ± 0.7 ppm (2014). This trend correlates (r = 0.958, p < 0.01) well with one third (May-August) of fossil source consumption of secondary industry in Beijing during 2007–2014. It is attributed to environmental policy programs and control measures at local scope in Beijing during 2007–2014. This study suggests that 14C measurements of tree rings by AMS provide an effective method to evaluate the effect of the environmental policies.

Influence of first-order lateral reflections on the localization of virtual source reproduced by crosstalk cancellation system

This study elucidates the influence of first-order lateral reflections on the localization of virtual sources reproduced by a crosstalk cancellation system. First, an experimental study was conducted to examine how the reflections with a delay within the scope of the summing localization and the precedence effect influence the localization of virtual sources with different frequency bands and target azimuths. The results show that the localization of virtual sources is influenced due to the reflections with a delay below the low limit of the precedence effect (1 ms universally), and the influence degree is related to the frequency band and target azimuth. When the delay is further increased, the effect of reflected sound is almost negligible. Particularly, for a virtual source in the region outside the loudspeaker on the contralateral side of the reflecting surface (at -60° or -90°), the perceived azimuth under reflections with a small delay was more lateral than that without reflections. Then, simulations were conducted under the same conditions as the experiments. Based on the binaural signals modified by the inhibition function of the precedence effect, binaural cues, including interaural phase delay difference (ITDp) and interaural level difference, were calculated to explain the experimental results. Particularly, the average ITDp over frequency can qualitatively predict the localization results below 1.5 kHz from the subjective experiment in most cases.

Charging Stations Selection Using a Graph Convolutional Network from Geographic Grid

Electric vehicles (EVs) have attracted considerable attention because of their clean and high-energy efficiency. Reasonably planning a charging station network has become a vital issue for the popularization of EVs. Current research on optimizing charging station networks focuses on the role of stations in a local scope. However, spatial features between charging stations are not considered. This paper proposes a charging station selection method based on the graph convolutional network (GCN) and establishes a charging station selection method considering traffic information and investment cost. The method uses the GCN to extract charging stations. The charging demand of each candidate station is calculated through the traffic flow information to optimize the location of charging stations. Finally, the cost of the charging station network is evaluated. A case study on charging station selection shows that the method can solve the EV charging station location problem.

Components.js: Semantic dependency injection

A common practice within object-oriented software is using composition to realize complex object behavior in a reusable way. Such compositions can be managed by Dependency Injection (DI), a popular technique in which components only depend on minimal interfaces and have their concrete dependencies passed into them. Instead of requiring program code, this separation enables describing the desired instantiations in declarative configuration files, such that objects can be wired together automatically at runtime. Configurations for existing DI frameworks typically only have local semantics, which limits their usage in other contexts. Yet some cases require configurations outside of their local scope, such as for the reproducibility of experiments, static program analysis, and semantic workflows. As such, there is a need for globally interoperable, addressable, and discoverable configurations, which can be achieved by leveraging Linked Data. We created Components.js as an open-source semantic DI framework for TypeScript and JavaScript applications, providing global semantics via Linked Data-based configuration files. In this article, we report on the Components.js framework by explaining its architecture and configuration, and discuss its impact by mentioning where and how applications use it. We show that Components.js is a stable framework that has seen significant uptake during the last couple of years. We recommend it for software projects that require high flexibility, configuration without code changes, sharing configurations with others, or applying these configurations in other contexts such as experimentation or static program analysis. We anticipate that Components.js will continue driving concrete research and development projects that require high degrees of customization to facilitate experimentation and testing, including the Comunica query engine and the Community Solid Server for decentralized data publication.

ULD-Net: 3D unsupervised learning by dense similarity learning with equivariant-crop.

Although many recent deep learning methods have achieved good performance in point cloud analysis, most of them are built upon the heavy cost of manual labeling. Unsupervised representation learning methods have attracted increasing attention due to their high label efficiency. How to learn more useful representations from unlabeled 3D point clouds is still a challenging problem. Addressing this problem, we propose a novel unsupervised learning approach for point cloud analysis, named ULD-Net, consisting of an equivariant-crop (equiv-crop) module to achieve dense similarity learning. We propose dense similarity learning that maximizes consistency across two randomly transformed global-local views at both the instance level and the point level. To build feature correspondence between global and local views, an equiv-crop is proposed to transform features from the global scope to the local scope. Unlike previous methods that require complicated designs, such as negative pairs and momentum encoders, our ULD-Net benefits from the simple Siamese network that relies solely on stop-gradient operation preventing the network from collapsing. We also utilize the feature separability constraint for more representative embeddings. Experimental results show that our ULD-Net achieves the best results of context-based unsupervised methods and comparable performances to supervised models in shape classification and segmentation tasks. On the linear support vector machine classification benchmark, our ULD-Net surpasses the best context-based method spatiotemporal self-supervised representation learning (STRL) by 1.1% overall accuracy. On tasks with fine-tuning, our ULD-Net outperforms STRL under fully supervised and semisupervised settings, in particular, 0.1% accuracy gain on the ModelNet40 classification benchmark, and 0.6% medium intersection of union gain on the ShapeNet part segmentation benchmark.

AGDS: adaptive goal-directed strategy for swarm drones flying through unknown environments

This paper aims to address a challenging problem of a drone swarm for a specific mission by reaching a desired region, through an unknown environment. A bio-inspired flocking algorithm with adaptive goal-directed strategy (AGDS) is proposed and developed for the drones swarmed across unknown environments. Each drone employs a biological visual mechanism to sense obstacles in within local perceptible scopes. Task information of the destination is only given to a few specified drones (named as informed agents), rather than to all other individual drones (uninformed agents). With the proposed flocking swarm, the informed agents operate collectively with the remaining uninformed agents to achieve a common and overall mission. By virtue of numerical simulation, the AGDS and non-adaptive goal-directed strategy (non-AGDS) are both presented and evaluated. Experiments by flying six DJI Tello quadrotors indoor are conducted to validate the developed flocking algorithm. Additional validations within canyon-like complicated scenarios have also been carried out. Both simulation and experimental results demonstrate the efficiency of the proposed swarm flocking algorithm with AGDS.

D-former: a U-shaped Dilated Transformer for 3D medical image segmentation

Computer-aided medical image segmentation has been applied widely in diagnosis and treatment to obtain clinically useful information of shapes and volumes of target organs and tissues. In the past several years, convolutional neural network (CNN)-based methods (e.g., U-Net) have dominated this area, but still suffered from inadequate long-range information capturing. Hence, recent work presented computer vision Transformer variants for medical image segmentation tasks and obtained promising performances. Such Transformers modeled long-range dependency by computing pair-wise patch relations. However, they incurred prohibitive computational costs, especially on 3D medical images (e.g., CT and MRI). In this paper, we propose a new method called Dilated Transformer, which conducts self-attention alternately in local and global scopes for pair-wise patch relations capturing. Inspired by dilated convolution kernels, we conduct the global self-attention in a dilated manner, enlarging receptive fields without increasing the patches involved and thus reducing computational costs. Based on this design of Dilated Transformer, we construct a U-shaped encoder–decoder hierarchical architecture called D-Former for 3D medical image segmentation. Experiments on the Synapse and ACDC datasets show that our D-Former model, trained from scratch, outperforms various competitive CNN-based or Transformer-based segmentation models at a low computational cost without time-consuming per-training process.

Early Results of Transformation of Pharmaceutical Industry in Turkey: Scope of Localization

Early Results of Transformation of Pharmaceutical Industry in Turkey: Scope of Localization

Factors Contributing to Forest Degradation in the Mountainous Tropical Forest: A Case Study of the Mutis-Timau Forest Complex, Indonesia

ABSTRACT The Mutis-Timau Forest Complex, one of the remaining mountainous tropical forest areas in Timor Island, eastern Indonesia, has been exposed to significant deforestation and forest degradation. Numerous studies have highlighted the factors affecting deforestation. However, information about the factors influencing forest degradation at the local scope is still limited. To address this gap, we analyzed the factors contributing to forest degradation during the 1999–2017 period in the Mutis-Timau Forest Complex using a spatial statistical approach. We developed logistic regression using the forest degradation data obtained from forest canopy density time series maps as dependent variables and the spatial representation of the factors contributing to forest degradation, namely distance to the settlement, distance to the road, distance to agriculture, distance to the river, distance to the savanna, distance to the forest edge, slope, elevation, population density, and forest management unit, as independent variables. Our finding showed the increase in distance to the forest edge and elevation would decrease the likelihood of forest degradation, whereas the probability of forest degradation increases in forest located far from the river and the settlement. Such findings are important for the forest authority in prioritizing actions for combating forest degradation and designing appropriate forest-related policies and supporting data for reducing carbon emissions from forest degradation implementation or other incentive schemes in reducing forest degradation.

Trayectoria del ejercicio del derecho a la educación de los estudiantes de la Universidad Autónoma Indígena de México

The person from the beginning of his life struggles to survive, he learns from everything around him to integrate it into his environment, learning has no end, education extends all stages of their human development, however, he can't do it alone, He has to search a the individuals around him, both from his family and social context. Formal education is a human aspiration, guided and protected by agreements and conventions of global and local scope, which has become a right to education. An investigation was developed in two phases: first, a bibliographical documentary research to recover text related to the right to education and in a second phase, an online questionnaire was applied to higher education students of the Universidad Autónoma Indígena de México (UAIM) to know the experiences of the student of what he has lived in each of the stages of his school way (elementary, middle school, high school and college), their motivations to study at the UAIM and their point of view on the right to education. Subsequently, through interpretive research, explain the findings obtained contrasted with the revised documentation. Referring particularly to the right to education in the Mexican historical context, we have found various intervention topics such as: the legal framework that supports said right, educational inequalities at the levels of basic education, half education higher and higher education; indigenous education; educational inequality caused by the COVID-19 pandemic; unofficial basic education; and higher education at UAIM. The indicators of the right to education made it possible to found out the fulfillment and commitment of the UAIM. and the confinement mechanisms due to COVID-19 cause new forms of inequality to emerge.

Modeling and optimization of concurrent execution process of coupled design-construction tasks under design-build mode

Concurrent execution of design and construction tasks is an important way to realize the integration of them in design-build (DB) mode, but it may bring about period risk due to multiple rework and frequent information transfer in local scope. To solve this problem, this study constructs a concurrent execution strategy model from the perspective of quantitative analysis with the decision goal of minimizing the total execution duration. The results show that: first, when the probability of information change between design and construction tasks decreases gradually, the optimal overlap degree increases and the optimal parallelism decreases. Second, learning effect can effectively shorten the minimum duration corresponding to the optimal overlap degree under any degree of parallelism. Thirdly, with the decrease of the probability of information change between design and construction tasks and the strengthening of learning effect, the shortening rate of the actual execution duration corresponding to the optimal parallelism will be gradually greater than the increasing rate of the actual execution duration. The findings of the study provide suggestions for exploring the mechanism of concurrent execution in DB mode, and also provide countermeasures for better realization of integration of design and construction in DB mode.

Containment Measures and Spillover Effects in Covid Times: A Global Study on Business Confidence

In this paper we take a global perspective to assess the impact of the exogenous COVID pandemic shock on business confidence. Through a quantitative analysis of 43 emerging and advanced economies over the period from January 2018 to December 2020, we provide a novel investigation of a unique worldwide event, in contrast to the most frequent exogenous shocks, which typically have a more limited local or regional scope. We proxy business expectations with the business confidence indicator or BCI. First, we find that the containment measures for the COVID pandemic have negatively affected business confidence, with the compulsory policies having a greater negative effect on BCI than the voluntary ones. Second, we find positive spillover effects on the local BCIs from the containment measures implemented in neighboring countries. This suggests that business people are not against compulsory measures per se, but rather that they are less inclined to assume the costs of these. Third, we find that while the severity of the containment measures has been greater in emerging countries, the negative impact on BCI of these containment measures has been larger in advanced economies. Keywords: Business confidence, containment measures, spillover effects

XAI for intrusion detection system: comparing explanations based on global and local scope

XAI for intrusion detection system: comparing explanations based on global and local scope

Detection and mapping of May 2021 flood in Beaufort, Sabah using Sentinel-1 SAR and Sentinel-2 multispectral in Google Earth Engine

Recurring floods severely impacted the livelihood and socio-economic. It causes disruption of clean water, electricity, communications, properties damages and sometimes loss of life. Information on flooded areas is crucial for effective emergency responses support. In this study we used Sentinel 1 (S-1) C-band and Sentinel 2 (S-2) Multispectral satellite imageries where wider area covered in 12 days repeat satellite pass. The flood event on the 26 May 2021 was identified and we retrieved the S-1 GRD SAR imagery and S-2 level-2A BOA in GEE environment. We analysed the S-1 VV, VH, VV/VH imagery by pixels clustering using object based SNIC classification and Machine Learning (ML) algorithm for extraction of waterbody. Meanwhile for the S-2 we used MNDWI and extracted the waterbody area using thresholding value. We obtained the final flooded area of S-1 and S-2 by subtraction with permanent waterbody. The S-2 flood estimation results were better than S-1. However, S-2 limited to cloud free and less cloudy coverage while S-1 lacking of ability to identify flood in detailed was influenced by slope shadow area. This study provides the basis of detection and mapping floods using S-1 and S-2 imageries through Machine Learning techniques in GEE for local scope of Sabah, Borneo region and Malaysia.

Minahasa-Kristen Dalam Gejolak Eksistensi Antara Budaya Dan Agama

This reasearch attempts to seek the possibilities and obstracles that impact Minahasan-Christian’s community in the fluctuation among culture and religion which involve in struggle and controversy in public. It leads to the unconfidence of Minahasan-Christian to express their social identity. Confirming its identity in local scope is essential and shares massive debates, moreover, in NKRI form, it faces challeges. Therefore, this essay employs qualitative approach intends to display on how culture and religion involves in intense discussion that relates to the existence of MInahasan-Christian in the state. In conclusion, the tension expresses possibilities and obstacles, the culture and religion should share common instead of involve in a battle. The Minahasan-Christian should engage deeply with religious teaching and preserve indigenous tradition and culture.

Developing a model for health determinants research within local government: lessons from a large, urban local authority.

Background: Socio-economic, cultural and environmental conditions strongly affect health across the life course. Local government plays a key role in influencing these wider determinants of health and levels of inequality within their communities. However, they lack the research infrastructure and culture that would enable them to develop an evidence-based approach to tackling the complex drivers of those conditions. Methods: We undertook a scoping project using some research methods and some descriptive summaries to explore the potential for, and what would be needed to develop a local authority research system for the City of Bradford, UK. This included identifying the current research landscape and any barriers and enablers to research activity within the local authority using qualitative individual and focus group interviews, a rapid review of existing local research system models, scoping and description of the use of evidence in decision making and training opportunities and existing support for local government research. Results: We identified four key themes important to developing and sustaining a research system: leadership, resource and capacity, culture, partnerships. Some use of research in decision making was evident but research training opportunities within the local authority were limited. Health research funders are slowly adapting to the local government environment, but this remains limited and more work is needed to shift the centre of gravity towards public health, local government and the community more generally. Conclusions: We propose a model for a local authority research system that can guide the development of an exemplar whole system research framework that includes research infrastructure, data sharing, research training and skills, and co-production with local partners, to choose, use, generate, and deliver research in local government.

Developing a model for health determinants research within local government: lessons from a large, urban local authority

Background: Socio-economic, cultural and environmental conditions strongly affect health across the life course. Local government plays a key role in influencing these wider determinants of health and levels of inequality within their communities. However, they lack the research infrastructure and culture that would enable them to develop an evidence-based approach to tackling the complex drivers of those conditions. Methods: We undertook a scoping project using some research methods and some descriptive summaries to explore the potential for, and what would be needed to develop a local authority research system for the City of Bradford, UK. This included identifying the current research landscape and any barriers and enablers to research activity within the local authority using qualitative individual and focus group interviews, a rapid review of existing local research system models, scoping and description of the use of evidence in decision making and training opportunities and existing support for local government research. Results: We identified four key themes important to developing and sustaining a research system: leadership, resource and capacity, culture, partnerships. Some use of research in decision making was evident but research training opportunities within the local authority were limited. Health research funders are slowly adapting to the local government environment, but this remains limited and more work is needed to shift the centre of gravity towards public health, local government and the community more generally. Conclusions: We propose a model for a local authority research system that can guide the development of an exemplar whole system research framework that includes research infrastructure, data sharing, research training and skills, and co-production with local partners, to choose, use, generate, and deliver research in local government.

자치경찰제의 성공적인 시행을 위한초기 운영의 쟁점 분석

With the introduction of the autonomous police system by the Moon Jae-in government, local governments are expected to provide local-specific public security services that reflect the needs of residents and comprehensive public security services through linking local administration and public security administration. The purpose of this study was to analyze the issues of the initial operation of the autonomous police system and to suggest improvement plans for the local police committee, which is a key organization for the successful implementation of the autonomous police system. To this end, a survey was conducted from July 1st to July 15th, 2021 on the organization and operation of the local police committee, personnel rights, budgetary rights, scope and linkage of the affairs targeting 18 local police committees. Based on the survey results, policy recommendations for the successful implementation of the autonomous police system were presented. First, it may be considered to allocate the recommended number of committee members to each recommendation institution in order to secure diversity of the local police committees. Second, the National Police Agency should cooperatively share personnel and budget information with the local police committees. Third, the independent personnel rights management system within the local police committee should be prospectively reviewed. Fourth, it is necessary to come up with various financial support measures that allow local governments to independently operate them. Fifth, it is necessary to continuously discuss improvement plans for local police affairs by reflecting the opinions and experiences of national police officers who perform local police affairs in the field. Sixth, for the personnel and budgetary authority of the local police committee to be effectively exercised, the facilities･manpower･budget of the national police must be completely transferred to the local governments.

Troops or Tanks? Rethinking COIN mechanization and force employment

ABSTRACT Are some conventional military units better equipped to conduct counterinsurgency (COIN) operations than others? COIN theorists and practitioners disagree about mechanization’s impact on COIN effectiveness. We suggest that mechanization is not the critical determinant of COIN effectiveness. Rather, mechanization’s effects vary based on a unit’s force employment approach within local scope conditions. To test our hypothesis, we explore how mechanization impacts COIN operations in Ramadi and Basra and find that force employment is the critical determinant in COIN effectiveness. We introduce an adaptive force employment hypothesis that outlines how mechanization includes costs to exposing dismounted troops and benefits in information-sharing, conditional on local insurgent strength. This hypothesis suggests that mechanized forces provide significant benefits to counterinsurgents when clearing high strength insurgents, outweighing benefits from dismounted civilian interaction. Following successful clearance operations when government control is increased and insurgent strength is reduced, requirements for armored protection decrease while civilian tips become increasingly important to target remaining insurgents. In the hold and build stages, counterinsurgents can increasingly rely on dismounted troops.