bluuebunny/crossref_metadata_2025 huggingface dataset:
Radiation background estimation for the GE11 Triple-GEM detectors in the CMS endcap
Authors: Bouhali O , Bhatnagar V , Castaneda A , Chauhan S S , Kamon T , Kang Y , Kumar S , Virdi A K
Published: December 2022

The Compact Muon Solenoid (CMS) is a general-purpose particle detector at the Large Hadron Collider (LHC) designed to study a wide range of particles produced in high energy collisions. The interaction of the beams with the pipe, shielding and detector supporting materials can produce neutrons, photons, electrons and positrons, forming a common background radiation field for CMS detector. A Monte-Carlo simulation is used to predict the background rate for a newly installed detector. In the forward region, the upgrade includes Gas Electron Multiplier (GEM) detectors called GE1/1. In this study, an estimate of the GE1/1 detector response to the background radiation is presented. The flux of background radiation is predicted using the FLUKA framework and the response of the detector is predicted using the GEANT4 framework. A comparison with actual GEM slice data is used as validation.
Characterization of passive CMOS sensors with RD53A pixel modules
Authors: Glessgen Franz , Backhaus Malte , Canelli Florencia , Dieter Yannick Manuel , Dingfelder Jochen Christian , Hemperek Tomasz , Huegging Fabian , Jofrehei Arash , Jin Weijie , Kilminster Ben , Macchiolo Anna , Muenstermann Daniel , Pohl David-Leon , Ristic Branislav , Wallny Rainer , Wang Tianyang , Wermes Norbert , Wolf Pascal
Published: December 2022

Both the current upgrades to accelerator-based HEP detectors (e.g. ATLAS, CMS) and also future projects (e.g. CEPC, FCC) feature large-area silicon-based tracking detectors. We are investigating the feasibility of using CMOS foundries to fabricate silicon radiation detectors, both for pixels and for large-area strip sensors. A successful proof of concept would open the market potential of CMOS foundries to the HEP community, which would be most beneficial in terms of availability, throughput and cost. In addition, the availability of multi-layer routing of signals will provide the freedom to optimize the sensor geometry and the performance, with biasing structures implemented in poly-silicon layers and MIM-capacitors allowing for AC coupling. A prototyping production of strip test structures and RD53A compatible pixel sensors was recently completed at LFoundry in a 150nm CMOS process. This presentation will focus on the characterization of pixel modules, studying the performance in terms of charge collection, position resolution and hit efficiency with measurements performed in the laboratory and with beam tests. We will report on the investigation of RD53A modules with 25x100 μm2 cell geometry.
Characterization of pixelated silicon detectors for daily quality assurance measurements in proton therapy
Authors: Schilling Isabelle , Bäcker Claus Maximillian , Bäumer Christian , Behrends Carina , Kröninger Kevin , Timmermann Beate , Weingarten Jens
Published: December 2022

The advanced imaging and delivery techniques in proton therapy allow conformal high-dose irradiation of the target volume with high accuracy using pencil beam scanning or beam shaping apertures. These irradiation methods increasingly include small radiation fields with large dose gradients, which require detector systems with high spatial resolution for quality assurance. In addition the measurement of all success parameters for daily quality assurance with only one proton field and one simple detector system would save a lot of time in clinical usage. Based on their good spatial resolution and high rate compatibility, pixelated silicon detectors could meet the new requirements. To assess their applicability in proton therapy, ATLAS pixelated silicon detectors are used to measure the lateral beam profile with high spatial resolution. Furthermore, a dose dependent detector calibration is presented to allow the measurement of the requested output constancy. A strategy to verify the proton energy during the daily quality assurance is under study. Promising results from proof-of-principle measurements at the West German Proton Therapy Centre in Essen, Germany, have been obtained.
Fragmentation Measurements in Particle Therapy: status and plans of the FOOT experiment
Authors: Ruzza Benedetto Di
Published: December 2022

Due to the advantageous characteristics of the charged particle’s energy deposition in matter, protons or ion beams are used in hadrontherapy to treat deep-seated solid tumors. Using these beams, the maximum of the dose is released to the tumor tissues at the end of the beam range. In this process, nevertheless, fragmentation of both projectile and target nuclei can occur in the nuclear interactions of the beam with the patient tissues and, as showed in recent studies, needs to be carefully taken into account in the delivered dose calculation. Nuclear fragmentation is also extremely relevant for space radioprotection studies, when the exposition of sensors and human crews to solar and galactic particle flows have to be minimized. The goal of the FOOT (FragmentatiOn Of Target) experiment is to estimate target and beam fragmentation performing cross section measurements (with respect to the kinetic energy and direction) with a precision of the order of 5% in the energy range of interest for hadrontherapy (protons in the energy range of 70-230 MeV or ion beams with energy up to 400 MeV/u) and space radioprotection (ion beams with energy up to 800 MeV/u) in order to provide new data for medical physicists, radio-biologists and to improve not only the new generation of oncological Treatment Planning Systems but also the design of shielding elements for the future long duration space missions eventually with human crews. In this paper will be presented the project, the present status of the different detector sub-systems construction and the data-taking plans.
A novel cylindrical detector for borehole muon radiography.
Authors: Cimmino Luigi , Ambrosino Fabio , D’Errico Mariaelena , Masone Vincenzo , Roscilli Lorenzo , Saracino Giulio
Published: December 2022

Muography (or muon radiography) is a recent imaging methodology that uses cosmic muons to investigate the mass distribution in large objects, such as volcanoes or mines, or to detect the presence of cavities in the subsoil or within buildings such as the pyramids. In recent years detectors with different geometries, sizes and technologies have been developed. In particular detectors with reduced size, that can be inserted in a borehole, are of particular interest in geophysical applications. We have developed, and patented, an innovative detector for well applications consisting of plastic scintillators with arc shape and rectangular section bars. Good spatial resolution was achieved with a reasonable number of channels. Detailed simulations based on Monte Carlo methods have shown excellent performance in cavity detection. Preliminary results of a prototype show good performance in terms of the number of photoelectrons produced by cosmic muons and track reconstruction.
A low insertion loss RF switch using a 130 nm SOI process
Authors: Zhang Caozheng , Zheng Libo , Yu Jinghao , Ye Pengfei , Liu Yi
Published: December 2022

To realize a better radio frequency (RF) communication system, the RF switch, as an important module, is used to cooperate with other devices to form an RF front end for optimizing communication performances. This paper designs a single-pole multi-throw (SPMT) RF switch, which exhibits a low insertion loss of 0.57 dB at 1.9 GHz under the voltage of 2.2 to 4.7 V. The RF switch is fabricated using a 130 nm silicon-on-insulator (SOI) complementary metal-oxide-semiconductor (CMOS) process. It can be used in multi-frequency and multi-mode applications such as 4G/5G. The proposed RF switch has competitive performance and can be combined with other modules to achieve better wireless communication and promote the development of ultra-wideband and multi-mode wireless communication systems.
Cascade detector for oriented airplane detection in remote sensing images
Authors: Gao Yangte , Gao Jianfeng , Nie Xiaoyu , Han Jianhong , Bi Fukun
Published: December 2022

Although the current target detection field has made remarkable progress, airplane detection in large field optical remote sensing images is still challenging. To this end, we innovatively propose an oriented airplane detector to solve the two difficulties. In response to the slow detection speed of optical remote sensing images, we use an anchor-free structure to avoid complex calculations related to anchor boxes and significantly reduce the use of memory and time in computing. Since the airplane target are distributed with arbitrary orientation, the accuracy of the general detector is reduced. We alleviate this problem by replacing the standard convolution with asymmetric convolution. In addition, we use a cascade structure in prediction, which can further improve accuracy with almost no increase in the calculation. Abundant experiments and detection results show our methods effectiveness. In airplane detection task, our proposed model achieves 88.14 mAP on DOTA-v1.0 and 90.46 on UCAS- AOD.
Performance analysis of RSMA-based cognitive networks with random distribution of eavesdroppers
Authors: Li Qinying , Tian Yue , Wang Xianling , Zhu Chen
Published: December 2022

Due to the pace of various emerging wireless communication technologies and new application services innovation, network information security, and capacity requirements have become a hot issue of concern. In order to improve network security performance and transmission capacity, researchers have proposed cognitive wireless network and physical layer security as potential solution. Most of the current research work focuses on the assumption that the location of the eavesdropping node is fixed. Starting from the physical layer security and based on the random geometry theory, this paper studies the security performance of cognitive wireless networks under the scenario of random distribution of eavesdropping nodes based on rate splitting multiple access, proposes a system model, and deduces the closed form solutions of connection interruption probability and security interruption probability of secondary users in the cognitive wireless network. The results show that the security interruption probability is related to the density and security of eavesdropping nodes. Finally, the correctness of the formulas is verified by simulation analysis.
The Research Process, Application, and the Future Development of Pulse-Position Modulation
Authors: Tang Wenzhe , Wang Sirui , Xu Yimeng , Yu Zhouyue
Published: December 2022

With the development of digital communication, pulse-position modulation technology has become an important technology in wireless communication due to its efficient bandwidth utilization and sensitive detection efficiency. This study aims to provide some basic information on PPM. In the trend of rapid development of PPM, researchers have found that PPM plays an irreplaceable role, no matter in daily life or scientific research and development. To arouse people’s interest in PPM research, more people are attracted to participate in the study. It began with an introduction to the development process of PPM, and briefly lists the advantages and disadvantages of PPM and the development trend to help readers quickly understand its background knowledge of it. The second part started to work on different modulations based on the previous research on PPM. Various modulation methods are related to each other, and the proposal of each modulation method is based on the shortcomings of existing modulation methods. This study discusses how PPM is used in experiments. Moreover, each experiment’s measurement values and corresponding advantages are analyzed through charts, like Bit Error Rate (BER) and Signal to Noise Ratio (SNR) conducted in each experiment. This study draws a conclusion by discussing the application and the broad horizon for future developments mainly used in communication engineering led by light communication. However, in recent years, PPM has been introduced in many other fields, such as detection and mechanic.
Time Domain Measurement System for Electric Field of Radio Interference Generated by Corona Discharge
Authors: Huang Huan , Hong Xiao Yan , Li Bin , Liu Lei , Yu Siwu , Mao Xianyin , Peng Chi , Du Hao
Published: December 2022

When corona discharge occurs around the wire, it will be accompanied by the movement of space charge, and then generate corona discharge effects such as radio interference and audible noise. In order to reveal time domain characteristics of the electric field of radio interference generated by corona discharge, a set of time domain measurement systems for radio interference, including oscillator antenna, sampling resistance, and data acquisition system, is developed. Based on the characteristics of corona-generated radio interference and antenna theory, the size of the antenna, the equivalent circuit model, and the selection of sampling resistance are discussed carefully to satisfy the requirements for the measurement of corona-generated radio interference. To verify the effectiveness of the measurement system, an experimental platform with a single corona source is built, and the time domain waveforms of corona current and radio interference are simultaneously measured. Besides, the attenuation law of radio interference is obtained by using a time domain measurement system. The results show that the amplitude of the measured voltage of radio interference decreases exponentially with distance. The time domain measurement system presented in this paper can lay a solid foundation for the time-domain evaluation of the radio interference level generated by corona discharge.
Separation Structure Speech Separation Model Fusing Conformer and NBC
Authors: Qiang Zhou , Xiangyang Cheng , Tian Ding
Published: December 2022

The quality of speech separation affects the entire speech technology ecosystem. Aiming at the problems of low utilization of local feature information, insufficient convergence speed, too many calculation parameters and too long calculation time in blind source separation in view of Transformer dual-path cyclic neural network, a blind source separation model based on fusion of Conformer and NBC (Narrow-band Conformer NBC) is proposed. First, for the problems of low utilization of speech local feature information and insufficient convergence speed, the Transformer block is replaced by the Conformer in the block of the dual-path recurrent network. It can improve the utilization and convergence speed of local features. Secondly, the NBC block is used to replace the Transformer block in the inter-block loop. The NBC block simplifies the calculation of vector similarity and vector aggregation, reduces lots of parameters and calculation cost, and reduces the computational complexity of the model. In the experiments on WSJ0-2mix[7] dataset and WHAM[13] dataset, contrast with other models, the convergence speech is faster and the blind source separation effect is better.
A New Bandwidth Reconfigurable Antenna
Authors: Zang Yiping , Li Weishan , Ma Jie , Yang Jiangjun , Xiong Wenjun
Published: December 2022

A bandwidth reconfigurable monopole antenna with disc-shaped is presented in this article. This presented antenna is integrated with a disc-shaped monopole antenna and a cascaded bandpass structure, and four different bandwidth states can be adjusted by the bandpass structure. By switching the diode’s on-off state integrated with the bandpass structure, this presented monopole antenna can switch quickly among four different bandwidth states whose fractional bandwidth is range from 16% to 55% centered at around 1.8 GHz. The experiment also shows good and stable electromagnetic properties of this presented antenna in different working bandwidths.
Slice resource allocation based on Comprehensive Utility
Authors: Shen Zhentao
Published: December 2022

Due to the increasing demand for quality of service in power communication services quality.smart grids require network slicing technology to provide differentiated services. However, the existing algorithms lack in-depth research on the comprehensive service, particularly in power services and system accessibility. To address these issues, this paper proposes an integrated utility-based resource allocation scheme. Under the premise of meeting user needs, the priority of slices is comprehensively considered to optimize system utility and access rate. By comparing to local optimization algorithms, the results show that the new algorithm can maximize the overall user utility of the system and optimize the slice access rate simultaneously.
Multi-path QoE Routing Algorithm for wireless mesh network Based on Q-Learning
Authors: Jie Tang , Yan Xu , Yali Che , Xuhui Liu
Published: December 2022

To address the problem of poor quality of experience(QoE) of wireless mesh network users in the current video streaming service, a wireless mesh network multi-path QoE algorithm based on Q-learning(QLMPQ) is proposed. The selection strategy is used to find the optimal master-slave path to improve the reliability of data transmission. Virtual simulation experiments are carried out under the conditions of different concurrent flows to verify the feasibility and accuracy of the algorithm. The experimental results show that compared with Q-Routing, QLAODV, and AODV algorithms, this algorithm can significantly improve the performance of wireless mesh networks.
Energy management optimization strategy of virtual power plant based on deep reinforcement learning
Authors: Ji Xiu , Li Cong , Wang Jiarui , Wang Yan , Hou Faming , Guo Shuanghao
Published: December 2022

The large-scale integration of new energy into the grid in the future will have an impact on the dispatching operation of the power grid. As a potential controllable resource, a controllable load is gradually being tapped, and the application of virtual power plants and artificial intelligence provides a solution. It can effectively perform resource aggregation scheduling and energy management optimization. Therefore, this paper proposes an energy management optimization strategy for virtual power plants based on deep reinforcement learning. The strategy first establishes three types of controllable load models and energy storage models and then combines the deep reinforcement learning Double-DQN algorithm with the internal model of the virtual power plant to construct an environment, action, and reward functions, and finally, conducts simulation and result analysis. The calculation example shows that this strategy can realize the optimal scheduling of the virtual power plant and improve the demand side response, and the experimental model in this paper has a higher yield curve compared with other learning algorithms, which verifies the effectiveness and rationality of the strategy. It is significant to inspire and promote the high-quality development of a green energy economy with new ideas of energy management models.
Forest fire detection algorithm based on Improved YOLOv5
Authors: Yu Shuailong , Sun Chunxia , Wang Xiaopeng , Li Baomin
Published: December 2022

Aiming at the problem that the existing target detection algorithms are difficult to detect sudden fire with high precision in real-time in the complex forest environment, an improved forest fire detection algorithm based on YOLOv5 is proposed by us. Firstly, the k-means clustering algorithm based on 1-IoU distance is used to preprocess the forest fire data set. Secondly, the Transformer attention mechanism is added to the model backbone network to improve the detection accuracy, and the regression box loss function GIoU-NMS is changed to EIoU-NMS. Finally, the network structure is changed, and the deep separable convolution is added to reduce the parameters. Comparing the algorithm we propose with the original YOLOv5 algorithm and the mainstream target algorithm, the experimental results show that the algorithm can have more efficient and high-precision detection results in fewer samples and complex environments and prove its feasibility and effectiveness.
Optimization of Interface layout of electrical equipment using Heuristic Algorithm
Authors: Yu Jianqin , Lu Guoying , Zhang Ting , Cheng Tianyu
Published: December 2022

Facility Layout Problem(FLP) is a major matter in the process of human-machine interface(HMI) design, which has a direct impact on how well operators perform their duties. Electrical equipment, which controls the power system, has a direct impact on how safely the system operates. The current optimization techniques prioritize improving the equipment’s performance characteristics at the expense of user aspects. This work aims to apply heuristic algorithms that introduce cognitive psychology and adopt the layout principle as a constraint condition to solve this problem. The improved analytic hierarchy process (AHP) is used to conduct the optimal design practice of the HMI of electrical switching equipment, develop the optimization mathematical model, and quantify the subjective and objective layout principles. Comparing the optimization results reveals that the genetic algorithm’s optimization impact is superior and the ant colony algorithm’s iterative pace is faster. The HMI’s optimized design based on a heuristic algorithm can improve the usability of interface operation, and has a certain reference for the optimization design of HMI of electrical equipment.
Design and Implementation of Microservice Secure Communication Framework based on National Secret Algorithm and Dynamic Key
Authors: Ding Hongyu , Meng Qingqiang , Sun Lihua , Liu Ming , Chen Xu , Liu Chang , Leng Duanjie
Published: December 2022

With the acceleration of the pace of the national electricity reform and the wide application of the micro-service architecture, various network threats in the field of information security are increasing, and the power industry has become the “hardest hit area” for cyber attacks. In order to improve the safe implementation of the National Power Grid’s electronic projects and effectively resist the “malicious exploration” of various network threats, this paper proposes a microservice secure communication framework based on the combination of dynamic keys and national secret algorithms for a high concurrency web system based on microservices. The framework uses the national secret SM2 algorithm to generate session-level paired keys and then generates a digital signature through the SM3 algorithm, which effectively guarantees the integrity of the communication. Finally, it extracts the encryption key and encryption offset from the paired keys and uses the SM4 algorithm to encrypt the original request text payload. Timestamps and random numbers are added to each request to prevent tampering and replay attacks effectively. The system framework has been practically applied on the State Grid’s digital capability open platform, which ensures the security of micro-service communication and the safety and efficient transmission of data.
Research on unmanned driving path planning algorithm based on Robot Operating System
Authors: Zheng Xinwang , Liang Chen , Huang Yixin , Li Bing , Yang Guangsong
Published: December 2022

With the rapid development of communication and computer technology In recent years, automatic driving has attracted much attention. By using path planning technology, the AV can automatically find an optimal path to the destination while avoiding obstacles, so as to reduce transportation costs, improve driving efficiency, improve customer satisfaction and reduce traffic accidents, which is of important meaning to spurring the further development of intelligent transportation. Two algorithms of driverless path planning are studied, one is and the A* algorithm is for global path planning, another is the timed elastic band (TEB) algorithm for local path planning. Firstly, the direction of the exploration domain of the traditional A* algorithm is improved by adding an exploration domain to smooth the planned path.t. Secondly, some parameters of the TEB algorithm are adjusted to make the car stay away from obstacles and run more safely. Finally, on the mobile robot platform equipped with ROS operating system, the hybrid path planning is realized by combining these two algorithms, and the simulation experiment under a real environment is carried out. Experiment results show that the proposed method can autonomously plan path and navigation smoothly.
Preparation of nano-magnesium oxide in ionic liquid by microwave irradiation
Authors: Chen Hukui , He Zhijun , Mu Xiaxia , Jiang Yu , Zhao Weixing
Published: December 2022

Nano-MgO was prepared by microwave irradiated ionic liquid method. The influence of the volume of ionic liquid ([BMIM]Ac), microwave radiation power and microwave radiation temperature on the morphology of nano-MgO were investigated. The prepared nano-MgO was characterized by TG, XRD, EDS and SEM. The results showed that the nano-MgO prepared by this method has a short rod-like shape, uniform particle size, and good dispersibility. The average diameter of the prepared rod-shaped nano-MgO was 25 nm, the length was 67 nm, and the specific surface area was 48.27 m2/g. During the preparation process with microwave radiation, [BMIM]Ac is a good dispersant, which effectively reduces the agglomeration of nano-MgO and is easy to remove.
Effect of Au Coating on the Electrolytic Migration of Ag Bonding Wires for Electronic Packaging Applications
Authors: Xiao Yuchen , Tang Huiyi , Zhang Hehe , Sun Ling , Xie Yong , Wu Baoan , Xie Weidong , Cai Xinnan , Li Feng
Published: December 2022

Ag-based bonding wires were generally accepted as the most promising material to substitute for Au bonding wires, however, the electrolytic migration concern of Ag limited its industrial application in electronic packaging, especially in ultra-fine pitch wire bonding. In the present study, the electrolytic migration behaviors of Au-coated Ag bonding wires and Ag wires were studied through water drop tests. The dendrites’ growth and morphology evolution were investigated by in-situ optical microscope observation and the microstructure of the cathodic and anodic bonding wires were characterized by scanning electron microscope test. It could be seen that when comparing Au-coated Ag bonding wires with Ag wires, the dendrite assembled by a large number of Ag nanoparticles grew much more slowly from cathode to anode. The Ag2O particle layer on the anodic wires was thinner, the dendrite contact time was delayed and the current densities at the dendrite contact time were much smaller when the Au coating layer exist. The above results show that Au coating could act as a barrier to inhibit electrolytic migration, which has contributed to electronic packaging applications of Au-coated Ag bonding wires.
Comparison of two different constitutive equations for hot deformation of AZ91 magnesium alloy
Authors: He Zhongtao , Xu Chun
Published: December 2022

AZ91 magnesium alloy is usually processed by casting because of its high strength and poor plasticity. Therefore, it is difficult to adopt the conventional rolling method for processing. In order to improve its processing plasticity, the content of some alloying elements is changed based on their conventional composition. On this basis, this project researches its thermal processing behavior and establishes relevant models. Gleeble-1500D thermal simulator was used for operation, the experimental conditions were 300~400°C and the strain rate was 0.01~10°C/s, and compression capacity of 70%, self-developed hot compression simulation experiment of AZ91 magnesium alloy was carried out. The research results proved that the magnesium alloy is sensitive to strain rate. For the sake of better fit true stress-strain curve of AZ91 magnesium material, modified Arrhenius equation and modified peak stress equation was used for prediction. The results show that the modified peak stress equation fits well when the strain rate is low. However, the fitting effect is poor when strain rate is 10°C/s.
Influence of Co on the microstructure and mechanical properties of M42 high-speed steel
Authors: Wang Shunduo , Chen Yunbo , Pan Peng , Lin Zhenghua , Zuo Lingli
Published: December 2022

M42 high-speed steel (W2Mo9Cr4VCo8) is a kind of high cobalt super-hard high-speed steel (HSS), which is extensively used in various cutting tool materials, but because of the high price of cobalt, high cobalt HSS does not bring good economic benefits. In this thesis, the consequences of various cobalt contents (0.0%, 2.0%, 4.0%, 6.0%, 8.0%) on the organization and mechanical properties of M42 HSS have been studied. The microstructure of HSS was analyzed by OM, SEM, and EDS, and the Rockwell hardness test, impact toughness test, and flexural strength test were conducted. The results show that cobalt helps to increase the nucleation rate of eutectic carbide precipitation and hinder its growth during tempering of HSS and refine the size of eutectic carbide, and the tempering hardness can be maintained above 68 HRC when the cobalt content is 4%-8%; however, cobalt also significantly reduces the impact toughness of HSS, and each 2% increase in cobalt impact toughness decreases about 4 J·cm−2. From the point of view of cobalt savings, cobalt content of 4%, high-speed steel tempering hardness, impact toughness, and other mechanical properties can be at a higher level.
Preparation and properties of low odor UV cured coating
Authors: Liu Xuewen , Zhang Yingqiang
Published: December 2022

Firstly, the urethane acrylate synthesized by IPDI and HEA was reacted with antioxidant 1010 in different proportions to prepare four kinds of macromolecular low odor monomers. Then a series of macromonomers and a macromolecular photo initiator, odorless reactive diluent, and odorless auxiliary were used to prepare a new odorless photocurable coating. The chemical structure of the macromonomer was characterized by Fourier transform infrared (FT-IR). The odor and properties of the coating film were tested. The results show that the macromolecular structure of the material can reduce the odor in the process of preparation and use and improve the properties of the material to a certain extent.
Changes to population-based emergence of climate change from CMIP5 to CMIP6
Authors: Douglas Hunter C , Harrington Luke J , Joshi Manoj , Hawkins Ed , Revell Laura E , Frame David J
Published: December 2022

The Coupled Model Intercomparison Project Phase 6 (CMIP6) model ensemble projects climate change emerging soonest and most strongly at low latitudes, regardless of the emissions pathway taken. In terms of signal-to-noise (S/N) ratios of average annual temperatures, these models project earlier and stronger emergence under the Shared Socio-economic Pathways than the previous generation did under corresponding Representative Concentration Pathways. Spatial patterns of emergence also change between generations of models; under a high emissions scenario, mid-century S/N is lower than previous studies indicated in Central Africa, South Asia, and parts of South America, West Africa, East Asia, and Western Europe, but higher in most other populated areas. We show that these global and regional changes are caused by a combination of higher effective climate sensitivity in the CMIP6 ensemble, as well as changes to emissions pathways, component-wise effective radiative forcing, and region-scale climate responses between model generations. We also present the first population-weighted calculation of climate change emergence for the CMIP6 ensemble, quantifying the number of people exposed to increasing degrees of abnormal temperatures now and into the future. Our results confirm the expected inequity of climate change-related impacts in the decades between now and the 2050 target for net-zero emissions held by many countries. These findings underscore the importance of concurrent investments in both mitigation and adaptation.
Corrigendum: Emergence of order in random languages (2019 J. Phys. A: Math. Theor.
52 504001)
Authors: De Giuli Eric
Published: December 2022

De Giuli (2019 J. Phys. A: Math. Theor.
52 504001) proposed a diagrammatic formulation of the random language model (RLM); explained why the model is dominated by saddle-points; and sought the solution to the disorder-averaged model by comparison to a simpler, solvable model. We discuss a hidden assumption of the latter analysis in De Giuli (2019 J. Phys. A: Math. Theor.
52 504001) that was neither explained nor motivated: the analytical solution to the Gaussian model, and its extension to the RLM, are predicated on a ‘downwards’ approximation that neglects information flow from the leaves to the root of derivation trees.
The social and environmental impact of building integrated photovoltaics technology
Authors: Pavlakis Sotiris , Teo Pauline , Jayasuriya Sajani
Published: December 2022

Various sustainable energy technologies are evolving around the world to reduce the carbon footprints in buildings. Building integrated photovoltaics (BIPV) is one of the emerging sustainable technologies and it refers to a technology where the elements of the building envelope such as façade and roof are replaced with solar cells. However, the adoptability of BIPV technology in buildings is limited as its costs and benefits are unknown to the public. This study aims to review the BIPV literature qualitatively, to explore the beneficial-related and cost-related factors of adopting BIPV technology. A thematic analysis was undertaken among journal papers published between 2011 to 2019 that focused their investigation on integrated solar renewable systems. The identified cost and benefit-related factors were classified into environmental, health, design, and social themes. It is recommended that further research can be undertaken to explore the importance of cost and beneficial factors identified in this study quantitatively. Finally, these factors will assist in quantitatively measuring the societal impacts of BIPV technology.
Shoreline Recognition Using Machine Learning Techniques
Authors: Zhong Yunshun , El-Diraby Tamer
Published: December 2022

Coastal areas have emerged to be the most significant and dynamic regions worldwide. Therefore, automating shoreline recognition will aid non-profit conservation authorities to reduce public budget expenditures, relieve erosion damage, and increase the climate resilience of the natural environment. In this paper, advanced ML boosting algorithms including XGBoost, and LGBM are firstly applied into shoreline recognition with aerial images (of Lake Ontario in this study). This paper first discussed the significance and a literature review of recent progress in shoreline detection. Then, this paper adopted semantic segmentation instead of detecting shoreline directly, which enables the (Machine Learning) ML model to achieve relatively high accuracy with a small amount of data. 5 high-resolution images are used for training the model in which shorelines are detected. The work was carried out in four steps: 1) labeling the contents of shoreline images as areas of water and banks; 2) training ML algorithms; 3) using the trained algorithms to classify the image content as either water or land objects; 4) post-processing by de-noising image pixels (applying a Fourier transform algorithm) to obtain a defined shoreline. The averaged training time per image for Random Forest, XGBoost, and LGBM algorithms are 195.2 sec, 71.0 sec, and 8.6 sec, respectively. The averaged accuracy is 95.6%, 96.0%, and 94.8%, respectively; the XGBoost algorithm has slightly higher accuracy, while LGBM has a significantly shorter runtime. Cross-validation of the LGBM algorithm reduced the training time by around 23% (7.0 sec) and increased the accuracy by only 1.1% (to 95.9%).
A review of sustainable technologies for energy efficient upgrade of existing buildings and systems
Authors: Dadzie John , Pratt Isaac , Frimpong-Asante James
Published: December 2022

Sustainable upgrades include actual case studies with before and after retrofit results, case studies with simulated results and use of computer systems to predict energy savings based on exiting building parameters. In the latter no actual renovations are done thus technologies and systems are assumed. The review used real cases including simulations and adopts a mix of systematic and content analysis approaches. Thus, over 288 articles were gathered from all the major scientific journals using Scopus, ProQuest, Web of Science etc. The sample was reduced to 230 articles based on the search themes; thereafter, a detailed focus on the methods used provided basis to trim the articles to 47. Sustainable technologies identified cover those installed to the external façade, indoor areas, air filtration, insulation systems, building elements, heating, ventilation and cooling (HVAC) systems, sensors, lighting, hot and cold water systems such as boilers, chillers, pumps, motors and renewable energy technologies. The results show sustainable technologies have been used to improve various existing buildings. Also, the results indicate high rate of adoption of insulation systems for external and internal walls, roofing and ceiling elements. This paper provides evidence to support the drive towards environmental sustainability through the adoption and installation of sustainable technologies. Policies to trigger demand and installation could further improve actions towards greenhouse gas reduction.
Assessment of Occupant Adaptive Heating Behaviour in Office Buildings - A Pilot Field Study
Authors: Andrade I , Krumdieck S , Becker S
Published: December 2022

Buildings are responsible for 39% of total global CO2 emissions worldwide, with 33% of energy consumption attributed to space heating in commercial buildings. Adaptive Heating Behaviour (AHB) of office users has a direct impact on energy use and depends on building characteristics which determine the heating load. This pilot field study focuses on the assessment of the AHB of office users of a recently retrofitted university building in Christchurch, New Zealand. AHB was reported by users during three consecutive days with a total heating degree days (HDD at 18°C) of 26.8. Indoor temperature and relative humidity were monitored during the study, and a qualitative analysis was performed to understand occupants’ behavioural patterns to maintain or recover thermal comfort in office settings. Participants were categorized according to (i) their reported thermal comfort issues at work, (ii) the adaptive responses they took to recover thermal comfort, and (iii) the effectiveness of such actions. The methods used in this pilot study to assess occupant AHB, were informative to future research on how to link environmental conditions to user behaviour in office settings, through the analysis of qualitative and quantitative data. Identifying patterns and triggers for AHB on a per-case basis is necessary, especially as AHB can significantly influence the building energy consumption.
Social sustainability indicators for road infrastructure projects: A systematic literature review
Authors: Vijayakumar Ankitha , Mahmood Muhammad Nateque , Gurmu Argaw , Kamardeen Imriyas , Alam Shafiq
Published: December 2022

Australia’s population is predicted to grow to 31.4 million by 2034, according to Infrastructure Australia Audit 2019, resulting in additional demand for road infrastructure. Although road infrastructure projects have a significant positive impact at the regional level regarding reduced travel time, improved accessibility, and amenities, they may also have negative social impacts at the local level. However, road infrastructure projects are often seen as key drivers of economic development. Social aspects are generally less considered in the decision-making process of the project lifecycle. The existing sustainability assessment models focus on economic and environmental aspects and lack social considerations. To bridge the gap, this paper aims to examine the existing literature on social sustainability in the construction industry and identify the social aspects and relevant indicators to be considered in assessing the social sustainability performance of road infrastructure projects. Through systematic literature review, using Scopus, Web of Science, EBSCO and google search databases from an initial set of 1178 journals, conference papers, books, book chapters and other relevant industry reports, 80 articles related to social sustainability in the construction industry were selected for the study. Based on the analysis, nine social sustainability criteria such as quality of life, equity, employment, health and safety, stakeholder participation, culture and heritage, compensation strategies, governance, macro-social activities and 78 respective indicators were identified. The findings of this study may assist policymakers and industry practitioners in developing comprehensive assessment models for benchmarking social sustainability in Australian road infrastructure projects.
Forecasting Green Building Growth in Different Regions of China
Authors: Chen Linyan , Chan Albert P.C. , Yang Qiang , Darko Amos , Gao Xin
Published: December 2022

Green building has significant merits in energy conservation and resource efficiency, making it prevalent in many countries. Forecasting green building growth helps governments develop relevant policies and benefits researchers to solve the problem of lack of data. Although there were various studies on green building development, few forecasted growth to inform green building policy. To fill the gap, this study aims to develop an innovative approach to predict green building growth in different regions of China. A long short-term memory (LSTM) model with an attention mechanism was put forward in this study. Results show that the innovative model performed well in forecasting green building growth. The green building development in China keeps an increasing trend and will continue the growth at a higher speed in the following years. Moreover, geographical clustering patterns of green buildings were investigated, and a three-step distribution pattern was observed. Although this research was conducted in the Chinese context, it provides references to other countries by proposing an innovative model, which helps them better understand the patterns of green building growth. This study developed an innovative approach to forecasting green buildings, contributing to the existing green building knowledge body. Furthermore, it benefits governments and practitioners in decision-making.
An investigation into green gentrification in the Silo District, Cape Town, South Africa
Authors: Ordor U , Spriestersbach R J , Aitken T
Published: December 2022

Despite the plethora of studies on gentrification, knowledge about green gentrification is limited, especially in a South African context. Green gentrification is the process in which providing green amenities and cleaning up pollution attracts wealthier residents or tenants to a previously rundown neighbourhood, thereby increasing property values. This paper explores whether the characteristics of green gentrification can be identified in the transformation of the Silo District of the V&A Waterfront, Cape Town, and therefore its effect on the perception of ‘green gentrification’, if it is found to occur in the area. The research was based on a single case study with three sources of data collection – a critical literature review, photographic and documentary evidence and semi-structured interviews. The findings show that the transforming of the Silo District, by implementing green and environmental amenities, attracted wealthy individuals and organisations to the area. The findings show that green gentrification occurs in the Silo District, and its transformation had a limited influence on this perception. The study reveals an inadvertent insidious phenomenon in redevelopment initiatives, especially with growing consciousness of sustainability issues. However, the limitations of a single case study research makes further research imperative, and results tested using contextually relevant methods.
Comparison of Perceived Data Skills of Quantity Surveying Undergraduates and Graduates in Malaysia
Authors: Chia F C , Sim Y C , Wong P F
Published: December 2022

The technological innovation is driving the future workforce proficient with a new set of data skills. Quantity Surveyors being one of the key actors in the construction industry have no exception. This paper aims to explore how the quantity surveying undergraduates and graduates perceived their own level of data skills possessed and their stances on data skills to be taught in quantity surveying programmes. Five categories of data skills namely, data collection, data processing, data analysis, data ethics and data privacy have been synthesised from 30 data skills through the literature reviewed. The questionnaire survey participated by 574 of undergraduates and graduates shows the self-perceived level of data skills in descending order are data collection, data processing, data analysis, data privacy and data ethics, which are differing with the stances on the data skills to be taught by the higher education institution, i.e. data analysis, data collection, data ethics, data processing and data privacy. Data analysis skill is mostly expected to be enhanced in the QS programme. The findings can be served as an input to the providers of QS programmes to enhance the course curriculum, particularly on the digital skill.
The influences of facilities management on mental health of underground development users during the pandemic in Hong Kong
Authors: Chen Hao , Chan Isabelle Y.S
Published: December 2022

The outbreak of COVID-19 has triggered an unprecedented health crisis across the world. Previous research indicated that the fear of being infected in public place has transportation hindered the commuters’ choice on. In fact, underground transportation systems, especially those located in high- density cities, have been perceived as high risk environments under the pandemic. In addition, the prolonged COVID-19 outbreak, together with the negative public impression towards underground environment, have to certain extent triggered various mental health responses amongst citizens (e.g., 42.3% increase of anxiety in Hong Kong). This study thus aims to investigate the impacts of FM on underground development users’ mental health in Hong Kong. To achieve this aim, a questionnaire survey approach is adopted. The survey is designed to contain three parts: background information, satisfaction towards underground FM (space management, building services, and supporting facilities related to the pandemic), and mental health level (emotional exhaustion, depersonalization, and claustrophobia). Data is collected over four underground subway stations in Hong Kong. Person correlation and regression analysis are conducted to determine the statistically significant relationships between underground FM and users’ mental health. The results indicated that satisfaction towards visual access, immediate access, and hygiene practices have negative relationship with the occurrence of emotional exhuastion and depersonalization, except for claustrophobia symptoms. The study results provide empirical evidence for practitioners to make informed decisions in FM plans for enhancing mental health of underground development users under and after the pandemic.