Impact of Polyethylene-Glycol-Induced Water Potential on Methane Yield and Microbial Consortium Dynamics in the Anaerobic Degradation of Glucose

This study investigated the relationship between water potential (Ψ) and the cation-induced inhibition of methane production in anaerobic digesters. The Ψ around methanogens was manipulated using polyethylene glycol (PEG) in a batch anaerobic reactor, ranging from -0.92 to -5.10 MPa. The ultimate methane potential (Bu) decreased significantly from 0.293 to 0.002 Nm3 kg-1-VSadded as Ψ decreased. When Ψ lowered from -0.92 MPa to -1.48 MPa, the community distribution of acetoclastic Methanosarcina decreased from 59.62% to 40.44%, while those of hydrogenotrophic Methanoculleus and Methanobacterium increased from 17.70% and 1.30% to 36.30% and 18.07%, respectively. These results mirrored changes observed in methanogenic communities affected by cation inhibition with KCl. Our findings strongly indicate that the inhibitory effect of cations on methane production may stem more from the water stress induced by cations than from their direct toxic effects. This study highlights the importance of considering Ψ dynamics in understanding cation-mediated inhibition in anaerobic digesters, providing insights into optimizing microbial processes for enhanced methane production from organic substrates.

Crack Detection of Reinforced Concrete Structure Using Smart Skin

The availability of carbon nanotube (CNT)-based polymer composites allows the development of surface-attached self-sensing crack sensors for the structural health monitoring of reinforced concrete (RC) structures. These sensors are fabricated by integrating CNTs as conductive fillers into polymer matrices such as polyurethane (PU) and can be applied by coating on RC structures before the composite hardens. The principle of crack detection is based on the electrical change characteristics of the CNT-based polymer composites when subjected to a tensile load. In this study, the electrical conductivity and electro-mechanical/environmental characterization of smart skin fabricated with various CNT concentrations were investigated. This was performed to derive the tensile strain sensitivity of the smart skin according to different CNT contents and to verify their environmental impact. The optimal CNT concentration for the crack detection sensor was determined to be 5 wt% CNT. The smart skin was applied to an RC structure to validate its effectiveness as a crack detection sensor. It successfully detected and monitored crack formation and growth in the structure. During repeated cycles of crack width variations, the smart skin also demonstrated excellent reproducibility and electrical stability in response to the progressive occurrence of cracks, thereby reinforcing the reliability of the crack detection sensor. Overall, the presented results describe the crack detection characteristics of smart skin and demonstrate its potential as a structural health monitoring (SHM) sensor.

Effect of Surface Hydrophilized Plastic Waste Aggregates Made by Mixing Various Kinds of Plastic on Mechanical Properties of Mortar

The surface hydrophilization of mixed plastic waste aggregates (MPAs) was conducted to improve the bond between an MPA and the surrounding cement matrix using two types of coating agents: a silicone amine resin and acrylic binders. The coating agents formed a physical bond with the MPAs, and the results of contact angle measurement also revealed that the surface of MPAs was hydrophilic. The workability of a mortar mix increased by up to 1.47 times with the surface hydrophilization of MPAs. Meanwhile, the compressive and flexural strengths of mortar mixes decreased by 29~43% and 72~86%, respectively, at 28 days with the surface hydrophilization of MPAs. Namely, the surface hydrophilization of MPAs was successively conducted, and the workability of mortar mixes was improved accordingly, but the compressive and flexural strengths of mortar mixes decreased as the physical bond was partially separated from not only the MPA but also the surrounding cement matrix and the surface friction was decreased.

Performance Assessment of Carbon Dioxide Sequestration in Cement Composites with a Granulation Technique

The cement industry emits a significant amount of carbon dioxide (CO2). Therefore, the cement industry should recycle the emitted CO2. However, sequestration by carbonation in cement composites absorbs a very small amount of CO2. Therefore, a direct way of achieving this is to improve the absorption performance of CO2 in cement composites. In this study, to improve absorption, unlike in existing studies, a granulation technique was applied, and the material used was calcium hydroxide (CH). In addition, granulated CH was coated to prevent a reaction during the curing of cement paste. The coated CH granule (CCHG) was applied to 5% of the cement weight as an additive material, and the specimens were cured for 91 days to wait for the coating of CCHG to fully phase-change. The experiment of CO2 absorption showed an unexpected result, where the use of blast furnace slag (BFS) and fly ash (FA) had a negative effect on CO2 sequestration. This was because BFS and FA had a filler effect in the cement matrix, and the filler effect caused the blocking of the path of CO2. In addition, BFS and FA are well-known pozzolanic materials; the pozzolan reaction caused a reduction in the amount of CH because the pozzolan reaction consumed the CH to produce a calcium silicate hydrate. Therefore, the pozzolan reaction also had a negative effect on the CO2 sequestration performance combined with the filler effect. The CO2 sequestration efficiency was decreased between ordinary cement paste and BFS-applied specimens by 45.45%. In addition, compared to cases of ordinary cement paste and FA-applied specimens, the CO2 sequestration performance was decreased by 63.64%. Comprehensively, CO2 sequestration performance depends on the porosity and amount of CH.

Deep Learning Based Fire Risk Detection on Construction Sites

The recent large-scale fire incidents on construction sites in South Korea have highlighted the need for computer vision technology to detect fire risks before an actual occurrence of fire. This study developed a proactive fire risk detection system by detecting the coexistence of an ignition source (sparks) and a combustible material (urethane foam or Styrofoam) using object detection on images from a surveillance camera. Statistical analysis was carried out on fire incidences on construction sites in South Korea to provide insight into the cause of the large-scale fire incidents. Labeling approaches were discussed to improve the performance of the object detectors for sparks and urethane foams. Detecting ignition sources and combustible materials at a distance was discussed in order to improve the performance for long-distance objects. Two candidate deep learning models, Yolov5 and EfficientDet, were compared in their performance. It was found that Yolov5 showed slightly higher mAP performances: Yolov5 models showed mAPs from 87% to 90% and EfficientDet models showed mAPs from 82% to 87%, depending on the complexity of the model. However, Yolov5 showed distinctive advantages over EfficientDet in terms of easiness and speed of learning.

A Methodology of Condition Monitoring System Utilizing Supervised and Semi-Supervised Learning in Railway

In this paper, research was conducted on anomaly detection of wheel flats. In the railway sector, conducting tests with actual railway vehicles is challenging due to safety concerns for passengers and maintenance issues as it is a public industry. Therefore, dynamics software was utilized. Next, STFT (short-time Fourier transform) was performed to create spectrogram images. In the case of railway vehicles, control, monitoring, and communication are performed through TCMS, but complex analysis and data processing are difficult because there are no devices such as GPUs. Furthermore, there are memory limitations. Therefore, in this paper, the relatively lightweight models LeNet-5, ResNet-20, and MobileNet-V3 were selected for deep learning experiments. At this time, the LeNet-5 and MobileNet-V3 models were modified from the basic architecture. Since railway vehicles are given preventive maintenance, it is difficult to obtain fault data. Therefore, semi-supervised learning was also performed. At this time, the Deep One Class Classification paper was referenced. The evaluation results indicated that the modified LeNet-5 and MobileNet-V3 models achieved approximately 97% and 96% accuracy, respectively. At this point, the LeNet-5 model showed a training time of 12 min faster than the MobileNet-V3 model. In addition, the semi-supervised learning results showed a significant outcome of approximately 94% accuracy when considering the railway maintenance environment. In conclusion, considering the railway vehicle maintenance environment and device specifications, it was inferred that the relatively simple and lightweight LeNet-5 model can be effectively utilized while using small images.

Effect of Dispersing Carbon Nanotube in Aqueous Solution by Poly-Carboxylic-Based Surfactants on Mechanical and Microstructural Properties as Cementitious Composites

The development of high-performance concrete using carbon nanotubes (CNTs), which is used in various industries owing to its excellent mechanical properties, has attracted much attention, leading to ongoing research in this area. However, when mixing CNTs into cement paste, there has been limited focus on the dispersibility, and, in most cases, aqueous dispersions of CNTs used in other industrial sectors are used. Because CNTs form the structures of bundles or aggregates owing to their high aspect ratio and van der Waals force between particles, the desired dispersibility cannot be obtained when mixing CNTs in powder form with other materials. Therefore, in this study, we examined the applicability of CNT aqueous dispersions using PC-based plasticizer used in concrete. Aqueous dispersions of CNT using PC-based surfactants are prepared and their properties are compared with those of a PVP-based aqueous dispersion. To analyze the mechanical properties, the compressive strength and flexural strength are measured on the 28th day. Then, the dispersibility and microstructure are analyzed using scanning electron microscopy image analysis, thermogravimetric analysis, and BET (Brunauer-Emmett-Teller) analysis. The analysis results show the enhancement of mechanical properties due to the mixing of the CNT dispersion, and the results confirm the applicability of the proposed CNT aqueous dispersions using PC-based surfactants.

Fusion localization for indoor airplane inspection using visual inertial odometry and ultrasonic RTLS

In this paper, the fusion localization system for the visual inertial odometry (VIO) and ultrasonic real-time localization system (RTLS) for indoor airplane inspection using drones is proposed. In a hangar environment, either trilateration-based RTLS or vision-based localization shows disadvantages and neither can be used alone. In this research, we design a configuration of VIO suitable for hangar environment and outlier filter on ultrasonic RTLS for non-line of sight situations, so that both can be fused using graph optimization. The proposed solution can provide more accurate localization than the visual odometry-only system as well as continue estimating positions in the absence of RTLS data. Localization and real-time performance of the proposed algorithm are evaluated through experimentation in a hangar and a flight test in an outdoor space.

A Study on Wheel Member Condition Recognition Using Machine Learning (Support Vector Machine)

The wheels of railway vehicles are of paramount importance in relation to railroad operations and safety. Currently, the management of railway vehicle wheels is restricted to post-event inspections of the wheels whenever physical phenomena, such as abnormal vibrations and noise, occur during the operation of railway vehicles. To address this issue, this paper proposes a method for predicting abnormalities in railway wheels in advance and enhancing the learning and prediction performance of machine learning algorithms. Data were collected during the operation of Line 4 of the Busan Metro in South Korea by directly attaching sensors to the railway vehicles. Through the analysis of key factors in the collected data, factors that can be used for tire condition classification were derived. Additionally, through data distribution analysis and correlation analysis, factors for classifying tire conditions were identified. As a result, it was determined that the z-axis of acceleration has a significant impact, and machine learning techniques such as SVM (Linear Kernel, RBF Kernel) and Random Forest were utilized based on acceleration data to classify tire conditions into in-service and defective states. The SVM (Linear Kernel) yielded the highest recognition rate at 98.70%.

The Detection System for a Danger State of a Collision between Construction Equipment and Workers Using Fixed CCTV on Construction Sites

According to data from the Ministry of Employment and Labor in Korea, a significant portion of fatal accidents on construction sites occur due to collisions between construction workers and equipment, with many of these collisions being attributed to worker negligence. This study introduces a method for accurately localizing construction equipment and workers on-site, delineating areas prone to collisions as 'a danger area of a collision', and defining collision risk states. Utilizing advanced deep learning models which specialize in object detection, video footage obtained from strategically placed closed-circuit television (CCTV) cameras across the construction site is analyzed. The positions of each detected object are determined using transformation or homography matrices representing the conversion relationship between a sufficiently flat reference plane and image coordinates. Additionally, 'a danger area of a collision' is proposed for evaluating equipment collision risk based on the moving equipment's speed, and the validity of this area is verified. Through this, the paper presents a system designed to preemptively identify potential collision risks, particularly when workers are located within the 'danger area of a collision', thereby mitigating accident risks on construction sites.

Special Traffic Event Detection: Framework, Dataset Generation, and Deep Neural Network Perspectives

Identifying early special traffic events is crucial for efficient traffic control management. If there are a sufficient number of vehicles equipped with automatic event detection and report gadgets, this enables a more rapid response to special events, including road debris, unexpected pedestrians, accidents, and malfunctioning vehicles. To address the needs of such a system and service, we propose a framework for an in-vehicle module-based special traffic event and emergency detection and safe driving monitoring service, which utilizes the modified ResNet classification algorithm to improve the efficiency of traffic management on highways. Due to the fact that this type of classification problem has scarcely been proposed, we have adapted various classification algorithms and corresponding datasets specifically designed for detecting special traffic events. By utilizing datasets containing data on road debris and malfunctioning or crashed vehicles obtained from Korean highways, we demonstrate the feasibility of our algorithms. Our main contributions encompass a thorough adaptation of various deep-learning algorithms and class definitions aimed at detecting actual emergencies on highways. We have also developed a dataset and detection algorithm specifically tailored for this task. Furthermore, our final end-to-end algorithm showcases a notable 9.2% improvement in performance compared to the object accident detection-based algorithm.

Theoretical and Experimental Analysis of Osmotically Assisted Reverse Osmosis for Minimum Liquid Discharge

Osmotically assisted reverse osmosis (OARO) is an innovative process that shows promising potential in the treatment of brine produced by conventional reverse osmosis (RO) systems. This study presents a theoretical and experimental analysis of the OARO process, focusing on its application to achieve minimum liquid discharge (MLD). This theoretical analysis includes the development of a mathematical model to describe the transport phenomena occurring during OARO. By considering mass balance equations coupled with transport equations, the theoretical model allows for the simulation of a full-scale system consisting of a single-stage RO and a four-stage OARO. Experimental investigations are also conducted to validate the theoretical model and to evaluate the performance of the OARO process. A laboratory-scale OARO system is designed and operated using a synthetic RO brine. Various operating conditions, including applied pressure, feed concentration, and draw concentration, are varied to investigate their effects on process performance. The experimental results demonstrate the feasibility of OARO as an MLD solution and also validate the predictions of the theoretical model, confirming its reliability for process optimization and design. The results of the theoretical analysis show that OARO has the potential to significantly improve water recovery compared to conventional RO. Based on the simulation, the optimal operating conditions are explored, leading to a significant reduction (up to 89%) in the volume of brine discharge.

Indoor Air Pollutant (Toluene) Reduction Based on Ultraviolet-A Irradiance and Changes in the Reactor Volume in a TiO2 Photocatalyst Reactor

This study experimentally confirmed the effect of TiO2 photocatalysts on the removal of indoor air pollutants. In the experiment, toluene, a representative indoor air pollutant, was removed using a coating agent containing TiO2 photocatalysts. Conditions proposed by the International Organization for Standardization (ISO) were applied mutatis mutandis, and a photoreactor for an experiment was manufactured. The experiment was divided into two categories. The first experiment was conducted under ISO conditions using the TiO2 photocatalyst coating agent. In the second experiment, the amount of ultraviolet-A (UV-A) light was varied depending on the lamp's service life, and the volume of the reactor was varied depending on the number of contaminants. The results showed that the TiO2 photocatalytic coating agent reduced the effect of toluene. This reduction effect can be increased as a primary function depending on the changes in the amount of UV-A light and reactor volume. However, because toluene is decomposed in this study, additional organic pollutants such as benzene and butadiene can be produced. Because these pollutants are decomposed by the TiO2 photocatalysts, the overall reduction performance may change. Nonetheless, TiO2 photocatalysts can be used to examine the effect of indoor pollutant reduction in indoor ventilation systems and building materials.

Evaluation of Camera Recognition Performance under Blockage Using Virtual Test Drive Toolchain

This study is the first to develop technology to evaluate the object recognition performance of camera sensors, which are increasingly important in autonomous vehicles owing to their relatively low price, and to verify the efficiency of camera recognition algorithms in obstruction situations. To this end, the concentration and color of the blockage and the type and color of the object were set as major factors, with their effects on camera recognition performance analyzed using a camera simulator based on a virtual test drive toolkit. The results show that the blockage concentration has the largest impact on object recognition, followed in order by the object type, blockage color, and object color. As for the blockage color, black exhibited better recognition performance than gray and yellow. In addition, changes in the blockage color affected the recognition of object types, resulting in different responses to each object. Through this study, we propose a blockage-based camera recognition performance evaluation method using simulation, and we establish an algorithm evaluation environment for various manufacturers through an interface with an actual camera. By suggesting the necessity and timing of future camera lens cleaning, we provide manufacturers with technical measures to improve the cleaning timing and camera safety.

Tracing the evolution of green logistics: A latent dirichlet allocation based topic modeling technology and roadmapping

Green logistics (GL) is the main development trend of modern logistics. The analysis of green logistics topics and their evolution is helpful in grasping its development trend and doing research facing the international frontier. Focusing on the hot topics and evolution process of green logistics, this paper analyzes from four aspects: firstly, this study divides the green logistics development progress into three stages based on life cycle theory, which are the emerging stage (1993-2003), slow growth stage (2004-2014) and rapid growth stage (2015-2021). Then, based on latent dirichlet allocation (LDA) topic model, this study summarizes and confirms related words and meaning of each topic in different stages. Furthermore, this study calculates the text similarity in each development stage of green logistics and analyzes the trend of hot topics in green logistics. Finally, this paper visualizes the development roadmap of green logistics and explores the progression among three stages. There are 4, 5, and 7 topics defined respectively in three development stages. The revolution of green logistics is analyzed, and the results show that "model and management on sustainable development of GL", "related issues and potential threats of GL", and "optimization analysis of low-carbon vehicle routing and time" are the primary development trends of green logistics. This study fills the gap in considering the evolution process of green logistics through topic modeling and roadmapping method. It provides a particular theoretical significance for the green and sustainable development of logistics.

The Characteristics of Self-Hydration and Carbonation Reaction of Coal Ash from Circulating Fluidized-Bed Boiler by Absorption of CO2

The by-products of the circulating fluidized-bed boiler combustion (CFBC) of coal exhibit self-hardening properties due to the calcium silicates generated by the reaction between SiO2 and CaO, and the ettringite generated by the reaction of gypsum and quicklime with activated alumina. These reactions exhibit tendencies similar to that of the hydration of ordinary Portland cement (OPC). In this study, the self-hydration and carbonation reaction mechanisms of CFBC by-products were analyzed. These CFBC by-products comprise a number of compounds, including Fe2O3, free CaO, and CaSO4, in large quantities. The hydration product calcium aluminate (and/or ferrite) of calcium aluminate ferrite and sulfate was confirmed through instrumental analysis. The CFBC by-products attain hardening properties because of the carbonation reaction between calcium aluminate ferrite and CO2. This can be identified as a self-hardening process because it does not require a supply of special ions from the outside. Through this study, it was confirmed that CFBC by-products generate CaCO3 through carbonation, thereby densifying the pores of the hardened body and contributing to the development of compressive strength.

Control of Mechanical Properties of FRP (Fiber-Reinforced Plastic) via Arrangement of High-Strength/High-Ductility Fiber in a Blended Fabric

Carbon fiber-reinforced plastic (CFRP) has been widely investigated as a reinforcement material to address the corrosion and durability issues of reinforced concrete (RC). To improve the strain of FRP grids, we investigated the effect of single-fiber types, hybrid ratios, and stacking patterns on the strain of the composite materials. Blended fabrics in which different fibers are woven were used to further improve the strain of carbon fibers (CFs). In the blended fabrics, CFs with high tensile strength were mixed with high-strain glass fibers (GFs) or aramid fibers (AFs). Fibers with different mechanical properties were mixed to improve the strain without reducing the tensile strength of the composite materials. The fiber arrangement direction was controlled by CF/GF blended fabric. CFs are arranged in the direction parallel to the tensile load direction with no strength degradation, and GFs are arranged in the direction perpendicular to the increase in strain. Compared to the mechanical properties of the single CF composites, the fabrics obtained via an FRP mixing method proposed in this study showed an increase in the tensile strength by 7% from 568.17 to 608.34 MPa with no strength degradation and an increase in strain by 34% from 0.97% to 1.30%.

Electrocatalytic Reactions for Converting CO2 to Value-Added Products: Recent Progress and Emerging Trends

Carbon dioxide (CO₂) emissions are an important environmental issue that causes greenhouse and climate change effects on the earth. Nowadays, CO₂ has various conversion methods to be a potential carbon resource, such as photocatalytic, electrocatalytic, and photo-electrocatalytic. CO₂ conversion into value-added products has many advantages, including facile control of the reaction rate by adjusting the applied voltage and minimal environmental pollution. The development of efficient electrocatalysts and improving their viability with appropriate reactor designs is essential for the commercialization of this environmentally friendly method. In addition, microbial electrosynthesis which utilizes an electroactive bio-film electrode as a catalyst can be considered as another option to reduce CO₂. This review highlights the methods which can contribute to the increase in efficiency of carbon dioxide reduction (CO₂R) processes through electrode structure with the introduction of various electrolytes such as ionic liquid, sulfate, and bicarbonate electrolytes, with the control of pH and with the control of the operating pressure and temperature of the electrolyzer. It also presents the research status, a fundamental understanding of carbon dioxide reduction reaction (CO₂RR) mechanisms, the development of electrochemical CO₂R technologies, and challenges and opportunities for future research.

Evaluation of a Hydrophobic Coating Agent Based on Cellulose Nanofiber and Alkyl Ketone Dimer

In this study, we report on the development and testing of hydrophobic coatings using cellulose fibers. The developed hydrophobic coating agent secured hydrophobic performance over 120°. In addition, a pencil hardness test, rapid chloride ion penetration test, and carbonation test were conducted, and it was confirmed that concrete durability could be improved. We believe that this study will promote the research and development of hydrophobic coatings in the future.

Refractive Index-Adaptive Nanoporous Chiral Photonic Crystal Film for Chemical Detection Visualized via Selective Reflection

Photonic crystals (PC) are of great importance in technology, especially in optics and photonics. In general, the structural color of PCs responds to external stimuli primarily by changing their periodicity. Herein, the authors report on refractive index (RI) adaptive PCs. Cross-linked cholesteric films with interconnected nanopores exhibit a very low RI without light scattering. Transparent PC films with maximum reflectance in the ultravoilet (UV) region respond to various chemicals by changing the reflective color of the PC. The authors demonstrate its unique colorimetric chemical detections of hazardous organic liquids. Loading various chemicals into nanopores significantly shifts the structural color into the visible range depending on the chemical's RI. These results are unique in that the structural color of photonic films is mediated by RI changes rather than periodicity changes. In principle, nanoporous photonic crystal films can detect the RI of a chemical substance by its unique color. In contrast to volumetric changes, this sensing mechanism offers several advantages, including durability, excellent sensitivity, fast response time, and wide detection range. These results provide useful insight into stimulus-responsive PCs. The structural color of PC films can be effectively tuned by adjusting average RIs instead of changing periodicity.

In vitro and in vivo evaluation of the genotoxicity of titanium dioxide, GST

Titanium dioxide (TiO2) was used in various applications in a wide range of products including food, cosmetics and photocatalyst. General toxicity studies of titanium dioxide, GST (Green Sludge Titanium) have been investigated in several reports, whereas studies concerning mutagenicity and genotoxicity have not been elucidated. Herein, we investigated the potential mutagenicity and genotoxicity of GST by genetic toxicology testing. The bacterial reverse mutation test was conducted by the pre-incubation method in the presence and absence of metabolic activation system (S9 mixture). The chromosome aberration test was performed using cultured Chinese hamster lung cell line in the absence and presence of S9 mixture. The micronucleus test was performed by using specific pathogen-free male ICR mice. Genotoxicity tests were conducted following the test guidelines of the Organisation for Economic Cooperation and Development with application of Good Laboratory Practice. No statistically significant increases were found in the bacterial reverse mutation test, in vitro chromosome aberration test, and in vivo micronucleus test when tested for induction of genotoxicity in GST. These results suggest that GST did not induce mutagenicity and genotoxicity in both in vitro and in vivo system.

Evaluation of Efficiently Removing Secondary Effluent Organic Matters (EfOM) by Al-Based Coagulant for Wastewater Recycling: A Case Study with an Industrial-Scale Food-Processing Wastewater Treatment Plant

The reuse of wastewater has been identified as an important initiative for the sustainable development of the environment; thus, the removal of secondary effluent organic matter (EfOM) to ensure the safety of reused wastewater is the key step and a subject of extensive research. In this study, Al₂(SO₄)₃ and anionic polyacrylamide were selected as coagulant and flocculant, respectively, for the treatment of secondary effluent from a food-processing industry wastewater treatment plant to meet the standard regulatory specifications for water reuse. In this process, the removal efficiencies of chemical oxygen demand (COD), components with UV₂₅₄, and specific ultraviolet absorbance (SUVA) were 44.61%, 25.13%, and 9.13%, respectively, with an associated reduction in chroma and turbidity. The fluorescence intensities (Fmax) of two humic-like components were reduced during coagulation, and microbial humic-like components of EfOM had a better removal efficiency because of a higher Log Km value of 4.12. Fourier transform infrared spectroscopy showed that Al₂(SO₄)₃ could remove the protein fraction of the soluble microbial products (SMP) of EfOM by forming a loose SMP protein complex with enhanced hydrophobicity. Furthermore, flocculation reduced the aromaticity of secondary effluent. The cost of the proposed secondary effluent treatment was 0.034 CNY t^-1 %COD^-1. These results demonstrate that the process is efficient and economically viable for EfOM removal to realize food-processing wastewater reuse.

Discovering Homogeneous Groups from Geo-Tagged Videos

The popularity of intelligent devices with GPS and digital compasses has generated plentiful videos and images with text tags, timestamps, and geo-references. These digital footprints of travelers record their time and spatial movements and have become indispensable information resources, vital in applications such as how groups of videographers behave and in future-movement prediction. In this paper, first we propose algorithms to discover homogeneous groups from geo-tagged videos with view directions. Second, we extend the density clustering algorithm to support fields-of-view (FoVs) in the geo-tagged videos and propose an optimization model based on a two-level grid-based index. We show the efficiency and effectiveness of the proposed homogeneous-pattern-discovery approach through experimental evaluation on real and synthetic datasets.

Health risk assessment and source apportionment of PM2.5-bound toxic elements in the industrial city of Siheung, Korea

The emission sources and their health risks of fine particulate matter (PM_2.5) in Siheung, Republic of Korea, were investigated as a middle-sized industrial city. To identify the PM_2.5 sources with error estimation, a positive matrix factorization model was conducted using daily mean speciated data from November 16, 2019, to October 2, 2020 (95 samples, 22 chemical species). As a result, 10 sources were identified: secondary nitrate (24.3%), secondary sulfate (18.8%), traffic (18.8%), combustion for heating (12.6%), biomass burning (11.8%), coal combustion (3.6%), heavy oil industry (1.8%), smelting industry (4.0%), sea salts (2.7%), and soil (1.7%). Based on the source apportionment results, health risks by inhalation of PM_2.5 were assessed for each source using the concentration of toxic elements portioned. The estimated cumulative carcinogenic health risks from the coal combustion, heavy oil industry, and traffic sources exceeded the benchmark, 1E-06. Similarly, carcinogenic health risks from exposure to As and Cr exceeded 1E-05 and 1E-06, respectively, needing a risk reduction plan. The non-carcinogenic risk was smaller than the hazard index of one, implying low potential for adverse health effects. The probable locations of sources with relatively higher carcinogenic risks were tracked. In this study, health risk assessment was performed on the elements for which mass concentration and toxicity information were available; however, future research needs to reflect the toxicity of organic compounds, elemental carbon, and PM_2.5 itself.

Corroded Bolt Identification Using Mask Region-Based Deep Learning Trained on Synthesized Data

The performance of a neural network depends on the availability of datasets, and most deep learning techniques lack accuracy and generalization when they are trained using limited datasets. Using synthesized training data is one of the effective ways to overcome the above limitation. Besides, the previous corroded bolt detection method has focused on classifying only two classes, clean and fully rusted bolts, and its performance for detecting partially rusted bolts is still questionable. This study presents a deep learning method to identify corroded bolts in steel structures using a mask region-based convolutional neural network (Mask-RCNN) trained on synthesized data. The Resnet50 integrated with a feature pyramid network is used as the backbone for feature extraction in the Mask-RCNN-based corroded bolt detector. A four-step data synthesis procedure is proposed to autonomously generate the training datasets of corroded bolts with different severities. Afterwards, the proposed detector is trained by the synthesized datasets, and its robustness is demonstrated by detecting corroded bolts in a lab-scale steel structure under varying capturing distances and perspectives. The results show that the proposed method has detected corroded bolts well and identified their corrosion levels with the most desired overall accuracy rate = 96.3% for a 1.0 m capturing distance and 97.5% for a 15° perspective angle.

A street-view-based method to detect urban growth and decline: A case study of Midtown in Detroit, Michigan, USA

Urban growth and decline occur every year and show changes in urban areas. Although various approaches to detect urban changes have been developed, they mainly use large-scale satellite imagery and socioeconomic factors in urban areas, which provides an overview of urban changes. However, since people explore places and notice changes daily at the street level, it would be useful to develop a method to identify urban changes at the street level and demonstrate whether urban growth or decline occurs there. Thus, this study seeks to use street-level panoramic images from Google Street View to identify urban changes and to develop a new way to evaluate the growth and decline of an urban area. After collecting Google Street View images year by year, we trained and developed a deep-learning model of an object detection process using the open-source software TensorFlow. By scoring objects and changes detected on a street from year to year, a map of urban growth and decline was generated for Midtown in Detroit, Michigan, USA. By comparing socioeconomic changes and the situations of objects and changes in Midtown, the proposed method is shown to be helpful for analyzing urban growth and decline by using year-by-year street view images.

Fabrication of Electrospun Ni0.5Zn0.5Fe2O4 Nanofibers Using Polyvinyl Pyrrolidone Precursors and Electromagnetic Wave Absorption Performance Improvement

Ni0.5Zn0.5Fe2O4 nanofibers with an average diameter of 133.56 ± 12.73 nm were fabricated by electrospinning and calcination. According to our thermogravimetric-differential thermal analysis and X-ray diffraction results, the calcination temperature was 650 °C. The microstructure, crystal structure, and chemical composition of the nanofibers were observed using field-emission scanning electron, X-ray diffraction, and energy-dispersive X-ray spectroscopy. Commercial particle samples and samples containing 10 wt% and 20 wt% nanofibers were fabricated, and the electromagnetic properties were analyzed with a vector network analyzer and a 7.00 mm coaxial waveguide. Regardless of the nanofiber content, Ni0.5Zn0.5Fe2O4 was dominantly affected by the magnetic loss mechanism. Calculation of the return loss based on the transmission line theory confirmed that the electromagnetic wave return loss was improved up to -59.66 dB at 2.75 GHz as the nanofiber content increased. The absorber of mixed compositions with Ni0.5Zn0.5Fe2O4 nanofibers showed better microwave absorption performance. It will be able to enhance the performance of commercial electromagnetic wave absorbers of various types such as paints and panels.

Metal-Organic-Framework- and MXene-Based Taste Sensors and Glucose Detection

Taste sensors can identify various tastes, including saltiness, bitterness, sweetness, sourness, and umami, and have been useful in the food and beverage industry. Metal-organic frameworks (MOFs) and MXenes have recently received considerable attention for the fabrication of high-performance biosensors owing to their large surface area, high ion transfer ability, adjustable chemical structure. Notably, MOFs with large surface areas, tunable chemical structures, and high stability have been explored in various applications, whereas MXenes with good conductivity, excellent ion-transport characteristics, and ease of modification have exhibited great potential in biochemical sensing. This review first outlines the importance of taste sensors, their operation mechanism, and measuring methods in sensing utilization. Then, recent studies focusing on MOFs and MXenes for the detection of different tastes are discussed. Finally, future directions for biomimetic tongues based on MOFs and MXenes are discussed.

Tacky-Free Polyurethanes Pressure-Sensitive Adhesives by Molecular-Weight and HDI Trimer Design

Polyurethane pressure-sensitive adhesives (PU-PSAs) with satisfactory tack, cohesion, and removability were newly developed through the synthetic process by reacting methylene diisocyanate, poly(ethylene glycol) (PEG), and a 1,4-butanediol chain extender based on the different HDI/HDI trimer ratios. The sticking properties of PU-PSAs depended on both the HDI/HDI trimer ratio and crosslinking-agent composition in the formulation. The molecular weight (MW) dependence of adhesion in PU-PSA was observed in the range of 1000 < Mn < 3000, suggesting that the increase in MW limits the pressure-sensitive adhesion of these samples. The differences in the crosslinking-density significantly affected the cohesion, adhesion, and tack in PU-PSA. The formulation of 50 wt.% 600PEG and 50 wt.% crosslinking-agent and an HDI/HDI trimer ratio of 1.0 led to the optimal balance between the adhesion and cohesion properties owing to the sufficient tack, high 180-peel strength, and good cohesion.

Comparative Analysis and Strength Estimation of Fresh Concrete Based on Ultrasonic Wave Propagation and Maturity Using Smart Temperature and PZT Sensors

Recently, the early-age strength prediction for RC (reinforced concrete) structures has been an important topic in the construction industry, relating to project-time reduction and structural safety. To address this, numerous destructive and NDTs (non-destructive tests) are applied to monitor the early-age strength development of concrete. This study elaborates on the NDT techniques of ultrasonic wave propagation and concrete maturity for the estimation of compressive strength development. The results of these comparative estimation approaches comprise the concrete maturity method, penetration resistance test, and an ultrasonic wave analysis. There is variation of the phase transition in the concrete paste with the changing of boundary limitations of the material in accordance with curing time, so with the formation of phase-transition changes, changes in the velocities of ultrasonic waves occur. As the process of hydration takes place, the maturity method produces a maturity index using the time-feature reflection on the strength-development process of the concrete. Embedded smart temperature sensors (SmartRock) and PZT (piezoelectric) sensors were used for the data acquisition of hydration temperature history and wave propagation. This study suggests a novel relationship between wave propagation, penetration tests, and hydration temperature, and creates a method that relies on the responses of resonant frequency changes with the change of boundary conditions caused by the strength-gain of the concrete specimen. Calculating the changes of these features provides a pattern for estimating concrete strength. The results for the specimens were validated by comparing the strength results with the penetration resistance test by a universal testing machine (UTM). An algorithm used to relate the concrete maturity and ultrasonic wave propagation to the concrete compressive strength. This study leads to a method of acquiring data for forecasting in-situ early-age strength of concrete, used for secure construction of concrete structures, that is fast, cost effective, and comprehensive for SHM (structural health monitoring).