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Generally, polyphonic piano music transcription systems are designed to estimate and determine pitch activities along with various note states for each audio frame. While the music transcription system has multiple uses in the Music Information Retrieval (MIR) field, due to the complicated structures of the note events, precisely predicting various note states is still regarded as a challenging task. Accordingly, approaches to designing neural network architectures have evolved to facilitate the joint prediction of each note state. However, recent models have not been able to efficiently exploit mutual correlations among different note states. The key contribution of our work is that we verified mutual correlations between the different note states and reflected them in the model architecture. It enables the transcription system to recognize clearer note events and produce high-quality real-world results. We propose a kernel-sharing feature extractor module for exploiting those mutual correlations in the feature extraction step. Moreover, to make a system recognize the shape of the pitch envelope, we added some connections between the note state-specific detector modules in the note state detection step. The efficacy of our architecture was thoroughly validated in a series of experiments using the publicly available MAESTRO datasets proposed by Google Magenta. Furthermore, ablation studies are performed to demonstrate notions of those mutual correlations and show the impact and significance of the suggested approach.

Objectives This study aims to assess the feasibility of simultaneously producing hydrogen and hypochlorous acid using a 2-compartment PEM water electrolysis cell with an installed membrane electrode assembly (MEA). Methods External power was applied to the PEM water electrolysis device through a potentiostat to induce the electrolysis reaction and to confirm the production of hydrogen and hypochlorite simultaneously. Linear Sweep Voltammetry (LSV) experiments were conducted, thereby determined the minimum voltage required for the electrolysis. Constant current experiments were performed by applying a fixed current of 1 A for 1 hour, measuring voltage changes every second to evaluate the stability and efficiency of hydrogen production. The hydrogen production rate was measured using a mass flow meter, while the hypochlorite production was determined using a portable free chlorine photometer. Results and Discussion In this study, the minimum voltage required for hydrogen generation in a PEM water electrolysis cell was investigated using LSV with Deionized (DI) water. The results showed that the current increased from around 1.4 V, indicating an approximate voltage efficiency of 82%. NaCl was introduced to the anode in the PEM electrolysis cell to induce electrochemical reactions. When the anolyte was NaCl, the voltage was observed to increase by 0.77 V compared to using DI water. Optimization experiments were conducted to investigate the influence of electrolyte concentration and flow rate on hypochlorite and hydrogen production. As the electrolyte concentration increased, the hypochlorite generation also increased, with a maximum of 104±0.50 mg/L observed with NaCl 10 g/L. Additionally, when the flow rate was reduced, the hypochlorite production increased, and at a reduced flow rate of 5 mL/min compared to 20 mL/min, hypochlorite generation increased by 127.3%. However, the hydrogen production showed no significant variation with NaCl concentration or flow rate. Conclusion This study explores a novel approach using PEM water electrolysis technology to simultaneously produce hydrogen and chlorinated disinfectants. The research employed a PEM electrolysis cell with the MEA to generate both chlorine-based compounds and hydrogen. By varying the concentration and flow rate of the anolyte, the production efficiencies of hypochlorite were compared. This system showed that PEM electrolysis can be a promising alternative for disinfection and energy production in terms of environmental protection and cost-effectiveness.

The transportation industry has played a crucial role in advancing human society, but it has also raised concerns about carbon emissions and environmental pollution. As a result, significant research is being conducted in various areas, such as electric vehicles, powertrain energy efficiency, shared mobility, and autonomous driving. However, vehicles remain essential, and reducing their energy consumption is vital. One effective way to achieve this is by light–weighting vehicle materials. In the past, steel was the primary material used, but the transition to materials like aluminum has shown better weight-to-strength ratios. To drive change, we must explore the use of diverse materials since no single material is perfect. By combining specific materials, we can complement their weaknesses and enhance their strengths, opening up new possibilities for the future of mobility. One critical factor in advancing future mobility is the joining of dissimilar materials. This paper delves into various methods of joining dissimilar materials, their characteristics, and techniques to improve their joining performance. The joint strength for each joining method is also summarized in this study. The joining methods are primarily divided into mechanical joining, chemical joining using adhesives, and thermal fusion welding. Methods to enhance the joint include combining each joining method and improving surface interactions through surface treatments. The joint exhibiting the highest strength was the laser-welded joint of strength 312 MPa between Ti3Al-Nb alloy and Ni-based superalloy, while the lightest and strong joint was the friction stir-welded joint of strength 63.4 MPa in Polyamide 6 GFRP. Welded joints of polymer materials appear promising for the future mobility industry, particularly in terms of being lightweight. Additionally, there is a need to develop new methods to measure strength as the units used to represent the physical characteristics of the joints are not standardized. Some joints suffer from issues due to unclear joint area, highlighting the necessity to develop new strength measurement methods for future advancements.

Increased safety risks and the difficulties of training excavator operators, combined with manpower shortages, have led to an increased demand for machine automation. This study applies a long short-term memory algorithm to automate a bucket tip-trajectory planning artificial intelligence (AI) system. Unlike other autonomous excavation techniques, our approach performs bucket-trajectory planning without prior knowledge of the nonlinear bucket–soil interaction dynamics during excavation, which require the parameters to be precisely adjusted via heuristic analysis of their correlations. Using data acquired from the excavations of excavator experts, this method uses three-dimensional point cloud of terrain and bucket motion data of excavation process to train and apply the AI modules. Especially, we transform the point cloud, which comprises a massive number of points and entails a considerable computation complexity, into much fewer values, which are enough to represent the three-dimensional shape of the target terrain. To ensure prevention of collisions with underground obstacles along a given excavation path, a collision avoidance algorithm, based on continuous pressure monitoring in the excavator’s hydraulic cylinder, is applied. Comparison experiments reveal that the bucket-tip trajectory planning AI system with collision avoidance algorithm generates a traceable trajectory for the machine controller, equipped in an excavator, and yields the desired excavation volume and lead time without collision, regardless of the topographic changes caused by successive excavations.

The Controller Area Network (CAN), widely used for vehicular communication, is vulnerable to multiple types of cyber-threats. Attackers can inject malicious messages into the CAN bus through various channels, including wireless methods, entertainment systems, and on-board diagnostic ports. Therefore, it is crucial to develop a reliable intrusion detection system (IDS) capable of effectively distinguishing between legitimate and malicious CAN messages. In this paper, we propose a novel IDS architecture aimed at enhancing the cybersecurity of CAN bus systems in vehicles. Various machine learning (ML) models have been widely used to address similar problems; however, although existing ML-based IDS are computationally efficient, they suffer from suboptimal detection performance. To mitigate this shortcoming, our architecture incorporates specially designed rule-based filters that cross-check outputs from the traditional ML-based IDS. These filters scrutinize message ID and payload data to precisely capture the unique characteristics of three distinct types of cyberattacks: DoS attacks, spoofing attacks, and fuzzy attacks. Experimental evidence demonstrates that the proposed architecture leads to a significant improvement in detection performance across all utilized ML models. Specifically, all ML-based IDS achieved an accuracy exceeding 99% for every type of attack. This achievement highlights the robustness and effectiveness of our proposed solution in detecting potential threats.

Tumor-associated macrophages (TAMs) play a pivotal role in the tumor microenvironment, influencing cancer progression and contributing to poor prognosis. However, in cervical cancer (CC), their significance and involvement are relatively less studied than in other gynecological cancers such as ovarian and endometrial cancer. This review aims to provide an overview of TAMs, covering their origins and phenotypes and their impact on CC progression, along with major TAM-targeted therapeutic approaches. Furthermore, we advocate for the integration of cutting-edge research methodologies, such as single-cell RNA sequencing and spatial RNA sequencing, to enable in-depth and comprehensive investigations into TAMs in CC, which would be beneficial in leading to more personalized and effective immunotherapy strategies for patients with CC.

Tumor budding (TB) is a small cluster of malignant cells at the invasive front of a tumor. Despite being an adverse prognosis marker, little research has been conducted on the tumor immune microenvironment of tumor buddings, especially in cervical cancer. Therefore, RNA sequencing was performed using 21 formalin-fixed, paraffin-embedded slides of cervical tissues, and differentially expressed genes (DEGs) were analyzed. Immune Pathway and Gene Database (IMPAGT) was generated for immune profiling. “Pathway in Cancer” was identified as the most enriched pathway for both up- and downregulated DEGs. Kyoto Encyclopedia of Genes and Genomes Mapper and Gene Ontology further revealed the activation of the PI3K/Akt signaling pathway. An IMPAGT analysis revealed immune dysregulation even at the tumor budding stage, especially in the PI3K/Akt/mTOR axis, with a high efficiency and integrity. These findings emphasized the clinical significance of tumor buddings and the necessity of blocking the overactivation of the PI3K/Akt/mTOR pathway to improve targeted therapy in cervical cancer.

Tumor microenvironment (TME) plays a pivotal role in immuno-oncology, which investigates the intricate interactions between tumors and the human immune system. Specifically, tumor-infiltrating lymphocytes (TILs) are crucial biomarkers for evaluating the prognosis of breast cancer patients and have the potential to refine immunotherapy precision and accurately identify tumor cells in specific cancer types. In this study, we conducted tissue segmentation and lymphocyte detection tasks to predict TIL scores by employing self-supervised learning (SSL) model-based approaches capable of addressing limited labeling data issues. Our experiments showed a 1.9% improvement in tissue segmentation and a 2% improvement in lymphocyte detection over the ImageNet pre-training model. Using these SSL-based models, we achieved a TIL score of 0.718 with a 4.4% improvement. In particular, when trained with only 10% of the entire dataset, the SwAV pre-trained model exhibited a superior performance over other models. Our work highlights improved tissue segmentation and lymphocyte detection using the SSL model with less labeled data for TIL score prediction.

Lymphovascular space invasion (LVSI) is the presence of tumor emboli in the endothelial-lined space at the tumor body’s invasive edge. LVSI is one of three Sedlis criteria components—a prognostic tool for early cervical cancer (CC)—essential for indicating poor prognosis, such as lymph node metastasis, distant metastasis, or shorter survival rate. Despite its clinical significance, an in-depth comprehension of the molecular mechanisms or immune dynamics underlying LVSI in CC remains elusive. Therefore, this study investigated tumor-immune microenvironment (TIME) dynamics of the LVSI-positive group in CC. RNA sequencing included formalin-fixed paraffin-embedded (FFPE) slides from 21 CC patients, and differentially expressed genes (DEGs) were analyzed. Functional analysis and immune deconvolution revealed aberrantly enriched PI3K/Akt pathway activation and a heterogenic immune composition with a low abundance of regulatory T cells (Treg) between LVSI-positive and LVSI-absent groups. These findings improve the comprehension of LSVI TIME and immune mechanisms, benefiting targeted LVSI therapy for CC.

Uterine cervical cancer (CC) is a complex, multistep disease primarily linked to persistent infection with high-risk human papillomavirus (HR-HPV). However, it is widely acknowledged that HR-HPV infection alone cannot account for the formation and progression of CC. Emerging evidence suggests that the cervicovaginal microbiome (CVM) also plays a significant role in HPV-related CC. Certain bacteria, such as Fusobacterium spp., Porphyromonas, Prevotella, and Campylobacter, are currently being considered as potential microbiomarkers for HPV-positive CC. However, the composition of the CVM in CC is inconsistent; thus, further studies are needed. This review comprehensively discusses the complex interplay between HPV and the CVM in cervical carcinogenesis. It is postulated that the dynamic interaction between HPV and the CVM creates an imbalanced cervicovaginal microenvironment that triggers dysbiosis, enhances HPV persistence, and promotes cervical carcinogenesis. Moreover, this review aims to provide updated evidence on the potential role of bacteriotherapy, particularly probiotics, in the treatment of CC.

The conversion of glycerol to high-value-added products via photoelectrochemical (PEC) oxidation has emerged as a promising approach for utilizing a sustainable and clean energy source with environmental and economic benefits. Moreover, the energy requirement for glycerol to produce hydrogen is lower than that for pure water splitting. In this study, we propose the use of WO3 nanostructures decorated with Bi-based metal–organic frameworks (Bi-MOFs) as the photoanode for glycerol oxidation with simultaneous hydrogen production. The WO3-based electrodes selectively converted glycerol to glyceraldehyde, a high-value-added product, with remarkable selectivity. The Bi-MOF-decorated WO3 nanorods enhanced the surface charge transfer and adsorption properties, thereby improving the photocurrent density and production rate (1.53 mA/cm2 and 257 mmol/m2·h at 0.8 VRHE). The photocurrent was maintained for 10 h, ensuring stable glycerol conversion. Furthermore, at 1.2 VRHE, the average production rate of glyceraldehyde reached 420 mmol/m2·h, with a selectivity of 93.6% between beneficial oxidized products over the photoelectrode. This study provides a practical approach for the conversion of glycerol to glyceraldehyde via the selective oxidation of WO3 nanostructures and demonstrates the potential of Bi-MOFs as a promising cocatalyst for PEC biomass valorization.

Abstract Van der Waals (vdW) heterostructures, consisting of a variety of low-dimensional materials, have great potential use in the design of a wide range of functional devices thanks to their atomically thin body and strong electrostatic tunability. Here, we demonstrate multi-functional indium selenide (InSe)/black phosphorous (BP) heterostructures encapsulated by hexagonal boron nitride. At a positive drain bias (V D), applied on the BP while the InSe is grounded, our heterostructures show an intermediate gate voltage (V BG) regime where the current hardly changes, working as a ternary transistor. By contrast, at a negative V D, the device shows strong negative differential transconductance characteristics; the peak current increases up to ~5 μA and the peak-to-valley current ratio reaches 1600 at V D = −2 V. Four-terminal measurements were performed on each layer, allowing us to separate the contributions of contact resistances and channel resistance. Moreover, multiple devices with different device structures and contacts were investigated, providing insight into the operation principle and performance optimization. We systematically investigated the influence of contact resistances, heterojunction resistance, channel resistance, and the thickness of BP on the detailed operational characteristics at different V D and V BG regimes.

Objectives : The purpose of this study is to analyze research trends based on text mining technology from the published papers in the Journal of Korean Society of Environmental Engineering. Methods : From 2000 to 2019, a total of 2,743 published papers were analyzed using text mining techniques. Term frequency, TF-IDF for document classification, word association analysis were applied to find the characteristics of text data. Results and Discussion : When confirming the high appearance of the word in the published paper during 2000-2019, ‘adsorption’, ‘heavy metals’, ‘activated carbon’, ‘sediment’, ‘sewage sludge’ was found in order. It implies that large number of studies in the journal were focused on the water quality field mainly. TF-IDF analysis classified the studies into five groups; 1) drinking water treatment field, 2) water quality modeling field, 3) heavy metal adsorption field, 4) biological sewage treatment field, 5) environmental catalyst field. These results by TF-IDF show that a large proportion of studies were published in the field of water quality modeling and biological sewage treatment. When we analyzed the term frequency every five years, “adsorption” and “heavy metals” were the highly-frequency occurrence words from 2000 to 2009, but in the last 5 years, new words such as “fine dust”, “cesium”, and “ecological toxicity” were appeared. It seems that the research was reflected in the recent environmental issues. Conclusions : A lot of studies has been focused on the field of water quality but in recent years, new research topics are being studied related to atmosphere, toxicity, and radiation. Applying a more sophisticated and diverse text mining technique will be of great help to improve the environmental engineering research field.

Fiber media filtration for combined sewer overflow (CSO) treatment was evaluated in this work. Pilot-scale experiments of CSO treatment involve seven layers that form radial-type fiber media filters. The fiber media filter was characterized using analysis techniques and exhibited extremely high hydrophobicity and negative charge. The results obtained for a sewer treatment plant at Tancheon in Seoul revealed potential for suspended solid, total organic carbon, and biochemical oxygen demand removal with efficiencies of 89.6%, 56%, and 42%, respectively. The results are consistent with the sieving effects and electrostatic repulsion between fiber media and pollutants. Head loss was induced by organic pollutants and was 10.5 cm after 20 h of operation. To mitigate the head loss induced by organic pollutants on the fiber media filter, sodium hypochlorite (NaClO) was used as a chemical backwashing agent. An NaClO concentration of 2000 mg/L with a soaking time of 24 h yielded an optimal head loss recovery of 96.2% of the initial head loss obtained for the virgin fiber media filter without media filter degradation. The result of the present study will provide practical insight and act as a technical guide for CSO treatment plant engineers.

Network pruning reduces the number of parameters and computational costs of convolutional neural networks while maintaining high performance. Although existing pruning methods have achieved excellent results, they do not consider reconstruction after pruning in order to apply the network to actual devices. This study proposes a reconstruction process for channel-based network pruning. For lossless reconstruction, we focus on three components of the network: the residual block, skip connection, and convolution layer. Union operation and index alignment are applied to the residual block and skip connection, respectively. Furthermore, we reconstruct a compressed convolution layer by considering batch normalization. We apply our method to existing channel-based pruning methods for downstream tasks such as image classification, object detection, and semantic segmentation. Experimental results show that compressing a large model has a 1.93% higher accuracy in image classification, 2.2 higher mean Intersection over Union (mIoU) in semantic segmentation, and 0.054 higher mean Average Precision (mAP) in object detection than well-designed small models. Moreover, we demonstrate that our method can reduce the actual latency by 8.15× and 5.29× on Raspberry Pi and Jetson Nano, respectively.

Group B Streptococcus (GBS, Streptococcus agalactiae) is a Gram-positive bacterium that is commonly found in the gastrointestinal and urogenital tracts. However, its colonization during pregnancy is an important cause of maternal and neonatal morbidity and mortality worldwide. Herein, we specifically looked at GBS in relation to the field of Obstetrics (OB) along with the field of Gynecology (GY). In this review, based on the clinical significance of GBS in the field of OBGY, topics of how GBS is being detected, treated, and should be prevented are addressed.

This study compared the biometric accuracy and refractive outcomes, and ocular surface changes after cataract surgery in patients with Sjögren’s syndrome (SS, S group), non-SS dry eye patients (D group), and healthy controls (C group). The medical records of patients who underwent cataract surgery and met certain inclusion criteria were reviewed. In total, 167 eyes of 87 patients were enrolled. Refractive parameters were analyzed via optical biometry and combined ultrasound biometry and automated refractokeratometry. The mean absolute errors (MAEs), the uncorrected distance visual acuities (UDVAs), changes in the ocular staining score (OSS), and anterior chamber cell grades were compared for 12 months postoperatively. The S group evidenced more severe and persistent OSS exacerbation after cataract surgery; the OSS returned to baseline by 3 months postoperatively. The mean keratometric values showed a significant linear correlation. There was no significant intergroup difference in either the MAEs (p > 0.530) or anterior chamber inflammation (p > 0.436). The postoperative UDVA of the S group was poorer than that of the C group from 3 months postoperatively (p < 0.047) but not different from that of the D group (p > 0.311). With preoperative ocular surface optimization and optimal postoperative treatment of superficial keratitis, the refractive outcomes of SS patients were comparable to those of other groups and the postoperative UDVA was not inferior to that of non-SS dry eye patients.

Abstract Alcohol increases the risk of both hepatocellular carcinoma (HCC) and colorectal neoplasia. In this hospital-based case-control and retrospective cohort study, we sought to determine whether development of colorectal neoplasia increases the risk of HCC in patients with alcoholic liver disease (ALD). In the phase I case-control analysis, the association between history of colorectal cancer (CRC) and HCC development was assessed in patients with ALD by logistic regression modeling (n = 1,659). In the phase II retrospective cohort analysis, the relative risk of HCC development was compared in ALD patients with respect to the history of CRC by a Cox model (n = 1,184). The history of CRC was significantly associated with HCC in the case-control analysis (adjusted odds ratio, 1.82; 95% CI, 1.06–3.15; P

Serial femtosecond crystallography (SFX) using an X-ray free-electron laser (XFEL) can be applied to determine the room-temperature structure of target molecules while minimizing radiation damage and visualizing molecular dynamics. In SFX, a sample delivery system is required to deliver microcrystals to the XFEL beam path in a serial manner. We recently developed a sample delivery method, the combined inject-and-transfer system (BITS), which is a hybrid method based on the injector and fixed-target scanning approach. In this study, we introduced recently upgraded hardware to move the injection needle in the direction of the XYZ-axis and a graphic user interface for user motion control. Furthermore, we report that the viscous solution containing 10% (w/v) PEG 3350 or PEG 6000 that is widely used for protein crystallization can be stably deposited on polyimide film with a hydrophobic surface without any special treatment. Moreover, the development of an inject-and-diffuse method for time-resolved studies with liquid applications in the BITS and its preliminary results are reported. This study provides up-to-date instrument information to SFX users using BITS and provides insights to instrument developers for SFX.

A crystal delivery system is essential in serial femtosecond crystallography experiments using an X-ray free-electron laser (XFEL). Investigating the XFEL-induced radiation damage to materials potentially applicable to sample delivery devices is vital for developing a sample delivery system. In this study, we investigated the radiation damage caused by an XFEL to polydimethylsiloxane (PDMS) and polyimide (PI), which are widely used as sample delivery materials in synchrotron X-rays. Upon XFEL exposure, the PDMS film produced irregularly shaped and sized holes, whereas the PI film produced relatively regular shaped and sized holes. When XFELs were exposed to the channel of the PDMS-based microfluidic device, holes were generated on the film by the radiation damage and the microfluidic device and the internal channel region were structurally destroyed. The PI-based microfluidic device experienced no structural destruction, except for the holes generated by the XFEL. However, as the XFELs were continuously exposed, bubbles generated from the solution due to radiation damage; the accumulation of these bubbles interfered with the path of the inner channel of the microfluidic device. Our results will not only help understand the phenomenon of radiation damage of PDMS and PI films by XFEL, but also provide insight into the directions to pursue in developing applications of PDMS and PI films in XFEL studies.

Generative adversarial networks (GANs) are used for image enhancement such as single image super-resolution (SISR) and deblurring. The conventional GANs-based image enhancement suffers from two drawbacks that cause a quality degradation due to a loss of detailed information. First, the conventional discriminator network adopts strided convolution layers which cause a reduction in the resolution of the feature map, and thereby resulting in a loss of detailed information. Second, the previous GANs for image enhancement use the feature map of the visual geometry group (VGG) network for generating a content loss, which also causes visual artifacts because the maxpooling layers in the VGG network result in a loss of detailed information. To overcome these two drawbacks, this paper presents a proposal of a new resolution-preserving discriminator network architecture which removes the strided convolution layers, and a new content loss generated from the VGG network without maxpooling layers. The proposed discriminator network is applied to the super-resolution generative adversarial network (SRGAN), which is called a resolution-preserving SRGAN (RPSRGAN). Experimental results show that RPSRGAN generates more realistic super-resolution images than SRGAN does, and consequently, RPSRGAN with the new content loss improves the average peak signal-to-noise ratio (PSNR) by 0.75 dB and 0.32 dB for super-resolution images with the scale factors of 2 and 4, respectively. For deblurring, the visual appearance is also significantly improved, and the average PSNR is increased by 1.54 dB when the proposed discriminator and content loss are applied to the deblurring adversarial network.

Fluidized-bed agglomeration (FBA) is known to modify the structure and rheology of food powders. In this study, guar gum (GG) powders with various concentrations of sucrose binder (0%, 10%, 20%, or 30%) were subjected to fluidized-bed agglomeration. Subsequently, changes in the characteristics of the GG powders were evaluated by using scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), particle size distribution (PSD) analysis, and rheological and dispersibility measurements. SEM images and FTIR spectra revealed surface morphology changes and structural modification, respectively, in the original GG powder after FBA, although the changes observed in FTIR spectra were only slightly dependent on sucrose concentration at low concentrations (0–20%). XRD patterns confirmed that the crystallinity of the GG powder was affected by FBA, but not greatly so by binder concentration. The PSD results showed that the GG particle size was increased by FBA and there was a clear relationship between sucrose concentration (10–30%) and mean particle size. The rheological behavior and dispersibility of GG (properties that are known to be affected by the structure of a powder) were also influenced by sucrose concentration. To sum up, FBA and the concentration of sucrose binder used can serve as factors for modifying GG powder.

Particle agglomeration of fine gum powders to improve their physical and morphological characteristics is of crucial importance. Changes in the physical properties of guar gum, locust bean gum, and carboxymethyl cellulose powders subjected to fluidized-bed agglomeration with various sugar types as the binder were examined. The agglomerates with sugar binders had much larger particles (D50) and higher porosity (ε) than the corresponding fine gum powders, as confirmed by particle-size-distribution analysis and scanning electron microscopy. In particular, the carboxymethyl cellulose agglomerate exhibited much higher D50 and ε values than the original fine gum powder, with sorbitol as the binder resulting in the highest D50 and ε values. Except for guar gum with sorbitol as the binder, the guar gum and locust bean gum agglomerates with the other sugar binders showed lower Carr index and Hausner ratio values (thus exhibiting better flowability and lower cohesiveness) than the original powders, whereas those of the carboxymethyl cellulose agglomerates were higher. These findings indicate that the physical and structural properties of gum powders can be greatly improved according to the type of gum and sugar solution used in the agglomeration process.

Widespread interest in environmental issues is growing. Many studies have examined the effect of distributed generation (DG) from renewable energy resources on the electric power grid. For example, various studies efficiently connect growing DG to the current electric power grid. Accordingly, the objective of this study is to present an algorithm that determines DG location and capacity. For this purpose, this study combines particle swarm optimization (PSO) and the Volt/Var control (VVC) of DG while regulating the voltage magnitude within the allowable variation (e.g., ±5%). For practical optimization, the PSO algorithm is enhanced by applying load profile data (e.g., 24-h data). The objective function (OF) in the proposed PSO method considers voltage variations, line losses, and economic aspects of deploying large-capacity DG (e.g., installation costs) to transmission networks. The case studies validate the proposed method (i.e., optimal allocation of DG with the capability of VVC with PSO) by applying the proposed OF to the PSO that finds the optimal DG capacity and location in various scenarios (e.g., the IEEE 14- and 30-bus test feeders). This study then uses VVC to compare the voltage profile, loss, and installation cost improved by DG to a grid without DG.

Friction between metals is a physical phenomenon that occurs in manufacturing machine tools. This annoying noise implies unnecessary metal contact and deterioration of a mechanical system. In this study, for the monitoring of the friction between two metal surfaces, the acoustic signature was extracted by applying the wavelet transform method to the noise measured from the change in contact force for each state of adhesive and abrasive wear. Experiments were conducted with a constant relative speed between the contacting metal surfaces. For the adhesive wear, the peak signal-to-noise ratio (PSNR) calculated by the wavelet transformation increases with the increasing contact pressure. Opposite trends were observed for the abrasive wear. The proposed index formed a group within a specific range. This ratio exhibited a strong relationship with the wear characteristics and the surface condition. From the proposed index calculated by the wavelet coefficients, the continuous monitoring of the wear influence on the failure of the machine movement operations is achieved by the sound radiation from the contacting surfaces.

Crowdsourcing systems have emerged as cornerstones to collect large amounts of qualified data in various human-powered problems with a relatively low budget. In eliciting the wisdom of crowds, many web-based crowdsourcing platforms have encouraged workers to select top-K alternatives rather than just one choice, which is called “K-approval voting”. This kind of setting has the advantage of inducing workers to make fewer mistakes when they respond to target tasks. However, there is not much work on inferring the correct answer from crowd-sourced data via a K-approval voting. In this paper, we propose a novel and efficient iterative algorithm to infer correct answers for a K-approval voting, which can be directly applied to real-world crowdsourcing systems. We analyze the average performance of our algorithm, and prove the theoretical error bound that decays exponentially in terms of the quality of workers and the number of queries. Through extensive experiments including the mixed case with various types of tasks, we show that our algorithm outperforms Expectation and Maximization (EM) and existing baseline algorithms.