Bone Scan Index (BSI) is an image biomarker for quantification on bone metastasis of cancers. To compute BSI, not only the hotspots (metastasis) but also the bones have to be segmented. Most related researches focus on binary classification on bone scintigraphy: having metastasis or none. Rare studies focus on pixelwise segmentation. In this study we compare three advanced convolutional neural network (CNN) based models to explore the bone segmentation on dataset in house. The best model is Mask R-CNN, which reaches the precision, sensitivity and F1-score: 0.93, 0.87, 0.90 for prostate cancer patients and 0.92, 0.86, 0.88 for breast cancer patients, respectively. The results are the average of 10-fold cross-validation, which reveals the reliability for clinical use on bone segmentation.

Bone metastasis detection and quantification on bone scintigraphy is challenging and clinical important for treatment and patient’s life quality. To develop a CNN based diagnostic system for automated segmentation on bone metastasis regions is non-trivial, especially for a small dataset. A dataset in house comprising 100 breast cancer patients and 100 prostate cancer patients is utilized for this research. The Double U-Net model is adapted through the integration of background removal, adding negative samples, and transfer learning methods for bone metastasis detection. The performance is investigated via 10-fold cross-validation and computed in pixel-wise scale. The best model we achieved has precision of 63.08%, sensitivity of 70.82%, and F1-score of 66.72%. The developed system has the potential to provide pre-diagnostic reports for physicians in final decisions and the calculation of the bone scan index (BSI) with the combination with bone skeleton segmentation.

The ever-burgeoning growth of autonomous unmanned aerial vehicles (UAVs) has demonstrated a promising platform for utilization in real-world applications. In particular, UAV equipped with a vision system could be leveraged for surveillance applications. This paper proposes a learning-based UAV system for achieving autonomous surveillance, in which the UAV can be of assistance in autonomously detecting, tracking, and following a target object without human intervention. Specifically, we adopted the YOLOv4-Tiny algorithm for semantic object detection and then consolidated it with a 3D object pose estimation method and Kalman Filter to enhance the perception performance. In addition, a back-end UAV path planning for surveillance maneuver is integrated to complete the fully autonomous system. The perception module is assessed on a quadrotor UAV, while the whole system is validated through flight experiments. The experiment results verified the robustness, effectiveness, and reliability of the autonomous object tracking UAV system in performing surveillance tasks. The source code is released to the research community for future reference.

For esophageal cancer patients treated with neoadjuvant chemoradiotherapy (nCRT), restaging using F-18-fluorodeoxyglucose (18F-FDG) positron emission tomography computed tomography (PET-CT) following nCRT can detect interval metastases, including liver metastases, in almost 10% of patients. However, in clinical practice, focal FDG liver uptake, unrelated to liver metastases, is observed after chemoradiotherapy. This radiation-induced liver injury (RILI) can potentially lead to overstaging. Liver radiation damage is also a concern during distal esophageal cancer radiotherapy due to proximity to the left liver lobe, typically included in the radiation field. Post-CRT, if FDG activity appears in the left or caudate liver lobes, thorough investigation is needed to confirm or rule out distant metastases. The increased FDG uptake in liver lobes post-CRT often presents a diagnostic dilemma. Distinguishing between radiation-induced liver disease and metastasis is vital for appropriate patient management, necessitating a combination of imaging techniques and an understanding of the factors influencing radiation response. Diagnosis involves identifying new foci of hepatic FDG avidity on PET/CT scans. Geographic regions of hypoattenuation on CT and well-demarcated regions with specific enhancement patterns on contrast-enhanced CT scan and MRI are characteristic of radiation-induced liver disease (RILD). Lack of mass effect on all three modalities (CT, MRI, PET) indicates RILD. Resolution of abnormalities on subsequent examinations also helps in diagnosing RILD. Moreover, it can also help to rule out occult metastases, thereby excluding those patients from further surgery who will not benefit from esophagectomy with curative intent.

When an aircraft malfunctions, quickly and accurately identifying the faulty unit is essential for ensuring normal operation. Unfortunately, maintenance engineers often struggle to acquire the necessary fault-related knowledge due to poor management and utilization of aircraft fault documents. To address this issue, we introduce knowledge graph technology into the field of aircraft fault diagnosis, exploring its construction and application for effective knowledge management. Our work starts by analyzing the critical knowledge elements required for aircraft fault diagnosis and designing a schema layer for fault knowledge graphs. We then we then combine deep learning and heuristic rules to extract fault knowledge from both structured and unstructured data, enabling the construction of aircraft fault knowledge graphs. Finally, we develop a fault question-answering system based on fault knowledge graphs that can accurately give solutions to questions posed by maintenance engineers. Our practice demonstrates that knowledge graphs provide an effective means of managing aircraft fault knowledge, assisting engineers in locating fault reasons accurately and quickly.

Federated learning trains a neural network model using the client's data to maintain the benefits of centralized model training, while maintaining the privacy. However, if the client data are not independently and identically distributed (non-IID) because of different environments, the accuracy of the model may suffer from client drift during training owing to discrepancies in each client's data. This study proposes a personalized federated learning algorithm based on the concept of multitask learning to divide each client model into two layers: a feature extraction layer and a category prediction layer. The feature extraction layer maps the input data to a low-dimensional feature vector space. Furthermore, the parameters of the neural network are aggregated with those of other clients using an adaptive method. The category prediction layer maps low-dimensional feature vectors to the label sample space, with its parameters remaining unaffected by other clients to maintain client uniqueness. The proposed personalized federated learning method produces faster learning model convergence rates and higher accuracy rates for the non-IID datasets in our experiments.

An oceanic radar altimeter such as TOPEX/Poseidon (T/P) is typically for observing elevation changes over the open oceans or large inland lakes/rivers, with limited applications over solid earth due to its large footprint and susceptibility to waveform contamination and slope effect. Here we demonstrate that it is possible to construct a long-term time series of glacier elevation change from T/P-series radar altimeters over two flat surfaces near a glacier terminus and an icefield (Sites A and B, with slopes of 2° and 0.8°) in Mt. Tanggula, Tibet, at elevations over 5400 m. We retracked radar waveforms using the subwaveform threshold algorithm, selected quality altimeter data (1/4 of the original) with nearly the same slope and adjusted the original elevations by fitting with a time-varying, 2nd order surface. The glacier elevation changes at the two sites from T/P (1993–2002) show seasonal elevation oscillations with linear rates at about −3 m/year and abnormal seasonal changes around the 1997–98 El Niño. Site A is over a deep valley in southern Tanggula. Its elevation dropped about 30 m over 1993–2002 (from T/P) and the glacier almost disappeared by 2016 (from altimeters and satellite images). Despite the sporadic Jason-2 and Jason-3 altimeter data, we also derived long-term rates of glacier elevation change over 1993–2017. Landsat-derived glacier area and elevation changes near the two sites confirm the rapid glacier thinning from the altimeters. The glacier meltwater near Site A supplied increasing source water to Chibuzhang Co west of Mt. Tanggula, contributing partially to its accelerated rising lake level. The glacier thinning at Site B (icefield) was correlated with the increased discharge of the Tuotuo River in eastern Mt. Tanggula, a source region of the Yangtze River. The successful detection of glacier thinning at the two sites shows that T/P-series altimeters can serve as a virtual station at a flat glacier spot to monitor long-term glacier elevation changes in connection to climate change. This virtual station concept is particularly useful for inaccessible glaciers, but its implementation faces two challenging issues: increasing the volume of quality altimeter data and improving the ranging accuracy over a targeted mountain glacier spot.

Patients receiving hemodialysis are at risk of vascular access infections (VAIs) and particularly vulnerable to the opportunistic pathogen Staphylococcus aureus. Hemodialysis patients were also at increased risk of infection during the COVID-19 pandemic. Therefore, this study determined the change in the molecular and antibiotic resistance profiles of S. aureus isolates from VAIs during the pandemic compared with before. 102 S. aureus isolates were collected from VAIs between November 2013 and December 2021. Before the pandemic, 69 isolates were collected, 58%, 39.1%, and 2.9% from arteriovenous grafts (AVGs), tunneled cuffed catheters (TCCs), and arteriovenous fistulas (AVFs), respectively. The prevalence of AVG and TCC isolates changed to 39.4% and 60.6%, respectively, of 33 isolates during the pandemic. Sequence type (ST)59 was the predominant clone in TCC-methicillin-resistant S. aureus (MRSA) and AVG-MRSA before the pandemic, whereas the predominant clone was ST8 in AVG-MRSA during the pandemic. ST59 carrying the ermB gene was resistant to clindamycin and erythromycin. By contrast, ST8 carrying the msrA gene was exclusively resistant to erythromycin. The ST distribution for different VAIs changed from before to during the pandemic. The change in antibiotic resistance rate for different VAIs was closely related to the distribution of specific STs.

This study presents an innovative approach for the utilization of industrial by-products and municipal waste in the production of sustainable and environmentally friendly cement mortar. We explored stabilized stainless-steel reduced slag (SSRS) and polyethylene (PE) plastic waste as partial replacements for aggregates. Various engineering properties of the resulting cement mortar specimens, including slump, slump flow, compressive strength, flexural strength, tensile strength, water absorption, and ultrasonic pulse velocity (UPV), were investigated through comprehensive experimental tests. The influence of different water-cement (w/c) ratios and substitution amounts on the engineering properties of the cement mortar samples was thoroughly examined. The findings revealed that an increase in PE substitution adversely affected the overall workability of the cement mortar mixtures, whereas an increase in SSRS amount contributed to enhanced workability. As for the hardened properties, a consistent trend was observed for both cases, with higher w/c ratios and substitution amounts leading to reduced mechanical properties. Water absorption and UPV test results validated the increased formation of porosity with higher w/c ratios and substitution amounts. This study proposes a promising method to effectively repurpose industrial by-products and municipal wastes, transforming them into sustainable construction and building materials. Additionally, a comparative analysis of transportation costs and carbon footprint emissions between SSRS-cement mortar and PE-cement mortar was conducted to assess their environmental impact and sustainability. Generally, higher w/c ratios and replacement levels corresponded to reduced carbon footprint. The geographical location of the source of SSRS and PE remains a challenge and studies to overcome this challenge must be further explored.

ST-segment elevation myocardial infarction (STEMI), a major cause of mortality worldwide, has been shown through epidemiological studies to have a relationship with short-term exposure to air pollutants; however, the association between air pollutants and the outcome of STEMI has not been well studied. The aim of this study was to estimate the impact of air pollutants on the outcomes of STEMI. Data on particulate matter <2.5 μm (PM2.5), <10 μm (PM10), nitrogen dioxide (NO2), and ozone (O3) at each of the 11 air monitoring stations in Kaohsiung City were collected between January 1, 2012, and December 31, 2017. Medical records of non-trauma patients aged > 20 years who had visited the ED with a principal diagnosis of STEMI were extracted. The primary outcome measure was in-hospital mortality. After adjusting for potential confounders and meteorological variables, we found that an increase in the interquartile range (IQR) in NO2 was associated with an elevated risk of in-hospital mortality in patients with STEMI. Moreover, during the warm season, an IQR increase in NO2 corresponded to a higher risk of in-hospital mortality in patients with STEMI on lag 3 (OR=3.266; 95% CI:1.203–8.864, p=0.02). Conversely, an IQR increase in PM10 was associated with an increased risk of in-hospital mortality in patients with STEMI on lag 3 (OR=2.792; 95%CI:1.115–6.993, p=0.028) during the cold season. Our study suggests that exposure to NO2 (during warm season) and PM10 (during cold season) may contribute to a higher risk of poor prognosis in patients with STEMI.

In this study, we propose a radiomics-clinical probability weighted model for the prediction of prognosis for NSCLC. The model combines radiomics features extracted from RT planning images with clinical factors such as age, gender, histology, and tumor stage. CT images with radiotherapy structures of 422 NSCLC patients were retrieved from The Cancer Imaging Archive (TCIA). Radiomic features were extracted from gross tumor volume (GTV). Five machine learning algorithms, namely decision trees (DT), random forests (RF), extreme boost (EB), support vector machine (SVM) and generalized linear model (GLM), were optimized by a voted ensemble machine learning (VEML) model. A probabilistic weighted approach is used to incorporate the uncertainty associated with both radiomic and clinical features and to generate a probabilistic risk score for each patient. The performance of the model is evaluated using a receiver operating characteristic (ROC). Radiomic model, clinical factors model and combined radiomic-clinical probability weighted model demonstrated good performance in predicting NSCLC survival with AUC of 0.941, 0.856 and 0.949 respectively. The combined radiomics-clinical probability weighted enhanced model achieved significantly better performance than radiomic model in 1-year survival prediction (chi-square test, p<0.05). The proposed model has the potential to improve NSCLC prognosis and facilitate personalized treatment decisions.

Blood glucose (BG) monitoring is an important issue for critically ill patients. Previous studies reported that poor sugar control was associated with increased mortality in admitted patients. However, repeated blood glucose monitoring can be resource-consuming and cause a healthcare burden in clinical practice. In this study, we aimed to develop a personalized machine-learning model to predict dysglycemia based on electrocardiogram (ECG) findings. The study included patients with more than 20 ECG records during single hospital admission in the Medical Information Mart for Intensive Care III database, focusing on the lead II recordings, along with the corresponding blood sugar data. We processed the data and used ECG features from each heartbeat as inputs to develop a one-class support vector machine (SVM) algorithm to predict dysglycemia. The model prediction for dysglycemia using a single heartbeat had an AUC level of 0.92 ± 0.09, with a sensitivity of 0.92 ± 0.10 and specificity of 0.84 ± 0.04. Based on 10 s majority voting, the model prediction for dysglycemia improved to an AUC of 0.97 ± 0.06. In this study, we found that a personalized machine-learning algorithm could accurately detect dysglycemia using a single-lead ECG.

Hyperuricemia (HC) was one of the important risk factors for gout, arteriosclerosis, and cardiovascular disease. Animal studies have shown that Lactobacillus plantarum can improve the microbiota, immune regulation, and inhibit the uric acid production. However, it was not clear whether L. plantarum can improve HC and intestinal microbiota. We used potassium oxonate (PO) to induce HC in male SD rats, and then treated L. plantarum TCI227 in a dose dependent (HC+LD, HC+MD, HC+HD) for 4 weeks, and examined weight organs, biochemical examination of blood and urine, and analyzed the intestinal microbiota in feces by 16s rDNA sequence analysis. In this study, TCI227 improved body weight, decreased creatinine, serum uric acid, and increased urine uric acid compared to the HC group. Furthermore, TCI227 increased short-chain fatty acids (SCFAs). In fecal microbiota (family), TCI227 increased the level of Lactobacillaceae, and then decreased the level of Deferribacteres and Prevotellaceae compared to the HC group. Finally, in fecal microbiota (genus), TCI227 decreased the level of Prevotella, and then increased the level of Lactobacillus and Ruminococcus compared to the HC group. This study suggested that TCI227 can improve HC and change the compositions of the intestinal microbiota in PO induced male HC SD rats.

Agriculture is an industry that heavily relies on the environment. The increasing frequency of ex-treme disasters attributable to climate change has adversely affected agricultural land and dis-rupted the stability of the supply of agricultural products. Therefore, we implemented a research program focused on current strategies for adapting to agricultural meteorological disasters. The primary goal was to actively promote disaster prevention information and techniques through information platforms and applications and to provide refined forecasting and an early warning system to enhance preparedness for and recovery from agricultural disasters. This initiative can transform passive mitigation into dynamic action by disseminating crucial information to farmers. We integrated meteorological data and crop characteristics to develop disaster prevention strate-gies for the predisaster, during-disaster, and postdisaster stages to empower farmers to implement autonomous disaster prevention, ensuring their profitability and minimizing the impact of disas-ters. This paper presents an agricultural disaster prevention system involving cross-disciplinary collaboration to establish an early warning system for crop disasters. However, the effectiveness of the early warning system relies on the availability of complete meteorological and crop infor-mation, necessitating high-density agricultural meteorological stations, refined weather forecast services, historical crop damage data, and ongoing research and development on disaster preven-tion technology. In the system, a diversified social media interface is used to provide local farmers with weather forecast information. When impending disasters are identified, the platform acti-vates disaster prevention measures to reduce agricultural losses. This article paper introduces Taiwan’s agricultural disaster prevention system as a complete disaster prevention strategy.

Emerging evidence has shown that microRNAs play pivotal roles in wound healing. MicroRNA-21 (miR-21) was previously found to upregulate in order to fulfill an anti-inflammation role for wounds. Exosomal miRNAs have been identified and explored as essential markers for diagnostic medicine. However, the role of exosomal miR-21 in wounds has yet to be well studied. In order to facilitate the early management of poorly healing wounds, we developed an easy-to-use, rapid, paper-based microfluidic-exosomal miR-21 extraction device to determine wound prognosis in a timely manner. We isolated and then quantitatively examined exosomal miR-21 in wound fluids from normal tissues, acute and chronic wounds. Eight improving wounds displayed lower levels of exosomal miR-21 expression after wound debridement. However, four instances of increased exosomal miR-21 expression levels were notably associated with patients with poor healing wounds despite aggressive wound debridement, indicating a predictive role of tissue exosomal miR-21 for wound outcome. Paper-based nucleic acid extraction device provides a rapid and user-friendly approach for evaluating exosomal miR-21 in wound fluids as a means of monitoring wounds. Our data suggest that tissue exosomal miR-21 is a reliable marker for determining current wound status.

STAT3 is an oncoprotein which has been shown to contribute to drug resistance in multiple myeloma (MM). Nonetheless, the clinical utility of STAT3 inhibitors in treating MM has been limited, partly related to some of their pharmacologic properties. To overcome these challenges, our group had previously packaged STAT3 inhibitors using a novel formulation of nanoparticles (NP) and found encouraging results. In this study, we aimed to further improve the pharmacologic properties of these NP by decorating them with monoclonal anti-CD38 antibodies. NP loaded with S3I-1757 (a STAT3 inhibitor), labeled as S3I-NP, were generated. S3I-NP decorated with anti-CD38 (labeled as CD38-S3I-NP) were found to have a similar nanoparticular size, drug encapsulation and loading as S3I-NP. The release of S3I-1757 at 24 hours was also similar between the two formulations. Using Cy5.5 labeling of the NP, we found that the decoration of anti-CD38 on these NP significantly increased the cellular uptake by two MM cell lines (p<0.001). Accordingly, CD38-S3I-NP showed a significantly lower inhibitory concentration at 50% (IC50) compared to S3I-NP in two IL6-stimulated MM cell lines (p<0.001). In a xenograft mouse model, CD38-S3I-NP significantly reduced the tumor size by 4 folds compared to S3I-NP on day 12 after drug administration (p=0.006). The efficacy of CD38-S3I-NP in suppressing STAT3 phosphorylation in the xenografts was confirmed by using immunocytochemistry and western blot analysis. In conclusion, our study suggests that the decoration of anti-CD38 on NP loaded with STAT3 inhibitors can further improve their therapeutic effects against MM.

Large quantities of clothing are routinely discarded, ending up in landfills that contribute to the environmental crisis; hence, it is worthwhile investigating how to dispose of this waste in a sustainable way. This study considered environmental values and prosocial behavior from a behaviorism theory. The aim was to set up an environmental protection model for the sustainable disposal of consumer clothing. A structural equation modeling analysis of 407 undergraduate and postgraduate consumer data in Taiwan revealed that consumers’ choice to donate clothing was influenced by environmental values and prosocial behaviors. Clothing resale was influenced by the factor of prosocial behavior. However, prosocial consumers used other disposal patterns instead of resale. Clothing reuse was not influenced by either environmental values or prosocial behavior. The results indicated that environmental values and prosocial behavior did in fact influence consumers’ choices regarding clothing donation. However, prosocial consumers used other disposal patterns instead of resale. Behaviorism theory model is effective in analyzing the factors influencing sustainable clothing disposal. To face clothing waste issues, one can intensify traditional values of industriousness and thriftiness, of compassion and sympathy to others, to accelerate the promotion of the sustainable disposal of clothing.

Certain studies have reported various insulin resistance responses to ambient heavy metal pollution, but few have reported such responses to occupational heavy metal pollution. Even fewer have demonstrated a relationship between mixture effects of heavy metals and insulin resistance in welding workers. Overall, we recruited 53 welders and 48 administrative staff from a shipyard located in northern Taiwan. Personal exposure to heavy metals was monitored for PM2.5 and urine. Blood samples from each participant were collected from the antecubital vein after fasting. Urine samples from each participant were collected in the same period as blood samples. The geometric mean levels for chromium (Cr), manganese (Mn), iron (Fe), cobalt (Co), nickel (Ni), copper (Cu), zinc (Zn), and cadmium (Cd) in the PM2.5 of the personal breathing zone and urinary Mn of welders were significantly higher than those in administrative staffs. Ambient Cr, Co, Ni, and Cu levels in the PM2.5 and urinary Cd were positively related to HOMA2-IR after adjusting for personal covariates (PM2.5-Cr: β=0.036, 95%C.I.: 0.002 to 0.070; PM2.5-Co: β=0.040, 95%C.I.: 0.002 to 0.077; PM2.5-Ni: β=0.054, 95%C.I.: 0.013 to 0.094; PM2.5-Cu: β=0.049, 95%C.I.: 0.010 to 0.088; U-Cd: β=0.209, 95%C.I.: 0.052 to 0.366, respectively). Our findings indicated the PM2.5 metal components and urinary metals were associated with increased insulin resistance in shipyard welders.

We study the generalized χ and η cross helicities for non-ideal non-barotropic magnetohydrodynamics (MHD). χ and η, the additional label translation symmetry group, have been used to generalize the cross helicity in ideal flows. Both new helicities are additional topological invariants of ideal MHD. To study there behaviour in non-ideal MHD, we derive the time derivative of both helicities using the non-ideal MHD equations taking viscosity, finite resistivity and heat conduction into account. Physical variables are divided into ideal and non-ideal quantities separately during the mathematical analysis for simplification. The analytical results indicate that χ and η cross helicities are not strict constant of motion in the non-ideal MHD and show the rate of dissipation which is compared to the dissipation of other topological constants of motion.

This study presents a novel approach for quantifying the dynamics of monthly hydraulic geometry relationships in the Jingjiang reach of the Yangtze River by integrating leaf area index (LAI) data as an indicator of riparian vegetation. A comprehensive system of ordinary differential equations (ODEs) couples traditional power-law hydraulic geometry equations with LAI, capturing the influence of erosion control on channel morphology. The periodic delayed response theory and automatic differentiation techniques are employed to enhance the modeling framework's capabilities. Calibration and verification demonstrate the models' effectiveness in capturing overall trends in discharge, width, and depth dynamics, outperforming traditional constant-parameter approaches. However, model predictive abilities are limited under extreme climate conditions due to training on normal climate data. Key assumptions, including the periodic delayed response theory's neglect of peak discharges, simplified representation of artificial cut-offs, and reliance on remotely sensed data with limited local validation, introduce uncertainties. The proposed modeling framework shows potential for broader application to other river systems with appropriate adjustments. Future research could incorporate additional environmental factors, integrate models across scales, couple with advanced simulation techniques, and expand stability assessments beyond the riverbed index. By addressing related challenges, this integrated approach lays a foundation for enhancing understanding of river morphodynamics and channel stability.

Keywords: mHealth; peritoneal dialysis; functionality; usability

Low-grade glioma (LGG) is a highly aggressive disease in the skull. On the other hand, anoikis, a specific form of cell death induced by the loss of cell contact with the extracellular matrix, plays a key role in cancer metastasis. In this study，anoikis-related genes (ANRGs) were used to identify LGG subtypes and to construct a prognostic model for LGG patients. In addition, we explored the immune microenvironment and enrichment pathways between different subtypes. We constructed an anoikis-related gene signature using the TCGA cohort and investigated the differences in clinical features, mutational landscape, immune cell infiltration, etc. between different risk groups. Kaplan-Meier analysis showed that the characteristics of ANRGs in the high-risk group were associated with poor prognosis in LGG patients. The risk score was identified as an independent prognostic factor. The high-risk group had higher immune cell infiltration, tumor mutation load, immune checkpoint gene expression, and ICB treatment response. Functional analysis showed that these high- and low-risk groups had different immune statuses and drug sensitivity. Risk scores were used together with LGG clinicopathological features to construct a nomogram, and DCA analysis showed that the model could enable patients to benefit from clinical treatment strategies.

Depressive disorder in childhood and adolescence is a highly prevalent mood disorder that tends to recur throughout life. Untreated mood disorders can adversely impact a patient's quality of life and cause socioeconomic loss. Thus, an accurate diagnosis and appropriate treatment is crucial. However, until now, diagnoses and treatments are conducted according to clinical symptoms. Objective and biological validation are lacking. This may result in a poor outcome for patients with depressive disorder. Research has been conducted to identify the biomarkers that are related to depressive disorder. Cumulative evidence has revealed that certain immunologic biomarkers in-cluding brain-derived neurotrophic factor (BDNF) and cytokines, gastrointestinal biomarkers, hormones, oxidative stress, and certain hypothalamus-pituitary axis biomarkers are associated with depressive disorder. This article reviews the biomarkers related to the diagnosis and treatment of pediatric depressive disorders. To date, clinical biomarker tests are not yet available for diagnosis or for the prediction of treatment prognosis. However, cytokines such as Interleukin-2, interfer-on-gamma, tumor necrosis factor-alpha, and BDNF have shown significant results in previous studies of pediatric depressive disorder. These biomarkers have the potential to be used for diagnosis, prognostic assessment, and group screening for those at high risk.

High-strength low-alloy steels (HSLAs) are widely used in the structural body components of many domestic motor vehicles owing to their better mechanical properties and greater resistance. The real production process of HSLA steelmaking can be regarded as a model that builds on the relationship between process parameters and product quality attributes. A surrogate modeling method is used, and the resulting production process model can be applied to predict the optimal manufacturing process parameters. We used different methods in this paper to build such a surrogate model, including linear regression, random forests, support vector regression, multilayer perception, and a simplified VGG model. We then applied three bio-inspired search algorithms, namely particle swarm optimization, the artificial bee colony algorithm, and the firefly algorithm, to search for the optimal controllable manufacturing process parameters. Through experiments on 9,000 test samples used for building the surrogate model, and 299 test samples for making the optimal process parameter selection, we found that the combination of a simplified VGG model and the firefly algorithm was the most successful at reaching a success rate of 100%—in other words, when the product quality attributes of all test samples satisfy the mechanical requirements of the end products.

In urban area, one of the great problem is the rise of temperature, which leads to form the urban heat island effect. This paper refers to the trend of the urban surface temperature extracted from the Landsat images from which to consider changes in the formation of surface urban heat island for the north of Ho Chi Minh city in period 1995-2015. Research has identified land surface temperature from thermal infrared band, according to the ability of the surface emission based on characteristics of normalized difference vegetation index NDVI. The results showed that temperature fluctuated over the city with a growing trend and the gradual expansion of the area of the high-temperature zone towards the suburbs. Within 20 years, the trend of the formation of surface urban heat island with two typical locations showed a clear difference between the surface temperature of urban areas and rural areas with space expansion of heat island in 4 times in 2015 compared to 1995. An extreme heat island located in the inner city has an area of approximately 18% compared to the total area of the region. Since then, the solution to reduce the impact of urban heat island has been proposed, in order to protect the urban environment and the lives of residents in Ho Chi Minh City becoming better

Various colors of clothing originating from synthetic dyes are presently causing serious environmental pollution problems, whereas natural dyes extracted from natural indigo plants help reduce the harm to the environment and extend the sustainable use of clothing. This study focuses on the relation between indigo dyeing colors and the environment on the basis of sustainable design. The results reveal that light colors are considered to conform to the sustainable spirit more than dark colors in different indigo colors. In fact, the recyclable light-colored indigo dyeing t-shirt is beneficial in its reuse. Indigo dyeing fabric can be decomposed by a variety of microorganisms; however, the light-colored indigo dyeing takes a short time. Light colored indigo dyeing is more resource-saving when considering dye, water and electricity costs. The results of colors and environmental protection provide scheme references to consumers or industry for clothing collocation of different indigo dyeing colors for a series of blue clothing and carry out the ideal of the sustainability and co-existence between clothing and natural resources.

This paper investigates the hydroelastic responses of offshore floating solar photovoltaic farms (OFPVs) to minimize structural motion. The OFPV usually occupies a large sea space in the order of hectares and due to its huge structural length-to-thickness ratio, the structural deformation under wave action has to be taken into consideration. The flexible deformation of the structure under hydrodynamic loading is termed the hydroelastic response. Due to the relatively long structural length with respect to the encountering wavelength, the diffraction and radiation of waves have to be taken into account to accurately represent the hydrodynamic loadings on the floating platform. The numerical model is first validated by comparing the eigenvalues and eigenvectors of the OFPV obtained from the proposed numerical scheme with their counterparts obtained from an established finite element software. This is followed by the investigation of the hydroelastic response of various OFPVs designed in varying layout configurations. The various layout configurations are obtained by altering the floating modular units’ dimensions as well as the spacing of the OFPVs when deployed adjacent to each other. The optimal configuration that gives the best performance in terms of the overall smallest response, known as compliance, is then suggested. The results suggest that a longish OFPV layout has a lower hydroelastic response and the motion could be further reduced by increasing the global flexural stiffness via layout arrangement.

Malignant cells cultured in three-dimensional (3D) models have been found to be phenotypically and biochemically different from their counterparts cultured conventionally. Since most of these studies employed solid tumor types, how 3D culture affects multiple myeloma (MM) cells is not well understood. Here, we compared MM cells (U266 and RPMI8226) in a 3D culture model with those in conventional culture. While the conventionally cultured cells were present in single cells or small clusters, MM-3D cells grew in large spheroids. We discovered that STAT3 was the pathway that was more activated in 3D in both cell lines. The active form of STAT3 (phospho-STAT3 or pSTAT3), which was absent in MM cells cultured conventionally, became detectable after 1-2 days in 3D culture. This elevated pSTAT3 level was dependent on the 3D environment, since it disappeared after transferring to conventional culture. STAT3 inhibition using a pharmacological agent, Stattic, significantly decreased the cell viability of MM cells and sensitized them to bortezomib in 3D culture. Using an oligonucleotide array, we found that 3D culture significantly increased the expression of several known STAT3 downstream genes implicated in oncogenesis. Since most primary MM tumors are naturally STAT3-active, studies of MM in 3D culture can generate results that are more representative of the disease.

Since sensor-based perception systems are used in autonomous vehicle applications, validating such systems is imperative to guarantee the robustness of the systems before they are being put to use. In this study, a comprehensive corruption-related simulation-based robustness verification and enhancement process for sensor-based perception systems is proposed. Firstly, we present a methodology and scenario-based corruption generation tools for creating diverse simulated test scenarios that can analogously represent real-world traffic environments, especially considering corruption types related to safety concern. Then, an effective corruption similarity filtering algorithm is proposed to remove corruption types with high similarity and identify the representative corruption types to represent all considered corruption types. As a result, we can generate efficient corruption-related robustness test scenarios with less testing time and good scenario coverage. Subsequently, we perform the vulnerability analysis of object detection models to identify model weaknesses and construct an effective training dataset for model vulnerability enhancement. This enhances the tolerance of object detection models to weather and noise-related corruptions, ultimately improving the robustness of the perception system. We employ case studies to demonstrate the feasibility and effectiveness of the proposed robustness verification and enhancement procedures. Additionally, we explore the impact of different "similarity overlap threshold" parameter settings on scenario coverage, effectiveness, scenario complexity (size of training and testing datasets), and time costs.

Based on the major, trace element, and isotope data of the Cenozoic basalts in the study area, it is indicated that the basalts in this region can be classified into three distinct magma systems: alka-li basalts, tholeiite basalts, and Nushan alkali basalts. The geochemical characteristics observed in the studied basalts indicate that the mantle source in this region might have experienced met-asomatic processes prior to partial melting, which could be linked to an ancient subduction pro-cess. Combining the geochemical characteristics in the studied basalts with previous research, we propose that the underlying mantle source in Anhui province may have undergone metaso-matism due to the carbonatitic melts. These melts are believed to have originated from car-bonated eclogite, derived from subducted Pacific slab materials present in the deeper mantle. According to the Sr-Nd isotope data, it is suggested that the mantle source in the Nushan region could be derived from a depleted asthenospheric mantle, while the basaltic composition in the Niugang area may be representative of the mantle's end components associated with the EM-I enriched mantle source present in this study area. Therefore, we propose that the Anhui basalts may have originated from the partial melting of a mantle source representing a mixing of two end-members: DMM (Depleted MORB Mantle) and EM-Ⅰ (Enriched Mantle 1). The Anhui basal-tic magmas may have originated from the partial melting of EM-Ⅰ, potentially influenced by upwelling asthenospheric mantle or asthenospheric diapirism processes. The mantle source in the study area experiences partial melting at 1-5%, 15%, and 1-10%, resulting in the formation of parent magmas for alkali basalt, tholeiitic basalt, and Nushan alkali basalt. Subsequently, the parent magmas experienced fractional crystallization processes, which included minerals like olivine, pyroxene, and plagioclase. This led to the formation of various types of basalt in the study area.

Concussion, also known as mild traumatic brain injury (mTBI), commonly causes transient neurocognitive symptoms, but in some cases, it causes cognitive impairment, including working memory (WM) deficit, which can be long-lasting and impede a patient’s return to work. The predictors of long-term cognitive outcomes following mTBI remain unclear because abnormality is often absent in structural imaging findings. The purpose of the study was to determine whether machine learning-based models using functional magnetic resonance imaging (fMRI) biomarkers and demographic or neuropsychological measures at baseline could effectively predict 1-year cognitive outcomes of concussion. We conducted a prospective, observational study of patients with mTBI who were compared with demographically-matched healthy controls enrolled between September 2015 to August 2020. Baseline assessments were collected within the first week of injury, and follow-ups were conducted at 6 weeks, 3 months, 6 months, and 1 year. Potential demographic, neuropsychological, and fMRI features were selected according to the significance of correlation with the estimated changes in WM ability. The support vector machine classifier was trained using these potential features and estimated changes in WM between the predefined time periods. Patients demonstrated significant cognitive recovery at the third month, followed by worsened performance after 6 months, which persisted until 1 year after concussion. Approximately half of the patients experienced prolonged cognitive impairment at 1-year follow up. Satisfactory predictions were achieved for patients whose WM function did not recover at 3 months (accuracy=87.5%), 6 months (accuracy=83.3%), 1 year (accuracy=83.3%), and performed worse at 1-year follow-up compared to baseline assessment (accuracy=83.3%). This study demonstrated the feasibility of personalized prediction for long-term postconcussive WM outcomes based on baseline fMRI and demographic features, opening a new avenue for early rehabilitation intervention in selected individuals with possible poor long-term cognitive outcomes.

The morphological and compositional complexities of keratinized components make feathers ingenious skin appendages adapted to diverse ecological needs. Frizzling feathers, characterized by their distinct curling phenotypes, offer a unique model to explore the intricate morphogenesis in developing a keratin-based bioarchitecture over a wide range of morphospace. Here, we investigated the heterogeneous allocation of α- and β-keratins in flight feather shafts of homozygous and heterozygous frizzle chickens by analyzing the medulla-cortex integrations using quantitative morphology characterizations across scales. Our results reveal intriguing construction of frizzling feather shaft through modified medulla development, leading to a perturbed balance of the internal biomechanics and, therefore, introducing the inherent natural frizzling compared to those from wild-type chickens. We elucidate how the localized developmental suppression of the α-keratin in the medulla interferes with the growth of the hierarchical keratin organization by changing the internal stress in the frizzling feather shaft. This research not only offers insights into the morphogenetic origin of the inherent bending of frizzling feathers but also facilitates our in-depth understanding of the developmental strategies toward the diverse integuments adapted for ecological needs.

Abstract Background: Occipital neuralgia is a debilitating condition and traditional treatments often provide limited or temporary relief. Recently, ultrasound-guided hydrodissection of the greater occipital nerve (GON) has emerged as a promising minimally invasive approach. Objectives: To describe two novel ultrasound-guided hydrodissection with 5% dextrose for GON and discuss their advantages, disadvantages, and considerations. Methods: Two case reports are presented. Case 1 describes a lateral decubitus approach for hydrodissecting the GON between the semispinalis capitis (SSC) and obliquus capitis inferior (OCI) muscles. Case 2 details a cranial-to-caudal approach for hydrodissecting the GON within the SSC and upper trapezius (UT) muscles when the GON pass through these two muscles. Results: Both patients experienced significant and sustained pain relief with improvements in function and quality of life. Conclusions: Ultrasound-guided GON hydrodissection using 5% dextrose is a promising treatment for occipital neuralgia. The lateral decubitus and cranial to caudal approaches provide additional options to address patient-specific anatomical considerations and preferences.

One of the industries with the fastest rate of growth is healthcare, and this industry's enormous data requires extensive cloud storage. The cloud may offer some protection, but there is no assurance that data owners can rely on it for refuge and privacy amenities. Therefore, it is essential to offer security and privacy protection. Increased customer satisfaction with security and privacy preservation is crucial since healthcare big data and its sources are stored in the cloud and contain increasingly private and sensitive information. However, maintaining privacy and security in an untrusted green cloud environment is difficult, thus the data owner should have complete data control. A new work SecPri-BGMPOP(security and privacy of Boost graph Convolutional Network -Pinpointing-Optimization Performance) is suggested that can offer a solution that involves several different steps in order to handle the numerous problems relating to security and protecting privacy. Initially Boost Graph Convolutional Network Clustering (BGCNC) algorithm, which reduces computational complexity in terms of time and memory measurements, is first applied to the input dataset to begin the clustering process. Second, enlarge by employing a piece of the magnifying bit string to generate a safe key, pinpointing based encryption avoids amplify leakage even if a rival or attacker decrypts the key or asymmetric encryption. Finally, to determine the accuracy of the method, an optimal key was created using a meta-heuristic algorithmic framework called Hybrid fragment horde bland lobo Optimization (HFHBLO). Our proposed method currently kept in a cloud environment, allowing analytics users to utilize it without risking their privacy or security.

Health services research is a multidisciplinary discipline that aims to improve population health by studying healthcare services' organization, delivery, and financing. Over time, the field has worked to define its boundaries and establish a set of core research topics and methods addressing population wellbeing, access, quality, and cost. Although the influence of health technology is significant, our literature survey on health services research showed that SCs in general—containing their management, cost, and policy—had received little attention. Importantly, our assessment also showed that the system’s readiness to handle supply policy and supply device deficiencies like those encountered during pandemic was hardly ever mentioned. Reduced open areas and water bodies, deteriorating infrastructure, and changes in the biological morphology all affect cities’ uncontrollable and unplanned growth. Urban amenities, facilities, and healthcare services were unevenly distributed due to this unchecked urban population development. In light of this, this research suggests two reliable models for site selection issues for one of Hong Kong’s main hospitals. Levels of uncertainty, infeasibility tolerance, and reliability are taken into consideration. Then, two categories of uncertainty—symmetric and bound-ed—were examined. As a result, the challenge of scheduling in uncertainty has been considered, and potential solutions have been presented using a specified probability distribution function. The paper concludes by introducing the specific justice and health problems and outlining four typical strategies. In order to ensure that medications and other healthcare supplies are produced, distributed, and given to patients, a vast network of systems, components, and procedures must be effectively managed. This research contributes in this regard. Also, its grantee fairness in health systems and the populations health locally, nationally, and globally. It highlights health inequity, advances the measurement of health inequity, and promotes public dialogues on health inequity.

The spatial heterogeneity in hydrologic simulations is a key difference between lumped and distributed models. Not all distributed models benefit from pedo-transfer functions based on soil properties and crop-vegetation dynamics. Mostly coarse scale meteorological forcing is used to estimate water balance at the catchment outlet only. Mesoscale hydrologic model (mHM) is one of the rare models that incorporates remote sensing data i.e. leaf area index (LAI) and aspect to improve actual evapotranspiration (AET) simulations and water balance together. The user can select either LAI or aspect to scale PET. However, herein we introduced a new weighting parameter “alphax” that allows user to incorporate both LAI and aspect together for PET scaling. With this mHM code enhancement, the modeler has an also option of using raw PET with no scaling. In this study, streamflow, and AET are simulated using the mesoscale Hydrological Model (mHM) in Main (Germany) basin for the period of 2002-2014. The additional value of PET scaling with LAI and aspect for model performance is investigated using Moderate Resolution Imaging Spectroradiometer (MODIS) AET and LAI products. From 69 mHM parameters, 26 parameters are selected for calibration using Optimization Software Toolkit (OSTRICH). For calibration and evaluation, KGE metric is used for water balance and SPAEF metric is used for evaluating spatial patterns of AET. Our results show that AET performance of the mHM is highest when using both LAI and aspect indicating that LAI and aspect contain valuable spatial heterogeneity information from topography and canopy (e.g., forests, grasslands, and croplands) that should be preserved during modeling. The additional “alphax” parameter makes the model physically more flexible and robust as the model can decide the weights according to the study domain.

This study explores the use of satellite-based LULC (Land Use / Land Cover) data while simultaneously correcting potential evapotranspiration (PET) input with Leaf Area Index (LAI) to increase the performance of a physically distributed hydrologic model. The mesoscale hydrologic model (mHM) was selected for this purpose due to its unique features. Since LAI input informs the model about vegetation dynamics, we incorporated the LAI based PET correction option together with multi-year LULC data. The Globcover land cover data was selected for the single land cover cases, and hybrid of CORINE (coordination of information on the environment) and MODIS (Moderate Resolution Imaging Spectroradiometer) land cover datasets were chosen for the cases with multiple land cover datasets. These two datasets complement each other since MODIS has no separate forest class but more frequent (yearly) observations than CORINE. Calibration period spans from 1990 to 2006 and corresponding NSE (Nash-Sutcliffe Efficiency) values varies between 0.23 and 0.42, while the validation period spans from 2007 to 2010 and corresponding NSE values are between 0.13 and 0.39. The results revealed that the best performance is obtained when multiple land cover datasets are provided to the model and LAI data is used to correct PET, instead of default aspect-based PET correction in mHM. This study suggests that to minimize errors due to parameter uncertainties in physically distributed hydrologic models, adequate information can be supplied to the model with care taken to avoid over-parameterizing the model.

In recent years, governments applied intelligent transportation system (ITS) technique to provide several convenience services (e.g., garbage truck app) for residents. This study proposes a garbage truck fleet management system (GTFMS) and data feature selection and data clustering methods for travel time prediction. A GTFMS includes mobile devices (MD), on-board units, fleet management server, and data analysis server (DAS). When user uses MD to request the arrival time of garbage truck, DAS can perform the procedure of data feature selection and data clustering methods to analyses travel time of garbage truck. The proposed methods can cluster the records of travel time and reduce variation for the improvement of travel time prediction. After predicting travel time and arrival time, the predicted information can be sent to user’s MD. In experimental environment, the results showed that the accuracies of previous method and proposed method are 16.73% and 85.97%, respectively. Therefore, the proposed data feature selection and data clustering methods can be used to predict stop-to-stop travel time of garbage truck.

In the wake of increasing digital fraud, this paper introduces an innovative application of Artificial Intelligence (AI) in detecting fraudulent activities across finance, healthcare, and e-commerce sectors. It presents a detailed analysis of machine learning methodologies, specifically focusing on the advantages of supervised, unsupervised, and deep learning techniques. The paper addresses the challenges such as data imbalance, model interpretability, and ethical implications in AI-based fraud detection. It also discusses the necessity of high-quality datasets and advocates for the integration of traditional and advanced machine learning methods to enhance accuracy and adaptability in fraud identification. However, it acknowledges the limitations including computational demands and overfitting risks. The study underscores the importance of collaborative efforts between AI experts and industry professionals to develop ethical, efficient, and reliable AI solutions for fraud detection.

Ho Chi Minh City (HCMC), the most crowded city and economic hub of Viet Nam, has been experiencing land subsidence over the past decades. This effort aims to contribute the spatial distribution of subsidence in HCMC in its horizontal and vertical components using synthetic aperture radar interferometry (InSAR) time series. To this purpose, an advanced Persistent Scatterers and Distributed Scatterers (PSDS) InSAR technique was applied to two European Space Agency (ESA) Sentinel-1 datasets consisting of 96 ascending and 202 descending images, acquired from 2014 to 2020 over the HCMC area. A time series of 33 COSMO-SkyMed ascending images was also used for comparison. The combination of ascending and descending satellite passes is used to decompose the light of sight velocities into horizontal east-west and vertical components. Taking into account the presence of east-west horizontal motion, our findings indicate that the accuracy of the decomposed vertical velocity can be improved by up to 3 mm/year for Sentinel-1 data. The obtained results revealed that subsidence is most pronounced in the areas along the Sai Gon River, in the northwest-southeast axis, and in the southwest of the city, with a maximum value of 80 mm/yr, which is in accordance with the findings of the literature. The amplitude of east-west horizontal velocities is relatively small and large-scale eastward movement can be observed in the west of the city at a rate of 3-5 mm/yr. This confirmed that the displacement in Ho Chi Minh City area is mainly vertical downward. Together, these results reinforced the remarkable suitability of ESA's SAR Sentinel-1 for subsidence applications, even for non-European countries such as Vietnam and Southeast Asia.

This paper proposes an approach for image data extraction and driving behavior analysis using geographic information and driving data. Information derived from geographic and global positioning systems was used for image data extraction. In addition, we used an onboard diagnostic II and a controller area network bus logger to record driving data for driving behavior analysis. Driving behavior was analyzed using sparse automatic encoders and data exploration to detect abnormal and aggressive behavior. A regression analysis was used to derive the relationship between aggressive driving behavior and road facilities. Several traffic improvements were proposed for specific intersections and roads. The results indicate that lane ratios without lane markings and with straight lane markings are important features that affect aggressive driving behaviors.

Music therapy has served as complementary and alternative medicine for various neurological disorders. Five Phases Music Therapy (FPMT) employs the theory of five phases and five music scales or tones (宫Gong (do), 商Shang (ri), 角Jue (mi), 徵Zhi (so) and 羽Yu (la)) to analyze and treat mind-body illness. In Chinese Medicine (CM), the five music scales are used to connect the human body and the universe, interpret personalities and constitution and analyze the influences of climatic changes on health. FPMT has a self-contained theory and routine of practice application. Large amounts of clinical and fundamental reports have been available and clinical benefits have been obtained. However more systemic clinic research esp. evidence-based and random controlled trials must be performed to validate and optimize its routines and biological and neurological mechanism must be further explored. It’s reasonable to believe that the effective music therapy will attract more attention from the world outside China with the introduction of FPMT.

Quantitative determination of the critical size of inclined coal pillar in old goaf water affected area is of great significance for water damage prevention and safe mining. The critical size of inclined waterproof coal pillar is derived by using theoretical analysis, numerical calculation and field engineering practice for the stability of the waterproof coal pillar in old goaf water affected area of 1303 working face of Dananhu I mine in Xinjiang region. Firstly, a force model of inclined waterproof coal pillar was established to reveal the law that the critical size of the coal pillar increases with the increase of coal seam inclination under the action of asymmetric load. Then, numerical simulation was applied to reveal the dynamic evolution process of plastic deformation-destabilization-destruction of section coal pillar under the influence of mining and single-side water pressure, and the critical size of waterproof coal pillar in study area was determined to be 19.09m. Finally, measures such as pumping and pressure relief and slurry reinforcement were adopted to reduce the deformation rate of the roadway on side of waterproof coal pillar, which ensured the stability of the coal pillar and roadway and the safe recovery of the working face.

High Efficiency Video Coding (HEVC) uses an 8-point filter and a 7-point filter, which are based on the discrete cosine transform (DCT), for the 1/2-pixel and 1/4-pixel interpolations, respectively. In this paper, discrete sine transform (DST)-based interpolation filters (IF) are proposed. The first proposed DST-based IFs (DST-IFs) use 8-point and 7-point filters for the 1/2-pixel and 1/4-pixel interpolations, respectively. The final proposed DST-IFs use 12-point and 11-point filters for the 1/2-pixel and 1/4-pixel interpolations, respectively. These DST-IF methods are proposed to improve the motion-compensated prediction in HEVC. The 8-point and 7-point DST-IF methods showed average BD-rate reductions of 0.7% and 0.3% in the random access (RA) and low delay B (LDB) configurations, respectively. The 12-point and 11-point DST-IF methods showed average BD-rate reductions of 1.4% and 1.2% in the RA and LDB configurations for the Luma component, respectively.

Background: The link between nocturia and aging male symptoms (AMS) has not been scientifically established. This study aimed to measure the degree of severity of AMS that impact on the health-related quality-of-life (HRQoL) in adult males living with nocturia, and to determine the predictive values of nocturnal factors on AMS. Methods: It is an extended analysis of new data collected by using the Hong Kong Traditional AMS (HK-AMS) scale and Cantonese version of the Pittsburgh Sleep Quality Index (PSQI) in a recently published cross-sectional population-based survey. Results: Of the 781 respondents that have completed the set of questionnaires, 68% and 61% of men living with nocturia reported clinically significant (at moderate-to-severe levels) somato-vegetative and sexual AMS, whereas the prevalence and severity were increased with advancing nighttime voiding frequency. The nocturia-specific QoL (NQoL) score and nocturnal frequency were found to be significant predictive factors for composite, somato-vegetative and sexual AMS, in addition to age, global OSQI score, and certain metabolic diseases. Conclusions: Current findings suggested the inclusion of nocturia when measuring the male-specific HRQoL related to aging.

This study explores the public library service evaluation domain through user-generated content on Google Maps, highlighting digital feedback's significant yet underexplored potential in understanding public library patronage across Taiwan's six major cities. Utilizing a mixed-methods research design, this study integrates Google Maps review content analysis with Social Network Analysis to delineate public perceptions and identify areas for service enhancement in public libraries. It innovatively leverages personal experiences extracted from over 60,000 Google Maps reviews to evaluate public library services in cities such as Taipei, New Taipei, Taoyuan, Taichung, Tainan, and Kaohsiung. The research taps into the National Library of Taiwan's National Library Statistics System to provide a robust analysis of library performance and user satisfaction, offering a novel perspective by emphasizing user-centric feedback from Google Maps as a primary data source. This approach provides quantitative data on library usage and geographic distribution and enriches our understanding of the qualitative experiences of library users. In analyzing the keywords from Google Maps reviews of public libraries, we categorize and interpret these under the three core LibQUAL+ dimensions—Affect of Service, Information Control, and Library as Place. The findings expose variances in perceived service quality among the cities, with Kaohsiung and Taichung receiving the highest accolades for service satisfaction. Simultaneously, the study identifies potential areas for improvement, particularly in cities with lower satisfaction ratings like Taipei. This personalized feedback illustrates the intimate relationship between public libraries and their communities, offering invaluable insights for policymakers and library management to enhance service delivery and user experience.

Mechanical ventilation (MV), used in patients with acute lung injury (ALI), induces diaphragmatic myofiber atrophy and contractile inactivity, termed ventilator-induced diaphragm dysfunction. Phosphoinositide 3-kinase- (PI3K-) is crucial in modulating fibrogenesis during the reparative phase of ALI; however, the mechanisms regulating the interactions among MV, myofiber fibrosis, and PI3K- remain unclear. We hypothesized MV with or without bleomycin treatment would increase diaphragm muscle fibrosis through the PI3K- pathway. Five days after receiving a single bolus of 0.075 units bleomycin intratracheally, C57BL/6 mice were exposed to 6 or 10 mL/kg MV for 8 h after receiving 5 mg/kg of AS605240 intraperitoneally. In wild-type mice, bleomycin exposure followed by MV 10 mL/kg prompted significant increases in disruptions of diaphragmatic myofibrillar organization, transforming growth factor-1, oxidative loads, Masson’s trichrome staining, extracellular collagen levels, positive staining of -smooth muscle actin, PI3K- expression, and myonuclear apoptosis (P &lt; 0.05). Decreased diaphragm contractility and peroxisome proliferator activated receptor- coactivator-1 levels were also observed (P &lt; 0.05). MV-augmented bleomycin-induced diaphragm fibrosis and myonuclear apoptosis were attenuated in PI3K-γ-deficient mice and through AS605240-induced inhibition of PI3K- activity (P &lt; 0.05). MV-augmented diaphragm fibrosis after bleomycin-induced ALI is partially mediated through PI3K-γ. Therapy targeting PI3K-γ may ameliorate MV-associated diaphragm fibrosis.

Construction productivity entails a wide range of work combinations. When human resources are scarce, it is critical to replace manpower with machinery, and calculating machinery productivity is crucial. Traditional labor productivity methods, however, cannot address dredging complex multi-attribute decision-making (MADM) problems. Aiming to address the limitations of the traditional labor productivity method, this paper extends the data envelopment analysis (DEA) and proposes a new dredging productivity evaluation method. The proposed method can solve the problem of evaluating various combinations of factors (single-input, multiple-input, sin-gle-output, and multiple-output) and the problem suggesting that the efficiency of the DEA method'scalculation results is equal to 1. The effectiveness of the proposed method was verified using reservoir dredging examples. The simulation results show that the proposed method has broad applicability, can accurately evaluate the related dredging performance issues, and provide directions for construction managers to improve labor productivity.

Huntington's disease (HD) is a progressive and fatal neurodegenerative disease caused by CAG repeat expansion in the coding region of huntingtin (HTT) protein. The accumulation of mutant HTT (mHTT) contributes to neurotoxicity by causing autophagy defects and oxidative stress that ultimately lead to neuronal death. Interestingly, epidemiologic studies have demonstrated that the prevalence of type-2 diabetes, a metabolic disease mainly caused by defective insulin signaling, is higher in patients with HD than in healthy controls. Although the precise mechanisms of mHTT-mediated toxicity remain unclear, the blockade of brain insulin signaling may initiate or exacerbate mHTT-induced neurodegeneration. In this study, we used an in vitro HD model to investigate whether neuronal insulin signaling is involved in mHTT-mediated neurotoxicity. Our results demonstrated that mHTT overexpression significantly impairs insulin signaling and causes apoptosis in neuronal cells. However, treatment with liraglutide, a GLP-1 analogue, markedly restores insulin sensitivity and enhances cell viability. This neuroprotective effect may be attributed to the contribution of the upregulated expression of genes associated with endogenous antioxidant pathways to oxidative stress reduction. In addition, liraglutide stimulates autophagy through AMPK activation, which attenuates the accumulation of HTT aggregates within neuronal cells. Our findings collectively suggest that liraglutide can rescue impaired insulin signaling caused by mHTT and that GLP-1 may potentially reduce mHTT-induced neurotoxicity in the pathogenesis of HD.

Regulatory T cells are key immune cells to initiate tolerable immune responses. TGFβ is the key cytokine of regulatory T cells. TGFβ is also the master cytokine to stimulate tissue fibrosis. Systemic sclerosis is an autoimmune disease related to overt organ fibrosis. Thus, systemic sclerosis is a disease related to regulatory T cell over-representation. Besides, many end stage organ failures including heart failure, liver cirrhosis, uremia, and pulmonary fibrosis also related to tissue fibrosis. Thus, TGFβ producing regulatory T cells are also involved in the pathogenesis of these end stage organ failure. Once we know the significance of TGFβ and it induced tolerable immune reactions in the pathophysiology of end organ failure, we can develop better prevention or therapeutic strategies to combat these detrimental disorders.

Aluminum alloy (Al6061) sheet micro-hole processing is extensively used in smartphones, tablet PC, and smart wearable devices. The micro-hole processing is commonly performed using laser, micro drilling, microstamping, micro discharge, and chemical etching technologies. Micro-stamping technology is characterized by precision and rapid processing, but the precision positioning of the punch head and lower die is one of the major difficulties in micro-hole stamping, especially in manufacturing array micro-holes. This study used stamping without die technology. This technology uses an array punch head to punch the lower die holes on the base, then performs array micro-hole stamping. The experimental results show that a micro-hole array with 37 micro-holes were successfully manufactured and can be reproduced many times. Tapered array micro-holes with a high aspect ratio can be manufactured using this stamping without die technology; the micro-hole depth can be 260μm, the inlet diameter is 116μm, and the outlet diameter is 25μm. This study has successfully developed the feasibility of array micro-hole stamping technology.

Corrosive burns lead to progressive esophageal stricture and dysphagia. There are many trials to prevent Esophageal stricture formation after corrosive burn. This study aimed to access the effects of EW-7197 on prevention for esophageal stricture formation after corrosive esophageal burn. animal study were classified divided into three groups: a healthy group, a control group (corrosive burn without EW-7197), and a treatment group (corrosive burn with EW-7197). Corrosive esophageal burns were produced using 30% NaOH on the lower esophagus. For 3 weeks, the control group received vehicle and the treatment group received 20 mg/kg/day EW-7197. Treatment efficacy was assessed by measuring the stenosis ratio by esophagogram with contrast media on day 21. histologic staining was performed to evaluate the fibrosis area ratio, and western blotting was performed to evaluate fibrotic markers. Among 20 rats that underwent surgery, 14 survived. Three in the treatment group died because of esophageal perforation, and three in the control group died due to their debilitating status. The esophageal stenosis ratio was significantly lower in the treatment group than in the control group (12.1 ± 9.5% and 42.2 ± 8.3%, respectively; p = 0.001). The histologic fibrosis area ratio was also significantly lower in the treatment group (12.5 ± 3.0% and 21.6 ± 2.1%, respectively; p = 0.001). The treatment group showed lower expressions of profibrogenic proteins such as TGF-β1, pSmad3, and α-SMA. EW-7197 may be a good alternative for the prevention esophageal stricture formation after corrosive burn.

As renewable energy generation prediction systems have been introduced into the energy trading market, accurate prediction of solar photovoltaic (PV) energy generation has become a crucial challenge for ensuring stable trade of variable energy. Therefore, it is essential to quantitatively investigate and analyze current prediction technology and develop more advanced prediction systems. In this study, three models for predicting PV energy generation were investigated. Multiple regression, random forest, and gradient boosting machine (GBM) models were con-structed for 15 utility-scale power plants throughout South Korea. Model performance was evaluated in terms of root mean square error (RMSE) and mean absolute error (MAE). The multiple regression-based model had an RMSE of 12.00% and MAE of 9.06%. The random forest model had an RMSE of 11.69% and MAE of 8.61%. The GBM model had an RMSE of 11.34% and MAE of 8.18%, indicating its superior accuracy among the three models in predicting PV energy generation. These findings provide valuable insights that may contribute to the development of advanced PV energy generation models and their application in grid operations.

No research to date has explored the possibility of context-specific, within-relationship fluctuation in attachment security. In this present article, two cross-sectional studies were designed (1) to develop and validate context-specific attachment scales in Traditional-Chinese, and (2) to explore fluctuations in within-parent attachment security between the contexts of sport and academics, in relation to global attachment patterns and indicators of psychological wellbeing. Results indicated that youth can and do perceive within-parent attachment patterns differently depending upon context but that the relationship of such differences to context-specific outcomes is complex. Of particular interest was that the degree of within-parent attachment variability between contexts was clearly and negatively related to indices of psychological wellbeing. This suggests that contextual variation may be a meaningful and useful way to explore within-parent attachment fluctuation.

Predictions of crop production in the Chi basin are of major importance for decision support tools in countries such as Thailand, which aim to increase domestic income and global food security by implementing the appropriate policies. This research aims to establish a predictive model for predicting crop production for an internal crop growth season prior of harvest at the province scale for fourteen provinces in Thailand's Chi basin between 2011 and 2019. We provide approaches for reducing redundant variables and multicollinearity in remotely sensed (RS) and meteorological data to avoid overfitting models using correlation analysis (CA) and variance inflation factor (VIF). Temperature condition index (TCI), normalized difference vegetation index (NDVI), land surface temperature (LSTnight), and mean temperature (Tmean) were the resulting variables in the prediction model with a p-value &lt; 0.05 and a VIF &lt; 5. The baseline data (2011–2017: June to November) were used to train four regression models, which revealed that eXtreme Gradient Boosting (XGBoost), random forest (RF), and XGBoost achieved R2 values of 0.95, 0.94, and 0.93, respectively. In addition, the testing dataset (2018–2019) displayed a minimum root mean square error (RMSE) of 0.18 ton/ha for the optimal solution by integrating variables and applying the XGBoost model. Accordingly, it is estimated that between 2020 and 2022, the total crop production in the Chi basin region would be 7.88, 7.64, and 7.72 million tons, respectively. The results demonstrated that the proposed model is proficient at greatly improving crop yield prediction accuracy when compared to a conventional regression method and that it may be deployed in different regions to assist farmers and policymakers in making more informed decisions about agricultural practices and resource allocation.

A simple bounded stochastic motion is employed for modelling equity price dynamics under shocks, where the prices are constrained to lie between two positive bounds. While the stochastic process has an inaccessible boundary, the other boundary is quasi-bounded, implying that the boundary can be breached if the probability leakage condition is met. The quasi-boundedness of the process at the boundary can thus provide an indicator of possible risk of equities under price shocks. Empirical calibration of model parameters of the proposed process for equities can be performed easily due to the availability of an analytically tractable probability density function which generates fat-tailed distributions consistent with empirical observations. The volatility and mean-reversion of the S&P500 dynamics calibrated by the process is positively and negatively co-integrated respectively with the VIX index representing the level of market distress. The process captures the high likelihood of Hertz’s default about two months earlier, using only information until that point, and before the firm filed for Chapter 11 bankruptcy in May 2020 as a result of the Covid-19 pandemic. Empirical calibration of the process for GameStop’s stock price shows that the short squeeze of the stock occurred when the condition for breaching an upper boundary was met on 14 January 2021, i.e., about two weeks before major short sellers closed out their positions with significant losses. The trading volume of the stock was positively co-integrated with the probability leakage ratio.

The purpose of this study is to apply the convergence-confinement method (CCM) based on the assumption of hydrostatic stress to the surrounding rock excavated in a circular tunnel whose rock mass characteristics meet the requirements of the non-linear Hoek-Brown failure criterion. Based on a consistent and rigorous method of theoretical analysis, a closed-form analytical solution for the stress/displacement of the ground reaction induced by the advancing excavation of the tunnel, especially in the elastic and plastic regions, is derived and investigated under the condition of an isotropic stress state. By taking the value of a confinement loss as an explicit analysis process in an incremental manner, not only is the computationally executable analytical solution achieved, but it can also be performed using a simple spreadsheet. The validity of the analytical solution implemented by the incremental procedure was examined with published data to study the effect of nonlinear failure criteria on the ground reaction curve; in particular the mechanical behavior at the intrados of the tunnel, and the distribution of stress/displacement on the cross-section of the tunnel. A comparison between the published results and the solution proposed in this study, which interprets the closed-form by the incremental procedure, shows consistent and favorable trends.

Change orders have received considerable attention from researchers thus far, but none have considered pricing strategies of change orders through the interaction between general contractors and subcontractors. Previous studies found that contractors’ opportunistic bidding considering beyond-contractual reward (BCR) in the execution stage can be reduced by improving the construction management system and strengthening the supervision of contractors’ performance. However, the BCR remains in ecology of construction engineering. This study proposes an integrated evolutionary game theory-system dynamics model (ET-SD model) and simulates the pricing strategy of change orders between general contractors and subcontractors to explore the root cause of BCR phenomenon. Sensitivity analysis on the evolutionary dynamics of payoff is explored. Results reveal that change orders with BCR maintain Nash equilibrium and evolutionary stable strategy (ESS) unless changing the payoff structure between general and subcontractors’ pricing strategies. This study presents important managerial insights from the evolutionary game perspectives, nature of change orders, and payoff of the alternative.

Postoperative adhesive arachnoiditis is an inflammatory response of the spinal leptomeninges that occurs after surgery, resulting in scar formation in the avascular nature of the arachnoid layer. Clinical manifestations of this condition include pain, sensory deficits, motor dysfunction, reflex abnormalities, and bladder/bowel impairments. In MRI scans, the signs of postoperative adhesive arachnoiditis can vary, but some specific indicators can assist surgeons in locating the lesion and planning surgical interventions more accurately. In this report, we reported a 37-year-old man with postoperative adhesive arachnoiditis after two surgeries for Chiari I malformation, demonstrating that the gradually-forming the delta cord sign with sequential syringomyelia. During the intraoperative examination, we identified the presence of the delta cord sign, which was formed by an arachnoid scar that was tethering the dorsal spinal cord to the dura. This discovery allowed us to precisely pinpoint the location of the arachnoid scar, thereby providing valuable guidance to avoid any unnecessary exploration of innocent structures during the procedure. Other localization signs were also reviewed.

Rose (Rosa spp.), especially R. hybrida, is one of the most popular ornamental plants in the world and the third largest cut flower crop in Taiwan. Rose mosaic disease (RMD), showing mosaic, line patterns and ringspots on leaves, is a common rose disease caused by complex infection of various viruses. Due to pests and diseases, the rose planting area in Taiwan has been decreasing since 2008; however, no rose virus disease has been reported in the past five decades. In the spring of 2020, rose samples showing RMD-like symptoms were observed at an organic farm in Chiayi, central Taiwan. The virome in the farm was analyzed by RNA-seq. Rose genomic sequences were filtered from the obtained reads. The remaining reads were de novo assembled to generate 294 contigs, 50 of which were annotated as viral sequences corresponding to 10 viruses. Through reverse transcription-polymerase chain reaction validation, a total of seven viruses were detected, including six known rose viruses, namely apple mosaic virus, prunus necrotic ringspot virus, rose partitivirus, apple stem grooving virus, rose spring dwarf-associated virus and rose cryptic virus 1, and a novel ilarvirus. After completing the whole genome sequencing and sequence analysis, the unknown ilarvirus was demonstrated as a new species, tentatively named rose ilarvirus 2. This is the first report of the rose viruses in Taiwan.

The mechanism of immune activation and de-activation is very important. Host should trigger effective immunity against pathogen infection. And, host cannot trigger immunity against self-antigens that will lead to detrimental autoimmune disorders. Spleen and liver have opposite roles in mediating immune responses. Spleen is the organ involving in immune activation, and liver is the organ involving in immune de-activation. Sympathetic nerve system and parasympathetic nerve system have opposite roles in mediating immune responses. Sympathetic nerve system is involving in pro-inflammatory, and parasympathetic nerve system is involving in anti-inflammatory. ACTH and glucocorticosteroid have opposite roles in mediating immune responses. ACTH is involving in activating adaptive immunity with inhibiting innate immunity, and glucocorticosteroid is involving in activating innate immunity with inhibiting adaptive immunity. The molecular chaperons, heat shock proteins, which are induced by fever, have the vital roles in immune activation. On the contrary, IgD B cells and gamma-delta T cells are causing immune de-activation via the mechanism of clonal anergy. These mechanism can help us the know the work of immunological pathways better for better control of infectious diseases and autoimmune disorders.

To study the influence of Latin dance and Tai Chi on knee joint proprioception and balance. Method: All experiments were performed in the Beijing Normal University School of Physical Education and Sport Biomechanics Laboratory. An isokinetic test system (Biodex system 4, BS4) and a balance testing system (Biodex Balance System, BBS)from the United States Biodex medical system were used to test related indexes. Results: In the LOS test, the overall score of Latin group is much better than sedentary group. In the ASL test, Latin group have a better balance ability in anterior and posterior direction and overall score than sedentary group(p＜0.05); TAI CHI group have a better balance ability than sedentary group in anterior and posterior direction(p＜0.05)and right and left direction (p＜0.01). When the knee joint was at 15, 30, and 45degree positions, the Tai Chi exercise group was much better than sedentary group (P < 0.05), and the 45 degree position in the Tai Chi group was much better than that in the Latin dance group (P < 0.01).When the knee joint was at the 30 degree position, the quadriceps force sense was significantly better in the Tai Chi group than in the Latin dance and sedentary groups. In a test of the four angles, the hamstring force sense in the Tai Chi exercise group was obviously better than that in the sedentary group (P < 0.05); at the 15 degree position, the Tai Chi group was significantly better than the Latin group (P < 0.05); and at 30 degree position, the hamstring force sense of the Latin group was obviously better than that of the sedentary group (P < 0.05).

Polluted rivers are a primary problem in Changhua County, Taiwan, due to rapid and massive developments in agriculture and industry. In this study, samples consisted of water influent from the Yangzi-Cuo River, Ci-Tong Jiao, Changhua County. Ecology technology, a combination of gravel contact oxidation and surface flow constructed wetlands, was employed to enhance the water quality. Ecology technology is a useful and ordinary process that focuses on original treatment of pollution through chemical, physical, and biological procedures based on the mediums of soil, plant, water, and microorganisms from pure environments. Moreover, this study aimed to minimize the river pollution index (RPI) of the Yangzi-Cuo River by using combinations of gravel contact oxidation and surface flow constructed wetlands. The inflow volume of the water samples in situ was found to be 1534–2261 cubic meters per day (CMD). The pollution in the water sample mostly involved suspended solid (SS), ammoniacal nitrogen (NH₃–N), total phosphorus (TP), total nitrogen (TN), and biochemical oxygen demand (BOD), and the values of the above indexes were found to be 10.0–26.7 mg/L, 0.9–14.0 mg/L, 1.2–14.1 mg/L, 11.1–18.2 mg/L, and 0.5–3.6 mg/L, respectively. Efficiencies of pollution elimination in SS, NH₃–N, TP, TN, and BOD were found to be 24.2–93.1%, 58.3–86.2%, 5.2–85.0%, 59.4–77.2%, and 46.3–76.4%, correspondingly, after purification treatment via ecology technology. Thus, ecology technology is a valid means of purification treatment for polluted rivers.

Current stock market forecasting methods encompass fundamental, technical, emotional, and bargaining factors. Predominantly, price prediction hinges on order volume and price, although correlating these two within existing models proves challenging. This study employs Cycle Generative Adversarial Network (Cycle GAN) to unravel the intricate price-volume relationship, combining it with Bollinger Bands for trading signal analysis, overcoming hurdles in short-term forecasting prevalent in numerical analysis and AI. Focusing on TSMC (2330.TW) stock price, the research leverages Cycle GAN in deep learning to master the price-volume nexus, juxtaposed with LSTM and RNN. Historical TSMC closing prices and transaction counts are model inputs, scrutinizing their interconnectedness for predictions. This innovative approach aligns stock price, volume, market value, taxes, and prior changes via system engineering. By intertwining Bollinger Bands with stock price forecasts, trading signals are distilled, factoring in extended index %b for a comprehensive market picture.

Red djulis (Chenopodium formosanum) is a native cereal plant in Taiwan; it contains abundant polyphenols, betalian and dietary fiber. The appearance of red djulis is bright red. Therefore, it is also called the “ruby of cereals”. The antioxidative activity of red djulis extract is well-understood. However, the antiaging function still remains unclear. This study examined the potential of red djulis extract for enhancing collagen secretion and preventing cutaneous aging using red djulis extracts. The red djulis extracts are comprised of an abundant active component that can effectively enhance the ability of collagen secretion of dermal fibroblasts, prevent the glycation of collagen and resist the damage of ultraviolet light exposure. After fibroblast treatment with red djulis extracts, TGM1, KRT1, KRT10 and SOD2 genes were up-regulated significantly by 2.3, 4.3, 4.4 and 27.3 times, respectively, compared to those of the control group. Additionally, it can increase COL1A2 gene expression by 43% and decrease MMP9 gene expression 33%. Therefore, it was demonstrated that red djulis extracts affect gene expressions related to the skin barrier, antioxidation and collagen. Moreover, we found positive effects on skin barrier integrity, endogenous antioxidant activity and skin collagen-preservation. The preparation of the red djulis extracts is environmental friendly and can promote the economic value of Chenopodium formosanum; thus, the proposed extract is suitable for applications in the development of food products, especially beverages, skin care and cosmetic products.

Conventional methods of crop mapping need ground truth information to train the classifier. Thanks to the frequent acquisition allowed by recent satellite missions (Sentinel 2), we can identify temporal patterns that depend on both phenology and crop management. Some of these patterns are specific to a given crop and thus can be used to map it. Thus, we can substitute ground truth information used in conventional methods with agronomic knowledge. This approach was applied to identify irrigated permanent grasslands (IPG) in the Crau area (Southern France) which play a crucial role in groundwater recharge. The grassland is managed by making three mows during the May-October period which leads to a specific temporal pattern of leaf area index (LAI). The mowing detection algorithm was designed using the temporal LAI signal derived from Sentinel 2 observations. The algorithm includes some filtering to remove noise in the signal that might lead to false mowing detection. A pixel is considered a grassland if the number of detected mows is greater than 1. A data set covering five years (2016-2020) was used. The detection mowing number was done at the pixel level and then results are aggregated at the plot level. A validation data set including 780 plots was used to assess the performances of the classification. We obtained a Kappa index ranging between 0.94-0.99 according to the year. These results were better than other supervised classification methods that include training data sets. The analysis of land-use changes shows that misclassified plots concern grasslands managed less intensively with strong intra-parcel heterogeneity due to irrigation defects or year-round grazing. Time series analysis, therefore, allows us to understand different management practices. Real land-use change in use can be observed, but long time series are needed to confirm the change and remove ambiguities with heterogeneous grasslands.

Ensuring optimal performance and reliability in rotor-bearing systems is crucial for industrial applications. Imbalance and shaft bow in these systems can lead to decreased efficiency and increased vibrations. Early detection and mitigation of a rotor’s faults are essential, and model-based fault identification has gained much attention in the manufacturing industry for years. Over the past two decades, however, the development of fault diagnosis rules with data-driven and artificial intelligence (AI) has become a trend, and in the foreseeable future, AI combined with big data will become mainstream. Nevertheless, the critical role of rotating machinery in manufacturing introduces a challenge, as the availability of fault data is often insufficient. This limitation renders the establishment of diagnostic rules using data-driven methods and AI technologies impractical. In light of these challenges, this study proposes a novel hybrid approach, that combines a physical model with machine learning (ML) techniques for the diagnosis of multi-faults (imbalance and shaft-bow demonstrated) in a Jeffcott rotor. To overcome the lack of real-world faulted, labeled datasets, a physics-based Jeffcott rotor model is first derived and then used to generate abundant fault datasets for ML. Subsequently, simulated data are employed for the training of an artificial neural network (ANN), enabling the network to learn from and analyze the vast array of generated data. The results prove that a well-trained feed-forward neural network (FNN) can accurately isolate and diagnose the imbalance and shaft-bow faults using the simulated data and the real data from the Jeffcott rotor experiment. These physics-based and ML approaches prove effective particularly for multi-fault, offering new possibilities for advanced rotor system monitoring and maintenance strategies in industrial applications.

This paper comprehensively reviews sensors and sensing devices developed or/and proposed so far utilizing two smart materials: electrorheological fluids (ERFs) and magnetorheological materials (MRMs) whose rheological characteristics such as stiffness and damping can be controlled by external stimuli, an electrical voltage for ERFs and a magnetic field for MRMs, respectively. In this review article, the MRMs are classified into magnetorheological fluid (MRF), magnetorheological elastomer (MRE) and magnetorheological plastomer (MRP). To easily understand the history of sensing research using the two smart materials, the order of this review article is organized in a chronological manner of ERF sensors, MRF sensors, MRE sensors and MRP sensors. Among many sensors fabricated from each smart material, one or two sensors or sensing devices are adopted to discuss on the sensing configuration, working principle and specifications such as accuracy and sensitivity. Some sensors adopted in this article include force sensor, tactile device, strain sensor, wearable bending sensor, magnetometer, display device and flux measurement sensor. After briefly describing what have been reviewed in a conclusion, several challenging future works, which should be done for practical applications as sensors or sensing devices, are remarked in terms of new technologies such as artificial intelligence neural network in which several parameters affecting the sensor signals can be precisely tuned or controlled. It is sure that this review article is very helpful to make creative sensors using not only the proposed smart materials but also several different types of smart materials including shape memory alloys and active polymers.

As governments in various countries and regions issue tourism-friendly policies in the post-pandemic period, the voices for tourism consumption recovery and innovative transformation have attracted great attention. Many scholars, experts and industry professionals in the field of festivals are actively exploring efficient solutions for festival marketing and sustainability. It is worth rethinking tourists’ consumption psychology and perceived image toward exiting festival products as well as the antecedents that affect festival loyalty. The Qingdao International Beer Festival, as one of the most influential festivals in China, was selected as the research case for this study. The main goal of this study was to investigate festival consumption promotion mechanism by proposing a conceptual framework in which the associations from destination familiarity and product familiarity to loyal intentions (re-patronage and recommendation) were examined, with mediators of overall festival image, perceived value and overall satisfaction. Furthermore, the moderating role of visitor involvement in the proposed framework was also adequately demonstrated. The research findings are expected to provide a significant reference for the recovery and competitiveness improvement of the entire festival industry.

Systemic lupus erythematosus (SLE) is a prevalent autoimmune condition, yet its alignment with a specific host immunological pathway remains unclear. The THαβ host immunological pathway, recognized for its defense against viruses and prions, has recently emerged as a response to DNA, RNA, and protein pathogens. SLE patients often produce anti-double strand DNA antibodies and anti-nuclear antibodies, suggesting a potential association with the THαβ host immune reaction. Throughout the course of SLE, elevated levels of type 1 interferons, type 3 interferons, interleukin-10, IgG1, and IgA1 are commonly observed. These cytokines and antibody isotypes are indicative of the THαβ host immunological pathway. Similarly, Myasthenia gravis, Grave’s disease, Graft versus host disease, autoimmune hemolytic anemia, immune thrombocytopenia, dermatomyositis, and Sjogren’s syndrome are also linked to THαβ-related type 2 hypersensitivities. Considering the potential association of these diseases with dysregulated THαβ immune responses, therapeutic strategies such as anti-interleukin-10 or anti-interferons α/β could be explored to manage these disorders effectively.

Confronting the dual challenges of rapid urbanization and climate change, although extensive research has investigated the influencing factors and practical strategies of urban carbon emissions and urban vibrancy, respectively, the unclear mutual nexus between them and the development strategy of collaborative optimization requires further in-depth analysis. This study explores the delicate balance between urban vibrancy and low-carbon sustainability within the confines of Beijing’s Fifth Ring Road. By integrating OpenStreetMap, land use, population, and building carbon emission data, we have developed a reproducible method to estimate total carbon emission and emission intensity per capita. Furthermore, we have introduced vibrancy index data to distinguish the vibrancy evaluation of residential and non-residential land and applied cross-combinational classification technology to dissect the spatial correlation between urban carbon emissions and urban vibrancy. The results reveal that the four combination typologies show more significant differences and regularity in residential land. Based on the discovery of spatial correlation, this study puts forward corresponding development strategy suggestions for each typology based on geographical location and the requirements of urban development policies of these four combination typologies. In conclusion, our study highlights the importance of integrating carbon emissions and urban vibrancy comprehensively in sustainable urban planning and proposes that various land use combinations need targeted development strategies to achieve this goal, which need to consider population, energy, service facilities, and other diverse aspects.

Three new secoiridoid constituents Gonocarin A-C (1-3) and a new derivative Gonocarin A monoacetate (4), along with two known lignins pinoresinol (5) and paulownin (6) were isolated from the seed of Gonocaryum calleryanum (Baill.) Becc. The structures of the new metabolites were determined on the basis of extensive spectroscopic analysis, particularly mass spectroscopy and 2D NMR (1H–1H COSY, HMQC, HMBC, and NOESY) spectroscopy. When mouse macrophages RAW264.7 were treated with compounds 1-6 together with LPS -stimulated, a concentration-dependent inhibition of nitric oxide (NO) and tumor necrosis factor (TNF-α) productions were detected. The results confirmed that the Gonocaryum calleryanumrrg could be a potential anti-inflammatory agent.

Solid state sensors having timing capabilities are becoming an absolute need in particle tracking techniques of future experiments at colliders. In this sense, silicon sensors having 3D structure are becoming an interesting solution, due to their intrinsic speed and radiation resistance. A characteristics of such devices is the strict dependence of their performance on their geometric structure, which can be widely optimised by design, thus requiring suitable tools for an accurate modeling of their behaviour. This paper illustrates the development, performance and use of the TCoDe simulator, specifically dedicated to the fast simulation of carrier transportation phenomena in solid state sensors. Some examples of its effectiveness in the design and analysis of 3D sensors is also given.

Arid- and semi-arid Central Asia is particularly sensitive to climate change. The changes in extreme precipitation in Central Asia stemming from climate warming are the subject of intense debate within the scientific community. This study employed a Morlet wavelet analysis to examine the annual occurrence number of extreme precipitation in Central Asia from May to September during the period of 1951–2005. Their modulating planetary-scale climate modes were identified by using a linear regression analysis. Two major scales of the temporal variability were derived: 2–3.9 yr and 4–6 yr. The dominant variability was 2–3.9-yr scale and was associated with the negative phase of the Polar/Eurasia (POL) pattern. The 4–6-yr scale provided a secondary contribution and was closely linked to the negative phase of the North Atlantic Oscillation (NAO). These planetary climate modes acted as precursors of extreme precipitation over Central Asia. The negative phase of POL directly contributed to a negative height anomaly over Central Asia, which was intimately related to extreme precipitation. In contrast, the negative NAO phase possibly manifested as a Rossby wave source, which was subsequently exported to Central Asia through a negative–positive–negative Rossby wave train.

Many theoretical models of iron-based superconductors have been proposed but Tc calculations based on the models are usually missing. We have chosen two models of iron-based superconductors in the literature and then compute the Tc values accordingly: Recently two models have been announced which suggest that superconducting electron concentration involved in the pairing mechanism of iron-based superconductors may have been underestimated, and that the antiferromagnetism and the induced xy potential may even have a dramatic amplification effect on electron-phonon coupling. We use bulk FeSe, LiFeAs and NaFeAs data to calculate the Tc based on these models and test if the combined model can predict the superconducting transition temperature (Tc) of the nanostructured FeSe monolayer well. To substantiate the recently announced xy potential in the literature, we create a two-channel model to separately superimpose the dynamics of the electron in the upper and lower tetrahedral plane. The results of our two-channel model support the literature data. While scientists are still searching for a universal DFT functional that can describe the pairing mechanism of all iron-based superconductors, we base on the ARPES data to propose an empirical combination of DFT functional for revising the electron-phonon scattering matrix in the superconducting state, which ensures that all electrons involved in iron-based superconductivity are included in the computation. Our computational model takes into account this amplifying effect of antiferromagnetism and the correction of the electron-phonon scattering matrix together with the abnormal soft out-of-plane lattice vibration of the layered structure, which allows us to calculate theoretical Tc values of LiFeAs, NaFeAs and FeSe as a function of pressure that correspond reasonably well to the experimental values. More importantly, by taking into account the interfacial effect between an FeSe monolayer and its SrTiO3 substrate as an additional gain factor, our calculated Tc value is up to 91 K high, and provides evidence that the strong Tc enhancement recently observed in such monolayers with Tc reaching 100 K may be contributed from the electrons within the ARPES range.

The oncologic merits of laparoscopic technique for colorectal cancer surgery remain debatable. Eligible patients with non-metastatic colorectal cancer who were scheduled for an elective resection by only one surgeon in a medical institution were randomized to either laparoscopic or open treatment. During this period, total 188 patients received laparoscopic surgery and other 163 patients to open approach. The primary endpoint was cancer-free 5-year survival after operative treatment and secondary endpoint was the tumor recurrence incidence. We found there was no statistically significant difference between open and laparoscopic groups regarding average number of lymph nodes dissected, overall mortality rate, cancer recurrence rate or cancer-free 5-year survival. Nevertheless, laparoscopic approach was more effective for colorectal cancer treatment with shorter hospital stay and less blood loss despite operation time was significantly longer. Meanwhile fewer patients receiving laparoscopic approach developed postoperative urinary tract infection, wound infection, pneumonia or anastomosis leakage, which reached statistical significance. For non-metastatic colorectal cancer patients, laparoscopic surgery resulted in better short-term outcomes whether in total complications and intra-operative blood loss. Though there was no significant statistical difference in terms of cancer-free 5-year survival and tumor recurrence, we favor patients receiving laparoscopic surgery if not contraindicated.

In Taiwan, colorectal cancer is ranked second and third in terms of mortality and cancer incidence, respectively. In addition, medical expenditures related to colorectal cancer are considered to be the third highest. While advances in treatment strategies have provided cancer patients with longer survival, potentially harmful second primary cancers can occur. Therefore, second primary colorectal cancer analysis is an important issue with regard to clinical management. In this study, a novel predictive scheme was developed for predicting the risk factors associated with second colorectal cancer in patients with colorectal cancer by integrating five data mining classification techniques, including support vector machine, random forest, multivariate adaptive regression splines, extreme learning machine, and extreme gradient boosting. In total, 4,287 patients in the datasets provided by three hospital tumor registries were used. Our empirical results revealed that this proposed predictive scheme provided promising classification results and the identification of important risk factors for predicting second colorectal cancer based on accuracy, sensitivity, specificity, and area under the curve metrics. Collectively, our clinical findings suggested that the most important risk factors were the combined stage, age at diagnosis, BMI, surgical margins of the primary site, tumor size, sex, regional lymph nodes positive, grade/differentiation, primary site, and drinking behavior. Accordingly, these risk factors should be monitored for the early detection of second primary tumors in order to improve treatment and intervention strategies.

Artificial sweeteners are widely used food ingredients in beverages and drinks to lower calorie intake which in turn prevent lifestyle diseases such as obesity. Epidemiological evidences suggest that an overdose of artificial sweeteners could result to adverse effects after consumption. Thus, our study aims to systematically explore the potential adverse effects of eight commercial artificial sweeteners, including acesulfame-K, alitame, aspartame, sodium cyclamate, dulcin, neotame, saccharin and sucralose on cardiac performances of zebrafish (Danio rerio) and Daphnia as model animals. Embryonic zebrafish and Daphnia were exposed to eight artificial sweeteners at 100 ppb concentrations and their cardiac performance (heart rate, ejection fraction, fractional shortening, stroke volume, cardiac output and heartbeat regularity) were measured and compared. Saccharin significantly increased the heart rate of zebrafish larvae while a significant decrease was observed in Daphnia. Significant increase was also noted in zebrafish heart rate variability after incubation in acesulfame K, dulcin, sodium cyclamate, and sucralose. However, a significant increase in Daphnia was only observed after incubation in dulcin. Based on Principal Component Analysis (PCA) and hierarchical clustering results, several artificial sweetener samples were species-specific to zebrafish and Daphnia. Our study demonstrates the potential adverse physiological effects of artificial sweeteners in cardiovascular systems of zebrafish larvae and Daphnia.

Background: Neurological disorders are the leading cause disability in Australia and the world. Combating the perceived difficulty of neurology or “neurophobia” and improving physician education is a key component in addressing this problem. We aim to conduct the first study to identify whether neurophobia exists in medical students and junior doctors in an Australian population and try to identify factors that may contribute to this in this population. Methods: A 24 question online validated survey was distributed via email broadcast to all medical students and junior doctors at a metropolitan tertiary care centre in Australia. Responses were collected over 6 weeks with weekly reminder emails for 4 weeks after the initial invitation email. Results: 114 medical students and junior doctors participated in the study. Participants perceived neurology as the most difficult medical speciality compared to 10 other medical specialties (p=0.001). The top three factors contributing to this perceived difficulty were: a lack of understanding of neuroanatomy, lack of diagnostic certainty and lack of clinical exposure. 65% of the participants stated that they had too little planned teaching in neurology with only 36% of the participants having performed a neurology rotation during medical school. Conclusion: The prevalence of neurophobia in this Australian cohort of medical students and junior doctors is consistent with previous findings from around the world. This concerning finding requires further examination into the contributing factors in order to created trials of targeted interventions in order to resolve this.

Due to their excellent physicochemical properties, fluorinated greenhouse gases (F-gases), including hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulfur hexafluoride (SF6), and nitrogen trifluoride (NF3), are used in a variety of applications, but they are potent greenhouse gases. Therefore, they have been blanketed into the list of phase-out under the international protocols or treaties. In this work, the updated statistics of the Taiwan’s national inventory report (NIR) were used to analyze the trends of F-gases (i.e., HFCs, PFCs, SF6 and NF3) emissions during the period of 2000-2020. Furthermore, the regulatory strategies and measures for reducing the emissions of the four F-gases will be summarized to be in accordance with the national and international regulations. With the progressive efforts by the regulatory requirements and the industry’s voluntary reduction, the total F-gases emissions indicated a significant increase from 2,462 kilotons of carbon dioxide equivalents (CO2eq) in 2000 to the peak value (i.e., 12,643 kilotons) of CO2eq in 2004, but sharply decreased from 10,284 kilotons of CO2eq in 2005 to 3,906 kilotons of CO2eq in 2020. It was also found that the most commonly used method for controlling the emissions of F-gases from the semiconductor and optoelectronic industries was based on the thermal destruction-local scrubbing technology.

Blockchain technology can achieve decentralization, multi-party verification, anti-tampering, anonymity, traceability of transactions, and the application of distributed ledger. Countries around the world continue to seek the blockchain business models, technologies and applications, and have different visions and policies for the development of blockchain. This study conducts a comparative policy framework of theoretical analysis of the blockchain technology between the USA and China. Using the innovative policy tools proposed by Rothwell and Zegveld, the above mentioned governments are analyzed from the viewpoint of twelve policy tools. The results show that the USA and China all prefer to use “Environmental-side” policy. The USA has paid more attention to “Legal and regulatory”, “Public services” and “Procurement”. China has the highest proportion of policies in “Political tools”, followed by “Legal & regulatory”, while “Scientific and technical”, “Education” and “Overseas agent” come in third . The blockchain technology has developed vigorously among industries and its applications have gradually diversified. The results are provided to various stakeholders as a reference for policy planning.

In Taiwan, farmers often apply excess compost to ensure adequate crop yield in highly frequent tillage, highly weathered, and lower fertility soils. The potential of biochar (BC) for diminishing soil C mineralization, and improving soil nutrient availability in compost over-fertilized soil is promising, but the study is still under-examined. To test the hypothesis, 434 days in vitro C mineralization kinetics of incubation experiment were conducted. Woody BC 0%, 0.5%, 1.0% and 2.0% (w/w) made of lead tree (Leucaena leucocephala (Lam.) de. Wit) were added to an Oxisols, and two Inceptisols of Taiwan. In each treatment, 5% swine manure compost (2 times recommended amount) was added and served as the over-fertilized soil. The results indicated that soil type strongly influenced the impact of BC addition on soil carbon mineralization potential. Respiration per unit of total organic carbon (total mineralization coefficient, TMC) of three studied soils significantly decreased with BC addition increased. Principal component analysis (PCA) suggested that for retaining more plant nutrients in addition to the effects of carbon sequestration, it is recommended that farmer could use locally produced biochars and composts in highly weathered and highly frequent tillage soil. Adding 0.5%-1% woody BC in soil should be reasonable and appropriate.

In Taiwan, farmers often apply excess compost to ensure adequate crop yield in highly frequent tillage, highly weathered, and lower fertility soils. The potential of biochar (BC) for diminishing soil C mineralization, and improving soil nutrient availability in compost over-fertilized soil is promising, but the study is still under-examined. To test the hypothesis, 434 days in vitro C mineralization kinetics of incubation experiment were conducted. Woody BC 0%, 0.5%, 1.0% and 2.0% (w/w) made of lead tree (Leucaena leucocephala (Lam.) de. Wit) were added to an Oxisols, and two Inceptisols of Taiwan. In each treatment, 5% swine manure compost (2 times recommended amount) was added and served as the over-fertilized soil. The results indicated that soil type strongly influenced the impact of BC addition on soil carbon mineralization potential. Respiration per unit of total organic carbon (total mineralization coefficient, TMC) of three studied soils significantly decreased with BC addition increased. Principal component analysis (PCA) suggested that for retaining more plant nutrients in addition to the effects of carbon sequestration, it is recommended that farmer could use locally produced biochars and composts in highly weathered and highly frequent tillage soil. Adding 0.5%-1% woody BC in soil should be reasonable and appropriate.

In the era of the post-quantum Internet of Things (IoT), the implementation of anti-quantum cryptographic algorithms in numerous terminals can successfully defend against prospective quantum computing assaults. Lattice-based cryptography can withstand quantum computing attacks, making it a viable substitute for the currently prevalent classical public-key cryptographic technique. Nevertheless, the algorithm’s significant time complexity will result in a substantial computational burden on the edge computing chip in the IoT terminal. The computation of polynomial multiplication is the most demanding task in lattice-based cryptographic algorithms. Therefore, the investigation into efficient methods for calculating polynomial multiplication is highly important. Quick number theory transformations (NTT) are a widely employed technique to accelerate polynomial multiplication. This study presents a hardware implementation of an efficient number theory transformation. We utilize a multi-level pipeline architecture in the design to accomplish parallel calculations and execute it on a low-profile Artix7-XC7A100T FPGA device. The performance evaluation results demonstrate that our implementation significantly enhances performance and reduces resource usage compared to other existing proposals on the same platform. We suggested NTT core can be implemented in edge computing chips to enhance computational speed due to its small and low-latency design. The experimental results show that the proposed design, which supports both NTT and inverse NTT, achieves 417-Megahertz and consumes only 541-LUTs on Artix-7 XC7A100T.

Bowlby’s (1969/1982) attachment theory has been employed as a broad and integrative framework to explore human wellness across a range of disciplines. Attachment theory has even been labelled one of the last surviving “grand theories” not to have been completely dismissed, replaced, or extensively reworked (e.g., Carr, 2012; Mercer, 2011). However, despite the ubiquitous nature of some of the theory’s fundamental tenets, there are always possibilities for new conceptual development, extension, and revision. In this paper, we critically explore the idea of “context-specific” attachment within parent-child relationships. We briefly outline critical assumptions and key areas of attachment and articulate potential rationale, conceptualisation, and relevance of contextual attachment.

Background: Most prevalence surveys on nocturia have focused on older populations. This study aimed to measure the nocturia prevalence across the full spectrum of male adults living in Hong Kong, where severity and associated quality-of-life (QoL) were also explored. Methods: A cross-sectional population-based survey was conducted in men aged 18 or above using the ICIQ-NQoL Questionnaire. Results: With 1,239 respondents at age ranged 18-99, the overall nocturia prevalence were found to be 63.0% (ranged 41.6-84.6% at different age groups) and 31.2% (ranged 13.0-56.3% at different age groups), for ≥1 and ≥2 bedtime voiding episodes, respectively. The chance of nocturia was dramatically increased at age 60 or above while both prevalence and voiding frequency were increased with advancing age. About 83% of the nocturia men experienced 1-2 voiding episodes per night, but many of them had self-rated their QoL poor or very poor and indicated moderate-to-high bothersome. Younger men at age 18-39 were found to have high prevalence as 41.6% and almost 30% of them rated poor or very poor QoL. Conclusions: Nocturia was not only affecting the older populations but also caused significant bothersome and negative impacts on QoL in younger males.

Postpartum haemorrhage remains a significant contributor to maternal mortality and morbidity worldwide, particularly in low- and middle-income countries. PPH continues to pose challenges in both developed and developing nations despite being preventable. This review examines the current state of knowledge regarding PPH within the context of Vietnam, a country with its unique healthcare landscape. This review identifies critical areas for improvement and future research directions based on a comprehensive literature analysis, including epidemiological insights, risk factors, prevention strategies, and management approaches. Discrepancies in PPH definitions and prevalence rates across regions highlight the need for tailored interventions and policies. Understanding the specific challenges and opportunities related to PPH in Vietnam is crucial for informing evidence-based interventions and policies to improve maternal health outcomes. This review contributes to the broader global effort to reduce maternal mortality by addressing gaps in knowledge and proposing strategies to enhance PPH prevention and management in Vietnam.

Early debridement and stabilization for pyogenic spondylodiscitis allow early mobilization of the patient and prevent subsequent spinal deformity. Tantalum (Ta) trabecular metal (TM) components have several potential advantages over conventional implant materials, such as its uniformity and structural continuity, strength, low stiffness, high porosity, high coefficient of friction and tantalum trabecular metal enhances the host defense mechanism by increasing leukocyte chemotaxis, phagocytosis, and the bacterial killing rate. A single-stage surgery with a tantalum trabecular metal cage combining two different approaches is sufficient to maintain vertebral stability. This retrospective cohort study included 57 patients, 31 (54%) patients were treated with single-stage debridement and reconstruction with trabecular metal cages, and 26 (45.6%) patients received spine surgery without trabecular metal cages received surgery between January 2018 and March 2021 in our tertiary academic teaching hospital. The CRP values dropped borderline significantly in the TM cage group compared with the non-TM cage group postoperatively. In patients with spondylodiscitis, it is advisable to perform single-stage debridement and reconstruction with a trabecular metal cage, which allows abscess drainage and rapid mobilization, prevents deformity.

Due to the rapid development and popularity of the Internet, cloud computing has become an indispensable application service. However, how to assign various tasks to the appropriate service nodes is an important issue. Based on the reason above, an efficient scheduling algorithm is necessary to enhance the performance of system. Therefore, a Three-Layer Cloud Dispatching (TLCD) architecture is proposed to enhance the performance of task scheduling. In first layer, the tasks need to be distinguished to different types by their characters. Subsequently, the Cluster Selection Algorithm is proposed to dispatch the task to appropriately service cluster in the secondly layer. Besides, a new scheduling algorithm is proposed to dispatch the task to a suitable server in a server cluster to improve the dispatching efficiency in the thirdly layer. Basically, the TLCD architecture can obtain better task completion time than previous works. Besides, our algorithm and can achieve load-balancing and reliability in cloud computing network.

It is a truism that whole energy system models underpin the development of policies for energy system decarbonisation. But recent reviews have thrown doubt on the appropriateness of such models for addressing the multiple goals for future energy systems, in the face of emergent real-world complexity and the evolution of stakeholder’s priorities. Without an understanding of the changing priorities of policy makers and expectations of stakeholders for future systems, system objectives and constraints are likely to be ill-defined, and there is a risk that models may be inadvertently instrumentalised. Adopting a system architecture perspective, the authors have undertaken a three-year programme of research to explore strategies for decarbonising heat in the UK, with interaction with and elicitation of needs from stakeholders at its heart. This paper presents the procedure, methods, and results of an exercise in which experts from stakeholder organisations across the energy system were interviewed. Analysis of interview data reveals two broad approaches to heat decarbonisation which can be broadly defined as either adaptive or transformative. Specific insights gained from these interviews enabled our modelling teams to refocus their work for exploration with a wider circle of stakeholders. Results suggests that this iterative approach to formalising model-policy interaction could improve the transparency and legitimacy of modelling and enhance its impact on policy making.

This paper reviews the requirements for the production, quality, and quality assurance of solid recovered fuels (SRF) that are increasingly used for the substitution of fossil fuels in the indus-trial utilities. The economical, technological and environmental aspects should be considered before using SRF as an alternative fuel. In general, the most important factors determining the commercial production of SRF referred to the calorific values and their available amounts adopted in the starting materials. Furthermore, the low-melting impurities and undesired constituents/elements, including chlorine, sulfur, potassium and sodium, may pose negative impacts on air pollutant emission and slagging & fouling in the process utilities. In this regard, the vent emitted from the industrial utilities must comply with the effluent standards of station-ary sources like particulate, sulfur oxides, nitrogen oxides, heavy metals (e.g., lead, cadmium, and mercury) and dioxins. For these reasons, this work will summarize the legal or regulatory requirements under development in Asian countries (i.e., Japan, South Korea, and Taiwan) and European Union (EU). Finally, the outlook of solid recovered fuel (SRF) in the waste manage-ment and industrial sectors will be addressed to echo the sustainable development goals (SDGs).

In recent years, the demand for coffee has increased tremendously. During the production process, green coffee beans are traditionally screened manually for defective beans before they are packed into coffee bean packages; however, this method is not only time-consuming but also increases the rate of human error due to fatigue. Therefore, this paper proposed a lightweight deep convolutional neural network (LDCNN) for the quality detection system of green coffee beans, which combined depthwise separable convolution (DSC), squeeze-and-excite block (SE block), skip block, and other frameworks. To avoid the influence of low parameters of the lightweight model caused by the model training process, rectified Adam (RA), lookahead (LA), and gradient centralization (GC) were included to improve efficiency; the model was also put into the embedded system. Finally, the local interpretable model-agnostic explanations (LIME) model was employed to explain the predictions of the model. The experimental results indicated that the accuracy rate of the model could reach up to 98.38% and the F1 score could be as high as 98.24% when detecting the quality of green coffee beans. Hence, it can obtain higher accuracy, lower computing time, and lower parameters. Moreover, the interpretable model verified that the lightweight model in this work is reliable, providing the basis for screening personnel to understand the judgment through its interpretability, thereby improving the classification and prediction of the model.

In this study, the resource blocks (RB) are allocated to user equipment (UE) according to the evolutional algorithms for long term evolution (LTE) systems. Particle Swarm Optimization (PSO) algorithm is one of the evolutionary algorithms, based on the imitation of a flock of birds foraging behavior through learning and grouping the best experience. In previous work, the Simple Particle Swarm Optimization (SPSO) algorithm was proposed for RB allocation to enhance the throughput of Device-to-Device (D2D) communications and improve the system capacity performance. In simulation results, with less population size of M = 10, the SPSO can perform quickly convergence to sub-optimal solution in the 100^th generation and obtained sub-optimum performance with more 2 UEs than the Rand method. Genetic algorithm (GA) is one of the evolutionary algorithms, based on Darwinian models of natural selection and evolution. Therefore, we further proposed a Refined PSO (RPSO) and a novel GA to enhance the throughput of UEs and to improve the system capacity performance. Simulation results show that the proposed GA with 100 populations, in 200 generations can converge to suboptimal solutions. Therefore, with comparing with the SPSO algorithm the proposed GA and RPSO can improve system capacity performance with 1.8 and 0.4 UEs, respectively.

Taiwan's bed and breakfast (B&B) development has been more than 25 years ago, from the first generation of retirement to the B&B industry, serving tourists from all over the world accom-modation, providing breakfast and accommodation mode to the present, after the change of employees from the second generation, more and more attention is paid to the cultivation and cultivation of business ability. With the advent of the era of AI technology networking, the ca-pabilities required for B&B operation are more focused on all aspects of service, and the good marketing ability of B&B can promote a green chimney-free tourism economy, which is also a favorite industry of various governments and the public. This study explores the basic vocational competency indicators required for running a B&B, with (1)EQ emotion management skills, (2)Financial management ability, (3)(SK) technical skills, and (4)Marketing and promotion management capabilities. Through the study method, the K-S Z test was determined by the con-sensus conclusion of expert reliability and validity p value

Synthetic cathinones, derived from cathinone found in the plant Catha edulis, represent the second-largest and most frequently seized group of new psychoactive substances. They are considered as β-keto analogs of amphetamine, sharing pharmacological effects with amphetamine and cocaine. This review describes the neurotoxic properties of synthetic cathinones, encompassing their capacity to induce neuroinflammation, dysregulate neurotransmitter systems, and alter in monoamine transporters and receptors. Additionally, it discusses the rewarding and abuse potential of synthetic cathinones drawing from findings obtained through various preclinical animal models, contextualized with other classical psychostimulants. The review also offers an overview of current abuse trends of synthetic cathinones on the illicit drug market, specifying the aspects covered, and underscores the risks they pose to public health.

As renewable energy generation prediction system has been introduced into the energy trading market, making a model to accurately predict the quantity of solar photovoltaic (PV) energy generation has become a significant problem. Moreover, to encourage an accurate prediction of the quantity of energy generation, an incentive system has been implemented for those who predict the quantity of solar PV energy under the error rate of 8%. Therefore, it has become more important to investigate and analyze current prediction technology numerically and develop more advanced prediction system. In this study, we tried to develop a better model to improve the accuracy of solar PV energy generation quantity by comparing three models made with gradient boosting machine (GBM), Model 1, Model 2, Model 3 respectively. Model 1 was built with the whole training data set without any additional preprocessing. After conducting some additional preprocessing procedure to predict solar energy generation more accurately, we made Model 2 with the whole training data set and Model 3 with only upper 10% of energy generation capacity. To compare the accuracy of three models, normalized mean absolute error (nMAE) was used as an evaluation index. The nMAE of Model 1 was 9.64% while the Model 2 showed 8.41%. Also, Model 3, which was constructed with the training set of upper 10% energy generation capacity, outperformed with the nMAE of 8.08%. For further study, to check the effectiveness of models constructed with GBM, a time series model, autoregressive integrated moving average (ARIMA), was also built and the nMAE was compared.

Insulated Gate Bipolar Transistor (IGBT) may convey high current and sustain high breakdown voltages; it may provide high power control. In a sense, how IGBT demonstrates the capability of power control was not specifically described, and the equivalent bipolar junction transistors (BJT) activated by an applied bias to the insulated gate is intriguing. As seen in Figure 1, IGBT shows an equivalent circuit structure containing two types of BJT, (NPN-type Bipolar transistor and PNP-type Bipolar transistor), which can be modeled as diode-associated transistor. Apparently, as a bias applies to the controlling gate, the channel beneath the gate oxide gets strongly inversed and turns out to be conductive just like what happens on metal-oxide-silicon field effect transistor (MOSFET), which provides necessary igniting currents to both BJTs and thus causes high current gain flowing through them-selves from the bottom. A proposed and renewed formula provides addressing the above scenario and successfully fitting the measured data that forms the characteristic curves. Some parameters in the proposed formula, which includes size and mobility associated constant, threshold voltage, and Early-Voltage-related lambda for MOSFET, explain and explore the underlying physics. IGBT thus enjoy high input impedance and high current gain due to MOSFET (Insulated Gate) and BJT, respectively. This formula combines both merits of MOSFET and BJT in order to fit the measured data. Indeed, the formula intimately fits IGBT electrical characteristic curves, and thus may be qualified and served as a model. The new model is then advisable for designing power integrated circuit to exactly achieve power saving, power control, and power efficiency.

The Vu Gia - Thu Bon River Basin (VG-TB) is confronting numerous challenges related to water security (WS), particularly in the face of the impacts of climate change. These challenges manifest in salinity intrusion, altered rainfall patterns, and reduced water supply in downstream areas. To assess the current state of water security in the basin, the study employed statistical analysis methods, Process Analysis Method (PAM), SMART, and Analytic Hierarchy Process (AHP) to develop a comprehensive Assessment Framework for Water Security (AFWS) in the VG-TB. The AFWS encompasses five key dimensions, 17 indicators, and 28 variables, aligning with the UN-Water definition of water security and addressing the Sustainable Development Goal 6 (SDG6) criteria for global water sustainability. It also adheres to the Asian Development Bank's (ADB) approach to water security assessment as outlined in the Asian Water Development Outlook (AWDO) reports. This allows for the computation of a comprehensive Water Security Index (WSI) for specific regions, serving as a foundation for decision-making and policy formulation aimed at enhancing water security in the context of climate change and facilitating sustainable basin development in the future.

The International Health Regulations (2005) promote national capacity in core institutions so countries can better detect, respond to and recover from public health emergencies. In accordance with the ‘all hazards’ approach to public health risk, this systematic review examines poisoning and toxic exposures in Myanmar. A systematic literature search was undertaken to find articles pertaining to poisoning in Myanmar published between 1998 and 2020. A number of poisoning risks are identified in this review including snakebites, heavy metals, drugs of abuse, agro-chemicals and traditional medicine. Patterns of poisoning presented in the literature diverge from poisoning priorities reported in other lower-middle income countries in the region. The expe-rience of professionals working in a Yangon-based poison treatment unit indicates that frequently observed poisoning as a result of substances including pharmaceuticals, methanol, and petroleum products were absent from the literature. Other notable gaps in the available research include assessments of the public health burden of poisoning through self-harm, household exposures to chemicals, paediatric risk and women’s occupational risk of poisoning. There is a limited amount of research available on poisoning outcomes and routes of exposure in Myanmar. Further inves-tigation and research is warranted to provide a more complete assessment of poisoning risk and incidence.

This study is the first to investigate the asymmetric effects of oil price volatility on stock returns for the Ho Chi Minh Stock Exchange (HOSE). We utilized weekly series of VN30-Index, WTI crude oil prices, geopolitical risks (GPR) index and gold prices spanning from 6 February 2012 to 31 December 2023 as data sources. Using a nonlinear Autoregressive Distributed Lag (NARDL) bounds testing approach, we found that in the short-term, oil price volatility has negative asymmetric effects on the market returns. Specifically, in the short-term, a one percent increase in oil price volatility immediately leads to a 2,6868 percent decrease in the market returns while a similar magnitude decrease in oil price volatility is associated with 6.3180 percent increase in the market returns. In addition, the results obtained from the NARDL model indicated that, in the long-term, the negative and positive changes of oil price volatility have significantly negative effects on the market returns. Finally, the findings derived from the error correction model (ECM) show that 98.21 percent of the disequilibria from the previous week are converged and corrected back to the long-term equilibrium in the current week.