Multidisciplinary Digital Publishing Institute (Switzerland)
Multidisciplinary Digital Publishing InstituteNot a member yet
688210 research outputs found
Sort by
Multi-Classification Using YOLOv11 and Hybrid YOLO11n-MobileNet Models: A Fire Classes Case Study
Fires are classified into five types: A, B, C, D, and F/K, according to the components involved in combustion. Recognizing fire classes is critical, since each kind demands a unique suppression approach. Proper fire classification helps to decrease the risk to both life and property. The fuel type is used to determine the fire class, so that the appropriate extinguishing agent can be selected. This study takes advantage of recent advances in deep learning, employing YOLOv11 variants (YOLO11n, YOLO11s, YOLO11m, YOLO11l, and YOLO11x) to classify fires according to their class, assisting in the selection of the correct fire extinguishers for effective fire control. Moreover, a hybrid model that combines YOLO11n and MobileNetV2 is developed for multi-class classification. The dataset used in this study is a combination of five existing public datasets with additional manually annotated images, to create a new dataset covering the five fire classes, which was then validated by a firefighting specialist. The hybrid model exhibits good performance across all classes, achieving particularly high precision, recall, and F1 scores. Its superior performance is especially reflected in the macro average, where it surpasses both YOLO11n and YOLO11m, making it an effective model for datasets with imbalanced classes, such as fire classes. The YOLO11 variants achieved high performance across all classes. YOLO11s exhibited high precision and recall for Class A and Class F, achieving an F1 score of 0.98 for Class A. YOLO11m also performed well, demonstrating strong results in Class A and No Fire with an F1 score of 0.98%. YOLO11n achieved 97% accuracy and excelled in No Fire, while also delivering good recall for Class A. YOLO11l showed excellent recall in challenging classes like Class F, attaining an F1 score of 0.97. YOLO11x, although slightly lower in overall accuracy of 96%, still maintained strong performance in Class A and No Fire, with F1 scores of 0.97 and 0.98, respectively. A similar study employing MobileNetV2 is compared to the hybrid model, and the results show that the hybrid model achieves higher accuracy. Overall, the results demonstrate the high accuracy of the hybrid model, highlighting the potential of the hybrid models and YOLO11n, YOLO11m, YOLO11s, and YOLO11l models for better classification of fire classes. We also discussed the potential of deep learning models, along with their limitations and challenges, particularly with limited datasets in the context of the classification of fire classes
Prototype of Self-Service Electronic Stethoscope to Be Used by Patients During Online Medical Consultations
This article presents the authors’ design of an electronic stethoscope intended for use during online medical consultations for patient auscultation. The goal of the project was to design an instrument that is durable, user-friendly, and affordable. Existing electronic components were used to create the device and a traditional single-sided chest piece. Three-dimensional printing technology was employed to manufacture the prototype. Following the selection of the material, a static tensile strength test was conducted on the printed samples as part of the pre-implementation investigations. Results: Tests on samples made of PLA with a 50% hexagonal infill demonstrated a tensile strength of 36 MPa and an elongation of 4–5%, which was deemed satisfactory for the intended application in the stethoscope’s manufacture. The designed and manufactured electronic stethoscope presented in the article can be connected to headphones or speakers, enabling remote medical consultation. According to the opinion of doctors who tested it, it provides the appropriate sound quality for auscultation. This stethoscope facilitates the rapid detection and recognition of cardiac and respiratory activity in humans
Targeting Asparagine Metabolism in Solid Tumors
Reprogramming of energy metabolism to support cellular growth is a “hallmark” of cancer, allowing cancer cells to balance the catabolic demands with the anabolic needs of producing the nucleotides, amino acids, and lipids necessary for tumor growth. Metabolic alterations, or “addiction”, are promising therapeutic targets and the focus of many drug discovery programs. Asparagine metabolism has gained much attention in recent years as a novel target for cancer therapy. Asparagine is widely used in the production of other nutrients and plays an important role in cancer development. Nutritional inhibition therapy targeting asparagine has been used as an anticancer strategy and has shown success in the treatment of leukemia. However, in solid tumors, asparagine restriction alone does not provide ideal therapeutic efficacy. Tumor cells initiate reprogramming processes in response to asparagine deprivation. This review provides a comprehensive overview of asparagine metabolism in cancers. We highlight the physiological role of asparagine and current advances in improving survival and overcoming therapeutic resistance
Doxycycline Restores Gemcitabine Sensitivity in Preclinical Models of Multidrug-Resistant Intrahepatic Cholangiocarcinoma
Background/Objectives: Intrahepatic cholangiocarcinoma (iCCA) is a malignant liver tumor with a rising global incidence and poor prognosis, largely due to late-stage diagnosis and limited effective treatment options. Standard chemotherapy regimens, including cisplatin and gemcitabine, often fail because of the development of multidrug resistance (MDR), leaving patients with few alternative therapies. Doxycycline, a tetracycline antibiotic, has demonstrated antitumor effects across various cancers, influencing cancer cell viability, apoptosis, and stemness. Based on these properties, we investigated the potential of doxycycline to overcome gemcitabine resistance in iCCA. Methods: We evaluated the efficacy of doxycycline in two MDR iCCA cell lines, MT-CHC01R1.5 and 82.3, assessing cell cycle perturbation, apoptosis induction, and stem cell compartment impairment. We assessed the in vivo efficacy of combining doxycycline and gemcitabine in mouse xenograft models. Results: Treatment with doxycycline in both cell lines resulted in a significant reduction in cell viability (IC50 ~15 µg/mL) and induction of apoptosis. Doxycycline also diminished the cancer stem cell population, as indicated by reduced cholangiosphere formation. In vivo studies showed that while neither doxycycline nor gemcitabine alone significantly reduced tumor growth, their combination led to marked decreases in tumor volume and weight at the study endpoint. Additionally, metabolic analysis revealed that doxycycline reduced glucose uptake in tumors, both as a monotherapy and more effectively in combination with gemcitabine. Conclusions: These findings suggest that doxycycline, especially in combination with gemcitabine, can restore chemotherapy sensitivity in MDR iCCA, providing a promising new strategy for improving outcomes in this challenging disease
The Putative Antidiabetic Effect of Hypericum perforatum on Diabetes Mellitus
Diabetes mellitus (DM), a global disease that significantly impacts public health, has become increasingly common over time. In this review, we aim to determine the potential benefits of St. John’s Wort (SJW) as an adjunct therapy for DM. We gathered information from studies conducted in vitro, in vivo, and in humans. In vitro studies investigated the concentrations of SJW extracts capable of inhibiting certain enzymes or factors involved in the inflammatory pathway, such as the β-signal transducer and activator of transcription 1, nuclear factor κB, methylglyoxal, and oxidative stress (OS). The extract was found to have positive effects on OS and anti-inflammatory properties in DM, suggesting it could serve as a protective agent against diabetic vascular complications, cell damage, and apoptosis. According to in vivo research, the essential components of the extract can stimulate thermogenesis in adipose tissue, inhibit several key inflammatory signaling pathways, and delay the early death of pancreatic β cells, all of which contribute to combating obesity. The extract may also help treat prediabetes and significantly reduce neuropathic pain. Human studies have also confirmed some of these results. However, some of the plant’s side effects need further investigation through clinical research before it can be used to treat DM
The Effect of the MgO/Al2O3 Ratio on the Thermal and Refractory Behaviors of Cordierite Ceramics
In this study, cordierite-based ceramics (2MgO·2Al2O3·5SiO2) were synthesized using high-purity MgO, Al2O3, and SiO2 as starting materials. The influence of the MgO/Al2O3 ratio on various properties, including the thermal behavior, pyrometric cone refractory behavior, phase formation, physical properties, and microstructure of the synthesized ceramics, was systematically analyzed. Increasing the MgO/Al2O3 ratio progressively weakened the cordierite network, leading to lower temperatures for liquid formation and melting. This resulted in reduced viscosity and increased fluidity. Subsequently, the thermal and refractory behaviors were observed at lower temperatures with higher deformation rates under higher MgO/Al2O3 ratios. The lower viscosity of the liquid formed at reduced temperatures contributed to an increase in the density of sintered bodies, reduced porosity, and enhanced shrinkage. X-ray diffraction analysis confirmed that cordierite was the predominant phase in samples sintered at 1300, 1350, and 1400 °C, with higher cordierite formation at higher temperatures. Conversely, the formation of secondary phases, such as spinel, cristobalite, and enstatite, decreased with increasing sintering temperature. Pyrometric cones were then constructed for a range of temperature settings, and their deformation characteristics at specific temperatures were used to evaluate the refractoriness under diverse conditions
Perspectives of Secondary School Educators Teaching Gender and Sexuality in Health Education
High-quality health education in schools plays a critical role in the formation of young people by developing the attitudes, beliefs, and skills needed to adopt and maintain healthy behaviours throughout their lives. Curriculum reform processes ensure that health education is adequately preparing adolescents for the world today and in the future. However, there is little consideration given to the teachers implementing these curriculum reforms, and their ability to integrate changes as they shape their learning and teaching. In this paper, we discuss the worldviews and beliefs of the teachers delivering health education in Western Australia. We present findings from a doctoral grounded theory study within secondary schools to explain the process teachers use as they approach curriculum, particularly after a reform. We investigate how teachers struggle to decide how to present themselves and the new curriculum content in class. Our findings evidence that teachers have determined gender and sexuality content to be controversial, uncomfortable, difficult to teach but also a favourite to teach. Teachers have expressed uncertainty as to what to say in class and have called for further guidance to teach these important life lessons. Curriculums need to constantly change to keep pace with a changing world, so how do we do this in a way that supports teachers and ultimately produces the best education for young people
Preparation and Applications of Multifunctional MXene/Tussah Silk Fabric
The development of functional textiles has become a key focus in recent years, aiming to meet the diverse requirements of modern society. MXene has excellent conductivity, hydrophilicity, and UV resistance, and is widely used in electromagnetic shielding, sensors, energy storage, and photothermal conversion. Tussah silk (TS) is a unique natural textile raw material and has a unique jewelry luster, natural luxury, and a smooth and comfortable feel. However, there are relatively few studies on the functional finishing of TS fabric with Ti3C2Tx MXene. Here, we developed a multifunctional MXene/tussah silk (MXene/TS) fabric by the deposition of Ti3C2Tx MXene sheets on the surface of TS fabric through a simple padding–drying–curing process. The obtained MXene/TS fabric (five cycles) exhibited excellent conductivity (4.8 S/m), air permeability (313.6 mm/s), ultraviolet resistance (ultraviolet protection factor, UPF = 186.3), photothermal conversion (temperature increase of 11 °C), and strain sensing. Thanks to these superior properties, the MXene/TS fabric has broad application prospects in motion monitoring, smart clothing, flexible wearables, and artificial intelligence
An Attention-Based Multidimensional Fault Information Sharing Framework for Bearing Fault Diagnosis
Deep learning has performed well in feature extraction and pattern recognition and has been widely studied in the field of fault diagnosis. However, in practical engineering applications, the lack of sample size limits the potential of deep learning in fault diagnosis. Moreover, in engineering practice, it is usually necessary to obtain multidimensional fault information (such as fault localization and quantification), while current methods mostly only provide single-dimensional information. Aiming at the above problems, this paper proposes an Attention-based Multidimensional Fault Information Sharing (AMFIS) framework, which aims to overcome the difficulties of multidimensional bearing fault diagnosis in a small sample environment. Specifically, firstly, a shared network is designed to capture the common knowledge of the Fault Localization Task (FLT) and the Fault Quantification Task (FQT) and save it to the global feature pool. Secondly, two branching networks for performing FLT and FQT were constructed, and an attentional mechanism (AM) was used to filter out features from the shared network that were more relevant to the task to enhance the branching network’s capability under small samples. Meanwhile, we propose an innovative Dynamic Adjustment Strategy (DAS) designed to adaptively regulate the training weights of FLT and FQT tasks to achieve optimal training results. Finally, extensive experiments are conducted in two cases to verify the effectiveness and superiority of AMFIS
Parameter Analysis and Optimization of a Leakage Localization Method Based on Spatial Clustering
Leakage in water distribution systems (WDSs) causes a waste of water resources and increased carbon emissions. Rapid and accurate leakage localization to reduce the waste of water resources caused by leakages is an important way to overcome the problem. Using spatiotemporal correlation in monitoring data forms the basis of a leakage localization method proposed in a previous study. It is crucial to acknowledge that the chosen parameter settings significantly influence the localization performance of this method. This paper primarily seeks to optimize three essential parameters of this method: localization metrics weight (LMW), score threshold (ST), and the indicator of detection priority (IDP). LMW evaluates the similarity between simulated and measured pressure residuals. ST determines the size of the datasets involved in the spatial clustering, and IDP quantifies the likelihood of a true leakage within the candidate region. The leakage localization method is tested on a realistic full-scale distribution network to assess leakage flow rates and sensor noise. The results show that the optimized parameter settings could improve the efficiency and accuracy of leakage localization. Further, the findings indicate that the optimized parameter settings can enhance the effectiveness and precision of leakage localization