Search Results (7)

There is a paradigm shift to hybrid (AC/DC) networks that integrate both AC and DC to meet growing energy demands, mitigate global warming, and interconnect distributed energy sources (DERs). However, the unique characteristics of AC/DC faults, the mutual interaction of hybrid lines, the harmonic components of converters/inverters, multiple directions of energy flow, and varying current levels have challenged the existing protection algorithms. Therefore, this paper presents a data-driven coordination AC/DC fault protection algorithm. The algorithm utilizes faulty voltage and current signals to retrieve the precise time-domain characteristics of AC, DC, and intersystem (IS) faults to develop the algorithm. The proposed algorithm consists of four stages: stage 1 includes the detection of faults, stage 2 identifies the fault as either AC or DC, stage 3 classifies the respective AC and DC faults, and stage 4 locates the AC/DC fault precisely. The hybrid test system is developed in a MATLAB/Simulink environment, and the data-driven algorithm is trained and tested in Python. The extensive simulation results for multiple fault cases, either AC or DC, and the comparisons of various performance indicators confirm the effectiveness of the developed algorithm, which performs efficiently under a noisy and extended hybrid AC/DC network. Compared to other schemes, the proposed coordination protection approach can enhance the speed and accuracy of hybrid AC/DC networks. Full article

The addition of transit routes transforms urban development by disrupting the existing equilibrium that land parcels have achieved over time and promotes revitalization. It is based on the relationships between land parcel variables and transit route characteristics, including feeder routes and road infrastructure. Traditional parametric methods for explaining this relationship have problems with multicollinearity and generalizability while non-parametric methods are not used with the multiple variables of both transit route and land parcel changes over time. This study applies the C5.0 decision tree algorithm, a non-parametric model that creates a decision tree with leaf nodes that predict the relationship. Using the BRT Lahore case study, the time series data of parcel variables in the 2 km circle of five transit stations before BRT 2010 and after BRT 2018, as well as transit route characteristics including feeder routes and road infrastructure, were collected and analyzed. The model identified eight important predictors and explained the relationship in the form of a flowchart. Property condition emerged as the strongest predictor, followed by property value, parking, population density, land use, building height, access routes, and distance from transit stations, in that order. The results show that well-developed transport infrastructure, parking spaces, and feeder routes enable sustainable urban transformation. Full article

This research aims to develop an intelligent framework for quality control and fault detection in pre-production and post-production systems in Industry 4.0. In the pre-production system, the health of the manufacturing machine is monitored. In this study, we examine the gear system of induction motors used in industries. In post-production, the product is tested for quality using a machine vision system. Gears are fundamental components in countless mechanical systems, ranging from automotive transmissions to industrial machinery, where their reliable operation is vital for overall system efficiency. A faulty gear system in the induction motor directly affects the quality of the manufactured product. Vibration data, collected from the gear system of the induction motor using vibration sensors, are used to predict the motor’s health condition. The gear system is monitored for six different fault conditions. In the second part, the quality of the final product is inspected with the machine vision system. Faults on the surface of manufactured products are detected, and the product is classified as a good or bad product. The quality control system is developed with different deep learning models. Finally, the quality control framework is validated and tested with the evaluation metrics. Full article

Accurate and efficient medicinal plant image classification is of utmost importance as these plants produce a wide variety of bioactive compounds that offer therapeutic benefits. With a long history of medicinal plant usage, different parts of plants, such as flowers, leaves, and roots, have been recognized for their medicinal properties and are used for plant identification. However, leaf images are extensively used due to their convenient accessibility and are a major source of information. In recent years, transfer learning and fine-tuning, which use pre-trained deep convolutional networks to extract pertinent features, have emerged as an extremely effective approach for image-identification problems. This study leveraged the power by three-component deep convolutional neural networks, namely VGG16, VGG19, and DenseNet201, to derive features from the input images of the medicinal plant dataset, containing leaf images of 30 classes. The models were compared and ensembled to make four hybrid models to enhance the predictive performance by utilizing the averaging and weighted averaging strategies. Quantitative experiments were carried out to evaluate the models on the Mendeley Medicinal Leaf Dataset. The resultant ensemble of VGG19+DensNet201 with fine-tuning showcased an enhanced capability in identifying medicinal plant images with an improvement of 7.43% and 5.8% compared with VGG19 and VGG16. Furthermore, VGG19+DensNet201 can outperform its standalone counterparts by achieving an accuracy of 99.12% on the test set. A thorough assessment with metrics such as accuracy, recall, precision, and the F1-score firmly established the effectiveness of the ensemble strategy. Full article

Diabetes and its complications, such as delayed wound healing, are increasing at an alarming rate in India, putting an enormous strain on the country’s limited healthcare resources. Hence, the present study proposes to screen/identify the possible mechanisms and to study the effect of the flavonoid-enriched fraction of Selaginella bryopteris extract against human keratinocyte cell lines (HaCaT) and streptozocin (STZ)-induced diabetic wounds in a male Wistar rat model. Chemical profiling was performed by an MTT assay. The obtained GC–MS analysis results showed the presence of amentoflavone, gallic acid, imidazole, palmitic acid, catechine, L-fucitol, lupeol, and myo-inositol as the major bioactive phytoconstituents. S. bryopteris induces the generation of ROS, the condensation of chromatin in the nucleus, and changes in the membrane potential of mitochondria in HaCaT cell lines. An S. bryopteris-dependent induction of apoptosis-mediated cell death in HaCaT cell lines was confirmed by an AO/PI analysis. Mitochondrial depolarization was reflected in JC-1 staining of cells. The wound size was reduced and epithelialization was enhanced. Keratinocyte migration decreased interleukins, TNF-α, IL-2, and IL-6 and the expression of pro-apoptotic (p53, caspase-3, caspase-9, and Bax) and anti-apoptotic (Bcl-2) genes in a dose-dependent manner. Keratinocyte migration increased antioxidant enzyme levels (CAT, SOD, MDA, and GSH). Wound healing is facilitated through the mitochondria-mediated apoptosis pathway, revealing a new area of diabetic wound therapy. Full article

In this study we aim to examine gene–environment interactions (GxEs) between genes involved with estrogen metabolism and environmental factors related to estrogen exposure. GxE analyses were conducted with 1970 Korean breast cancer cases and 2052 controls in the case-control study, the Seoul Breast Cancer Study (SEBCS). A total of 11,555 SNPs from the 137 candidate genes were included in the GxE analyses with eight established environmental factors. A replication test was conducted by using an independent population from the Breast Cancer Association Consortium (BCAC), with 62,485 Europeans and 9047 Asians. The GxE tests were performed by using two-step methods in GxEScan software. Two interactions were found in the SEBCS. The first interaction was shown between rs13035764 of NCOA1 and age at menarche in the GE|2df model (p-2df = 1.2 × 10⁻³). The age at menarche before 14 years old was associated with the high risk of breast cancer, and the risk was higher when subjects had homozygous minor allele G. The second GxE was shown between rs851998 near ESR1 and height in the GE|2df model (p-2df = 1.1 × 10⁻⁴). Height taller than 160 cm was associated with a high risk of breast cancer, and the risk increased when the minor allele was added. The findings were not replicated in the BCAC. These results would suggest specificity in Koreans for breast cancer risk. Full article

Power loss and voltage instability are major problems in distribution systems. However, these problems are typically mitigated by efficient network reconfiguration, including the integration of distributed generation (DG) units in the distribution network. In this regard, the optimal placement and sizing of DGs are crucial. Otherwise, the network performance will be degraded. This study is conducted to optimally locate and sizing of DGs into a radial distribution network before and after reconfiguration. A multi-objective particle swarm optimization algorithm is utilized to determine the optimal placement and sizing of the DGs before and after reconfiguration of the radial network. An optimal network configuration with DG coordination in an active distribution network overcomes power losses, uplifts voltage profiles, and improves the system stability, reliability, and efficiency. For considering the actual power system scenarios, a penalty factor is also considered, this penalty factor plays a crucial role in the minimization of total power loss and voltage profile enhancement. The simulation results showed a significant improvement in the percentage power loss reduction (32% and 68.05% before and after reconfiguration, respectively) with the inclusion of DG units in the test system. Similarly, the minimum bus voltage of the system is improved by 4.9% and 6.53% before and after reconfiguration, respectively. The comparative study is performed, and the results showed the effectiveness of the proposed method in reducing the voltage deviation and power loss of the distribution system. The proposed algorithm is evaluated on the IEEE-33 bus radial distribution system, using MATLAB software. Full article