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The rapidly solidified prealloyed alpha brass powder with a size range of 40 to 100 μm produced by water atomization process was consolidated using liquid phase sintering process. The relationships between sintering temperature, physic-mechanical properties and microstructural characteristics were investigated. Maximum densification was obtained at 930 °C, under 600 MPa compacting pressure, with 60 min holding time. The microstructure of the sintered brass was influenced by dezincification and structural coarsening during supersolidus liquid phase sintering. As a consequence of Kirkendall effect atomic motion between Cu and Zn atoms caused to dezincification at the grain boundaries and formation of ZnO particles on the pore surfaces. It was concluded that microstructural analysis is in a well agreement with obtained physical and mechanical properties. Also, the amount of liquid phase, which depends on sintering temperature, results in different load bearing cross section areas, and it affects the type of fracture morphologies.

Abstract The DC–DC conversion stage of a three‐stage solid‐state transformer is the most challenging due to the combination of high power, medium voltage, and medium frequency. This combination also causes most of the losses in the DC–DC stage. To address this issue, a new asymmetrical quadruple active bridge series resonant DC–DC converter (AQAB‐SRC) has been proposed as a building block for solid‐state transformers. The converter achieves soft switching of all switches throughout the operating range by extending and using the half‐cycle continuous conduction mode control strategy. It also has fewer high‐frequency transformers compared to other converters, as more bridges are connected to the same multi‐winding transformer. The optimal design of AQAB‐SRC has been analyzed theoretically, and simulation and lab‐scale experimental results have been obtained to validate the feasibility and validity of the proposed topology.

Abstract This paper investigates the changes in the waveform of a sinusoidal carrier resulting from amplitude modulation (AM) process. Based on this analysis, a novel method for extracting amplitude information is proposed. The proposed method uses the behaviour of the amplitude limitation which does not significantly affect the slope of the sinusoidal signal near zero crossing points. A simple comparator is used to convert the changes in sinusoidal slope near zero crossing points into pulse width changes. A simple circuit is proposed which keeps the output pulse width of the comparator constant by a simple control loop. The accuracy of the method is evaluated through simulation and is experimentally tested. If the modulation index is high and the amplitude of the input signal to the detector is limited, the proposed method can yield up at least 9 dB improvement in relative error power. However, if the modulation index is small, the improvement in relative error power can be at least 35 dB compared to other conventional types of AM demodulators.

Abstract DC microgrids are integral to smart grids, enhancing grid reliability, power quality, and energy efficiency while enabling individual grid independence. They combine distributed and renewable energy sources, reducing overall energy consumption. High-gain DC–DC converters are crucial for elevating voltages from low-voltage DC sources like solar panels and wind turbines in DC microgrids. This paper introduces a non-isolated DC–DC converter designed to achieve ultra-high step-up (UHSU) voltage conversion utilizing a two-winding coupled inductor (CI). The propounded UHSU configuration achieves a substantial voltage increase by employing low duty cycles and a decreased turn ratio for the CI, resulting in a smaller core size. Moreover, this UHSU circuit incorporates soft-switching capabilities for both power switches and diodes, enhancing its efficiency. By keeping the voltage stress on the switches low, the design minimizes losses and improves overall efficiency. The operational modes are thoroughly analyzed, and comparisons with other topologies are presented to demonstrate the effectiveness of the proffered UHSU circuit. Finally, the performance of the UHSU circuit is validated through the construction and testing of a 150-W laboratory prototype operating at a switching frequency of 50 kHz, with Vin = 20 V and Vout = 300 V.

Abstract This paper proposes a new soft‐switching bidirectional multiport DC/DC converter for PV/EV applications. The proposed topology is capable of using several different sources simultaneously. Also, it has some benefits including high voltage gain, bidirectional, soft‐switching technique, and multi‐input multi‐output (MIMO) topology. In addition, all inputs of the proposed converter are separate from each other and it causes control of the outputs to be simple in the wake of the common ground of the inputs and outputs. The proposed converter can work on zero current switching (ZCS) conditions on some power semiconductors (power diode/switch) and decreases the switching and conduction losses. It also makes use of semiconductors with lower resistance on‐state (Rds‐on). Using coupled inductor techniques and working of semiconductors under ZCS conditions eliminates the total loss affected by reverse recovery of the power diodes, and a passive clamp circuit can recover the leakage energy released by the leakage inductance of the used coupled inductor. The proposed topology is built and tested in the laboratory. To confirm the accurate operation of the proposed topology, it is tested by Typhoon HIL‐404 emulator at 12 V–12 V–12 V inputs/270 W with 50 kHz switching frequency.

Abstract Background Navigating the complexity of chronic myeloid leukemia (CML) diagnosis and management poses significant challenges, including the need for accurate prediction of disease progression and response to treatment. Artificial intelligence (AI) presents a transformative approach that enables the development of sophisticated predictive models and personalized treatment strategies that enhance early detection and improve therapeutic interventions for better patient outcomes. Methods An extensive search was conducted to retrieve relevant articles from PubMed, Scopus, and Web of Science databases up to April 24, 2023. Data were collected using a standardized extraction form, and the results are presented in tables and graphs, showing frequencies and percentages. The authors adhered to the PRISMA-ScR checklist to ensure transparent reporting of the study. Results Of the 176 articles initially identified, 12 were selected for our study after removing duplicates and applying the inclusion and exclusion criteria. AI’s primary applications of AI in managing CML included tumor diagnosis/classification (n = 9, 75%), prediction/prognosis (n = 2, 17%), and treatment (n = 1, 8%). For tumor diagnosis, AI is categorized into blood smear image-based (n = 5), clinical parameter-based (n = 2), and gene profiling-based (n = 2) approaches. The most commonly employed AI models include Support Vector Machine (SVM) (n = 5), eXtreme Gradient Boosting (XGBoost) (n = 4), and various neural network methods, such as Artificial Neural Network (ANN) (n = 3). Furthermore, Hybrid Convolutional Neural Network with Interactive Autodidactic School (HCNN-IAS) achieved 100% accuracy and sensitivity in organizing leukemia data types, whereas MayGAN attained 99.8% accuracy and high performance in diagnosing CML from blood smear images. Conclusions AI offers groundbreaking insights and tools for enhancing prediction, prognosis, and personalized treatment in chronic myeloid leukemia. Integrated AI systems empower healthcare practitioners with advanced analytics, optimizing patient care and improving clinical outcomes in CML management.

Obsessive-compulsive disorder (OCD) is the fourth most common psychiatric disorder with a significant morbidity rate. Despite various treatment modalities and medications, some patients show no definitive response. The aim of this study is to classify the medications of OCD with machine learning (ML) methods and to compare the classification performances of the decision tree (DT), chi-square automatic interaction detection (CHAID) algorithm, and linear model in ML methods. This research is a descriptive analytical study based on co-word and artificial intelligence methods. The DT models were created with a target (total weight link strength). For hyperparameter optimization, the Gini index was used as the weight total link strength. The performance of the DT model was evaluated based on the prediction model. A total of 116 drugs were extracted from 6574 articles based on co-word analysis, and 56 drugs were classified as the DT's root. These drugs were categorized into six groups in the EWKM diagram. The DT was constructed using the weight.total.link index, with 7 items in Label 3 and 42 items in Label 5 serving as DT leaves. The ML analysis provided valuable insights into the efficacy of various medications such as clomipramine, duloxetine, and pindolol, as well as supplements such as folate, in the treatment of OCD. Treating concomitant diseases, namely hypothyroidism and streptococcal infection could improve the efficacy of treatment.

Abstract Background Global communication, an integral part of modern life, increases the risk of transmitting infectious diseases to individuals. Based on the transtheoretical model (TTM), this study aimed to identify the most effective factors in adherence to health protocols among marketers and guilds. Methods This cross-sectional study was conducted among 400 market sellers and guilds of Ardabil City, Iran, in 2023. The TTM questionnaire was distributed among the participants which included four sub-constructs: (1) stages of change, (2) process of change, (3) self-efficacy, and (4) decisional balance. The data were analyzed using SPSS version 20. One-way ANOVA and linear regression tests were employed to evaluate the prediction of effective factors of the stage transition. Results Most participants (63.5%) were between 21 and 40. Most participants (65.5%) were in the passive stages of change (precontemplation, contemplation, and preparation). Pros (β = 0.133, P

Carrot pomace is a significant agricultural byproduct. Obtained during carrot juice processing. This residue is an appropriate reservoir of constituents with bioactive properties that could be investigated in the development of food constituents and nutritional supplements and in improving the quality and safety of foods. For this purpose, the objective of the present investigation was to extract the polyphenols from carrot pomace utilizing maceration and ultrasound-based extraction (UAE) procedures and to evaluate the antioxidant properties of phenolic constituents. To maximize the extraction of carrot pomace, a response surface approach was used. The optimal mixture of extraction time (A, min), ultrasonication power (B, w), and solvent type (C, v/v) for the highest yield of carrot pomace was found using a three-variable composite rotatable design (CRD). In order to assess different functional groups, Fourier transform infrared spectroscopy was utilized to investigate the extract collected under optimal circumstances. The highest polyphenols (26.53 %) were extracted by ethanol 70 % at 10 min with a sonication power of 250 w. The optimized extract also exhibited significant antioxidant and antimicrobial functions. The total phenolic compounds and scavenging of the DPPH radical were 85 mg GAE/gr and EC50: 55 ± 1 μg/mL, respectively. Together with Staphylococcus aureus, the highest zone of inhibition (12 mm) was identified. Our finding revealed that carrot pomace is an appropriate source of bioactive phenolic constituents, exhibiting antioxidant and antibacterial attributes, and could be applied as a natural preserver for promoting safety and quality properties in food products on an industrial scale.