bluuebunny/crossref_metadata_2025 huggingface dataset:
Utilizing intelligent technologies in construction and demolition waste management: From a systematic review to an implementation framework
Authors: Wu Zezhou , Pei Tianjia , Bao Zhikang , Ng S. Thomas , Lu Guoyang , Chen Ke
Published: June 2024

The rapid increase in global urbanization, along with the growth of the construction industry, highlights the urgent need for effective management of construction and demolition (C&D) waste. Intelligent technologies offer a viable solution to this critical challenge. However, there remains a significant challenge in integrating these technologies into a cohesive framework. This study conducts a quantitative analysis of 214 papers from 2000 to 2023, highlighting the extensive use of artificial intelligence (AI) and building information modeling (BIM), along with geographic information systems (GIS) and big data (BD). A further qualitative analysis of 73 selected papers investigates the use of seven different intelligent technologies in the context of C&D waste management (CDWM). To overcome current limitations in knowledge, future research should concentrate on (1) the comprehensive integration of technology, (2) inclusive studies throughout all lifecycle phases of CDWM, and (3) the continued examination of new technologies, such as blockchain. Based on these insights, this study suggests a strategic framework for the effective implementation of intelligent technologies in CDWM. This framework aims to assist professionals in merging various technologies, undertaking lifecycle-wide research, and narrowing the divide between existing and new technologies. It also lays a solid foundation for future academic work to examine specific intelligent technologies, conduct comparative studies, and refine strategic decisions. Regular updates on technological developments are essential for stakeholders to consistently enhance CDWM standards.
Prospective clinical study of the efficacy and safety of different doses of PEG recombinant human granulocyte colony-stimulating factor (PEG-rhG-CSF) for preventing leukopenia/neutropenia caused by concurrent chemoradiotherapy for cervical cancer
Authors: Yu Tingting , Zhang Yiwei , Li Jiawen , Li Zhuo , Tong Rui
Published: June 2024

Objective

The efficacy and safety of different doses of PEG recombinant human granulocyte colony-stimulating factor (PEG-rhG-CSF) in the prevention of leukopenia/neutropenia during concurrent chemoradiotherapy for cervical cancer were investigated.

Methods

The patients who received concurrent radiotherapy and chemotherapy for cervical cancer from June 2020 to January 2023 were selected to evaluate the difference in efficacy, adverse reactions, and treatment cost of PEG-rhG-CSF in the prevention of leukopenia/neutropenia between the test group and the control group.

Results

The minimum value of leukopenia/neutropenia in the test group was significantly higher than that in the control group (P < 0.05). The incidence of grade ≥ 3 leukopenia/neutropenia was significantly lower than that of the control group (P < 0.05). The number of interrupted days of radiotherapy was less than that of the control group (P < 0.05). PEG-rhG-CSF of different prophylactic doses (100 μg/kg dose or 50 μg/kg dose) had similar prophylactic effects in the test group, OR=1.192 (0.761, 1.868), P=0.443.

Conclusion

Prophylactic application of PEG-rhG-CSF could reduce the incidence of leukopenia/neutropenia in cervical cancer during concurrent chemoradiotherapy. There was no significant difference in efficacy and safety with prophylactic use of drug at doses of 100 μg/kg and 50 μg/kg.
The Effects of Short Video-App-Guided Loving-Kindness Meditation on Affect, Oneness, and Proenvironmental Attitude
Authors: Chen Hao , Liu Chao , Hsu Szu-Erh , Wu Kan , Liu Chia-Yih , Chiou Wen-Ko
Published: June 2024

Abstract: Objective: The study investigated the effects of a short video-app-guided loving-kindness meditation (LKM) on affect, oneness belief, and proenvironmenal behavior. Methods: We recruited 93 college students from a university in China and randomized them to an LKM group (n = 47) and a wait-list control group (n = 46). The app-group engaged in an 8-week LKM intervention, while the wait-list control group underwent no intervention. We measured five major variable factors (positive affect, negative affect, oneness belief, and proenvironmenal behavior) before and after the LKM intervention. Results: In the app-group, oneness belief, positive affect, and proenvironmenal behavior were significantly higher and negative affect was significantly lower than in the wait-list control group. The wait-list control group displayed no noticeable differences in any of the four variants[variables??] between the pretest and posttest. Conclusions: Our findings demonstrate that the short video-app-guided LKM may help to improve oneness belief, positive affect, proenvironmenal behavior and reduce negative affect. The finding of the short video-app-guided LKM’s effect extends our understanding of the integrative effects of positive psychology and digital media on proenvironmenal behavior.
Parameterized Internal Wave Mixing in Three Ocean General Circulation Models
Authors: Brüggemann Nils , Losch Martin , Scholz Patrick , Pollmann Friederike , Danilov Sergey , Gutjahr Oliver , Jungclaus Johann , Koldunov Nikolay , Korn Peter , Olbers Dirk , Eden Carsten
Published: June 2024

The non‐local model of mixing based on internal wave breaking, IDEMIX, is implemented as an enhancement of a turbulent kinetic energy closure model in three non‐eddy resolving general circulation ocean models that differ in the discretization and choice of computational grids. In IDEMIX internal wave energy is generated by an energy flux resulting from near‐inertial waves induced by wind forcing at the surface, and at the bottom, by an energy flux that parameterizes the transfer of energy between baroclinic and barotropic tides. In all model simulations with IDEMIX, the mixing work is increased compared to the reference solutions without IDEMIX, reaching values in better agreement with finestructure observations. Furthermore, the horizontal structure of the mixing work is more realistic as a consequence of the heterogeneous forcing functions. All models with IDEMIX simulate deeper thermocline depths related to stronger shallow overturning cells in the Indo‐Pacific. In the North Atlantic, deeper mixed layers in simulations with IDEMIX are associated with an increased Atlantic overturning circulation and an increase of northward heat transports toward more realistic values. The response of the deep Indo‐Pacific overturning circulation and the weak bottom cell of the Atlantic to the inclusion of IDEMIX is incoherent between the models, suggesting that additional unidentified processes and numerical mixing may confound the analysis. Applying different tidal forcing functions leads to simulation differences that are small compared to differences between the different models or between simulations with IDEMIX and without IDEMIX.
Black Space Week Celebrates Fifth Anniversary
Authors: Cartier Kimberly
Published: June 2024

The annual event has grown from a viral hashtag to a global uplifting of Black experiences with space.
Utility of plasma anti-gSG6-P1 IgG levels in determining changes in Anopheles gambiae bite rates in a rural area of Cameroon
Authors: Nguetsa Glwadys Cheteug , Elanga-Ndille Emmanuel , Essangui Same Estelle Géraldine , Nganso Keptchouang Tatiana , Mandeng Stanilas Elysée , Ekoko Eyisap Wolfgang , Binyang Jérome Achille , Fogang Balotin , Nouage Lynda , Piameu Micheal , Ayong Lawrence , Etang Josiane , Wanji Samuel , Eboumbou Moukoko Carole Else
Published: June 2024

The applicability of the specific human IgG antibody response to Anopheles gambiae salivary Gland Protein-6 peptide 1 (gSG6-P1 salivary peptide) as a biomarker able to distinguish the level of exposure to mosquito bites according to seasonal variations has not yet been evaluated in Central African regions. The study aimed to provide the first reliable data on the IgG anti-gSG6-P1 response in rural area in Cameroon according to the dry- and rainy-season. Between May and December 2020, dry blood samples were collected from people living in the Bankeng village in the forest area of the Centre region of Cameroon. Malaria infection was determined by thick-blood smear microscopy and multiplex PCR. The level of IgG anti-gSG6-P1 response, was assessed by enzyme-linked immunosorbent assay. Anopheles density and aggressiveness were assessed using human landing catches. The prevalence of malaria infection remains significantly higher in the rainy season than in the dry season (77.57% vs 61.44%; p = 0.0001). The specific anti-gSG6-P1 IgG response could be detected in individuals exposed to few mosquito bites and showed inter-individual heterogeneity even when living in the same exposure area. In both seasons, the level of anti-gSG6-P1 IgG response was not significantly different between Plasmodium infected and non-infected individuals. Mosquito bites were more aggressive in the rainy season compared to the dry season (human biting rate-HBR of 15.05 b/p/n vs 1.5 b/p/n) where mosquito density was very low. Infected mosquitoes were found only during the rainy season (sporozoite rate = 10.63% and entomological inoculation rate-EIR = 1.42 ib/p/n). The level of IgG anti-gSG6-P1 response was significantly higher in the rainy season and correlated with HBR (p ˂ 0.0001). This study highlights the high heterogeneity of individual’s exposure to the Anopheles gambiae s.l vector bites depending on the transmission season in the same area. These findings reinforce the usefulness of the anti-gSG6-P1 IgG response as an accurate immunological biomarker for detecting individual exposure to Anopheles gambiae s.l. bites during the low risk period of malaria transmission in rural areas and for the differentiating the level of exposure to mosquitoes.
Effects of different types of exercise intensity on improving health-related physical fitness in children and adolescents: a systematic review
Authors: Zhou Xianxian , Li Jiayu , Jiang Xiaoping
Published: June 2024

A substantial body of empirical evidence reveals that physical activity is associated with a wide range of positive physical and mental health outcomes. However, an absence of comprehensive syntheses is observed concerning the varying effects of different exercise intensities on the improvement of physical health among children and adolescents. The aim of this review is to systematically investigate the effects of different exercise intensities on the physical fitness of children and adolescents, to analyses the optimal exercise intensities for improving physical fitness, and to provide a relevant theoretical basis for optimizing school physical education curricula. A systematic search strategy was used in this study in four online databases (PubMed, Scopus, EBSCO and Web of Science). Intervention studies that met the inclusion criteria underwent a thorough screening process, and their methodological quality was assessed utilizing the PEDro scale. The selected literature was systematically analyzed and evaluated through induction, summary, analysis, and evaluation. These findings indicate that high-intensity exercise training exerts significant positive effects on body composition, cardiopulmonary function and muscle fitness in children and adolescents. Therefore, we suggest that schools should focus on high-intensity sports in their physical education curriculum, which can further improve the student's PHYSICAL FITNESS.
Validity of OSDI-6 questionnaire in a Chinese adult population
Authors: Qin Guanghao , Moutari Salissou , Chen Jiayan , Xu Ling , He Wei , He Xingru , Pazo Emmanuel Eric , Yu Sile
Published: June 2024

This study aimed to evaluate the validity of the Chinese translation version of OSDI-6 (C-OSDI-6) using a virtual set-up questionnaire for dry eye disease. A total of 270 participants (136 males, 50.4% and 134 females, 49.6%) with a mean age of 28.22 ± 9.01 years were assessed, diagnosed under the criteria put forth by Dry Eye Workshop completed the Chinese translated version of the OSDI-12 questionnaire (C-OSDI-12). Validity and psychometric properties were analyzed using the study data on the selected items (a new approach called virtual validation). The six items were extracted from the C-OSDI-12 as suggested by the authors of OSDI-6 and compared. The total scores of C-OSDI-12 and C-OSDI-6 were 30.27 ± 13.19 and 6.95 ± 3.53, respectively. Significant reliability was found between the total C-OSDI-6 score and the total C-OSDI-12 score (r = 0.865, p < 0.001). Infits and outfits of the C-OSDI-6 were between 1.26 and 0.78.The C-OSDI-6 proved valid and psychometrically responsive in Chinese adult dry eye participants. The findings of this virtual validation study need to be confirmed in a longitudinal validation study on real-world use.
NOTCH3 as a prognostic biomarker and its correlation with immune infiltration in gastrointestinal cancers
Authors: Xu Jia , Jin Xiao-li , Shen Hao , Chen Xuan-wei , Chen Jin , Huang Hui , Xu Bin , Xu Jian
Published: June 2024

NOTCH receptor 3 (NOTCH3) is known to regulate the transcription of oncogenes or tumor suppressor genes, thereby playing a crucial role in tumor development, invasion, maintenance, and chemotherapy resistance. However, the specific mechanism of how NOTCH3 drives immune infiltration in gastrointestinal cancer remains uncertain. The expression of NOTCH3 was analyzed through Western blot, PCR, Oncomine database, and the Tumor Immune Estimation Resource (TIMER) site. Kaplan–Meier plotter, PrognoScan database, and gene expression profile interactive analysis (GEPIA) were used to assess the impact of NOTCH3 on clinical prognosis. The correlation between NOTCH3 expression and immune infiltration gene markers was investigated using TIMER and GEPIA. NOTCH3 was found to be commonly overexpressed in various types of gastrointestinal tumors and was significantly associated with poor prognosis. Furthermore, the expression level of NOTCH3 showed a significant correlation with the tumor purity of gastrointestinal tumors and the extent of immune infiltration by different immune cells. Our findings suggest that NOTCH3 may act as a crucial regulator of tumor immune cell infiltration and can serve as a valuable prognostic biomarker in gastrointestinal cancers.
Comparing human text classification performance and explainability with large language and machine learning models using eye-tracking
Authors: Divya Venkatesh Jeevithashree , Jaiswal Aparajita , Nanda Gaurav
Published: June 2024

To understand the alignment between reasonings of humans and artificial intelligence (AI) models, this empirical study compared the human text classification performance and explainability with a traditional machine learning (ML) model and large language model (LLM). A domain-specific noisy textual dataset of 204 injury narratives had to be classified into 6 cause-of-injury codes. The narratives varied in terms of complexity and ease of categorization based on the distinctive nature of cause-of-injury code. The user study involved 51 participants whose eye-tracking data was recorded while they performed the text classification task. While the ML model was trained on 120,000 pre-labelled injury narratives, LLM and humans did not receive any specialized training. The explainability of different approaches was compared based on the top words they used for making classification decision. These words were identified using eye-tracking for humans, explainable AI approach LIME for ML model, and prompts for LLM. The classification performance of ML model was observed to be relatively better than zero-shot LLM and non-expert humans, overall, and particularly for narratives with high complexity and difficult categorization. The top-3 predictive words used by ML and LLM for classification agreed with humans to a greater extent as compared to later predictive words.
Clinical prediction model for red cell blood transfusion in elective primary posterior lumbar spine fusion
Authors: Pirot Chatchawan , Tantrakansakun Chakkraphan , Sirithiantong Todsaporn
Published: June 2024

Overestimated the cross-match of preoperative PRC preparation for elective primary lumbar spinal fusion needs revision for cost-effectiveness. We aimed to develop a novel preoperative predictive model for appropriate PRC preparation. This clinical prediction model in a retrospective cohort was studied between January 2015 and September 2022. Multivariate logistic regression models were used to assess predictive variables. The logistic coefficient of each predictor generated scores to establish a predictive model. The area under the receiver operating characteristic curve (AuROC) was used to evaluate the model. The predictive performance was validated using bootstrapping techniques and externally validated in 102 independent cases. Among 416 patients, 178 (43%) required transfusion. Four final predictors: preoperative hematocrit level, laminectomy level, transforaminal lumbar interbody fusion level, and sacral fusion. When categorized into two risk groups, the positive predictive values for the low-risk score (≤ 4) were 18.4 (95% Cl 13.9, 23.6) and 83.9 (95% CI 77.1, 89.3) for the high-risk score (> 4). AuROC was 0.90. Internal validation (bootstrap shrinkage = 0.993) and external validation (AuROC: 0.91). A new model demonstrated exemplary performance and discrimination in predicting the appropriate preparation for PRC. This study should be corroborated by rigorous external validation in other hospitals and by prospective assessments.
Intermediates involved in the reduction of SO2: insight into the mechanism of sulfite reductases
Authors: Bhattacharya Aishik , Samanta Soumya , Nath Arnab Kumar , Ghatak Arnab , Dey Somdatta Ghosh , Dey Abhishek
Published: June 2024

Sulfite reductases (SiRs) catalyze the reduction of SO32− to H2S in biosynthetic sulfur assimilation and dissimilation of sulfate.
Oxidation mechanism of phenols by copper(ii)–halide complexes
Authors: Yang Lan , Ito Rin , Sugimoto Hideki , Morimoto Yuma , Itoh Shinobu
Published: June 2024

The oxidation of phenols by tetrahedral copper(ii)–halide complexes was investigated to explore the oxidation mechanism as well as the importance of the tetrahedral geometry of the metal centre and the effects of halide ligands of the substrates.
Theoretical search for characteristic atoms in supported gold nanoparticles: a large-scale DFT study
Authors: Li Shengzhou , Miyazaki Tsuyoshi , Nakata Ayako
Published: June 2024

The size and site dependences of isolated and supported gold nanoparticles have been investigated based on local density of states and principal component analysis using large-scale DFT calculations with multi-site support functions.
Deciphering the shape selective conformational equilibrium of E- and Z-locked azobenzene–tetraethylammonium ion in regulating photo-switchable K+-ion channel blocking
Authors: Cholasseri Rinsha , De Susmita
Published: June 2024

Reversible blocking with a shape-shifting photo-switchable blocker.
I2 or I2/PIFA one-pot induced system for rapid synthesis of chalcogenated enamides, uracils or 2-pyridones under mild conditions
Authors: Sanaa Hamdi , Dondasse Ismaël , Retailleau Pascal , Nicolas Cyril , Gillaizeau Isabelle
Published: June 2024

An I2-mediated β-C(sp2)–H chalcogenylation of enamides under mild and rapid conditions has been developed. Sequential addition of PIFA led to valuable 5-thio- and 5-seleno-2-pyridones with a free C3 position.
Novel Control Technique for Power Quality Improvement in PV - Wind Operated Hybrid Standalone System with TSK Fuzzy Controllers
Authors: Singh Shweta , Jakhar Sunil Kumar , Ezhilarasan G. , Kulhar Kuldeep Singh
Published: June 2024

Power generation and supplying to electric loads through hybrid renewable energy sources is one of the best choices to reduce global warming as well as fulfill the needs of electric loads in many rural places. Hence establishing standalone hybrid power supply systems are becoming increasing day by day. However, due to atmospheric changes, a storage device (i.e., battery bank unit (BBU)) along with proper energy management system must be integrated into standalone system to manage power balance between load and generation. Photovoltaic-wind-battery powered standalone power supply system is considered in this work. An effective control mechanism is proposed to supply the quality power at load bus. Due to drawbacks of conventional PI controllers, a Takagi Sugeno Kang Fuzzy controller is used in proposed control methodology. Dual inverter configuration is implemented in this research work, one is operated between AC load bus and dc-link, the other one is in between BBU and AC load bus. In order to manage power balance in the system, a dc to dc bidirectional circuit is placed between batteries and inverter. In this configuration, the operation similar to inter line power flow controller is achieved by operating the inverter between BBU and AC load bus by proposed method. The Hardware-in the-Loop is designed for analyzing various case studies in the results by connecting two OPAL-RT devices.
Power Quality Improvement of PV-Wind-Battery Powered Standalone Hybrid System
Authors: Sahoo Girija Shankar , Jain Narendra Kumar , J Bhuvana , Kulhar Kuldeep Singh
Published: June 2024

Hybrid standalone power supply system’s especially operating with renewable energy sources are becoming popular and establishing in many places worldwide. Two major renewable energy sources for
electricity generation are solar and wind. Usually Photovoltaic modules (PVMs) are using for producing electricity from solar energy and in the similar way, PMSG based wind power conversion systems are becoming popular for medium power ranges. Therefore, a standalone hybrid system consisting of PVMs, wind, and battery can ensure a reliable power supply to various loads. The combination of these energy sources, along with the battery storage system, offers a flexible and dependable power supply to consumers at all times. This innovative approach involves the implementation of a novel control technique in a microgrid setup to uphold power quality at the PCC. The system integrates devices for maximizing power point to improve energy utilization efficiency. A DC to DC bidirectional circuit connects the battery bank unit (BBU) to the system to efficiently regulate energy. The bidirectional DC to DC circuit controls the dc-link voltage by accurately managing the discharging and charging of the battery. The three-phase inverter is situated between the AC loads and dclink, designed with a control system that ensures a constant RMS voltage at the PCC, regardless of changes in load and energy sources. MATLAB platform is used to present the results under various case studies in this paper.
Novel Control of Wind-PV-Battery based Standalone Supply System with LSTM Controllers
Authors: Ravikumar N.V.A. , Ramasekhara Reddy M. , Manoj Vasupalli
Published: June 2024

Integrated Wind - Photovoltaic (PV) based standalone electric power supply systems are widely used in many areas for various applications. These systems require a battery storage system to ensure a continuous power supply to loads, regardless of fluctuations in loads, wind speed, and solar irradiance. To ensure a reliable and stable power supply to consumers, several changes need to be made in these hybrid systems. Power quality plays a crucial role in standalone power systems, especially in hybrid energy sources based standalone power supply systems. The battery needs to charge when there is surplus power generation and discharge when there is a demand from the loads. To achieve this, a bidirectional DC to DC converter is used to connect the battery to the network, with a proper controlling mechanism. Additionally, maximum power point tracking devices with appropriate algorithms are incorporated for PV and wind turbines to optimize their utilization in all weather conditions. This paper considers multiple PV systems and wind turbines, each with proper arrangements of series and parallel combinations of PV modules, to determine the appropriate rating for the power supply system. Long short term memory (LSTM) based artificial neural network (ANN) controllers are implemented for various control units in this hybrid standalone power system. These proposed control techniques significantly improve power quality under various situations. The performance of the proposed method is evaluated using MATLAB/Simulink, and the results are presented in this paper.
Hybrid Renewable Energy Systems: An Integrated Approach to Rural Electrification
Authors: Mishra Ajit Kumar , Fezaa Laith H.A. , Bisht Yashwant Singh , Nivedha C.S. , Senthil Kumar R. , Sasipriya S.
Published: June 2024

In recent years, there has been a marked shift in focus towards sustainable and renewable energy solutions, particularly in the context of developing nations. This article explores the immense potential of hybrid renewable energy (RE) systems for ushering in a transformative change in electrifying remote and rural areas within these countries. The importance of such endeavours becomes paramount as a multitude of nations globally seek strategies to diminish carbon footprints. Renewable energy systems, combining sources such as solar, wind, hydro, and biomass, emerge as crucial assets in this drive, especially when considering regions that remain largely isolated from major electrical grids. The growing emphasis on research in this domain, coupled with favourable policies, signifies a promising trajectory for RE in developing nations. Such hybrid models, by capitalising on the diverse spectrum of renewable resources at hand, are on the brink of initiating a revolution in rural electrification strategies. This review accentuates the urgency and significance of rapidly integrating these avant-garde systems, setting the stage for a future that is not only environmentally conscious but also ensures broader and more equitable access to energy.
Neural Network based Direct Torque Controller of SRM for EV Application
Authors: D Ganesh , Saxena Vijay , Singh Daljeet Pal , Faujdar Pramod Kumar
Published: June 2024

the control aspects of the elecytric vehicle are presented in this paper. The electric car which is driven by a 6/4 Switched Reluctance Motor (SRM) powered by four battery banks is presented in this paper. The new topology of converter is proposed to drive SRM effectively for application of electric car. The direct torque controller is implemented with the help of neural network for effective speed controller with minimum ripples in torque. The required pulses are generated with space vector Pulse Width Modulation (PWM) technique. The electromagnetic torque generated by SRM needs to be maintained at ripples free for smooth operation of electric car. The mathematical validation is implemented to achieve the required power rating of SRM for Toyota Car. The proposed topology of converter has a facility of using four battery banks; hence the charging time of batteries will be minimized. The proposed model is designed on the platform of the MATLAB/Simulink package which is dumped into OPAL-RT modules to establish Hardware – in the – Loop (HIL) for presentation of various results. Various results are discussed with validate explanations of the proposed method.
Improved Direct Torque Control of Induction Motor in MATLAB
Authors: R Murugan , Raizada Aasheesh , Naval Preeti , Kulhar Kuldeep Singh
Published: June 2024

Induction motors based electric drives are dominated in many applications due to their superior characteristics over other drives including cost. An effect control method is required to reduce ripples in the torque. A direct torque control (DTC) is
implemented by various scholars recently, however for further reduction of ripples in torque a modified DTC is implemented in this paper. The proposed method considerably alleviates the computational burden of control technique on chip. The selection of inverter switching vectors is identified for restricting the torque and flux errors within their respective hysteresis bands. Fastest responses of torque, speed and flux are achieved. This paper also includes the incorporation of modeling and designing of DTC with induction motor to enhance comprehension. Extensive results under various operating conditions are presented in this paper to enhance the importance of the improved DTC.
Speed Control of 6-Phase PMSM using Fuzzy Controllers
Authors: Ravikumar N.V.A. , Manoj Vasupalli , Kumar Yegireddy Narendra
Published: June 2024

the article introduces a method for controlling the speed of a six-phase permanent magnet synchronous motor (PMSM) drive with the help of fuzzy logic controllers (FLCs). By utilizing the vector control technique, the fuzzy logic controller achieves a high level of dynamic performance. The FLC is implemented in the current and speed controllers of the six-phase PMSM drive. To evaluate its effectiveness under different operating conditions, the FLC with PMSM drives is designed and simulated using MATLAB. The FLC proves to be more robust and capable of overcoming the nonlinearity issues associated with the six-phase PMSM. The simulation outcomes confirm the efficiency of the suggested control technique for the PMSM drive system.
Fault Mitigation in Seven-Level Diode Clamped with Static Switch Based Fourth Leg Inverter Topology for Induction Motor Drives
Authors: Manoj Vasupalli , Krishnam Naidu R.S.R. , Ramasekhara Reddy M.
Published: June 2024

Modern day life demands the use of inverters at the point of utilization for many of the applications especially for motor drives used in industrial applications. Conventional inverters suffer from a setback of high harmonics at the output of inverter. Multilevel inverters produce stepped output nearer to sinusoidal reducing the harmonic distortion at the output. Faults are quite common in occurrence in inverters and needs to be identified and mitigated as quickly as possible. This paper presents seven-level diode clamped multi-level inverter topology with fourth leg for fault mitigation. Fourth leg is operated with thyristor based control and gets activated from the signals obtained from algorithm. An algorithm was given to identify the fault and send activating signals to fourth leg of inverter. Fault was internally generated only in phase-A in inverter circuits which can be applied the effectiveness of four-leg topology to phase-B and phase-C of inverter as like in phase-A. Simulation work was carried out and results were obtained using MATLAB/SIMULINK software.
ANN based Grid Charger for Electrical Vehicle Applications
Authors: Jyothsna  , Verresham K. , Bhavani J.
Published: June 2024

This paper present to electrical vehicle charger by using ANN (Artificial Neural Network) controller is presented in this study. The charger configuration consists of an AC/DC rectifier up front and a DC/DC converter down back, with a DC link capacitor separating the two. And its power flow, the charger’s design makes it possible to adjust the grid’s voltage. To test the charger’s efficacy, it was implemented in a MATLAB/Simulink environment. The simulation findings attest to the efficiency of an ANN based charger in controlling grid voltage when electric vehicles (EVs) are being charged and discharged in a different way.
Efficient Dual Mode Photovoltaic Power System for Improved Drive Span for Electric Vehicles
Authors: Rahman F. , Kumar Pawan
Published: June 2024

The problem of drive span maximization in electric vehicle is well studied. There exist number of approaches around the problem which uses residual energy and sleep mode as key in maximizing the drive span. However, they suffer to achieve higher performance in drive span maximization. This article presents a novel Dual Model Photovoltaic Power System Based Drive Span Maximization Model (DPDSM). The model is focused on maximizing the storage of voltage in the PV cells and utilizing maximum battery supply. The model generates the voltage from the photovoltaic system and regulates the power to the electric vehicle. The model has been connected with number of photovoltaic cells, which has been connected in serial. Using them, the required voltage has been regulated to the electric motor. In another mode, the residual voltage of the vehicle battery has been changed to discharge mode which support the regulation of voltage for the vehicle. The dual mode photovoltaic power system regulates effective power for the electric vehicle continuously with less voltage loss.
Drive State Analysis Based Electric Drive Control Model for Improved Power Stabilization
Authors: Pandey Dev Ras , Sasmita Dash Sushree
Published: June 2024

The problem of power stabilization in electric drives has been well studied. There exist numbers of approaches around the problem which consider the input power alone and suffer to achieve higher performance in power stabilization. To handle this issue, an efficient Drive State Analysis based Electric Drive Control model (DSA-EDCM) is presented in this article. The model monitors the drive state of electric drive at each duty cycle. According to the drive state and its previous conditions like voltage consumption, voltage leak, rpm and torque required, the method performs drive state analysis. The drive state analysis algorithm computes the power required at different conditions by computing Power Support value (PSV). Based on the PSV value, the method selects specific drive according to the input voltage received. Selected drive has been triggered for the cycle to maintain power stability. The proposed model improves the performance of power stability and maximizes the utilization performance.
Promoting Sustainable Transportation Solutions Through Electric Vehicles in Smart Cities
Authors: Pratap Singh Akhilendra , Sharma Khushboo , Rengarajan A. , Gautam Anupam Kumar
Published: June 2024

This paper meticulously reviews the work in the realms of Smart Mobility and Electric Vehicles (EVs) integration within the framework of Smart Cities, aiming to foster sustainable transportation solutions. The discourse delves into the broad areas of Smart City conceptualizations, focusing on the amalgamation of digital, green, and knowledge city policies to address urban challenges like traffic congestion, pollution, and energy consumption. A significant portion of the review is dedicated to exploring Smart Mobility initiatives, emphasizing the pivotal role of Information and Communication Technologies (ICT) in optimizing traffic flows and enhancing the liveability and public value of urban areas. Furthermore, the paper investigates the transformative potential of EVs in not only advancing the smart grid through Vehicle-to-Grid (V2G) and Grid-to- Vehicle (G2V) communications but also in mitigating environmental detriments associated with conventional transportation systems. The integration of EVs is posited as a catalyst for transitioning towards sustainable energy models, by capitalizing on the storage capabilities of EV batteries for harnessing renewable energy. The review also touches upon the necessity of advanced optimization techniques in designing sustainable logistic routes, thereby contributing to the broader sustainability objectives of Smart Cities. Through a comprehensive examination of secondary literature, the paper underscores the imperative of EVs integration in propelling Smart Cities towards a sustainable transportation paradigm, offering insightful recommendations for stakeholders and policymakers in the urban development sector.
Machine Learning-Driven Energy Management for Electric Vehicles in Renewable Microgrids
Authors: J Sharon Sophia , Winster Praveenraj D. David , Al-Attabi Kassem , Bijlwan Sheela , Nagar Mayank , Ikhar Sharayu
Published: June 2024

The surge in demand for sustainable transportation has accelerated the adoption of electric vehicles (EVs). Despite their benefits, EVs face challenges such as limited driving range and frequent recharging needs. Addressing these issues, innovative energy optimization techniques have emerged, prominently featuring machine learning-driven solutions. This paper reviews work in the areas of Smart EV energy optimization systems that leverage machine learning to analyse historical driving data. By understanding driving patterns, road conditions, weather, and traffic, these systems can predict and optimize EV energy consumption, thereby minimizing waste and extending driving range. Concurrently, renewable microgrids present a promising avenue for bolstering power system security, reliability, and operation. Incorporating diverse renewable sources, these microgrids play a pivotal role in curbing greenhouse gas emissions and enhancing efficiency. The review also delves into machine learning-based energy management in renewable microgrids with a focus on reconfigurable structures. Advanced techniques, such as support vector machines, are employed to model and estimate the charging demand of hybrid electric vehicles (HEVs). Through strategic charging scenarios and innovative optimization methods, these approaches demonstrate significant improvements in microgrid operation costs and charging demand prediction accuracy.
Lightening Driven Wind Turbines Blade Damages
Authors: Agarwal Ankita , Kumar Ravindra , Ezhilarasan  , Goswami Sachin
Published: June 2024

Lightning-induced damages to wind turbine blades are widely recognized as severe and can result in unplanned plant shutdowns. Therefore, it is crucial to thoroughly investigate the different causes of blade damage and categorize them based on their severity and other relevant factors. By conducting a comprehensive study on the classification of damage characteristics caused by wind turbine blades, taking into account various causes and protective measures, this research aims to provide valuable insights for individuals involved in wind turbine design, construction, and operation. Specifically focusing on damages caused by lightning strikes, this paper also proposes several potential countermeasures to safeguard the blades.
Numerical study for the improvement of performance of Savonius wind turbine having elliptical blades
Authors: Debnath Pinku , Debbarma Ronald
Published: June 2024

The S-rotor wind turbine operates based on drag, resulting in lower power coefficients. Furthermore, a multitude of geometric and aerodynamic factors have been meticulously crafted to enhance the overall efficiency. The augmentation technique decreased the adverse drag force produced by the S-rotor. Therefore, in this investigation, the elliptical bladed profile has been selected for simulation in conjunction with the curtain plate preceding the rotor. The simulation has been conducted in Ansys fluent platform. So far the comparison has been performed for power vs RPM for S-rotor blades and elliptical rotor blades. For elliptical rotor blades, the peak power magnitude of 10 W and average power of 7 W are achieved. From conventional S-rotor blades, the peak power magnitude of 8 W and average power of 4 W are obtained. It has been determined through contour plot analysis that the aerodynamic performance of an elliptical bladed profile with curtain plates surpasses that of an elliptical profile without curtains.
Machine Learning Approaches for Short-Range Wind Power Estimation: A Perspective
Authors: Kazem Al-Samalek Ahmed Saleh Mahdi , Lal Sonu , Sharmila P. , Abootharmahmoodshakir  , Joshi Ankita , Vimala Devi R.
Published: June 2024

The evolution of wind energy production, especially in near and offshore farms, has seen significant advancements due to the integration of novel technologies and the reduction in economic costs. This paper reviews the work in the domain of wind power estimation, emphasizing the innovative approaches leveraging satellite data and artificial intelligence (AI) methodologies. A notable method integrates Sentinel satellite imagery analysis in a two-phased approach, combined with machine learning techniques, to forecast wind speed. This method utilizes sentinel-1 and sentinel-2 satellite images for wind speed and bathymetry analysis, respectively. Furthermore, a hybrid forecasting model, comprising the generalized regression neural network (GRNN) and the whale optimization algorithm (WOA), has been introduced. Another pivotal advancement comes from the National Center for Atmospheric Research (NCAR), which has revamped its wind power forecasting system. This enhancement focuses on short-term forecasting, uncertainty quantification in wind speed prediction, and the prediction of extreme events like icing. The integration of numerical weather prediction with machine-learning methods, such as the fuzzy logic artificial intelligence system, has further elevated the accuracy and efficiency of these forecasting models. Collectively, these advancements offer a comprehensive perspective on the future of shortrange wind power estimation.
AI-Controlled Wind Turbine Systems: Integrating IoT and Machine Learning for Smart Grids
Authors: A Madeswaran , Bisht Deepa , Yuvaraj S. , Udayapal Reedy M. , Al-Attabi Kassem , Dhablia Anishkumar
Published: June 2024

Advances in renewable energy technologies are pivotal in addressing the challenges posed by the depletion of traditional energy sources and their associated environmental impacts. Among these, wind energy stands out as a promising avenue, with wind turbine farms proliferating globally. However, the unpredictable nature of wind and intricate interplay between turbines necessitate innovative solutions for efficient operation and maintenance. This paper reviews advancements in intelligent control systems, notably those proposed by Smart Wind technologies. These systems leverage a network of sensors and IoT devices to gather real-time data, such as wind speed, temperature, and humidity, to optimize turbine performance. A significant focus is on turbines employing doubly-fed induction generators, which offer benefits like adjustable speed and consistent frequency operation. Their integration into smart grids introduces challenges concerning power system dynamics’ security and reliability. This review delves into the dynamics, characteristics, and potential instabilities of such integrations, emphasizing the uncertainties in wind and nonlinear load predictions. A noteworthy finding is the rising prominence of artificial intelligence, particularly machine and deep learning, in predictive diagnostics. These methodologies offer costeffective, accurate, and efficient solutions, holding potential for enhancing power system stability and accuracy in the smart grid context.
Solar Energy in Developing Countries: Challenges and Opportunities for Smart Cities
Authors: Saraswat Nidhi , Pandeya Megha , Pareek Ravi Kant , Kulhar Kuldeep Singh
Published: June 2024

This perspective article explores the dynamic landscape of solar energy adoption in developing countries, particularly within the framework of smart cities. Developing nations face a compelling need to bridge the energy gap, reduce emissions, foster economic growth, and enhance energy security. Solar energy offers potential solutions to these challenges. Despite the promise of solar energy, obstacles exist. Financial constraints, outdated infrastructure, grid instability, technical expertise gaps, and regulatory hurdles hinder widespread adoption. However, technological advances in solar panels, energy storage, smart grids, and data analytics present avenues for overcoming these challenges. Collaborative initiatives, consistent policies, and public-private partnerships offer support for sustainable development. Smart cities play a crucial role in this transition. Sustainable urban planning, smart infrastructure, and digitalization contribute to the efficient use of solar power. Case studies from India, Rwanda, and Brazil exemplify successful integration of solar energy within smart city projects. Balancing challenges with opportunities is the key to success. By addressing obstacles and seizing available prospects, developing countries can lead the charge toward a sustainable energy future.
Applications of Sustainable Business Models for PV Systems in Developing Countries
Authors: Ras Pandey Dev , Sasmita Dash Sushree
Published: June 2024

The global push to boost the adoption of renewable energy resources and decrease our dependency on fossil fuels for electricity generation has experienced substantial growth. Solar Photovoltaic (PV) panels have now achieved a level of extensive implementation and global economic feasibility. These panels, compact and resilient, require only sunlight exposure to generate electricity. Since their commercial use began in Europe in the 1990s, solar PV power has been electrifying countless households worldwide and providing energy access to numerous remote communities in less developed regions. As a result, the extensive global deployment of solar energy systems strengthens the energy industry and fosters job growth, thereby facilitating substantial progress. This study emphasizes the importance of Photovoltaic (PV) technologies and their contribution to advancing sustainability, particularly in emerging economies. It provides valuable perspectives and examinations of the sustainability of solar energy, covering both ecological and economic facets.Furthermore, it delineates the crucial contribution of PV technologies to sustainable development, as they meet energy needs, create job prospects, and enhance environmental conservation initiatives.
Novel MPPT of PV System with MIWO Algorithm for Water Pumping Application
Authors: Raghutu Ramakrishna , Manoj Vasupalli , Kumar Yegireddy Narendra
Published: June 2024

The water pumping system (WPS) utilized in this research is powered by a photovoltaic (PV) system and is regulated by an inverter that is equipped with a TS-Fuzzy controller. To enhance the power extraction from the PV system when faced with partial shading conditions (PSCs), a Modified Invasive Weed Optimization (MIWO) technique is incorporated into the Perturb and Observes (P&O) algorithm. The conventional P&O algorithm faces difficulties in harnessing the full potential of the PV system when confronted with partial shading scenarios, primarily because of the presence of numerous maximum points. In such instances, optimization methods can be employed to locate the global maximum point. The MIWObased P&O algorithm proposed will modify the reference voltage according to the prevailing weather conditions in order to guarantee optimal operation of the PV system at the Maximum Power Point (MPP). The PV-based WPS is designed to cater to both irrigation and domestic water supply needs. In this study, a sensorless vector controlbased induction motor is utilized to drive the pump. The primary goal of this study is to showcase the efficiency of a PV-based WPS without the necessity of battery storage. The TS-Fuzzy controller guarantees smooth motor operation during transient periods. The efficacy of the proposed approach is confirmed through MATLAB/Simulink simulations as well as Hardware – in the – Loop and real-time data is also used, taking into account various water pumping scenarios and the fluctuating solar irradiance levels during rapid climate changes.