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Enhancing the Efficiency of Lead-Less Cs2TiBr6 Solar Cells through Dual Hole Transport Layer Integration 4

Enhancing the Efficiency of Lead-Less Cs2TiBr6 Solar Cells t...

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4th international conference on Power, control and computing Technologies, ICPC2T 2025

作者： Agarwala, Raja Sabbir, Safra Binte Tanim, Mehedi Hasan Kumar, Prince Department of Electrical and Electronic Engineering Hajee Mohammad Danesh Science and Technology University Dinajpur Bangladesh Department of Electrical Engineering National Institute of Technology Silchar India

ISBN: (纸本)9798331530402

The potential of lead-free perovskite solar cells (PSCs) has garnered prominent interest, particularly due to concerns surrounding the toxicity and instability of lead halide compounds. Enhancing the performance of lead-free, ecologically friendly PSCs utilizing the perovskite absorber layer (PAL) Cs2TiBr6, is the goal of this endeavor. This simulation investigation reveals comprehensive findings from particular modeling techniques for PSCs. The proposed study incorporates an innovative dual-hole transport layer (HTL) to enhance the power conversion efficiency (PCE) of the device. The PSC achieves the highest PCE by combining two HTLs, notably Copper Antimony Sulfide (CuSbS2) and Copper Barium Thiostannate (CBTS). The proposed device structure, FTO/TiO2/Cs2TiBr6/CuSbS2/CBTS/Ni, is analyzed using SCAPS-1D simulations to compare and optimize key parameters, including energy band alignment, current-voltage (J-V), and quantum efficiency (QE) properties, absorber and transport layer thickness, parasitic resistances, interface defects, and temperature. Our findings reveal that the dual HTL configuration performs better than previously cited single HTL-based structures. The proposed Cs2TiBr6-based PSC structure improves the device's photovoltaic (PV) parameters, achieving an enhanced open-circuit voltage (Voc) of 1.34 V, a short-circuit current density (Jsc) of 21.28 mA/cm2, and a fill factor (FF) of 86.24%, culminating in PCE of 24.64%. This research demonstrates the efficacy of employing dual HTLs in enhancing the performance and stability of PSCs, providing promising insights for the future advancement of high-efficiency PV devices. © 2025 IEEE.

关键词： Bromine compounds

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Exploring the Influence of Kesterites as Hole Transport Layers in Cs2TiBr6-Based Eco-Friendly Solar Cells 4

Exploring the Influence of Kesterites as Hole Transport Laye...

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4th international conference on Power, control and computing Technologies, ICPC2T 2025

ISBN: (纸本)9798331530402

Lead-free, eco-friendly perovskites have gained significant attention for their non-toxicity, addressing environmental concerns while enhancing the sustainability of photovoltaic (PV) technologies and maintaining high power conversion efficiency (PCE). Through detailed numerical simulations this study investigates the impact of kesterite materials as hole transport layers (HTLs) in perovskite solar cells (PSCs), utilizing Caesium Titanium Bromide (Cs2TiBr6) as the perovskite absorber layer (PAL) and Titanium Dioxide (TiO2) as the electron transport layer (ETL). The objective is to enhance performance, and stability and reach a PCE greater than 25%. Various kesterite compounds, including copper zinc tin selenide (CZTSe), copper manganese tin sulphide (CMTS), copper iron tin sulphide (CFTS), copper nickel tin sulphide (CNTS), copper zinc tin sulphide (CZTS), and copper barium tin sulphide (CBTS), were analyzed to evaluate their effectiveness as HTLs. Using the SCAPS-1D tool, key factors such as band alignment, optimal layer thickness, and current-voltage (J-V) characteristics, along with quantum efficiency (QE), were evaluated to identify the most effective HTL. Additional simulations investigated the impact of series resistance (Rs), shunt resistance (Rsh) and temperature on device performance. The findings demonstrated that CZTSe outperformed other kesterites as HTL due to its superior absorption coefficient, favorable band alignment, and higher charge carrier formation and extraction capabilities. The configuration resulted in an open-circuit voltage (VOC) of 0.78 V, a short-circuit current density (JSC) of 40.19 mA/cm2 and a fill factor (FF) of 80.30%, leading to an overall PCE of 25.36% These findings highlight the potential of CZTSe as a promising HTL for high-PCE, lead-free PSCs, advancing sustainable solar technology. © 2025 IEEE.

关键词： Titanium dioxide

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Tuberculosis Diagnosis from Chest X-Ray Image Using Deep Learning Techniques 5

Tuberculosis Diagnosis from Chest X-Ray Image Using Deep Lea...

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5th IEEE international conference on Advances in electrical, computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Shams, Karib Saha, Ratul Rodela, Mahmuda Islam Simran, Sanjana Rashid Khan, Musharrat East West University Computer Science and Engineering Department Dhaka Bangladesh

ISBN: (纸本)9798350356236

Tuberculosis (TB) is a significant global health challenge, necessitating precise and efficient diagnostic tools. This work investigates the effectiveness of various deep learning models such as EfficientNetB3, DenseNet121, VGG19, DenseNet201, and ResNet50 in detecting TB from chest X-ray images. Utilizing the TB Chest X-ray Database, we evaluated pre-trained models, developed custom architectures, and employed the k-fold validation method. The EfficientNetB3, with a customized design, demonstrated superior performance, while the DenseNet201 excelled in the k-fold validation setup. Our findings highlight the potential of the EfficientNetB3 and DenseNet201 in advancing TB diagnostics, offering significant improvements in speed and accuracy. We got 100% accuracy in EfficientnetB3. This study not only contributes to the growing field of AI in medical imaging but also underscores the ethical considerations required in deploying AI for healthcare, ensuring patient privacy and equitable algorithmic decisions. The outcomes of this study hold promise for improving real-time healthcare through more effective TB detection and patient care. © 2025 IEEE.

关键词： Disease control

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Transforming Multinational Enterprises in a Fragmented World the Intersection of Industry 4.0 Technologies and Geopolitical Dynamics in Finance and Operations

Transforming Multinational Enterprises in a Fragmented World...

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2025 international conference on Intelligent control, computing and Communications, IC3 2025

作者： Shukla, Priyanka Gundlapalle, Rajesh Ashlin Deepa, R.N. Srinija, K. Mohammed, Baydaa Fathil Gupta, Manish Galgotias university Department of Electrical Electronics & Communication Engineering Uttar Pradesh Gerater Noida203201 India New Horizon College of Engineering Department of Electronics and Communication Engineering Bangalore India Griet Department of Cse Bachupally Telangana Hyderabad India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India College of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq Lovely Professional University Phagwara India

ISBN: (纸本)9798331527495

The Global Resilience Integration Model is a major strategy adjustment. It illustrates how multinational firms are addressing Industry 4.0 and geopolitical risks. This brief overview of GRIM's key features and achievements demonstrates how it has transformed foreign commerce. Integrating, protecting, being adaptable, open, working together, and upholding ethics are GRIM's priorities. GRIM handles the complexity of a divided society using blockchain, AI, and dynamic economic flexibility algorithms. One model can leverage several data sources. This offers decision-makers a complete view of the globe at any moment. In today's linked world, stringent security measures are needed to protect international financial operations' privacy and integrity. Blockchain technology is a safety precaution. Open choice: GRIM's dynamic economic freedom program allows firms to adjust their financial strategies as the economy changes. This strategy encourages openness and morality by applying AIethics governance frameworks to help companies take greater responsibility. After this drawing, GRIM is a game-changer for worldwide corporations. By integrating Industry 4.0 technologies with global variables, GRIM will transform global businesses. © 2025 IEEE.

关键词： Ethical technology

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Application of Indirect Methods to Optimal control Problems in Epidemiology 16th

Application of Indirect Methods to Optimal Control Problem...

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16th APCA international conference on automatic control and Soft computing, controlO 2024

作者： Caio, Pedro Silva, Cristiana J. Department of Mathematics University of Aveiro Aveiro3810-193 Portugal Department of Mathematics ISTA Iscte - Instituto Universitário de Lisboa Av. das Forças Armadas Lisboa1649-026 Portugal

ISBN: (纸本)9783031817236

Currently most of the numerical resolution of optimal control problems is done using direct methods where the increase accuracy of indirect methods is overshadowed by the necessary analytical derivation required beforehand. With recent developments from the control-toolbox ecosystem team the application of indirect methods as become more streamline enabling a wider range of problems to be solved, like, for example, optimal control problems applied to the transmission of infectious diseases. In this work, we aim to extend the application of indirect methods to optimal control problems applied to epidemiological models, using the control-toolbox [25]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Malaria control

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Safe Reinforcement Learning for Social Motor Coordination in Two Dimensional Space 26

Safe Reinforcement Learning for Social Motor Coordination in...

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26th international conference on Industrial Technology, ICIT 2025

作者： Yang, Fan Huang, Yuening Zhai, Chao Zhang, Hehong China University of Geosciences School of Automation Wuhan Campus China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Rice University George R. Brown School of Engineering and Computing HoustonTX United States Fuzhou University School of Computer and Data Science Fuzhou China

ISBN: (纸本)9798331521950

This paper presents an interactive motion control method based on reinforcement learning, designed to assist children with autism who have social motor impairments through a mirror game intervention. The virtual teacher uses the Deep Deterministic Policy Gradient (DDPG) algorithm to optimize its actions, guiding the participant to follow a Lissajous trajectory. To ensure safety, a motion-correction mechanism was developed, which automatically adjusts actions when the predicted trajectory surpasses predefined safety boundaries. The reward function considers both the distance between the virtual teacher and the target trajectory, as well as the distance between the virtual teacher and the participant, with dynamic adjustments applied by the motion-correction mechanism. Experimental results demonstrate that the virtual teacher effectively guides the participant towards the target trajectory while adhering to safety constraints. © 2025 IEEE.

关键词： Reinforcement learning

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Digitalization of Integrated Industrial IoT Technologies for Monitoring and Cloud Analytics in Sustainable Horticultural Practices

Digitalization of Integrated Industrial IoT Technologies for...

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2025 international conference on Intelligent control, computing and Communications, IC3 2025

作者： Malik, Praveen Kumar Bharti, Veena Thapliyal, Shailendra Lande, Jaypal Mahajan, Anshul Singh, Parulpreet School of Electronics and Electrical Engineering Lovely Professional University Punjab Phagwara India Raj Kumar Goel Institute of Technology Ghaziabad India Uttaranchal Institute of Magagement Uttaranchal University Uttarakhand Dehradun India School of Computer Science & Artificial Intelligence Sr University Warangal India

ISBN: (纸本)9798331527495

Agriculture has been one of the most important areas that human being has to rely on but due to ever rising global population and ecological crises, there is an increasing demand for ecological farming techniques. Industry 4.0 is the latest evolution of industries that integrates tools such as IoT, AI, and big data and has the potential to transform agriculture. These technologies allow for precise agriculture, resource management, and better decisions to be made, catalyzing farming practices to be more eco-friendly and effective. One of the important limitations of modern farms is the ability to assess environmental attributes influencing plants' growth and yield. Most traditional techniques do not provide timely assessments and insights, which leads to excessive or insufficient use of inputs, especially water, fertilizers, and pesticides. This kind of mismanagement not only reduces the efficiency of agricultural output but also brings adverse effects to the surrounding nature. By employing digital systems that measure and evaluate state variables of the environment, farmers can intervene appropriately in the crop production process to improve plant health, conserve resources, and protect the environment. © 2025 IEEE.

关键词： Precision agriculture

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Computational Modeling of Spiking in the Layer 5 Projection Neurons of the Mouse Motor Cortex 5th

Computational Modeling of Spiking in the Layer 5 Projection ...

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5th international conference on computing and Network Communications, CoCoNet 2023

作者： Rajendran, Arathi Ajith, Navya Nambiar, Aiswarya Chandrabhanu Naldi, Giovanni Diwakar, Shyam Amrita Mind Brain Center Amrita Vishwa Vidyapeetham Amritapuri Campus Clappana P.O. Kerala Kollam690525 India Amrita School of Biotechnology Amrita Vishwa Vidyapeetham Amritapuri Campus Clappana P.O. Kerala Kollam690525 India Department of Environmental Science and Policy Università Degli Studi Di Milano Via C Saldini 50 Milano20133 Italy Department of Electronics and Communication Engineering School of Engineering Amrita Vishwa Vidyapeetham Amritapuri Campus Clappana P.O Kerala Kollam690525 India

ISBN: (纸本)9789819747108

Mathematical models have allowed the reconstruction of realistic behavior of large and complex neurons under various physiological functions. Understanding the electrophysiological behavior regarding neuronal gain, process, and connectivity of such complex pyramidal projection neurons in the motor cortex is essential for unraveling the neural basis of motor control and developing therapies for conditions related to motor dysfunctions. This modeling study analyzed the input/output properties of L5 cortico-striatal projection neurons and their sensitivity to abnormal firing behavior. The study reconstructs the excitability of layer 5 cortico-striatal neurons and suggests how they integrate the inputs temporally matching experimental observations. The study also showed variability in the output patterns in layer 5 cortico-striatal neurons with varying current stimuli. This model predicted that the firing rate is proportional to the delayed input currents, which may affect the neuron’s feature integration property. The modeling methodology showed several single-neuron excitability characteristics relevant to current disease mechanism theories and recognized Parkinson’s disease-associated symptoms. These results help to uncover the sensory integration at the single neuron level and pathophysiology of motor deficit along with other interconnected motor systems in the context of motor control and information processing. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Disease control

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An IoT-Driven Workers Health Monitoring System Utilizing LoRa Technology for Real-Time Vital Signs Collection, Tracking and Cloud-Based Data Logging at Industrial Entry Points

An IoT-Driven Workers Health Monitoring System Utilizing LoR...

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2025 international conference on Intelligent control, computing and Communications, IC3 2025

作者： Kaur, Navjot Malik, Praveen Kumar Krishna, Gopal Kumar, Manoj Aluvala, Srinivas Singh, Parulpreet School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India School of Electronics and Electrical Engineering Lovely Professional University Punjab Phagwara India Uttaranchal Institute of Technology Uttaranchal University Uttarakhand Dehradun India Mlr Institute of Technology Department of Electronics and Communication Engineering Hyderabad India School of Computer Science & Artificial Intelligence Sr University Warangal India

ISBN: (纸本)9798331527495

With the rapid rise of IoT tech., new possibilities have emerged in areas such as health monitoring, especially in industries that require OHS. Traditionally, workplace health monitoring took a reactive stance resolving any adverse health conditions whenever they arise. Nonetheless, given that there have been shifts in technology and operational practices, what was once impossible has now become realizable - and that is the implementation of systems that continuously perform health monitoring in real-time. Such systems offer a constant evaluation of the most important health parameters with the aim of detecting health problems and preventing related adverse events at the early stage. This is particularly important in the workplace where the physical demand and hazards related to the occupation are high. With the advent of IoT technologies, the way of health insight which was very mechanized and physical in nature has changed to an intact automation system. Such a shift is, however, not simply improvement of technologies but a great efficiency advancement in health management at workplaces. Because of the connection of the cloud technology, the health status information is kept, and tested in one place, and relevant superiors and medical professionals are provided with the information they require to act quickly. Within this setting, IoT oriented technologies develop systems that act as the first line of defense in ensuring minimal delay response time in case there are health adversities, active surveillance on worker's health is also achieved. This paper presents a comprehensive improvement of an existing product intended to solve such problems. It shows how the problem of safety in the workplace can be achieved easily. © 2025 IEEE.

关键词： Medical problems

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MRI Brain Tumor Classification Using Optimized APSO-LLRBFNN

MRI Brain Tumor Classification Using Optimized APSO-LLRBFNN

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Machine Learning and Autonomous Systems (ICMLAS), international conference on

作者： Tiruveedula Gopi Krishna Nune Sreenivas M. Ajay Kumar Chandra Sekhar Reddy Davinder Sing Rathee Teklu Urgessa Department of Computer Science and Engineering College of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia Department of Electrical and Electronics Engineering Andhra Loyala institute of Engineering and Technology Vijayawada India Department of Electrical Power and Control Engineering College of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia Department of ECE College of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

The research presents a new efficient machine learning method to classify brain tumors because this task remains vital in fighting the high incidence of brain cancers. The proposed approach unites all its operations including image segmentation with feature extraction and classification into a single processing system. This study uses Particle Swarm Optimization (PSO) and Adaptive PSO (APSO) methodologies to optimize Locally Linear Radial Basis Function Neural Network (LLRBFNN) resulting in PSO-LLRBFNN and APSO-LLRBFNN model implementations. The research examined the optimized APSO-LLRBFNN models against Support Vector Machines (SVM) and Least Mean Squares (LMS) based LLRBFNN classifier methods. Results from experiments show that APSO-LLRBFNN gives superior outcomes with 98.93% accuracy and takes 12.23 seconds for execution, which surpasses currently available methods.

关键词： Support vector machines Image segmentation Adaptation models Accuracy Magnetic resonance imaging Machine learning Feature extraction Brain modeling Data models Numerical models

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