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An improved method for edge detection based on neighbor distance for processing hemispheric photography

Journal of Plant Ecology 2024年第2期17卷 134-145页

作者： yasi Liu Dayong Fan Han Sun Xiangping Wang State Key Laboratory of Effcient Production of Forest Resources School of Ecology and Nature ConservationBeijing Forestry UniversityBeijing 100083China The Key Laboratory for Silviculture and Conservation of Ministry of Education Beijing Forestry UniversityBeijing 100083China

Hemisphere photos are now widely applied to provide information about solar radiation dynamics,canopy structure and their contribution to biophysical processes,plant productivity and ecosystem *** present study aims to improve the original‘edge detection’method for binary classifcation between sky and canopy,which works not well for closed *** supposed such inaccuracy probably is due to the infuence of sky pixels on their neighbor canopy ***,we introduced a new term‘neighbor distance’,defned as the distance between pixels participated in the calculation of contrast at the edges between classifed canopy and sky,into the‘edge detection’*** showed that choosing a suitable neighbor distance for a photo with a specifc gap fraction can signifcantly improve the accuracy of the original‘edge detection’*** developed an ND-IS(Neighbor Distance-Iteration Selection)method that can automatically determine the threshold values of hemisphere photos with high accuracy and *** combines the modifed‘edge detection’method and an iterative selection method,with the aid of an empirical power function for the relationship between neighbor distance and manually verifed gap *** procedure worked well throughout a broad range of gap fractions(0.019-0.945)with different canopy compositions and structures,in fve forest biomes along a broad gradient of latitude and longitude across Eastern *** results highlight the necessity of integrating neighbor distance into the original‘edge detection’*** advantages and limitations of the method,and the application of the method in the feld were also discussed.

关键词： hemisphere photos ‘edge detection’method neighbor distance gap fraction forest

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Interpretable Detection of Malicious Behavior in Windows Portable Executables Using Multi-Head 2D Transformers

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Big Data Mining and Analytics 2024年第2期7卷 485-499页

作者： sohail Khan Mohammad Nauman Computer Science Department Effat College of EngineeringEffat UniversityJeddah 23341Kingdom of Saudi Arabia

Windows malware is becoming an increasingly pressing problem as the amount of malware continues to grow and more sensitive information is stored on *** of the major challenges in tackling this problem is the complexity of malware analysis,which requires expertise from human *** developments in machine learning have led to the creation of deep models for malware ***,these models often lack transparency,making it difficult to understand the reasoning behind the model’s decisions,otherwise known as the black-box *** address these limitations,this paper presents a novel model for malware detection,utilizing vision transformers to analyze the Operation Code(OpCode)sequences of more than 350000 Windows portable executable malware samples from real-world *** model achieves a high accuracy of 0.9864,not only surpassing the previous results but also providing valuable insights into the reasoning behind the *** model is able to pinpoint specific instructions that lead to malicious behavior in malware samples,aiding human experts in their analysis and driving further advancements in the *** report our findings and show how causality can be established between malicious code and actual classification by a deep learning model,thus opening up this black-box problem for deeper analysis.

关键词： machine learning malware vision transformers Windows Protable Executable(PE)

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An empirical investigation of financial development and ecological footprint in South Asia:Bridging the EKC and pollution haven hypotheses

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Geoscience Frontiers 2024年第4期15卷 427-436页

作者： Ilhan Ozturk sohail Farooq Muhammad Tariq Majeed Marinko Skare College of Business Administration University of SharjahSharjahUnited Arab Emirates Faculty of Economics Administrative and Social SciencesNisantasi UniversityIstanbulTurkey Hazara University Mansehra Pakistan School of Economics Quaid-i-Azam UniversityIslamabadPakistan Faculty of Economics and Tourism“Dr.Mijo Mirkovic ”Juraj Dobrila University of PulaPulaCroatia University of Economics and Human Sciences in Warsaw Poland

This research explores the dynamic relationships between ecological footprint,economic performance,financial development,energy usage,and foreign direct investment(FDI)in South Asian economies utilizing the panel data from 1971 to *** panel data analysis,conventional methods generally ignore the issues of cross-sectional dependency and the heterogenous nature of cross-sectional *** other concern with the existing research is that most of the studies ignore the significance of ecological footprint while evaluating financial development and FDI as sources of environmental *** longterm relationship among the indicators is tested utilizing the Westerlund cointegration *** findings support both environmental Kuznets curve and pollution haven hypotheses for South Asian ***,the empirical findings suggest that financial development increases environmental conservation while energy usage substantially disrupts the environment of the selected south Asian ***,the heterogeneous causality analysis reveals the causal relationships between the ***,overall results recommend that the South Asian economies need to boost economic growth without compromising the environment,decrease fossil fuel usage,enhance financial sector growth and incentivize environmentally friendly FDI to conserve the environment in the region.

关键词： Energy consumption Financial development Ecological footprint FDI South Asia

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Enhancing ChatGPT’s Querying Capability with Voice-Based Interaction and CNN-Based Impair Vision Detection Model

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Computers, Materials & Continua 2024年第3期78卷 3129-3150页

作者： Awais Ahmad sohail Jabbar Sheeraz Akram Anand Paul Umar Raza Nuha Mohammed Alshuqayran College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudia Arabia 2 School of Computer Science and EngineeringKyungpook National UniversityDaegu41566South Korea 3 Department of EngineeringManchester Metropolitan UniversityManchesterM156BHUK

This paper presents an innovative approach to enhance the querying capability of ChatGPT,a conversational artificial intelligence model,by incorporating voice-based interaction and a convolutional neural network(CNN)-based impaired vision detection *** proposed system aims to improve user experience and accessibility by allowing users to interact with ChatGPT using voice ***,a CNN-based model is employed to detect impairments in user vision,enabling the system to adapt its responses and provide appropriate *** research tackles head-on the challenges of user experience and inclusivity in artificial intelligence(AI).It underscores our commitment to overcoming these obstacles,making ChatGPT more accessible and valuable for a broader *** integration of voice-based interaction and impaired vision detection represents a novel approach to conversational ***,this innovation transcends novelty;it carries the potential to profoundly impact the lives of users,particularly those with visual *** modular approach to system design ensures adaptability and scalability,critical for the practical implementation of these ***,the solution places the user at its *** responses for those with visual impairments demonstrates AI’s potential to not only understand but also accommodate individual needs and preferences.

关键词： Accessibility in conversational AI CNN-based impair vision detection ChatGPT voice-based interaction recommender system

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Deep Learning-Based ECG Classification for Arterial Fibrillation Detection

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Computers, Materials & Continua 2024年第6期79卷 4805-4824页

作者： Muhammad sohail Irshad Tehreem Masood Arfan Jaffar Muhammad Rashid Sheeraz Akram Abeer Aljohani Faculty of Computer Science&Information Technology The Superior UniversityLahore54000Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54000Pakistan Department of Computer Science National University of TechnologyIslamabad45000Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia Department of Computer Science Applied CollegeTaibah UniversityMedina42353Saudi Arabia

The application of deep learning techniques in the medical field,specifically for Atrial Fibrillation(AFib)detection through Electrocardiogram(ECG)signals,has witnessed significant *** and timely diagnosis increases the patient’s chances of ***,issues like overfitting and inconsistent accuracy across datasets remain *** a quest to address these challenges,a study presents two prominent deep learning architectures,ResNet-50 and DenseNet-121,to evaluate their effectiveness in AFib *** aim was to create a robust detection mechanism that consistently performs *** such as loss,accuracy,precision,sensitivity,and Area Under the Curve(AUC)were utilized for *** findings revealed that ResNet-50 surpassed DenseNet-121 in all evaluated *** demonstrated lower loss rate 0.0315 and 0.0305 superior accuracy of 98.77%and 98.88%,precision of 98.78%and 98.89%and sensitivity of 98.76%and 98.86%for training and validation,hinting at its advanced capability for AFib *** insights offer a substantial contribution to the existing literature on deep learning applications for AFib detection from ECG *** comparative performance data assists future researchers in selecting suitable deep-learning architectures for AFib ***,the outcomes of this study are anticipated to stimulate the development of more advanced and efficient ECG-based AFib detection methodologies,for more accurate and early detection of AFib,thereby fostering improved patient care and outcomes.

关键词： Convolution neural network atrial fibrillation area under curve ECG false positive rate deep learning classification

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Metamaterial terahertz device with temperature regulation function that can achieve perfect absorption and complete reflection conversion of ultrawideband

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Communications in Theoretical Physics 2024年第7期76卷 123-133页

作者： Hao Sun Zao Yi Can Ma Shubo Cheng Bin Tang Qingdong Zeng sohail Ahmad School of Mathematics and Science Joint Laboratory for Extreme Conditions Matter PropertiesState Key Laboratory of Environment-Friendly Energy MaterialsTianfu Institute of Research and InnovationSouthwest University of Science and TechnologyMianyang 621010China Department of Oncology Sichuan Science City HospitalMianyangSichuan Province621000China School of Chemistry and Chemical Engineering Jishou UniversityJishou 416000China School of Physics and Optoelectronic Engineering Yangtze UniversityJingzhouHubei 434023China School of Microelectronics and Control Engineering Changzhou UniversityChangzhou 213164China School of Physics and Electronic Information Engineering Hubei Engineering UniversityXiaogan 432000China Institute of Physics Bahauddin Zakariya UniversityMultan 66000Pakistan

The field of terahertz devices is important in terahertz ***,most of the current devices have limited functionality and poor *** improve device performance and achieve multifunctionality,we designed a terahertz device based on a combination of VO_(2)and *** device can be tuned using the phase-transition characteristics of VO_(2),which is included in the triple-layer structure of the device,along with SiO_(2)and *** terahertz device exhibits various advantageous features,including broadband coverage,high absorption capability,dynamic tunability,simple structural design,polarization insensitivity,and incidentangle *** simulation results showed that by controlling the temperature,the terahertz device achieved a thermal modulation range of spectral absorption from 0 to *** 313 K,the device exhibited complete reflection of terahertz *** the temperature increased,the absorption rate also *** the temperature reached 353 K,the device absorption rate exceeded 97.7%in the range of 5-8.55 *** study used the effective medium theory to elucidate the correlation between conductivity and temperature during the phase transition of VO_(2).Simultaneously,the variation in device performance was further elucidated by analyzing and depicting the intensity distribution of the electric field on the device surface at different ***,the impact of various structural parameters on device performance was examined,offering valuable insights and suggestions for selecting suitable parameter values in real-world *** characteristics render the device highly promising for applications in stealth technology,energy harvesting,modulation,and other related fields,thus showcasing its significant potential.

关键词： terahertz perfect absorption complete reflection temperature regulation ultrawideband smart device

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An enhanced semi-supervised learning method with self-supervised and adaptive threshold for fault detection and classification in urban power grids

Energy and AI

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Energy and AI 2024年第3期17卷 65-79页

作者： Jiahao Zhang Lan Cheng Zhile Yang Qinge Xiao sohail Khan Rui Liang Xinyu Wu Yuanjun Guo Taiyuan University of Technology TaiyuanShanxiChina Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhenGuangdongChina Guangdong Institute of Carbon Neutrality(Shaoguan) ShaoguanGuangdongChina Sino-Pak Center for Artificial Intelligence Pak-Austria Fachhochschule–Institute of Applied Sciences and TechnologyHaripurKhyber PakhtunkhwaPakistan Chengxi Company State Grid Tianjin Electric Power CompanyTianjinChina

With the rapid development of urban power grids and the large-scale integration of renewable energy, traditional power grid fault diagnosis techniques struggle to address the complexities of diagnosing faults in intricate power grid systems. Although artificial intelligence technologies offer new solutions for power grid fault diagnosis, the difficulty in acquiring labeled grid data limits the development of AI technologies in this area. In response to these challenges, this study proposes a semi-supervised learning framework with self-supervised and adaptive threshold (SAT-SSL) for fault detection and classification in power grids. Compared to other methods, our method reduces the dependence on labeling data while maintaining high recognition accuracy. First, we utilize frequency domain analysis on power grid data to filter abnormal events, then classify and label these events based on visual features, to creating a power grid dataset. Subsequently, we employ the Yule–Walker algorithm extract features from the power grid data. Then we construct a semi-supervised learning framework, incorporating self-supervised loss and dynamic threshold to enhance information extraction capabilities and adaptability across different scenarios of the model. Finally, the power grid dataset along with two benchmark datasets are used to validate the model’s functionality. The results indicate that our model achieves a low error rate across various scenarios and different amounts of labels. In power grid dataset, When retaining just 5% of the labels, the error rate is only 6.15%, which proves that this method can achieve accurate grid fault detection and classification with a limited amount of labeled data.

关键词： Power grid fault detection Semi-supervised learning Data driven Deep learning Smart grid

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Innovative genome editing in plants: a transposase and CRISPR combination approach

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Science China Life Sciences 2024年

作者： Hamza sohail Iqra Noor Xuehao Chen Xiaodong Yang School of Horticulture and Landscape Architecture Yangzhou University

Transgenic plants are extensively utilized in research, enabling scientists to explore gene functions in controlled environments, leading to crops with higher productivity and greater resistance to biotic and abiotic stressors (Oyebamiji et al., 2024). Traditional transgene integration techniques, such as particle bombardment and Agrobacterium T-DNA integration, often lead to unintended mutations and unpredictable insertion sites, emphasizing the necessity for developing targeted transgene integration(Ahuja and Fladung, 2014).

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Deactivation mechanism for water splitting:Recent advances

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Carbon Energy 2024年第7期6卷 53-82页

作者： Yansong Jia Yang Li Qiong Zhang sohail yasin Xinyu Zheng Kai Ma Zhengli Hua Jianfeng Shi Chaohua Gu Yuhai Dou Shixue Dou Hydrogen Energy Institute Zhejiang UniversityHangzhouChina Institute of Process Equipment Zhejiang UniversityHangzhouChina Donghai Laboratory ZhoushanChina Department of Mechanical Engineering Faculty of EngineeringUniversiti MalayaKuala LumpurMalaysia Institute of Energy Materials Science University of Shanghai for Science and TechnologyShanghaiChina

Hydrogen(H2)has been regarded as a promising alternative to fossil-fuel *** H2 produced via water electrolysis(WE)powered by renewable energy could achieve a zero-carbon *** attention has been focused on developing highly active catalysts to facilitate the reaction kinetics and improve the energy efficiency of ***,the stability of the electrocatalysts hampers the commercial viability of *** studies have elucidated the origin of catalyst *** this review,we first discuss the WE mechanism,including anodic oxygen evolution reaction(OER)and cathodic hydrogen evolution reaction(HER).Then,we provide strategies used to enhance the stability of *** that,the deactivation mechanisms of the typical commercialized HER and OER catalysts,including Pt,Ni,RuO_(2),and IrO_(2),are ***,the influence of fluctuating energy on catalyst degradation is highlighted and in situ characterization methodologies for understanding the dynamic deactivation processes are described.

关键词： deactivation mechanism hydrogen evolution in situ characterization oxygen evolution water splitting

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Distributed scheduling for multi-energy synergy system considering renewable energy generations and plug-in electric vehicles:A level-based coupled optimization method

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Energy and AI 2024年第2期16卷 213-226页

作者： Linxin Zhang Zhile Yang Qinge Xiao Yuanjun Guo Zuobin Ying Tianyu Hu Xiandong Xu sohail Khan Kang Li Faculty of Data Science City University of MacaoTaipa999078Macao Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhenGuangdong518055China Tianjin University Tianjin 300072China School of Electronic and Electrical Engineering University of LeedsLeedsLS29JTUK Institute of Applied Sciences&Technology Pak-Austria FachhochschuleHaripurKhyber PakhtunkhwaPakistan

Multi-energy synergy systems integrating high-penetration large-scale plug-in electric vehicles, distributed renewable energy generations, and battery energy storage systems have great potential to reduce the reliance of the grid on traditional fossil fuels. However, the random charging characteristics of plug-in electric vehicles and the uncertainty of photovoltaics may impose an additional burden on the grid and affect the supply–demand equilibrium. To address this issue, judicious scheduling optimization offers an effective solution. In this study, considering charge and discharge management of plug-in electric vehicles and intermittent photovoltaics, a novel Multi-energy synergy systems scheduling framework is developed for solving grid instability and unreliability issues. This formulates a large-scale mixed-integer problem, which calls for a powerful and effective optimizer. The new binary level-based learning optimization algorithm is proposed to address nonlinear large-scale high-coupling unit commitment problems. To investigate the feasibility of the proposed scheme, numerical experiments have been carried out considering multiple scales of unit numbers and various scenarios. Finally, the results confirm that the proposed scheduling framework is reasonable and effective in solving unit commitment problems, can achieve 3.3% cost reduction and demonstrates superior performance in handling large-scale energy optimization problems. The integration of plug-in electric vehicles, distributed renewable energy generations, and battery energy storage systems is verified to reduce the output power of 192.72 MW units during peak periods to improve grid stability. Therefore, optimizing energy utilization and distribution will become an indispensable part of future power systems.

关键词： Electric vehicle Unit commitment Renewable energy Battery energy storage system Synergy optimization

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