YOLOv8 for Fire and Smoke Recognition Algorithm Integrated with the Convolutional Block Attention Module

The complexity of fire and smoke in terms of shape, texture, and color presents significant challenges for accurate fire and smoke detection. To address this, a YOLOv8-based detection algorithm integrated with the Convolutional Block Attention Module (CBAM) has been developed. This algorithm initially employs the latest YOLOv8 for object recognition. Subsequently, the integration of CBAM enhances its feature extraction capabilities. Finally, the WIoU function is used to optimize the network’s bounding box loss, facilitating rapid convergence. Experimental validation using a smoke and fire dataset demonstrated that the proposed algorithm achieved a 2.3% increase in smoke and fire detection accuracy, surpassing other state-of-the-art methods.

Impact of artificial intelligence in the management of esophageal,gastric and colorectal malignancies

The incidence of gastrointestinal malignancies has increased over the past decade at an alarming rate.Colorectal and gastric cancers are the third and fifth most commonly diagnosed cancers worldwide but are cited as the second and third leading causes of mortality.Early institution of appropriate therapy from timely diagnosis can optimize patient outcomes.Artificial intelligence(AI)-assisted diagnostic,prognostic,and therapeutic tools can assist in expeditious diagnosis,treatment planning/response prediction,and post-surgical prognostication.AI can intercept neoplastic lesions in their primordial stages,accurately flag suspicious and/or inconspicuous lesions with greater accuracy on radiologic,histopathological,and/or endoscopic analyses,and eliminate over-dependence on clinicians.AI-based models have shown to be on par,and sometimes even outperformed experienced gastroenterologists and radiologists.Convolutional neural networks(state-of-the-art deep learning models)are powerful computational models,invaluable to the field of precision oncology.These models not only reliably classify images,but also accurately predict response to chemotherapy,tumor recurrence,metastasis,and survival rates post-treatment.In this systematic review,we analyze the available evidence about the diagnostic,prognostic,and therapeutic utility of artificial intelligence in gastrointestinal oncology.

The State-of-the-Art Review on Applications of Intrusive Sensing,Image Processing Techniques,and Machine Learning Methods in Pavement Monitoring and Analysis

In modern transportation,pavement is one of the most important civil infrastructures for the movement of vehicles and pedestrians.Pavement service quality and service life are of great importance for civil engineers as they directly affect the regular service for the users.Therefore,monitoring the health status of pavement before irreversible damage occurs is essential for timely maintenance,which in turn ensures public transportation safety.Many pavement damages can be detected and analyzed by monitoring the structure dynamic responses and evaluating road surface conditions.Advanced technologies can be employed for the collection and analysis of such data,including various intrusive sensing techniques,image processing techniques,and machine learning methods.This review summarizes the state-ofthe-art of these three technologies in pavement engineering in recent years and suggests possible developments for future pavement monitoring and analysis based on these approaches.

State-of-the-Art and Perspectives in the Field of Particulate Nanostructured Materials

A great attention has been paid to the research and development of nanostructured materials.The main preparation methods of ultrafine particles and nanostructured materials have been summarized. The applications of zone typical nanostructured materials have also been reviewed.The peculiar characteristics and properties. such as density, grain size, hardness, superplasticity,magnetic and catalytic properties have been discussed

China's study on coal mining beneath surface buildings——state-of-the-art and developing trend

Extensive coal deposits are sterilized beneath surface buildings in China. The paper elaborates on the present status of the study on mining beneath surface buildings from several aspects involving the techniques for reducing ground subsidence and deformation and the measures for protecting buildings. Following that, it is pointed out that, in the future, more efforts should be directed forwards the following aspects: the laws governing the ground movement; the techniques for constructing anti-deformation buildings; coal-bed gasification and protection of buildings; grouting of the overlying strata with bed separation phenomena; and the method for evaluating the stability of the ground above a worked-out area.

State-of-the-art Super Server Developed at CAS

Apowerful computer superserver developed at the In-stitute of Computer Tech-nology (ICT), under the ChineseAcademy of Sciences (CAS), hasjust passed the accreditation of

A state-of-the-art photoemission spectroscopy

CAS researchers have developed a VUV laser-based angle-resolved photoemission spectroscopy. making it possible to probe the electronic structure of materials at a sharper level.

Deep Learning Applied to Computational Mechanics:A Comprehensive Review,State of the Art,and the Classics

Three recent breakthroughs due to AI in arts and science serve as motivation:An award winning digital image,protein folding,fast matrix multiplication.Many recent developments in artificial neural networks,particularly deep learning(DL),applied and relevant to computational mechanics(solid,fluids,finite-element technology)are reviewed in detail.Both hybrid and pure machine learning(ML)methods are discussed.Hybrid methods combine traditional PDE discretizations with ML methods either(1)to help model complex nonlinear constitutive relations,(2)to nonlinearly reduce the model order for efficient simulation(turbulence),or(3)to accelerate the simulation by predicting certain components in the traditional integration methods.Here,methods(1)and(2)relied on Long-Short-Term Memory(LSTM)architecture,with method(3)relying on convolutional neural networks.Pure ML methods to solve(nonlinear)PDEs are represented by Physics-Informed Neural network(PINN)methods,which could be combined with attention mechanism to address discontinuous solutions.Both LSTM and attention architectures,together with modern and generalized classic optimizers to include stochasticity for DL networks,are extensively reviewed.Kernel machines,including Gaussian processes,are provided to sufficient depth for more advanced works such as shallow networks with infinite width.Not only addressing experts,readers are assumed familiar with computational mechanics,but not with DL,whose concepts and applications are built up from the basics,aiming at bringing first-time learners quickly to the forefront of research.History and limitations of AI are recounted and discussed,with particular attention at pointing out misstatements or misconceptions of the classics,even in well-known references.Positioning and pointing control of a large-deformable beam is given as an example.

Radiofrequency combined with immunomodulation for hepatocellular carcinoma: State of the art and innovations

Hepatocellular carcinoma(HCC)is the most common primary liver tumor and has been considered a very immunogenic tumor.The treatment with radiofrequency ablation(RFA)has been established as the standard ablative therapy for early HCC,and is currently recognized as the main ablative tool for HCC tumors<5 cm in size;however,progression and local recurrence remain the main disadvantages of this approach.To solve this clinical problem,recent efforts were concentrated on multimodal treatment,combining different strategies,including the combination of RFA and immunotherapy.This article reviewed the combination treatment of RFA with immunotherapy and found that this treatment strategy leads to an increased response of anti-tumor T cells,significantly reduces the risk of recurrence and improves survival rates compared to RFA alone.This review highlighted scientific evidence that supports the current recommendations for pre-clinical studies,and discuss the need for further research on this topic.

Recent advances in structural control research and applications in China mainland

The recent developments of theoretical research, model tests and engineering applications of structural control in China's Mainland are reviewed in this paper. It includes seismic isolation, passive energy dissipation, active and semi-active control, smart materials and smart structural systems. It can be seen that passive control methods, such as seismic isolation and energy dissipation methods, have developed into the mature stage in China. At the same time, great progress has been made in active and semi-active control, and smart actuators or smart dampers and smart structural systems. Finally, some future research initiatives for structural control in civil engineering are suggested. Keywords state-of-the-art review - structural control - seismic isolation - passive energy dissipation - active and semi-active control - smart material and smart structure Supported by : National Natural Science Foundation of China (Grant No. 50025821)

CD-R产品检测:高的品质来自成熟的工艺

根据最新的市场调查显示：在2004年，CD-R产品的市场需求将小于之前的2年。这源于在2003年期间，CD-R产品的生产能力以非常迅速的速度同市场的需求之间达到平衡。特别是在印度、中东和远东地区，新的风险企业家在偶然情况下发现可记录光盘领域的市场需求，从而大量进入来填补这个空白。

The issues on the commercialization of perovskite solar cells

Perovskite solar cells have aroused a worldwide research upsurge in recent years due to their soaring photovoltaic performance,ease of solution processing,and low cost.The power conversion efficiency record is constantly being broken and has recently reached 26.1%in the lab,which is comparable to the established photovoltaic technologies such as crystalline silicon,copper indium gallium selenide and cadmium telluride(CdTe)solar cells.Currently,perovskite solar cells are standing at the entrance of industrialization,where huge opportunities and risks coexist.However,towards commercialization,challenges of up-scaling,stability and lead toxicity still remain,the proper handling of which could potentially lead to the widespread adoption of perovskite solar cells as a low-cost and efficient source of renewable energy.This review gives a holistic analysis of the path towards commercialization for perovskite solar cells.A comprehensive overview of the current state-of-the-art level for perovskite solar cells and modules will be introduced first,with respect to the module efficiency,stability and current status of industrialization.We will then discuss the challenges that get in the way of commercialization and the corresponding strategies to address them,involving the upscaling,the stability and the lead toxicity issue.Insights into the future direction of commercialization of perovskite photovoltaics was also provided,including the flexible perovskite cells and modules and perovskite indoor photovoltaics.Finally,the future perspectives towards commercialization are put forward.

BiCuSeO as state-of-the-art thermoelectric materials for energy conversion:from thin films to bulks

BiCuSeO-based thermoelectric material has attracted great attention as state-of-the-art thermoelectric materials since it was first reported in 2010. In this review, we update the studies on the BiCuSeO thin films first. Then, we focus on the most recent progress of multiple approaches that enhance the thermoelectric performance including advanced synthesized technologies, notable mechanisms for higher power factor （optimizing carrier concentration, carrier mobility, Seebeck coefficient） and doping effects predicted by calculation. And finally, aiming at further enhancing the performance of these materials and ultimately commercial application, we give a brief discussion on the urgent issues to which should be paid close attention.

Words of the Editor-in-Chief——A state-of-the-art academic paper: Submarine fans: A critical retrospective(1950-2015)

Prof. Dr. G. Shanmugam is a distinguished, pragmatic and iconoclastic geologist, especially a deep water process sedimentologist. This paper entitled “Submarine fans： A critical retrospective （1950-2015）” by Prof. Dr. G. Shanmugam published in the 2016 April issue of Journal of Palaeogeography, is not only a research result and a retrospective of submarine fans worldwide （1950-2015）, but also a guide for future research.

Vision-Based Hand Gesture Recognition for Human-Computer Interaction——A Survey

Recently,vision-based gesture recognition(VGR)has become a hot research spot in human-computer interaction(HCI).Unlike other gesture recognition methods with data gloves or other wearable sensors,vision-based gesture recognition could lead to more natural and intuitive HCI interactions.This paper reviews the state-of-the-art vision-based gestures recognition methods,from different stages of gesture recognition process,i.e.,(1)image acquisition and pre-processing,(2)gesture segmentation,(3)gesture tracking,(4)feature extraction,and(5)gesture classification.This paper also analyzes the advantages and disadvantages of these various methods in detail.Finally,the challenges of vision-based gesture recognition in haptic rendering and future research directions are discussed.

Characterization and anticancer potential of Withania somnifera fruit bioactives(a native species to Pakistan)using gas chromatography-mass spectrometer,nuclear magnetic resonance and liquid chromatography-mass spectrometry-electrospray ionization

INTRODUCTION:Withania somnifera(W.somnifera)is a plant with remarkable pharmacological properties.The plant has an impressive profile of medicinal uses in the folk medicine system of several civilizations.AIM:This comprehensive study is aimed to characterize phytochemicals in fruit of W.somnifera and tested for in vitro anticancer potential to find out active candidate in disease prevention and treatment.METHODS:The bioactive components from W.somnifera fruit were extracted with polar and non-polar solvents.Anticancer potential of the isolated bioactive was assessed against different cancer cell lines through MTT assay and Incucytes imaging analysis.The extracts were characterized for secondary metabolites using GCMS(gas chromatography-mass spectrometer),LCMS(liquid(electrospray Ionization)and 1H-NMR(electrospray Ionization)techniques.RESULTS:Both freeze-dried and rotary evaporator condensed extracts exhibited anticancer potential against MDA-MB-231,MCF7-SKOV3 and SKBR3 cell lines.The tested extracts have cell growth inhibition potential against mammalian cancer cell line.Hexacosanedioic acid purified from n-hexane extract through HPLC was investigated for its cytotoxicity against breast cancer cell line SKBR3 by using Incucytes imaging analysis.Conclusion:We found that a variety of bioactive compounds existed in this plant.One identified compound that was not investigated for cytotoxicity in previous studies was purified and its application showed cytotoxicity on breast cancer cell lines.A number of bioactive identified from W.somnifera fruit may have an effective potential for development into chemotherapy drugs.

Intelligent classification of lung malignancies using deep learning techniques

Purpose-Computed tomography(CT)scan can provide valuable information in the diagnosis of lung diseases.To detect the location of the cancerous lung nodules,this work uses novel deep learning methods.The majority of the early investigations used CT,magnetic resonance and mammography imaging.Using appropriate procedures,the professional doctor in this sector analyses these images to discover and diagnose the various degrees of lung cancer.All of the methods used to discover and detect cancer illnesses are time-consuming,expensive and stressful for the patients.To address all of these issues,appropriate deep learning approaches for analyzing these medical images,which included CT scan images,were utilized.Design/methodology/approach-Radiologists currently employ chest CT scans to detect lung cancer at an early stage.In certain situations,radiologists’perception plays a critical role in identifying lung melanoma which is incorrectly detected.Deep learning is a new,capable and influential approach for predicting medical images.In this paper,the authors employed deep transfer learning algorithms for intelligent classification of lung nodules.Convolutional neural networks(VGG16,VGG19,MobileNet and DenseNet169)are used to constrain the input and output layers of a chest CT scan image dataset.Findings-The collection includes normal chest CT scan pictures as well as images from two kinds of lung cancer,squamous and adenocarcinoma impacted chest CT scan images.According to the confusion matrix results,the VGG16 transfer learning technique has the highest accuracy in lung cancer classification with 91.28% accuracy,followed by VGG19 with 89.39%,MobileNet with 85.60% and DenseNet169 with 83.71% accuracy,which is analyzed using Google Collaborator.Originality/value-The proposed approach using VGG16 maximizes the classification accuracy when compared to VGG19,MobileNet and DenseNet169.The results are validated by computing the confusion matrix for each network type.

Prometheus’revelation:Deciphering the mysteries of the liver,towards a shared healthful future

iLIVER is an open-access peer-reviewed journal dedicated to the presentation and dissemination of scientific findings and novel practices in the field of hepatobiliary system.As an excellent source of scientific information,the journal seeks to publish high-quality articles,including original research,meta-analysis,reviews,consensus or practical guidelines,and editorials regarding hepatobiliary system.We intend to build a global community in which clinicians,scientists,engineers,scholars and other healthcare professionals are able to scope the whole spectrum of research and exchange frontier academic advances.