BACKGROUND: Sepsis is a life-threatening inflammatory condition in which the invading pathogen avoids the host's defense mechanisms and continuously stimulates and damages host cells. Consequently, many immune res...BACKGROUND: Sepsis is a life-threatening inflammatory condition in which the invading pathogen avoids the host's defense mechanisms and continuously stimulates and damages host cells. Consequently, many immune responses initially triggered for protection become harmful because of the failure to restore homeostasis, resulting in ongoing hyperinflammation and immunosuppression. METHODS: A literature review was conducted to address bacterial sepsis, describe advances in understanding complex immunological reactions, critically assess diagnostic approaches, and emphasize the importance of studying bacterial bottlenecks in the detection and treatment of sepsis.RESULTS: Diagnosing sepsis via a single laboratory test is not feasible;therefore, multiple key biomarkers are typically monitored, with a focus on trends rather than absolute values. The immediate interpretation of sepsis-associated clinical signs and symptoms, along with the use of specific and sensitive laboratory tests, is crucial for the survival of patients in the early stages. However, long-term mortality associated with sepsis is now recognized, and alongside the progression of this condition, there is an in vivo selection of adapted pathogens.CONCLUSION: Bacterial sepsis remains a significant cause of mortality across all ages and societies. While substantial progress has been made in understanding the immunological mechanisms underlying the inflammatory response, there is growing recognition that the ongoing host-pathogen interactions, including the emergence of adapted virulent strains, shape both the acute and long-term outcomes in sepsis. This underscores the urgent need for novel high-throughput diagnostic methods and a shift toward more pre-emptive, rather than reactive, treatment strategies in sepsis care.展开更多
A bottleneck algebra is a linearly ordered set(B,≤)with two operations a⊕b=max{a,b}and a⊗b=min{a,b}.A finite nonempty set of vectors of order m over a bottleneck algebra B is said to be 2 B-independent if each vecto...A bottleneck algebra is a linearly ordered set(B,≤)with two operations a⊕b=max{a,b}and a⊗b=min{a,b}.A finite nonempty set of vectors of order m over a bottleneck algebra B is said to be 2 B-independent if each vector of order m over B can be expressed as a linear combination of vectors in this set in at most one way.In 1996,Cechlárováand Plávka posed an open problem:Find a necessary and sufficient condition for a finite nonempty set of vectors of order m over B to be 2 B-independent.In this paper,we derive some necessary and sufficient conditions for a finite nonempty set of vectors of order m over a bounded bottleneck algebra to be 2 B-independent and answer this open problem.展开更多
Diagnosing gastrointestinal tract diseases is a critical task requiring accurate and efficient methodologies.While deep learning models have significantly advanced medical image analysis,challenges such as imbalanced ...Diagnosing gastrointestinal tract diseases is a critical task requiring accurate and efficient methodologies.While deep learning models have significantly advanced medical image analysis,challenges such as imbalanced datasets and redundant features persist.This study proposes a novel framework that customizes two deep learning models,NasNetMobile and ResNet50,by incorporating bottleneck architectures,named as NasNeck and ResNeck,to enhance feature extraction.The feature vectors are fused into a combined vector,which is further optimized using an improved Whale Optimization Algorithm to minimize redundancy and improve discriminative power.The optimized feature vector is then classified using artificial neural network classifiers,effectively addressing the limitations of traditional methods.Data augmentation techniques are employed to tackle class imbalance,improving model learning and generalization.The proposed framework was evaluated on two publicly available datasets:Hyper-Kvasir and Kvasir v2.The Hyper-Kvasir dataset,comprising 23 gastrointestinal disease classes,yielded an impressive 96.0%accuracy.On the Kvasir v2 dataset,which contains 8 distinct classes,the framework achieved a remarkable 98.9%accuracy,further demonstrating its robustness and superior classification performance across different gastrointestinal datasets.The results demonstrate the effectiveness of customizing deep models with bottleneck architectures,feature fusion,and optimization techniques in enhancing classification accuracy while reducing computational complexity.展开更多
INTRODUCTION Contemporary human living environments present complex and pervasive health risks,and environmental health challenges are becoming increasingly prominent.These risks encompass diverse domains,such as chem...INTRODUCTION Contemporary human living environments present complex and pervasive health risks,and environmental health challenges are becoming increasingly prominent.These risks encompass diverse domains,such as chemical factors(e.g.,heavy metals,nanomaterials,per-and polyfluoroalkyl substances),physical factors(e.g.,noise,radiation,and extreme weather)biological factors(e.g.,pathogenic microorganisms and parasites),natural disasters(e.g.,earthquakes and floods),and anthropogenic incidents(e.g.,chemical spills,fires,and explosions).展开更多
Aimed at the remanufacturing system, the effect of the uncertainty of returns＇ quality on bottleneck shifting is investigated. A novel definition of bottleneck station is presented and the probability of a station be...Aimed at the remanufacturing system, the effect of the uncertainty of returns＇ quality on bottleneck shifting is investigated. A novel definition of bottleneck station is presented and the probability of a station becoming a bottleneck is also given. By calculating the effective output, the effective operation time （EOT） and the ratio of EOT of each station, the system＇s current bottleneck of effective output time is determined. By calculating the probability coefficient of variation and index of bottleneck shifting, the quantitative performance of bottleneck shifting is obtained. Discrete event simulation and the experiment design method are adopted to simulate the system, in which the proportion of quality grading, repair rates and process routes are considered. The case study shows that the uncertainty of returns＇ quality greatly increases the probability of bottleneck shifting, and with the increase of the discrete degree of the returns＇ repair rate, the bottleneck shifting phenomenon is more obvious. Furthermore, bottleneck shifting is closely related to the process route of the dominating returns＇ quality grade.展开更多
American white moth is a remarkable worldwide quarantine pest. By the results combination of indoor incubation and field observation of American white moth in Langfang City of Hebei Province, series prevention and con...American white moth is a remarkable worldwide quarantine pest. By the results combination of indoor incubation and field observation of American white moth in Langfang City of Hebei Province, series prevention and control bottleneck factors for the invasive agricultural pest are analyzed, such as the main operation mode of the pest against host trees, diffusion and migration charaeteristies, biological characteristics, natural enemy control, pesticide prevention and so on. The re- search aims to search for the breakthrough point of the development of environment-friendly control techniques against American white moth, which also provides the reference for further improvement of integrated pest management system.展开更多
Photonic neural networks(PNNs)of sufficiently large physical dimensions and high operation accuracies are envisaged as ideal candidates for breaking the major bottlenecks in the current artificial intelligence archite...Photonic neural networks(PNNs)of sufficiently large physical dimensions and high operation accuracies are envisaged as ideal candidates for breaking the major bottlenecks in the current artificial intelligence architectures in terms of latency,energy efficiency,and computational power.To achieve this vision,it is of vital importance to scale up the PNNs while simultaneously reducing the high demand on the dimensions required by them.The underlying cause of this strategy is the enormous gap between the scales of photonic and electronic integrated circuits.Here,we demonstrate monolithically integrated optical convolutional processors on thin film lithium niobate(TFLN)that harness inherent parallelism in photonics to enable large-scale programmable convolution kernels and,in turn,greatly reduce the dimensions required by subsequent fully connected layers.Experimental validation achieves high classification accuracies of 96%(86%)on the MNIST(Fashion-MNIST)dataset and 84.6%on the AG News dataset while dramatically reducing the required subsequent fully connected layer dimensions to 196×10(from 784×10)and 175×4(from 800×4),respectively.Furthermore,our devices can be driven by commercial field-programmable gate array systems;a unique advantage in addition to their scalable channel number and kernel size.Our architecture provides a solution to build practical machine learning photonic devices.展开更多
基金funded by the Deanship of Scientific Research (DSR) at King Abdulaziz UniversityJeddah+1 种基金Saudi Arabiaunder grant number G-150-248-1443。
文摘BACKGROUND: Sepsis is a life-threatening inflammatory condition in which the invading pathogen avoids the host's defense mechanisms and continuously stimulates and damages host cells. Consequently, many immune responses initially triggered for protection become harmful because of the failure to restore homeostasis, resulting in ongoing hyperinflammation and immunosuppression. METHODS: A literature review was conducted to address bacterial sepsis, describe advances in understanding complex immunological reactions, critically assess diagnostic approaches, and emphasize the importance of studying bacterial bottlenecks in the detection and treatment of sepsis.RESULTS: Diagnosing sepsis via a single laboratory test is not feasible;therefore, multiple key biomarkers are typically monitored, with a focus on trends rather than absolute values. The immediate interpretation of sepsis-associated clinical signs and symptoms, along with the use of specific and sensitive laboratory tests, is crucial for the survival of patients in the early stages. However, long-term mortality associated with sepsis is now recognized, and alongside the progression of this condition, there is an in vivo selection of adapted pathogens.CONCLUSION: Bacterial sepsis remains a significant cause of mortality across all ages and societies. While substantial progress has been made in understanding the immunological mechanisms underlying the inflammatory response, there is growing recognition that the ongoing host-pathogen interactions, including the emergence of adapted virulent strains, shape both the acute and long-term outcomes in sepsis. This underscores the urgent need for novel high-throughput diagnostic methods and a shift toward more pre-emptive, rather than reactive, treatment strategies in sepsis care.
基金Supported by National Natural Science Foundation of China(Grant Nos.11771004 and 11971111).
文摘A bottleneck algebra is a linearly ordered set(B,≤)with two operations a⊕b=max{a,b}and a⊗b=min{a,b}.A finite nonempty set of vectors of order m over a bottleneck algebra B is said to be 2 B-independent if each vector of order m over B can be expressed as a linear combination of vectors in this set in at most one way.In 1996,Cechlárováand Plávka posed an open problem:Find a necessary and sufficient condition for a finite nonempty set of vectors of order m over B to be 2 B-independent.In this paper,we derive some necessary and sufficient conditions for a finite nonempty set of vectors of order m over a bounded bottleneck algebra to be 2 B-independent and answer this open problem.
基金supported by Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia through the Researchers Supporting Project PNURSP2025R333.
文摘Diagnosing gastrointestinal tract diseases is a critical task requiring accurate and efficient methodologies.While deep learning models have significantly advanced medical image analysis,challenges such as imbalanced datasets and redundant features persist.This study proposes a novel framework that customizes two deep learning models,NasNetMobile and ResNet50,by incorporating bottleneck architectures,named as NasNeck and ResNeck,to enhance feature extraction.The feature vectors are fused into a combined vector,which is further optimized using an improved Whale Optimization Algorithm to minimize redundancy and improve discriminative power.The optimized feature vector is then classified using artificial neural network classifiers,effectively addressing the limitations of traditional methods.Data augmentation techniques are employed to tackle class imbalance,improving model learning and generalization.The proposed framework was evaluated on two publicly available datasets:Hyper-Kvasir and Kvasir v2.The Hyper-Kvasir dataset,comprising 23 gastrointestinal disease classes,yielded an impressive 96.0%accuracy.On the Kvasir v2 dataset,which contains 8 distinct classes,the framework achieved a remarkable 98.9%accuracy,further demonstrating its robustness and superior classification performance across different gastrointestinal datasets.The results demonstrate the effectiveness of customizing deep models with bottleneck architectures,feature fusion,and optimization techniques in enhancing classification accuracy while reducing computational complexity.
基金supported by the commissioned project of the Department of Health and Immunization Planning under the National Disease Control and Prevention Administration(No.BX2024100800015)The preliminary study project on standardization of the Chinese Center for Disease Control and Prevention(No.BZ2025-Q155)The National Natural Science Foundation of China(No.82404299).
文摘INTRODUCTION Contemporary human living environments present complex and pervasive health risks,and environmental health challenges are becoming increasingly prominent.These risks encompass diverse domains,such as chemical factors(e.g.,heavy metals,nanomaterials,per-and polyfluoroalkyl substances),physical factors(e.g.,noise,radiation,and extreme weather)biological factors(e.g.,pathogenic microorganisms and parasites),natural disasters(e.g.,earthquakes and floods),and anthropogenic incidents(e.g.,chemical spills,fires,and explosions).
基金The Program for Special Talent in Six Fields of Jiangsu Province(No.2013ZBZZ-046)the Program of Lanzhou Technology Development(No.2014-1-175)
文摘Aimed at the remanufacturing system, the effect of the uncertainty of returns＇ quality on bottleneck shifting is investigated. A novel definition of bottleneck station is presented and the probability of a station becoming a bottleneck is also given. By calculating the effective output, the effective operation time （EOT） and the ratio of EOT of each station, the system＇s current bottleneck of effective output time is determined. By calculating the probability coefficient of variation and index of bottleneck shifting, the quantitative performance of bottleneck shifting is obtained. Discrete event simulation and the experiment design method are adopted to simulate the system, in which the proportion of quality grading, repair rates and process routes are considered. The case study shows that the uncertainty of returns＇ quality greatly increases the probability of bottleneck shifting, and with the increase of the discrete degree of the returns＇ repair rate, the bottleneck shifting phenomenon is more obvious. Furthermore, bottleneck shifting is closely related to the process route of the dominating returns＇ quality grade.
基金Supported by Science and Technology Planning Project in Langfang City(2009012009)~~
文摘American white moth is a remarkable worldwide quarantine pest. By the results combination of indoor incubation and field observation of American white moth in Langfang City of Hebei Province, series prevention and control bottleneck factors for the invasive agricultural pest are analyzed, such as the main operation mode of the pest against host trees, diffusion and migration charaeteristies, biological characteristics, natural enemy control, pesticide prevention and so on. The re- search aims to search for the breakthrough point of the development of environment-friendly control techniques against American white moth, which also provides the reference for further improvement of integrated pest management system.
基金supported by the National Natural Science Foundation of China (Grant Nos.12192251,12334014,62335019,12134001,1230441812474378)+1 种基金the Quantum Science and Technology National Science and Technology Major Project(Grant No.2021ZD0301403)the Shanghai Municipal Science and Technology Major Project (Grant No.2019SHZDZX01)。
文摘Photonic neural networks(PNNs)of sufficiently large physical dimensions and high operation accuracies are envisaged as ideal candidates for breaking the major bottlenecks in the current artificial intelligence architectures in terms of latency,energy efficiency,and computational power.To achieve this vision,it is of vital importance to scale up the PNNs while simultaneously reducing the high demand on the dimensions required by them.The underlying cause of this strategy is the enormous gap between the scales of photonic and electronic integrated circuits.Here,we demonstrate monolithically integrated optical convolutional processors on thin film lithium niobate(TFLN)that harness inherent parallelism in photonics to enable large-scale programmable convolution kernels and,in turn,greatly reduce the dimensions required by subsequent fully connected layers.Experimental validation achieves high classification accuracies of 96%(86%)on the MNIST(Fashion-MNIST)dataset and 84.6%on the AG News dataset while dramatically reducing the required subsequent fully connected layer dimensions to 196×10(from 784×10)and 175×4(from 800×4),respectively.Furthermore,our devices can be driven by commercial field-programmable gate array systems;a unique advantage in addition to their scalable channel number and kernel size.Our architecture provides a solution to build practical machine learning photonic devices.
