Risk assessment is a crucial component of collision warning and avoidance systems for intelligent vehicles.Reachability-based formal approaches have been developed to ensure driving safety to accurately detect potenti...Risk assessment is a crucial component of collision warning and avoidance systems for intelligent vehicles.Reachability-based formal approaches have been developed to ensure driving safety to accurately detect potential vehicle collisions.However,they suffer from over-conservatism,potentially resulting in false–positive risk events in complicated real-world applications.In this paper,we combine two reachability analysis techniques,a backward reachable set(BRS)and a stochastic forward reachable set(FRS),and propose an integrated probabilistic collision–detection framework for highway driving.Within this framework,we can first use a BRS to formally check whether a two-vehicle interaction is safe;otherwise,a prediction-based stochastic FRS is employed to estimate the collision probability at each future time step.Thus,the framework can not only identify non-risky events with guaranteed safety but also provide accurate collision risk estimation in safety-critical events.To construct the stochastic FRS,we develop a neural network-based acceleration model for surrounding vehicles and further incorporate a confidence-aware dynamic belief to improve the prediction accuracy.Extensive experiments were conducted to validate the performance of the acceleration prediction model based on naturalistic highway driving data.The efficiency and effectiveness of the framework with infused confidence beliefs were tested in both naturalistic and simulated highway scenarios.The proposed risk assessment framework is promising for real-world applications.展开更多
Chinese culture provides inexhaustible sources driving Chinese nation for constant progress. We should improve education on the best of Chinese culture, develop and explore rich resources of national culture by means ...Chinese culture provides inexhaustible sources driving Chinese nation for constant progress. We should improve education on the best of Chinese culture, develop and explore rich resources of national culture by means of modem technology, further cultivate and protect cultures of different ethnic groups, attach more importance to the conservation of tangible and intangible cultural heritage, and make greater efforts in sorting out historical literature.展开更多
BACKGROUND:Tracheal intubation(TI)is a fundamental procedure for securing the airway or assisting ventilation in emergency medicine.Tracheal intubation in the lateral position(TILP)has been utilized in clinical practi...BACKGROUND:Tracheal intubation(TI)is a fundamental procedure for securing the airway or assisting ventilation in emergency medicine.Tracheal intubation in the lateral position(TILP)has been utilized in clinical practice,demonstrating potential advantages in specific scenarios,including emergency settings.However,there is a lack of comprehensive reviews and practical protocols on TILP application.To address this gap,we performed a narrative review,and provided evidence-based recommendations to formulate a practice protocol,to assist clinicians to effectively apply TILP.METHODS:We conducted a narrative review of TILP applications and developed recommendations based on clinical research evidence and clinical experience.Delphi method was used among the TILP consortium to grade the strength of the recommendations and to help reach consensus.The practice protocols were formulated as warranted by advancements in medical knowledge,technology,and practice.RESULTS:This narrative review summarized the current evidence on TILP application,highlighting its safety,efficacy,challenges,and potential complications.In total,24 recommendations and a clinical protocol for TILP application in emergency patients were established.CONCLUSION:TILP is a valuable technique in emergency medicine.We reviewed its application in emergency settings and formulated recommendations along with a clinical practice protocol.Future studies are needed to evaluate the safety and efficacy of TILP,broaden its scope of application,and explore effective training protocols.展开更多
Utilizing machine learning techniques for data-driven diagnosis of high temperature PEM fuel cells is beneficial and meaningful to the system durability. Nevertheless, ensuring the robustness of diagnosis remains a cr...Utilizing machine learning techniques for data-driven diagnosis of high temperature PEM fuel cells is beneficial and meaningful to the system durability. Nevertheless, ensuring the robustness of diagnosis remains a critical and challenging task in real application. To enhance the robustness of diagnosis and achieve a more thorough evaluation of diagnostic performance, a robust diagnostic procedure based on electrochemical impedance spectroscopy (EIS) and a new method for evaluation of the diagnosis robustness was proposed and investigated in this work. To improve the diagnosis robustness: (1) the degradation mechanism of different faults in the high temperature PEM fuel cell was first analyzed via the distribution of relaxation time of EIS to determine the equivalent circuit model (ECM) with better interpretability, simplicity and accuracy;(2) the feature extraction was implemented on the identified parameters of the ECM and extra attention was paid to distinguishing between the long-term normal degradation and other faults;(3) a Siamese Network was adopted to get features with higher robustness in a new embedding. The diagnosis was conducted using 6 classic classification algorithms—support vector machine (SVM), K-nearest neighbor (KNN), logistic regression (LR), decision tree (DT), random forest (RF), and Naive Bayes employing a dataset comprising a total of 1935 collected EIS. To evaluate the robustness of trained models: (1) different levels of errors were added to the features for performance evaluation;(2) a robustness coefficient (Roubust_C) was defined for a quantified and explicit evaluation of the diagnosis robustness. The diagnostic models employing the proposed feature extraction method can not only achieve the higher performance of around 100% but also higher robustness for diagnosis models. Despite the initial performance being similar, the KNN demonstrated a superior robustness after feature selection and re-embedding by triplet-loss method, which suggests the necessity of robustness evaluation for the machine learning models and the effectiveness of the defined robustness coefficient. This work hopes to give new insights to the robust diagnosis of high temperature PEM fuel cells and more comprehensive performance evaluation of the data-driven method for diagnostic application.展开更多
Since 2015, community forests have been promoted in Togo as an alternative to protect areas from degradation and as a means of contributing to forest landscape restoration. The study focuses on the Nakpadjouak Communi...Since 2015, community forests have been promoted in Togo as an alternative to protect areas from degradation and as a means of contributing to forest landscape restoration. The study focuses on the Nakpadjouak Community Forest (NCF) in Tami (Togo, West Africa) which contributes to community forests sustainable management. It aims in (i) mapping forest ecosystems and analysing their dynamic and (ii) characterizing the floristic diversity of the NCF. The ecosystems were mapped and their dynamic was evaluated based on Google Earth images of 2014 and 2020. Floristic and forestry inventories were carried out using the transect technique in a sample of 20 plots of 50 m × 20 m. The NCF was made up mainly by wooded/shrub savannahs (95.37%) and croplands/fallow (4.63%) in 2014. These two land use types undergone changes over the 6 years prior to 2020. By 2020, the NCF had 3 land use types: wooded/shrub savannahs (77.59%), open forest/wooded savannahs (22.23%), and croplands/fallows (0.18%). A total of 89 plant species belonging to 70 genera and 28 families were recorded within the NCF. The dominant species are: Heteropogon contortus (L.) P.Beauv. and Combretum collinum Fresen. followed by Pteleopsissuberosa Engl. & Diels, Annona senegalensis Pers. The most common species are: Lannea acida A.Rich. s.l., A. senegalensis, Vitellaria paradoxa C.F.Gaertner subsp. paradoxa, C. collinum and Acacia dudgeonii Craib ex Holland. Due to its small area of just 40 hectares and its diverse plant life, this community forest of Savannahs Region is a significant biodiversity hotspot and warrants conservation efforts.展开更多
This study focuses on the landscape dynamics of the savannahs’ region in the far north of Togo. Based on a literature review and satellite images analysis using GIS and remote sensing, the study aims to ascertain the...This study focuses on the landscape dynamics of the savannahs’ region in the far north of Togo. Based on a literature review and satellite images analysis using GIS and remote sensing, the study aims to ascertain the effects of anthropogenic threats on the forest coverage of the Savannahs’ Region between 1984 to 2020. The objective is to clarify the dynamics of land use in the region from 1984 to 2000 and from 2000 to 2020. The findings indicate a significant decline in forest coverage within the region from 1984 to 2020, a trend attributed to land use patterns. Dry forests in the Savannah region are largely converted to farmlands, housing, dry savannahs or agroforestry parks, leading to a steady reduction in forest areas.展开更多
基金supported by the proactive SAFEty systems and tools for a constantly UPgrading road environment(SAFE-UP)projectfunding from the European Union’s Horizon 2020 Research and Innovation Program(861570)。
文摘Risk assessment is a crucial component of collision warning and avoidance systems for intelligent vehicles.Reachability-based formal approaches have been developed to ensure driving safety to accurately detect potential vehicle collisions.However,they suffer from over-conservatism,potentially resulting in false–positive risk events in complicated real-world applications.In this paper,we combine two reachability analysis techniques,a backward reachable set(BRS)and a stochastic forward reachable set(FRS),and propose an integrated probabilistic collision–detection framework for highway driving.Within this framework,we can first use a BRS to formally check whether a two-vehicle interaction is safe;otherwise,a prediction-based stochastic FRS is employed to estimate the collision probability at each future time step.Thus,the framework can not only identify non-risky events with guaranteed safety but also provide accurate collision risk estimation in safety-critical events.To construct the stochastic FRS,we develop a neural network-based acceleration model for surrounding vehicles and further incorporate a confidence-aware dynamic belief to improve the prediction accuracy.Extensive experiments were conducted to validate the performance of the acceleration prediction model based on naturalistic highway driving data.The efficiency and effectiveness of the framework with infused confidence beliefs were tested in both naturalistic and simulated highway scenarios.The proposed risk assessment framework is promising for real-world applications.
文摘Chinese culture provides inexhaustible sources driving Chinese nation for constant progress. We should improve education on the best of Chinese culture, develop and explore rich resources of national culture by means of modem technology, further cultivate and protect cultures of different ethnic groups, attach more importance to the conservation of tangible and intangible cultural heritage, and make greater efforts in sorting out historical literature.
基金National Natural Science Foundation of China(U24A20714 to XMF and 82102238 to PC)。
文摘BACKGROUND:Tracheal intubation(TI)is a fundamental procedure for securing the airway or assisting ventilation in emergency medicine.Tracheal intubation in the lateral position(TILP)has been utilized in clinical practice,demonstrating potential advantages in specific scenarios,including emergency settings.However,there is a lack of comprehensive reviews and practical protocols on TILP application.To address this gap,we performed a narrative review,and provided evidence-based recommendations to formulate a practice protocol,to assist clinicians to effectively apply TILP.METHODS:We conducted a narrative review of TILP applications and developed recommendations based on clinical research evidence and clinical experience.Delphi method was used among the TILP consortium to grade the strength of the recommendations and to help reach consensus.The practice protocols were formulated as warranted by advancements in medical knowledge,technology,and practice.RESULTS:This narrative review summarized the current evidence on TILP application,highlighting its safety,efficacy,challenges,and potential complications.In total,24 recommendations and a clinical protocol for TILP application in emergency patients were established.CONCLUSION:TILP is a valuable technique in emergency medicine.We reviewed its application in emergency settings and formulated recommendations along with a clinical practice protocol.Future studies are needed to evaluate the safety and efficacy of TILP,broaden its scope of application,and explore effective training protocols.
基金supported by the Chinese Scholarship Council(Nos.202208320055 and 202108320111)the support from the energy department of Aalborg University was acknowledged.
文摘Utilizing machine learning techniques for data-driven diagnosis of high temperature PEM fuel cells is beneficial and meaningful to the system durability. Nevertheless, ensuring the robustness of diagnosis remains a critical and challenging task in real application. To enhance the robustness of diagnosis and achieve a more thorough evaluation of diagnostic performance, a robust diagnostic procedure based on electrochemical impedance spectroscopy (EIS) and a new method for evaluation of the diagnosis robustness was proposed and investigated in this work. To improve the diagnosis robustness: (1) the degradation mechanism of different faults in the high temperature PEM fuel cell was first analyzed via the distribution of relaxation time of EIS to determine the equivalent circuit model (ECM) with better interpretability, simplicity and accuracy;(2) the feature extraction was implemented on the identified parameters of the ECM and extra attention was paid to distinguishing between the long-term normal degradation and other faults;(3) a Siamese Network was adopted to get features with higher robustness in a new embedding. The diagnosis was conducted using 6 classic classification algorithms—support vector machine (SVM), K-nearest neighbor (KNN), logistic regression (LR), decision tree (DT), random forest (RF), and Naive Bayes employing a dataset comprising a total of 1935 collected EIS. To evaluate the robustness of trained models: (1) different levels of errors were added to the features for performance evaluation;(2) a robustness coefficient (Roubust_C) was defined for a quantified and explicit evaluation of the diagnosis robustness. The diagnostic models employing the proposed feature extraction method can not only achieve the higher performance of around 100% but also higher robustness for diagnosis models. Despite the initial performance being similar, the KNN demonstrated a superior robustness after feature selection and re-embedding by triplet-loss method, which suggests the necessity of robustness evaluation for the machine learning models and the effectiveness of the defined robustness coefficient. This work hopes to give new insights to the robust diagnosis of high temperature PEM fuel cells and more comprehensive performance evaluation of the data-driven method for diagnostic application.
文摘Since 2015, community forests have been promoted in Togo as an alternative to protect areas from degradation and as a means of contributing to forest landscape restoration. The study focuses on the Nakpadjouak Community Forest (NCF) in Tami (Togo, West Africa) which contributes to community forests sustainable management. It aims in (i) mapping forest ecosystems and analysing their dynamic and (ii) characterizing the floristic diversity of the NCF. The ecosystems were mapped and their dynamic was evaluated based on Google Earth images of 2014 and 2020. Floristic and forestry inventories were carried out using the transect technique in a sample of 20 plots of 50 m × 20 m. The NCF was made up mainly by wooded/shrub savannahs (95.37%) and croplands/fallow (4.63%) in 2014. These two land use types undergone changes over the 6 years prior to 2020. By 2020, the NCF had 3 land use types: wooded/shrub savannahs (77.59%), open forest/wooded savannahs (22.23%), and croplands/fallows (0.18%). A total of 89 plant species belonging to 70 genera and 28 families were recorded within the NCF. The dominant species are: Heteropogon contortus (L.) P.Beauv. and Combretum collinum Fresen. followed by Pteleopsissuberosa Engl. & Diels, Annona senegalensis Pers. The most common species are: Lannea acida A.Rich. s.l., A. senegalensis, Vitellaria paradoxa C.F.Gaertner subsp. paradoxa, C. collinum and Acacia dudgeonii Craib ex Holland. Due to its small area of just 40 hectares and its diverse plant life, this community forest of Savannahs Region is a significant biodiversity hotspot and warrants conservation efforts.
文摘This study focuses on the landscape dynamics of the savannahs’ region in the far north of Togo. Based on a literature review and satellite images analysis using GIS and remote sensing, the study aims to ascertain the effects of anthropogenic threats on the forest coverage of the Savannahs’ Region between 1984 to 2020. The objective is to clarify the dynamics of land use in the region from 1984 to 2000 and from 2000 to 2020. The findings indicate a significant decline in forest coverage within the region from 1984 to 2020, a trend attributed to land use patterns. Dry forests in the Savannah region are largely converted to farmlands, housing, dry savannahs or agroforestry parks, leading to a steady reduction in forest areas.
