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| 1 | Retronasal triangle: a sonographic landmark for the screening of cleft palate in the first trimester显示文摘 | W.Sepulveda A. E.Wong P.Martinez‐Ten J.Perez‐Pedregosa | 2009 | Ultrasound Obstet Gynecol2009，，1： | 2 |
| 2 | Adalimumab induction and maintenance therapy for patients with ulcerative colitis previously treated with infliximab显示文摘 | C.Taxonera J.Estellés I.Fernández‐Blanco O.Merino I.Marín‐Jiménez M.Barreiro‐de Acosta C.Saro V.García‐Sánchez E.Gento G.Bastida J. P.Gisbert I.Vera P.Martinez‐Montiel S.Garcia‐Morán M. C.Sánchez J. L.Mendoza | 2011 | Alimentary Pharmacology & Therapeutics2011，，3： | 2 |
| 3 | Absent mandibular gap in the retronasal triangle view: a clue to the diagnosis of micrognathia in the first trimester显示文摘 | W.Sepulveda A. E.Wong F.Vi?als E.Andreeva N.Adzehova P.Martinez‐Ten | 2012 | Ultrasound Obstet Gynecol2012，，2： | 1 |
| 4 | Estimation of morphological traits of foliage and effective plant spacing in NFT-based aquaponics system显示文摘Deep learning and computer vision techniques have gained significant attention in the agriculture sector due to their non-destructive and contactless features.These techniques are also being integrated into modern farming systems,such as aquaponics,to address the challenges hindering its commercialization and large-scale implementation.Aquaponics is a farming technology that combines a recirculating aquaculture system and soilless hydroponics agriculture,that promises to address food security issues.To complement the current research efforts,a methodology is proposed to automatically measure the morphological traits of crops such as width,length and area and estimate the effective plant spacing between grow channels.Plant spacing is one of the key design parameters that are dependent on crop type and its morphological traits and hence needs to be monitored to ensure high crop yield and quality which can be impacted due to foliage occlusion or overlapping as the crop grows.The proposed approach uses Mask-RCNN to estimate the size of the crops and a mathematical model to determine plant spacing for a self-adaptive aquaponics farm.For common little gem romaine lettuce,the growth is estimated within 2 cm of error for both length and width.The final model is deployed on a cloud-based application and integrated with an ontology model containing domain knowledge of the aquaponics system.The relevant knowledge about crop characteristics and optimal plant spacing is extracted from ontology and compared with results obtained from the final model to suggest further actions.The proposed application finds its signifi-cance as a decision support system that can pave the way for intelligent system monitoring and control. | R.Abbasi P.Martinez R.Ahmad | 2023 | Artificial Intelligence in Agriculture2023，，3： | 0 |
| 5 | 频率不受运放参数影响的文氏桥振荡器显示文摘本文介绍一组(12个)新的正弦振荡器,这组振荡器使用两个运算放大器,其振荡频率不受单极点有源元件增益带宽之积的影响。这种特性是由本文所介绍的电路结构取得的,即不需要调节。 | A.Carlosena P.Martinez S.Porta 阎军 | 1992 | 电讯技术1992，32，4： | 0 |
| 6 | Nonlinear ionization dynamics of hot dense plasma observed in a laser-plasma amplifier显示文摘Understanding the behaviour of matter under conditions of extreme temperature,pressure,density and electromagnetic fields has profound effects on our understanding of cosmologic objects and the formation of the universe.Lacking direct access to such objects,our interpretation of observed data mainly relies on theoretical models.However,such models,which need to encompass nuclear physics,atomic physics and plasma physics over a huge dynamic range in the dimensions of energy and time,can only provide reliable information if we can benchmark them to experiments under well-defined laboratory conditions.Due to the plethora of effects occurring in this kind of highly excited matter,characterizing isolated dynamics or obtaining direct insight remains challenging.High-density plasmas are turbulent and opaque for radiation below the plasma frequency and allow only near-surface insight into ionization processes with visible wavelengths.Here,the output of a high-harmonic seeded laser-plasma amplifier using eightfold ionized krypton as the gain medium operating at a 32.8 nm wavelength is ptychographically imaged.A complexvalued wavefront is observed in the extreme ultraviolet(XUV)beam with high resolution.Ab initio spatio-temporal Maxwell–Bloch simulations show excellent agreement with the experimental observations,revealing overionization of krypton in the plasma channel due to nonlinear laser-plasma interactions,successfully validating this four-dimensional multiscale model.This constitutes the first experimental observation of the laser ion abundance reshaping a laserplasma amplifier.The presented approach shows the possibility of directly modelling light-plasma interactions in extreme conditions,such as those present during the early times of the universe,with direct experimental verification. | F.Tuitje P.Martinez Gil T.Helk J.Gautier F.Tissandier J.-P.Goddet A.Guggenmos U.Kleineberg S.Sebban E.Oliva C.Spielmann M.Zurch | 2020 | Light(Science & Applications)2020，9，1： | 0 |
| 7 | Crop diagnostic system:A robust disease detection and management system for leafy green crops grown in an aquaponics facility显示文摘Crops grown on aquaponics farms are susceptible to various diseases or biotic stresses during their growth cycle,just like traditional agriculture.The early detection of diseases is crucial to witnessing the efficiency and progress of the aquaponics system.Aquaponics combines recirculating aquaculture and soilless hydroponics methods and promises to ensure food security,reduce water scarcity,and eliminate carbon footprint.For the large-scale imple-mentation of this farming technique,a unified system is needed that can detect crop diseases and support re-searchers and farmers in identifying potential causes and treatments at early stages.This study proposes an automatic crop diagnostic system for detecting biotic stresses and managing diseases in four leafy green crops,lettuce,basil,spinach,and parsley,grown in an aquaponics facility.First,a dataset comprising 2640 images is con-structed.Then,a disease detection system is developed that works in three phases.The first phase is a crop clas-sification system that identifies the type of crop.The second phase is a disease identification system that determines the crop's health status.The final phase is a disease detection system that localizes and detects the diseased and healthy spots in leaves and categorizes the disease.The proposed approach has shown promising results with accuracy in each of the three phases,reaching 95.83%,94.13%,and 82.13%,respectively.The final dis-ease detection system is then integrated with an ontology model through a cloud-based application.This ontol-ogy model contains domain knowledge related to crop pathology,particularly causes and treatments of different diseases of the studied leafy green crops,which can be automatically extracted upon disease detection allowing agricultural practitioners to take precautionary measures.The proposed application finds its significance as a de-cision support system that can automate aquaponics facility health monitoring and assist agricultural practi-tioners in decision-making processes regarding crop and disease management. | R.Abbasi P.Martinez R.Ahmad | 2023 | Artificial Intelligence in Agriculture2023，，4： | 0 |
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