Systema respiratorium

Multi tool use

Piscis per branchias respirat
Systema respiratorium, vel systema respirationis, vel apparatus respiratorius est congeries organorum quae ad respiratum pertinent, saepissime apparatus quo corpus cuiuslibet animalis gasa inter suam sanguinem et atmosphaeram permutat. In hominibus et aliis mammalibus, anatomicae systematis respiratorii proprietates vias aereas, pulmones, et musculos respiratorios comprehendunt. Moleculae oxygenii et carbonii dioxidi patienter commutantur, a diffusione inter exterius circumiectum gaseosum et sanguinem. Haec commutatio in alveolare pulmonum regione fit.[1]
Alia animalia, sicut insecta, systemata respiratoria simplicissimis proprietatibus anatomicis habent, et in amphibiis etiam cutis graves partes in commutatione gasium agit. Plantis quoque sunt systemata respiratoria, sed directionalitas? commutationis gasium conversa sit directionalitati? commutationis gasium animalium. Systema respiratorium in plantis etiam proprietates anatomicas sicut foramina in imis foliis, appellata stomata comprehendit.[2] (Vide etiam tractus respiratorius.)
Nexus interni
- Asthma
- Cancer pulmonis
- Meditatio
- Oxygenium
- Pneumonologia
- Pneumonia
- Spirometer
- Thorax
- Phthisis
Notae |
↑ Maton, Anthea; Jean, Hopkins Susan, Johnson Charles William, McLaughlin Maryanna Quon Warner David, LaHart Wright, Jill D. (2009). Human Biology and Health. Englewood Cliffs,: Prentice Hall. pp. 108–118. ISBN 0-12-981176-1 .
↑ West, John B.. Respiratory physiology-- the essentials. Baltimore: Williams & Wilkins. pp. 1–10. ISBN 0-683-08937-4 .
Nexus externi |
- A high school level description of the respiratory system
- Introduction to Respiratory System
- Science aid: Respiratory System
- The Respiratory System
.mw-parser-output .stipula{padding:3px;background:#F7F8FF;border:1px solid grey;margin:auto}.mw-parser-output .stipula td.cell1{background:transparent;color:white}

|
Haec pagina est stipula. Amplifica, si potes!
|
1DeekZy5aCst3 G0vI8u17Uxk5RAC,kwHTrx,ObEQQLXnzobGG ZBX
Popular posts from this blog
Tabula multilinguis Rosettana in Museo Britannico ostenditur. Tabula Rosettana, [1] etiam titulo OGIS 90 agnita, est stela decreto de rebus sacris in Aegypto anno 196 a.C.n. lato inscripta. Tabula iuxta Rosettam Aegypti, urbem in delta Nili et ad oram maris Mediterranei iacentem, anno 1799 a milite Francico reperta est. Inventio stelae, linguis duabus scripturisque tribus inscriptae, eruditis Instituti Aegypti statim nuntiata est; ibi enim iussu imperatoris Napoleonis eruditi omnium scientiarum (sub aegide Commissionis Scientiarum et Artium) properaverant cum expeditione Francica. Qua a Britannis mox debellata, tabula Rosettana Londinium missa hodie apud Museum Britannicum iacet. Textus Graecus cito lectus interpretationi textuum Aegyptiorum (in formis hieroglyphica et demotica expressorum) gradatim adiuvit. Denique textum plene interpretatus est Ioannes Franciscus Champollion. Ab opere eruditorum cumulativo coepit hodiernus scripturae hieroglyphicae linguaeque Aegyptiae a...
1
$begingroup$
This is what I mean as document text image: I want to label the texts in image as separate blocks and my model should detect these labels as classes. NOTE: This is how the end result should be like: The labels like Block 1, Block 2, Block 3,.. should be Logo, Title, Date,.. Others, etc. Work done: First approach : I tried to implement this method via Object Detection, it didn't work. It didn't even detect any text. Second approach : Then I tried it using PixelLink. As this model is build for scene text detection, it detected each and every text in the image. But this method can detect multiple lines of text if the threshold values are increased. But I have no idea how do I add labels to the text blocks. PIXEL_CLS_WEIGHT_all_ones = 'PIXEL_CLS_WEIGHT_all_ones' PIXEL_C...
1
$begingroup$
I have this LSTM model model = Sequential() model.add(Masking(mask_value=0, input_shape=(timesteps, features))) model.add(LSTM(100, dropout=0.2, recurrent_dropout=0.2, return_sequences=False)) model.add(Dense(features, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) and shapes X_train (21, 11, 5), y_train (21, 5) . Each timestep is represented by 5 features and return_sequences is set to False because I want to predict one 5D array (the next timestep) for each input sequence of 11 timesteps. I get the error ValueError: y_true and y_pred have different number of output (5!=1) If I reshape the data as X_train (21, 11, 5), y_train (21, 1, 5) instead I get the error ValueError: Inva...