A Comparative Study of Grade 6 Students’ Academic Achievement and Motivation for Learning English as a Foreign Language Under Cooperative Teaching Method and Traditional Teaching Method at Watbangchalongnai School, Samut Prakan, Thailand

The purpose of this study was to find if cooperative teaching methods in Watbangchalongnai School, Samut Prakan, Thailand, would enhance Grade 6 students’ English academic achievement, measured by their abilities in grammar, reading comprehension, and vocabulary. The study also sought to measure students’ motivation for learning English as a foreign language (EFL). For the purpose of the study, the researcher conducted an intervention experiment for 12 weeks, from December 2018 to March 2019, in which two intact Grade 6 groups were chosen, one as experimental group and the other as control group. During the experiment, subjects in the experimental group (39 students) attended classes based on cooperative teaching method. The control group (39 students), on the other hand, did not carry out any additional activities to traditional teaching method. To control as many variables as possible, the number of classes per week, duration of each class and components to be taught were all the same in both groups. In order to test the effectiveness of such strategy, two quantitative tools for data collection were used to compile the findings of the study. To begin with, a pre-test and a post-test were administered to measure students’ academic achievement in learning EFL before and after the treatment. Afterwards, a questionnaire was distributed to both groups in order to measure students’ motivation for learning EFL. The results of the independent samples t-test on a difference of academic achievement revealed a significant difference between the two groups. The sample means showed that subjects in the experimental group increased their academic achievement from the pre-test to the post-test significantly more than subjects in the control group. Results regarding motivation for learning EFL failed to reveal a significant difference between the two groups. The sample means showed that subjects in the experimental group demonstrated high mean scores on motivation for learning EFL, which were statistically similar to those shown by subjects in the control group. Based on the research findings, recommendations for students, teachers, administrators and future researchers are provided

Modeliranje bioloških sekvenci dubokim neuronskim mrežama

Informacije o primarnoj strukturi proteina koriste se kao ulazni podaci u mnogim klasifikacijskim problemima iz područja biologije. Pri tome se značajke standardno konstruiraju uprosječivanjem svojstava aminokiselina u sekvenci. Međutim, u takvoj reprezentaciji gube se informacije o slijedu aminokiselina u sekvenci. Umjesto ručnog konstruiranja značajki koje zahtijeva dodatno vrijeme te poznavanje specifičnog problema koji se rješava, duboke neuronske mreže omogućavaju automatsko učenje reprezentacije u obliku realnog vektora fiksne dimenzije. Takve reprezentacije mogu se koristiti za rješavanje različitih klasifikacijskih problema. Budući da danas raspolažemo velikim brojem sekvenciranih genoma, taj pristup je iznimno prikladan za proteinske sekvence. U ovom radu bavimo se modeliranjem realnih vektorskih reprezentacija proteinskih sekvenci kroz nekoliko faza. U prvoj fazi proteinske sekvence dijelimo u sekvence trigrama aminokiselina i povezujemo problem traženja reprezentacija trigrama s problemom traženja reprezentacija riječi koristeći metode word2vec i Glove. Naučene reprezentacije trigrama koristimo u drugoj fazi za treniranje dvosmjerne LSTM rekurentne mreže na problemu prepoznavanja originalne od permutirane sekvence. U tu svrhu definiramo tri različita načina permutiranja koji odgovaraju različitoj težini problema. Na problemu razlikovanja stvarne od permutirane sekvence, rekurentna mreža s Glove reprezentacijama trigrama postigla je visoku točnost (> 93 %) već nakon 30 epoha. Za razliku od Glove reprezentacija, word2vec je postigla nešto lošije rezultate (> 72 %). Skriveni sloj LSTM mreže odgovara reprezentaciji sekvence koju smo zatim usporedili s reprezentacijama dobivenih sumom reprezentacija trigrama. U zadnjoj fazi, testirali smo kvalitete dobivenih reprezentacija na tri različita klasifikacijska problema: problemu predviđanja familije proteina, razlikovanja termofilnih od netermofilnih proteina te predviđanju klase ribosoma. Rezultati pokazuju da reprezentacije dobivene sumiranjem daju najbolje rezultate, dok Glove daje bolje rezultate od word2vec. Nadalje, uočava se korelacija između težine problema rekurentne mreže i kvalitete reprezentacija. Iako je zbog hardverskih ograničenja mreža trenirana na malom skupu podataka, rezultati pokazuju da postoje primjeri reprezentacija dobivenih rekurentnim mrežama koji postižu veću točnost od reprezentacija dobivenih sumom.Protein sequences are used as features in many biological classification problems. Those sequences are usually represented using biophysical properties of amino acids the sequence is built from. However, that kind of representation does not include information about the order of amino acids in the original sequence. Deep neural networks enable learning dense vector representations of sequences automatically, instead of building features by hand which is time-consuming and requires domain knowledge. These representations can be used to solve different classification tasks. In this work, we propose different methods of extracting dense vector representations from protein sequences through couple of phases. In the first phase, we divide each protein sequence into trigrams of amino acids. In order to map trigrams to distributed vectors, we relate trigrams to words and use natural language processing models word2vec and Glove. In the second phase, learned representations of trigrams are used as an input to bidirectional LSTM recurrent network to differentiate between a real protein sequence and a permuted sequence. For this purpose, we define three different permutation methods corresponding to distinct levels of complexity. On the hardest permutation problem, recurrent networks in case of Glove trigram representations achieve high accuracy (> 93 %) after only 30 epochs. On the other hand, recurrent networks with word2vec trigrams as inputs reach lower accuracy (> 72 %). Hidden layer of trained LSTM network corresponds to sequence representation which we have compared to representations obtained by sum of trigram representations. The final phase was used to test quality of all eight representations by solving three different classification tasks: protein family classification, distinguishing between thermophilic and non-thermophilic protein and predicting class of ribosomes. Results suggest that representations obtained from trigram summation outperform those from recurrent network and that Glove recurrent representations exceed those from word2vec. Furthermore, we have noticed a correlation between complexity of permutation task and achieved results. Although hardware limitations allow us to train the network only on small subset of the original dataset, there are individual cases where representations from recurrent networks perform better than summation representation

Modeliranje bioloških sekvenci dubokim neuronskim mrežama

Informacije o primarnoj strukturi proteina koriste se kao ulazni podaci u mnogim klasifikacijskim problemima iz područja biologije. Pri tome se značajke standardno konstruiraju uprosječivanjem svojstava aminokiselina u sekvenci. Međutim, u takvoj reprezentaciji gube se informacije o slijedu aminokiselina u sekvenci. Umjesto ručnog konstruiranja značajki koje zahtijeva dodatno vrijeme te poznavanje specifičnog problema koji se rješava, duboke neuronske mreže omogućavaju automatsko učenje reprezentacije u obliku realnog vektora fiksne dimenzije. Takve reprezentacije mogu se koristiti za rješavanje različitih klasifikacijskih problema. Budući da danas raspolažemo velikim brojem sekvenciranih genoma, taj pristup je iznimno prikladan za proteinske sekvence. U ovom radu bavimo se modeliranjem realnih vektorskih reprezentacija proteinskih sekvenci kroz nekoliko faza. U prvoj fazi proteinske sekvence dijelimo u sekvence trigrama aminokiselina i povezujemo problem traženja reprezentacija trigrama s problemom traženja reprezentacija riječi koristeći metode word2vec i Glove. Naučene reprezentacije trigrama koristimo u drugoj fazi za treniranje dvosmjerne LSTM rekurentne mreže na problemu prepoznavanja originalne od permutirane sekvence. U tu svrhu definiramo tri različita načina permutiranja koji odgovaraju različitoj težini problema. Na problemu razlikovanja stvarne od permutirane sekvence, rekurentna mreža s Glove reprezentacijama trigrama postigla je visoku točnost (> 93 %) već nakon 30 epoha. Za razliku od Glove reprezentacija, word2vec je postigla nešto lošije rezultate (> 72 %). Skriveni sloj LSTM mreže odgovara reprezentaciji sekvence koju smo zatim usporedili s reprezentacijama dobivenih sumom reprezentacija trigrama. U zadnjoj fazi, testirali smo kvalitete dobivenih reprezentacija na tri različita klasifikacijska problema: problemu predviđanja familije proteina, razlikovanja termofilnih od netermofilnih proteina te predviđanju klase ribosoma. Rezultati pokazuju da reprezentacije dobivene sumiranjem daju najbolje rezultate, dok Glove daje bolje rezultate od word2vec. Nadalje, uočava se korelacija između težine problema rekurentne mreže i kvalitete reprezentacija. Iako je zbog hardverskih ograničenja mreža trenirana na malom skupu podataka, rezultati pokazuju da postoje primjeri reprezentacija dobivenih rekurentnim mrežama koji postižu veću točnost od reprezentacija dobivenih sumom.Protein sequences are used as features in many biological classification problems. Those sequences are usually represented using biophysical properties of amino acids the sequence is built from. However, that kind of representation does not include information about the order of amino acids in the original sequence. Deep neural networks enable learning dense vector representations of sequences automatically, instead of building features by hand which is time-consuming and requires domain knowledge. These representations can be used to solve different classification tasks. In this work, we propose different methods of extracting dense vector representations from protein sequences through couple of phases. In the first phase, we divide each protein sequence into trigrams of amino acids. In order to map trigrams to distributed vectors, we relate trigrams to words and use natural language processing models word2vec and Glove. In the second phase, learned representations of trigrams are used as an input to bidirectional LSTM recurrent network to differentiate between a real protein sequence and a permuted sequence. For this purpose, we define three different permutation methods corresponding to distinct levels of complexity. On the hardest permutation problem, recurrent networks in case of Glove trigram representations achieve high accuracy (> 93 %) after only 30 epochs. On the other hand, recurrent networks with word2vec trigrams as inputs reach lower accuracy (> 72 %). Hidden layer of trained LSTM network corresponds to sequence representation which we have compared to representations obtained by sum of trigram representations. The final phase was used to test quality of all eight representations by solving three different classification tasks: protein family classification, distinguishing between thermophilic and non-thermophilic protein and predicting class of ribosomes. Results suggest that representations obtained from trigram summation outperform those from recurrent network and that Glove recurrent representations exceed those from word2vec. Furthermore, we have noticed a correlation between complexity of permutation task and achieved results. Although hardware limitations allow us to train the network only on small subset of the original dataset, there are individual cases where representations from recurrent networks perform better than summation representation

Utjecaj mikorize i gnojidbe na rast i razvoj vrste pelargonija (Pelargonium zonale)

U Hrvatskoj, kao i u Europi, postoji puno proizvođača proljetnog cvijeća različitih proizvodnih kapaciteta. Svi proizvođači susreću se s istim problemom, a to je visoka cijena ulaznih troškova: gnojiva, goriva, uzgojnih posuda, zaštitnih sredstava, vode, supstrata i ostalog, kao i sve niža cijena sadnica. Jedan od načina smanjenja uzgojnih troškova je i ušteda na ulaznim troškovima. Budući da su vrste roda „Pelargonium“ jedne od najzastupljenijih biljaka u proljetnom uzgoju, ovim radom je istražena mogućnost uštede na gnojivima u uzgoju vrste Pelargonium zonale ‘Anthony’ na način da se sadnice inokuliraju s mikorizom Glomus intraradices. U radu se istraživalo može li se inokulacijom sorte pelargonije Antony smanjiti količina utrošenog gnojiva te dobiti isti rezultat kao i kod neinokuliranih biljaka koje su zalijevane s većom koncentracijom gnojiva. Osim utjecaja mikorize, utvrđuje se i utjecaj različitih koncentracija gnojiva na rast i razvoj pelargonija. Kroz mjerene parametre kao što su: visina biljaka, broj listova, cvjetnih pupova i cvatova, prosječna težina nadzemne mase prije i nakon sušenja, težina korijena, ukupna težina biljaka, i analiza supstrata, utvrđena je reakcija biljaka na gnojidbu i mikorizu tijekom 12 tjedana.In Croatia, like in Europe, there are a lot of spring flowers growers, with different production size. All flower growers are facing the same problem of high production costs such as: fuel, pots, water, substratum, fertilization, and low flower price. One of the ways to reduce production costs is to save on inputs. Pelargonium varieties are one of the top produced plants in spring, and this work intends to research possibilities to reduce fertilization costs on Pelargonium zonale “Anthony”, with mycorrhiza inoculation Glomus intraradices. This work researches possibility to reduce fertilization on plants inoculated with mycorrhiza and get the same result like on plants fertilized with higher fertilization concentration without mycorrhiza. Except mycorrhiza effect, we will be able to see effect of different fertilization concentration on pelargonium's growth. Trough measurement of plant height, number of leaves, flowers and flower buds, weight of over ground mass before and after drying, weight of root mass, total plant mass and substrate analysis, we will be able to see pelargonium reaction on mycorrhiza and fertilization trough twelve weeks

Theft crimes against tourists and visitors in Slovenia

Purpose – This paper investigates the nature of theft crimes against tourist and visitors in Slovenia. The main objective of the study was to identify the characteristics of theft victims in tourist areas and to analyze the differences in risk between various crime scenes and types of crime. Design/Methodology – Using data from police crime statistics and applying a binary logistic regression, we analyzed the relationship between the crime scene and nature of the crime, as well as between the domestic/foreign status of the visitors and type of crime. Since the study uses data from official police database, one of the major challenges was to eliminate crime events from the analysis that occurred outside tourism areas.. This problem was addressed by using geotagged photos from Flickr platform with different spatial queries. Findings – The findings offer evidence indicating that the victims’ backgrounds (status and age group) and accommodation type, determine their vulnerability to certain crimes. The results show that foreign victims are more likely to suffer burglary than their domestic counterparts. In addition, crimes against foreign visitors and tourists are more likely to involve seniors, amount to larger losses, and take place in hotels, motels, and camps. On the other hand, crimes against domestic visitors are more likely to take place during evenings in bars, restaurants, and in vacation homes. Originality of the research – This study shows that combining data from different public sources into one analysis allows a more reliable analysis of police crime records

Modeliranje bioloških sekvenci dubokim neuronskim mrežama

Informacije o primarnoj strukturi proteina koriste se kao ulazni podaci u mnogim klasifikacijskim problemima iz područja biologije. Pri tome se značajke standardno konstruiraju uprosječivanjem svojstava aminokiselina u sekvenci. Međutim, u takvoj reprezentaciji gube se informacije o slijedu aminokiselina u sekvenci. Umjesto ručnog konstruiranja značajki koje zahtijeva dodatno vrijeme te poznavanje specifičnog problema koji se rješava, duboke neuronske mreže omogućavaju automatsko učenje reprezentacije u obliku realnog vektora fiksne dimenzije. Takve reprezentacije mogu se koristiti za rješavanje različitih klasifikacijskih problema. Budući da danas raspolažemo velikim brojem sekvenciranih genoma, taj pristup je iznimno prikladan za proteinske sekvence. U ovom radu bavimo se modeliranjem realnih vektorskih reprezentacija proteinskih sekvenci kroz nekoliko faza. U prvoj fazi proteinske sekvence dijelimo u sekvence trigrama aminokiselina i povezujemo problem traženja reprezentacija trigrama s problemom traženja reprezentacija riječi koristeći metode word2vec i Glove. Naučene reprezentacije trigrama koristimo u drugoj fazi za treniranje dvosmjerne LSTM rekurentne mreže na problemu prepoznavanja originalne od permutirane sekvence. U tu svrhu definiramo tri različita načina permutiranja koji odgovaraju različitoj težini problema. Na problemu razlikovanja stvarne od permutirane sekvence, rekurentna mreža s Glove reprezentacijama trigrama postigla je visoku točnost (> 93 %) već nakon 30 epoha. Za razliku od Glove reprezentacija, word2vec je postigla nešto lošije rezultate (> 72 %). Skriveni sloj LSTM mreže odgovara reprezentaciji sekvence koju smo zatim usporedili s reprezentacijama dobivenih sumom reprezentacija trigrama. U zadnjoj fazi, testirali smo kvalitete dobivenih reprezentacija na tri različita klasifikacijska problema: problemu predviđanja familije proteina, razlikovanja termofilnih od netermofilnih proteina te predviđanju klase ribosoma. Rezultati pokazuju da reprezentacije dobivene sumiranjem daju najbolje rezultate, dok Glove daje bolje rezultate od word2vec. Nadalje, uočava se korelacija između težine problema rekurentne mreže i kvalitete reprezentacija. Iako je zbog hardverskih ograničenja mreža trenirana na malom skupu podataka, rezultati pokazuju da postoje primjeri reprezentacija dobivenih rekurentnim mrežama koji postižu veću točnost od reprezentacija dobivenih sumom.Protein sequences are used as features in many biological classification problems. Those sequences are usually represented using biophysical properties of amino acids the sequence is built from. However, that kind of representation does not include information about the order of amino acids in the original sequence. Deep neural networks enable learning dense vector representations of sequences automatically, instead of building features by hand which is time-consuming and requires domain knowledge. These representations can be used to solve different classification tasks. In this work, we propose different methods of extracting dense vector representations from protein sequences through couple of phases. In the first phase, we divide each protein sequence into trigrams of amino acids. In order to map trigrams to distributed vectors, we relate trigrams to words and use natural language processing models word2vec and Glove. In the second phase, learned representations of trigrams are used as an input to bidirectional LSTM recurrent network to differentiate between a real protein sequence and a permuted sequence. For this purpose, we define three different permutation methods corresponding to distinct levels of complexity. On the hardest permutation problem, recurrent networks in case of Glove trigram representations achieve high accuracy (> 93 %) after only 30 epochs. On the other hand, recurrent networks with word2vec trigrams as inputs reach lower accuracy (> 72 %). Hidden layer of trained LSTM network corresponds to sequence representation which we have compared to representations obtained by sum of trigram representations. The final phase was used to test quality of all eight representations by solving three different classification tasks: protein family classification, distinguishing between thermophilic and non-thermophilic protein and predicting class of ribosomes. Results suggest that representations obtained from trigram summation outperform those from recurrent network and that Glove recurrent representations exceed those from word2vec. Furthermore, we have noticed a correlation between complexity of permutation task and achieved results. Although hardware limitations allow us to train the network only on small subset of the original dataset, there are individual cases where representations from recurrent networks perform better than summation representation

Utjecaj mikorize i gnojidbe na rast i razvoj vrste pelargonija (Pelargonium zonale)

U Hrvatskoj, kao i u Europi, postoji puno proizvođača proljetnog cvijeća različitih proizvodnih kapaciteta. Svi proizvođači susreću se s istim problemom, a to je visoka cijena ulaznih troškova: gnojiva, goriva, uzgojnih posuda, zaštitnih sredstava, vode, supstrata i ostalog, kao i sve niža cijena sadnica. Jedan od načina smanjenja uzgojnih troškova je i ušteda na ulaznim troškovima. Budući da su vrste roda „Pelargonium“ jedne od najzastupljenijih biljaka u proljetnom uzgoju, ovim radom je istražena mogućnost uštede na gnojivima u uzgoju vrste Pelargonium zonale ‘Anthony’ na način da se sadnice inokuliraju s mikorizom Glomus intraradices. U radu se istraživalo može li se inokulacijom sorte pelargonije Antony smanjiti količina utrošenog gnojiva te dobiti isti rezultat kao i kod neinokuliranih biljaka koje su zalijevane s većom koncentracijom gnojiva. Osim utjecaja mikorize, utvrđuje se i utjecaj različitih koncentracija gnojiva na rast i razvoj pelargonija. Kroz mjerene parametre kao što su: visina biljaka, broj listova, cvjetnih pupova i cvatova, prosječna težina nadzemne mase prije i nakon sušenja, težina korijena, ukupna težina biljaka, i analiza supstrata, utvrđena je reakcija biljaka na gnojidbu i mikorizu tijekom 12 tjedana.In Croatia, like in Europe, there are a lot of spring flowers growers, with different production size. All flower growers are facing the same problem of high production costs such as: fuel, pots, water, substratum, fertilization, and low flower price. One of the ways to reduce production costs is to save on inputs. Pelargonium varieties are one of the top produced plants in spring, and this work intends to research possibilities to reduce fertilization costs on Pelargonium zonale “Anthony”, with mycorrhiza inoculation Glomus intraradices. This work researches possibility to reduce fertilization on plants inoculated with mycorrhiza and get the same result like on plants fertilized with higher fertilization concentration without mycorrhiza. Except mycorrhiza effect, we will be able to see effect of different fertilization concentration on pelargonium's growth. Trough measurement of plant height, number of leaves, flowers and flower buds, weight of over ground mass before and after drying, weight of root mass, total plant mass and substrate analysis, we will be able to see pelargonium reaction on mycorrhiza and fertilization trough twelve weeks

Evaluación cuantitativa de la comunidad de peces criptobenónicos en los acantilados profundos del litoral Mediterráneo

The present study provides the first quantitative assessment of cryptobenthic fish species diversity and abundance on hard bottoms below 20 m depth by examining Mediterranean underwater reefs with deep vertical cliffs. Quantitative sampling was performed at depths down to about 45 m and yielded 220 cryptobenthic and 61 epibenthic individuals belonging to 21 species, showing that the cryptobenthic fishes highly outnumbered the epibenthic individuals. The study highlights the high diversity and abundance of this unexplored part of the benthic fish community. The cryptobenthic fish assemblage was dominated by the family Gobiidae in terms of both biodiversity ( > 60% of all species) and abundance ( > 90% of all specimens). Fish species previously considered rare were present and some of them were even numerous in this assemblage. Three out of fourteen recorded habitat variables (depth, rocky cliff surface area vs. the bottom of the cliff area, and the presence of sand as the bottom substrate) were identified as significant for species occurrence. Species were generalist in their choice of shelters since the size and quantity of semi-caves, caves, cavities and even bio-cover type had no significant influence on the species distribution and abundance.Este trabajo presenta una primera evaluación cuantitativa de la diversidad y abundancia de los peces criptobentónicos de fondos duros por debajo de 20 m de profundidad, como ejemplo de acantilados litorales sumergidos del Mediterráneo. Se realizó un muestreo a profundidades cercanas a 45 m de profundidad y se obtuvieron 220 individuos criptobentònicos y 61 epibentónicos pertenecientes a 21 especies. El estudio pone de manifiesto la gran diversidad y abundancia de esta fracción inexplorada de la comunidad de peces bentónicos. La familia Gobiidae domina la comunidad de peces criptobentónicos en biodiversidad y abundancia ( > 60% y > 90% del total de especies respectivamente). Se han observado especies de peces de este grupo consideradas raras, incluso algunas son numerosas. Tres de cada 14 variables registradas del hábitat (profundidad, superficie rocosa del acantilado vs fondo del acantilado y presencia de arena en el sustrato del fondo) fueron significativas para la presencia de las especies. En la elección de los refugios las especies fueron generalistas ya que el tamaño y la cantidad de semicuevas, cuevas, cavidades e incluso tipos de cubierta biológica no tienen influencia significativa en la distribución y abundancia de las especies

Utjecaj dubine na zajednicu riba stjenovitog dna u Mediteranu istraženu novom metodom videa sa podvodnog skutera

The new video on underwater scooter method in combination with rebreather diving techniques was introduced and applied for visual census of fish assemblages. The presently applied method facilitates deeper visual census studies below 40 m, where they have rarely been done before due to the increasing research challenges with depth. The video on underwater scooter method is also expected to be less disturbing and faster compared to swimming divers. The method was applied in visual census study of fish assemblages from 8 to 50 m depth conducted at two locations in the east Adriatic using 102 video transects. The environmental variables contributing to the variation of the reef fish community in the depth range of the infralittoral and upper circalittoral bottoms were also recorded. Thirty-one fish species were recorded on transects and 10.3% of all individuals were not identified to the species level. The average density on transects was 0.8 individuals/m², with C. chromis, C. julis and G. auratus being both the most abundant and the most frequent species. Three environmental variables, depth, bottom with zoocover and eastern orientation of the coast, were identified as highly significant for species occurrence. Abundance and species richness of fish assemblages showed no significant variation with depths, inclinations, orientations and sites. A significant difference in the fish assemblage structure was found among orientations, depths and inclinations with the gradient change of species composition with increasing depth and with increasing inclination.Nova metoda videa sa podvodnog skutera uz rebreather ronilačku tehniku uvedena je i primjenjena za vizualni census zajednica riba. Ova metoda omogućuje istraživanja vizualnim cenzusom na dubinama većim od 40 m, na kojima se, zbog teškoća u ronjenju na većim dubinama, vizualni cenzus rijetko izvodi. Metoda videa sa podvodnog skutera uz rebreather ronilačku tehniku također bi trebala biti manje uznemirujuća i brža u odnosu na plivajuće ronioce. Metoda je primjenjena u istraživanju vizualnim cenzusom na dubinama 8 do 50 m na dvije postaje na istočnom Jadranu sa 102 video transekta. Zabilježene su također varijable okoliša koje utječu na varijaciju u sastavu zajednice riba stjenovitog dna infralitorala i cirkalitorala.Trideset i jedna vrsta riba je zabilježena, uz 10,3% primjerka koji nisu određeni do razine vrste. Prosječna gustoća riba po transektu bila je 0,8 primjeraka/m², a najčešće i najbrojnije vrste bile su C. chromis, C. julis i G. auratus. Tri varijable okoliša: dubina, dno sa životinjskim obraštajem i istočna orjentacija dna, bile su izrazito značajne za prisutnost vrsta. Brojnost primjeraka i bogatstvo vrsta nisu pokazale značajnu promjenjivost sa promjenama dubine, nagiba, orjentacije i postaja. Utvrđena je značajna promjena u sastavu zajednice riba sa promjenom orjentacije obale, dubine i nagiba dna na transektima. Sastav vrsta postepeno se mijenjao sa porastom dubine dna i s povećanjem nagiba dna

Utjecaj mikorize i gnojidbe na rast i razvoj vrste pelargonija (Pelargonium zonale)

U Hrvatskoj, kao i u Europi, postoji puno proizvođača proljetnog cvijeća različitih proizvodnih kapaciteta. Svi proizvođači susreću se s istim problemom, a to je visoka cijena ulaznih troškova: gnojiva, goriva, uzgojnih posuda, zaštitnih sredstava, vode, supstrata i ostalog, kao i sve niža cijena sadnica. Jedan od načina smanjenja uzgojnih troškova je i ušteda na ulaznim troškovima. Budući da su vrste roda „Pelargonium“ jedne od najzastupljenijih biljaka u proljetnom uzgoju, ovim radom je istražena mogućnost uštede na gnojivima u uzgoju vrste Pelargonium zonale ‘Anthony’ na način da se sadnice inokuliraju s mikorizom Glomus intraradices. U radu se istraživalo može li se inokulacijom sorte pelargonije Antony smanjiti količina utrošenog gnojiva te dobiti isti rezultat kao i kod neinokuliranih biljaka koje su zalijevane s većom koncentracijom gnojiva. Osim utjecaja mikorize, utvrđuje se i utjecaj različitih koncentracija gnojiva na rast i razvoj pelargonija. Kroz mjerene parametre kao što su: visina biljaka, broj listova, cvjetnih pupova i cvatova, prosječna težina nadzemne mase prije i nakon sušenja, težina korijena, ukupna težina biljaka, i analiza supstrata, utvrđena je reakcija biljaka na gnojidbu i mikorizu tijekom 12 tjedana.In Croatia, like in Europe, there are a lot of spring flowers growers, with different production size. All flower growers are facing the same problem of high production costs such as: fuel, pots, water, substratum, fertilization, and low flower price. One of the ways to reduce production costs is to save on inputs. Pelargonium varieties are one of the top produced plants in spring, and this work intends to research possibilities to reduce fertilization costs on Pelargonium zonale “Anthony”, with mycorrhiza inoculation Glomus intraradices. This work researches possibility to reduce fertilization on plants inoculated with mycorrhiza and get the same result like on plants fertilized with higher fertilization concentration without mycorrhiza. Except mycorrhiza effect, we will be able to see effect of different fertilization concentration on pelargonium's growth. Trough measurement of plant height, number of leaves, flowers and flower buds, weight of over ground mass before and after drying, weight of root mass, total plant mass and substrate analysis, we will be able to see pelargonium reaction on mycorrhiza and fertilization trough twelve weeks