712 research outputs found
Image data set for AI-aided printed line smearing analysis of the roll-to-roll screen printing process for printed electronics
No full textA total of 20 images were collected with cross-directional (CD) printed lines of various line widths and smearing areas using the in-house roll-to-roll screen printing system. Then these images were labeled pixel-wise into three classes: smearing, printed line, and background, and labels were saved as one channel 8-bit images with corresponding intensity 1, 2, and 3. This data set was used to train a U-Net-like deep convolutional neural network (DCNN) to detect continuous printed line smearing defects
Image data set for AI-aided printed line smearing analysis of the roll-to-roll screen printing process for printed electronics
No full textA total of 20 images were collected with cross-directional (CD) printed lines of various line widths and smearing areas using the in-house roll-to-roll screen printing system. Then these images were labeled pixel-wise into three classes: smearing, printed line, and background, and labels were saved as one channel 8-bit images with corresponding intensity 1, 2, and 3. This data set was used to train a U-Net-like deep convolutional neural network (DCNN) to detect continuous printed line smearing defects
Image data set for AI-aided printed line smearing analysis of the roll-to-roll screen printing process for printed electronics
No full textA total of 20 images were collected with cross-directional (CD) printed lines of various line widths and smearing areas using the in-house roll-to-roll screen printing system. Then these images were labeled pixel-wise into three classes: smearing, printed line, and background, and labels were saved as one channel 8-bit images with corresponding intensity 1, 2, and 3. This data set was used to train a U-Net-like deep convolutional neural network (DCNN) to detect continuous printed line smearing defects
Image data set for AI-assisted reliability assessment for gravure offset printing system
No full textIn total, 299 images of printed lines were collected using the in-house roll-based gravure offset printing system. Then they were labeled for overall printing quality classification and local printing defect detection tasks. For overall printing quality classification, these images were divided into two classes: 225 with satisfactory quality and 74 with defects. For local printing defect detection, all 74 images with distinctive local defects were chosen and labeled following the YOLO object detection format. During the training process, these images were split for training and validation as follows: 75/25 % for the overall printing quality classification model and 80/20 % for the local printing defect detection model respectively
Image data set for AI-assisted reliability assessment for gravure offset printing system
No full textIn total, 299 images of printed lines were collected using the in-house roll-based gravure offset printing system. Then they were labeled for overall printing quality classification and local printing defect detection tasks. For overall printing quality classification, these images were divided into two classes: 225 with satisfactory quality and 74 with defects. For local printing defect detection, all 74 images with distinctive local defects were chosen and labeled following the YOLO object detection format. During the training process, these images were split for training and validation as follows: 75/25 % for the overall printing quality classification model and 80/20 % for the local printing defect detection model respectively
Bobodzhon Gafurovich Gafurov, 1908-1977
No full textAmong Soviet Central Asians who have achieved international attention, Bobodzhon Gafurovich Gafurov, the late director of the Institute of Oriental Studies of the Academy of Sciences of the USSR, stands out as a leading figure. Gafurov was politically the most prominent Tadzhik, if not Central Asian, in the Soviet Union. During the twenty-one years that he served as the Institute's director, he influenced and made decisions which have led to an overall increase in scholarly research and publication about West Asia, and about Soviet Central Asia in particular, emanating from the Soviet Union.</jats:p
Performance and security analysis of Gait-based user authentication
No full textVerifying the identity of a user, usually referred to as user authentication, before granting access to the services or objects is a very important step in many applications. People pass through some sorts of authentication process in their daily life. For example, to prove having access to the computer the user is required to know a password. Similarly, to be able to activate a mobile phone the owner has to know its PIN code, etc. Some user authentication techniques are based on human physiological or behavioral characteristics such as fingerprints, face, iris and so on. Authentication methods differ in their advantages and disadvantages, e.g. PIN codes and passwords have to be remembered, eye-glasses must be taken off for face authentication, etc. Security and usability are important aspects of user authentication. The usability aspect relates to the unobtrusiveness, convenience and user-friendliness of the authentication technique. Security is related to the robustness of the authentication method against attacks. Recent advances in electronic chip development offer new opportunities for person authentication based on his gait (walking style) using small, light and cheap sensors. One of the primary advantages of this approach is that it enables unobtrusive user authentication. Although studies on human recognition based on gait indicate encouraging performances, the security per se (i.e. robustness and/or vulnerability) of gait-based recognition systems has received little or no attention.
The overall goal of the work presented in this thesis is on performance and security analysis of gait-based user authentication. The nature of the contributions is not on developing novel algorithms, but rather on enhancing existing approaches in gait-based recognition using small and wearable sensors, and developing new knowledge on security and uniqueness of gait.
The three main research questions addressed in this thesis are: (1) What are the performances of recognition methods that are based on the motion of particular body parts during gait? (2) How robust is the gait-based user authentication? (3) What aspects do influence the uniqueness of human gait?
In respect to the first research question, the thesis identifies several locations on the body of the person, whose motion during gait can provide identity information. These body parts include hip, trouser pockets, arm and ankle. Analysis of acceleration signals indicates that movements of these body segments have some discriminative power. This might make these modalities suitable as an additional factor in multi-factor authentication. For the research question on security as far as we know, this thesis is the first extensive analysis of gait authentication security (in case of hip motion). A gait-based authentication system is studied under three attack scenarios. These attack scenarios include a minimal effort-mimicry (with restricted time and number of attempts), knowing the closest person in the database (in terms of gait similarity) and knowing the gender of the user in the database. The findings of the thesis reveal that the minimal effort mimicking does not help to improve the acceptance chances of impostors. However, impostors who know their closest person in the database or the genders of the users in the database can be a threat to gait-based authentication systems.
In the third research question, the thesis provides some insights towards understanding the uniqueness of gait in case of ankle/foot motion. In particular, it reveals the following: heavy footwear tends to diminish foot discriminativeness; a sideway motion of the foot provides the most discrimination, compared to an up-down or forward-backward direction of the motion; and different parts of the gait cycle provide different level of discrimination.
In addition, the thesis proposes taxonomy of user recognition methods based on gait.
In addition, the thesis work has also resulted in the follwoing paper which is closely related or overlapping with papers mentioned below.
Davrondzhon Gafurov, Kirsi Helkala and Torkjel Søndrol, Biometric Gait Authentication Using Accelerometer Sensor, Journal of Computers, 1(7), pp.51-59, 2006: http://www.academypublisher.com/jcp/vol01/no07/jcp01075159.pdf
List of papers. The 8 research papers that constitute the main research part of the thesis are
Kaukokartoituksen ja koneoppimisen soveltaminen dataköyhien rajavesistöjen ihmis- ja ilmastoperäisen kuivumisen tutkimuksessa
No full textAbstractAn unsustainable anthropogenic action upstream has environmental consequences such as lake drying downstream, and climatic drivers can in turn intensify the human impact. The independence of hydrological boundaries and political borders increases the complexity of transboundary water resources management, causing a lack of transparency and information. In this thesis, the lack of data was tackled by remote sensing and machine learning. These models/products were evaluated by in-situ records to investigate the spatiotemporal accuracy. Thus, deep learning and random forest (RF) methods were trained by local rain gauge observations and compared against global gridded remote-sensing precipitation products to determine the best option for solving data issues. Then, the developed framework was applied to determine the climatic and anthropogenic drivers of water bodies drying in some transboundary basins (e.g., Caspian Sea, Tigris River, and Helmand River). Also, the costs of lake drying on the economy of the surrounding region were compared against the economic gain (e.g., agricultural development). The standardized indices for precipitation (SPI) and discharge (SDI) were utilized to detect anthropogenic or climatic droughts that change the inflow downstream. Also, River Impact (RI) index was used to classify the level of river flow alteration in different locations. It was found that although training machine learning methods require local observations and increase the complexity of modeling, they considerably outperform global remote sensing alternatives. In the studied area (Tanzania), the trained RF had the best Pearson correlation coefficient (0.53), root mean square error (6.9 mm), and bias (-2%) among globally widely used precipitation products. Mitigating the issue of information in the studied transboundary basins helped quantify the share of different factors (climate or human) in drying the studied lakes. In the Helmand (Hamun Lake) and Tigris River basins, it was found that the water resource regulations upstream are the leading cause of environmental and social costs downstream. However, in the Caspian Sea basin, the water level of the sea is decreasing mainly due to climatic variations in recent years. Also, twenty-five thousand square km of the Caspian Sea is vulnerable to sea water level fluctuation. Although Kazakhstan has no control over the main feeding rivers to this sea, major part of the vulnerable area (70%) is in this country, followed by Russia (5055 km2), Iran (1089 km2), Azerbaijan (578 km2), and Turkmenistan (482 km2). Since last two decades, the Hamun Lake drying was a very expensive disaster for the people living in the Sistan region of Iran, which has negative huge impact (about half billion US dollar) on the economy of the region. This led to migration from this region due to high rate of unemployment and dust storms. In transboundary basins, unilateral flow legislations by upstream states are unsustainable and cause political and security challenges and crises. On the other hand, inclusive collaboration, and water resource management among all states within a transboundary basin will mitigate the lack of transparency on ongoing hydrological processes, minimize negative outcomes downstream, and improve sustainability.Original papersOriginal papers are not included in the electronic version of the dissertation.Akbari, M., Baubekova, A., Roozbahani, A., Gafurov, A., Shiklomanov, A., Rasouli, K., Ivkina, N., Kløve, B., & Torabi Haghighi, A. (2020). Vulnerability of the Caspian Sea shoreline to changes in hydrology and climate. Environmental Research Letters, 15(11), 115002. https://doi.org/10.1088/1748-9326/abaad8Self-archived versionAkbari, M., Mirchi, A., Roozbahani, A., Gafurov, A., Kløve, B., & Haghighi, A. T. (2022). Desiccation of the transboundary Hamun Lakes between Iran and Afghanistan in response to hydro-climatic droughts and anthropogenic activities. Journal of Great Lakes Research, 48(4), 876–889. https://doi.org/10.1016/j.jglr.2022.05.004Self-archived versionFaramarzzadeh, M., Ehsani, M. R., Akbari, M., Rahimi, R., Moghaddam, M., Behrangi, A., Klöve, B., Haghighi, A. T., & Oussalah, M. (2023). Application of machine learning and remote sensing for gap-filling daily precipitation data of a sparsely gauged basin in East Africa. Environmental Processes, 10(1), 8. https://doi.org/10.1007/s40710-023-00625-ySelf-archived versionGhajarnia, N., Akbari, M., Saemian, P., Ehsani, M. R., Hosseini‐Moghari, S., Azizian, A., Kalantari, Z., Behrangi, A., Tourian, M. J., Klöve, B., & Haghighi, A. T. (2022). Evaluating the evolution of ECMWF precipitation products using observational data for Iran: From ERA40 to ERA5. Earth and Space Science, 9(10), e2022EA002352. https://doi.org/10.1029/2022EA002352Self-archived versionAkbari, M., & Torabi Haghighi, A. (2022). Satellite-based agricultural water consumption assessment in the ungauged and transboundary Helmand Basin between Iran and Afghanistan. Remote Sensing Letters, 13(12), 1236–1248. https://doi.org/10.1080/2150704X.2022.2142074Self-archived versionTorabi Haghighi, A., Akbari, M., Noori, R., Danandeh Mehr, A., Gohari, A., Sönmez, M. E., Abou Zaki, N., Yilmaz, N., & Kløve, B. (2023). The impact of Turkey’s water resources development on the flow regime of the Tigris River in Iraq. Journal of Hydrology: Regional Studies, 48, 101454. https://doi.org/10.1016/j.ejrh.2023.101454Self-archived versionAkbari, M., Rozbahani, A., Hajimoradi, A., Miri, A., Labbaf, M., Madani, K., Kløve,
B., and Torabi Haghighi, A. (2023). Costs of the Lake Hamun death for the Sistan human life. Manuscript submitted for publication.TiivistelmäKestämätön ihmisperäinen toiminta valuma-alueiden yläosilla aiheuttaa ympäristövaikutuksia, kuten alueiden alapuolisissa osissa tapahtuvaa järvien kuivumista. Ilmastolliset tekijät voivat puolestaan voimistaa näitä vaikutuksia. Hydrologisten ja poliittisten rajojen riippumattomuus toisistaan monimutkaistaa ylirajallisten vesivarojen hallintaa, mikä aiheuttaa läpinäkymättömyyttä ja tiedon puutetta. Tässä väitöskirjassa tätä pyrittiin edistämään kaukokartoitusmenetelmien ja koneoppimisen avulla. Käytettyjä malleja verrattiin paikallisesti tehtyihin mittauksiin. Syväoppimis- (deep learning) ja satunnaismetsämenetelmiä (random forest - RF) koulutettiin paikallisesti mitatulla sademäärällä ja verrattiin globaaleihin kaukokartoitusmenetelmin määritettyihin sademäärähiloihin, jotta pystyttiin löytämään tietoaukkoja parhaiten paikkaavia menetelmiä. Tämän jälkeen kehitettyä viitekehystä sovellettiin vesimuodostumien kuivumiseen liittyviin ilmasto- ja ihmisperäisten ajureiden määrittämiseen joillakin ylirajallisilla vesimuodostumilla (esim. Kaspianmeri, Tigris-joki, Helmand-joki). Myös järven kuivumisen vaikutuksia ympäröivän alueen talouteen verrattiin kuivattamisesta saatavaan taloudelliseen hyötyyn (esim. maatalouden kehittämiseen). Standardoidut sademäärän (SPI) ja virtauksen (SDI) indeksit otettiin käyttöön, jotta voitiin havaita ihmis- tai ilmastoperäisiä kuivuuksia. Myös jokiympäristöille sovellettiin omaa tunnuslukua RI (River Impact), jota käytettiin virtausten muutosten tason luokitteluun eri paikoissa. Huomattiin, että vaikka koneoppimismenetelmien kouluttaminen edellyttää paikallisia havaintoja ja lisää mallinnuksen monimutkaisuutta, ne suoriutuvat huomattavasti paremmin kuin globaalit kaukokartoitusmenetelmät. Eräällä tutkitulla alueella (Tansania) koulutetulla RF:llä oli paras Pearsonin korrelaatio (0.53), neliöllinen keskihajonta (6.9 mm) ja poikkeama (-2 %) verrattuna maailmanlaajuisesti laajasti käytettyihin sademääräaineistoihin. Tietojen puutteen lieventäminen tutkituissa ylirajallisissa vesistöissä auttoi määrittämään erilaisten tekijöiden (ilmasto tai ihminen) osuutta tutkittujen järvien kuivumisessa. Helmandissa (Hamun-järvi) ja Tigris-joen valuma-alueilla havaittiin, että vesivarojen säännöstely yläjuoksulla on ympäristöllisten ja sosiaalisten paineiden pääsiallinen aiheuttaja vesistön alapuolisissa osissa. Sen sijaan Kaspianmeren viimeaikaisen tason vaihtelun pääasiallinen syy on ollut ilmaston vaihtelussa. Lisäksi 25000 km2 Kaspianmeren alasta on haavoittuvaista vedenpinnan vaihtelulle. Vaikka Kazakstanilla ei ole vaikutusvaltaa tämän vesistön laskeviin pääjokiin, suurin osa pinnan vaihtelulle alttiista alueesta (70 %) sijaitsee sen alueella, seuraavina Venäjä (5055 km2), Iran (1089 km2), Azerbaidžan (578 km2) ja Turkmenistan (482 km2). Viimeisten kahden vuosikymmenen aikana tapahtunut Hamun-järven kuivuminen on ollut taloudellinen katastrofi Iranin Sistanin alueella (n. 0,5 mrd. Yhdysvaltojen dollaria). Tämä johti muuttoliikkeeseen alueelta korkean työttömyyden ja pölymyrskyjen vuoksi. Ylirajallisten vesistöjen yläpuolisilla alueilla esiintyvät yksipuoliset säädökset ja sääntelyt ovat kestämättömiä ja aiheuttavat poliittisia ja turvallisuuteen kohdistuvia haasteita ja kriisejä. Toisaalta kaikkien osapuolten välinen osallistava yhteistyö ja vesivarojen hallinta tällaisten vesistöalueiden valtioiden kesken lieventää läpinäkymättömyyttä jatkuvissa hydrologisissa prosesseissa, pienentää negatiivisia vaikutuksia vesistöjen alapuolisilla alueilla sekä parantaa kestävyyttä.OsajulkaisutOsajulkaisut eivät sisälly väitöskirjan elektroniseen versioon.Akbari, M., Baubekova, A., Roozbahani, A., Gafurov, A., Shiklomanov, A., Rasouli, K., Ivkina, N., Kløve, B., & Torabi Haghighi, A. (2020). Vulnerability of the Caspian Sea shoreline to changes in hydrology and climate. Environmental Research Letters, 15(11), 115002. https://doi.org/10.1088/1748-9326/abaad8Rinnakkaistallennettu versioAkbari, M., Mirchi, A., Roozbahani, A., Gafurov, A., Kløve, B., & Haghighi, A. T. (2022). Desiccation of the transboundary Hamun Lakes between Iran and Afghanistan in response to hydro-climatic droughts and anthropogenic activities. Journal of Great Lakes Research, 48(4), 876–889. https://doi.org/10.1016/j.jglr.2022.05.004Rinnakkaistallennettu versioFaramarzzadeh, M., Ehsani, M. R., Akbari, M., Rahimi, R., Moghaddam, M., Behrangi, A., Klöve, B., Haghighi, A. T., & Oussalah, M. (2023). Application of machine learning and remote sensing for gap-filling daily precipitation data of a sparsely gauged basin in East Africa. Environmental Processes, 10(1), 8. https://doi.org/10.1007/s40710-023-00625-yRinnakkaistallennettu versioGhajarnia, N., Akbari, M., Saemian, P., Ehsani, M. R., Hosseini‐Moghari, S., Azizian, A., Kalantari, Z., Behrangi, A., Tourian, M. J., Klöve, B., & Haghighi, A. T. (2022). Evaluating the evolution of ECMWF precipitation products using observational data for Iran: From ERA40 to ERA5. Earth and Space Science, 9(10), e2022EA002352. https://doi.org/10.1029/2022EA002352Rinnakkaistallennettu versioAkbari, M., & Torabi Haghighi, A. (2022). Satellite-based agricultural water consumption assessment in the ungauged and transboundary Helmand Basin between Iran and Afghanistan. Remote Sensing Letters, 13(12), 1236–1248. https://doi.org/10.1080/2150704X.2022.2142074Rinnakkaistallennettu versioTorabi Haghighi, A., Akbari, M., Noori, R., Danandeh Mehr, A., Gohari, A., Sönmez, M. E., Abou Zaki, N., Yilmaz, N., & Kløve, B. (2023). The impact of Turkey’s water resources development on the flow regime of the Tigris River in Iraq. Journal of Hydrology: Regional Studies, 48, 101454. https://doi.org/10.1016/j.ejrh.2023.101454Rinnakkaistallennettu versioAkbari, M., Rozbahani, A., Hajimoradi, A., Miri, A., Labbaf, M., Madani, K., Kløve,
B., and Torabi Haghighi, A. (2023). Costs of the Lake Hamun death for the Sistan human life. Manuscript submitted for publication.Academic dissertation to be presented with the assent of the Doctoral Programme Committee of Technology and Natural Sciences of the University of Oulu for public defence in the Tönning auditorium (L4), Linnanmaa, on 13 October 2023, at 12 noonAbstract
An unsustainable anthropogenic action upstream has environmental consequences such as lake drying downstream, and climatic drivers can in turn intensify the human impact. The independence of hydrological boundaries and political borders increases the complexity of transboundary water resources management, causing a lack of transparency and information. In this thesis, the lack of data was tackled by remote sensing and machine learning. These models/products were evaluated by in-situ records to investigate the spatiotemporal accuracy. Thus, deep learning and random forest (RF) methods were trained by local rain gauge observations and compared against global gridded remote-sensing precipitation products to determine the best option for solving data issues. Then, the developed framework was applied to determine the climatic and anthropogenic drivers of water bodies drying in some transboundary basins (e.g., Caspian Sea, Tigris River, and Helmand River). Also, the costs of lake drying on the economy of the surrounding region were compared against the economic gain (e.g., agricultural development). The standardized indices for precipitation (SPI) and discharge (SDI) were utilized to detect anthropogenic or climatic droughts that change the inflow downstream. Also, River Impact (RI) index was used to classify the level of river flow alteration in different locations. It was found that although training machine learning methods require local observations and increase the complexity of modeling, they considerably outperform global remote sensing alternatives. In the studied area (Tanzania), the trained RF had the best Pearson correlation coefficient (0.53), root mean square error (6.9 mm), and bias (-2%) among globally widely used precipitation products. Mitigating the issue of information in the studied transboundary basins helped quantify the share of different factors (climate or human) in drying the studied lakes. In the Helmand (Hamun Lake) and Tigris River basins, it was found that the water resource regulations upstream are the leading cause of environmental and social costs downstream. However, in the Caspian Sea basin, the water level of the sea is decreasing mainly due to climatic variations in recent years. Also, twenty-five thousand square km of the Caspian Sea is vulnerable to sea water level fluctuation. Although Kazakhstan has no control over the main feeding rivers to this sea, major part of the vulnerable area (70%) is in this country, followed by Russia (5055 km2), Iran (1089 km2), Azerbaijan (578 km2), and Turkmenistan (482 km2). Since last two decades, the Hamun Lake drying was a very expensive disaster for the people living in the Sistan region of Iran, which has negative huge impact (about half billion US dollar) on the economy of the region. This led to migration from this region due to high rate of unemployment and dust storms. In transboundary basins, unilateral flow legislations by upstream states are unsustainable and cause political and security challenges and crises. On the other hand, inclusive collaboration, and water resource management among all states within a transboundary basin will mitigate the lack of transparency on ongoing hydrological processes, minimize negative outcomes downstream, and improve sustainability.Tiivistelmä
Kestämätön ihmisperäinen toiminta valuma-alueiden yläosilla aiheuttaa ympäristövaikutuksia, kuten alueiden alapuolisissa osissa tapahtuvaa järvien kuivumista. Ilmastolliset tekijät voivat puolestaan voimistaa näitä vaikutuksia. Hydrologisten ja poliittisten rajojen riippumattomuus toisistaan monimutkaistaa ylirajallisten vesivarojen hallintaa, mikä aiheuttaa läpinäkymättömyyttä ja tiedon puutetta. Tässä väitöskirjassa tätä pyrittiin edistämään kaukokartoitusmenetelmien ja koneoppimisen avulla. Käytettyjä malleja verrattiin paikallisesti tehtyihin mittauksiin. Syväoppimis- (deep learning) ja satunnaismetsämenetelmiä (random forest - RF) koulutettiin paikallisesti mitatulla sademäärällä ja verrattiin globaaleihin kaukokartoitusmenetelmin määritettyihin sademäärähiloihin, jotta pystyttiin löytämään tietoaukkoja parhaiten paikkaavia menetelmiä. Tämän jälkeen kehitettyä viitekehystä sovellettiin vesimuodostumien kuivumiseen liittyviin ilmasto- ja ihmisperäisten ajureiden määrittämiseen joillakin ylirajallisilla vesimuodostumilla (esim. Kaspianmeri, Tigris-joki, Helmand-joki). Myös järven kuivumisen vaikutuksia ympäröivän alueen talouteen verrattiin kuivattamisesta saatavaan taloudelliseen hyötyyn (esim. maatalouden kehittämiseen). Standardoidut sademäärän (SPI) ja virtauksen (SDI) indeksit otettiin käyttöön, jotta voitiin havaita ihmis- tai ilmastoperäisiä kuivuuksia. Myös jokiympäristöille sovellettiin omaa tunnuslukua RI (River Impact), jota käytettiin virtausten muutosten tason luokitteluun eri paikoissa. Huomattiin, että vaikka koneoppimismenetelmien kouluttaminen edellyttää paikallisia havaintoja ja lisää mallinnuksen monimutkaisuutta, ne suoriutuvat huomattavasti paremmin kuin globaalit kaukokartoitusmenetelmät. Eräällä tutkitulla alueella (Tansania) koulutetulla RF:llä oli paras Pearsonin korrelaatio (0.53), neliöllinen keskihajonta (6.9 mm) ja poikkeama (-2 %) verrattuna maailmanlaajuisesti laajasti käytettyihin sademääräaineistoihin. Tietojen puutteen lieventäminen tutkituissa ylirajallisissa vesistöissä auttoi määrittämään erilaisten tekijöiden (ilmasto tai ihminen) osuutta tutkittujen järvien kuivumisessa. Helmandissa (Hamun-järvi) ja Tigris-joen valuma-alueilla havaittiin, että vesivarojen säännöstely yläjuoksulla on ympäristöllisten ja sosiaalisten paineiden pääsiallinen aiheuttaja vesistön alapuolisissa osissa. Sen sijaan Kaspianmeren viimeaikaisen tason vaihtelun pääasiallinen syy on ollut ilmaston vaihtelussa. Lisäksi 25000 km2 Kaspianmeren alasta on haavoittuvaista vedenpinnan vaihtelulle. Vaikka Kazakstanilla ei ole vaikutusvaltaa tämän vesistön laskeviin pääjokiin, suurin osa pinnan vaihtelulle alttiista alueesta (70 %) sijaitsee sen alueella, seuraavina Venäjä (5055 km2), Iran (1089 km2), Azerbaidžan (578 km2) ja Turkmenistan (482 km2). Viimeisten kahden vuosikymmenen aikana tapahtunut Hamun-järven kuivuminen on ollut taloudellinen katastrofi Iranin Sistanin alueella (n. 0,5 mrd. Yhdysvaltojen dollaria). Tämä johti muuttoliikkeeseen alueelta korkean työttömyyden ja pölymyrskyjen vuoksi. Ylirajallisten vesistöjen yläpuolisilla alueilla esiintyvät yksipuoliset säädökset ja sääntelyt ovat kestämättömiä ja aiheuttavat poliittisia ja turvallisuuteen kohdistuvia haasteita ja kriisejä. Toisaalta kaikkien osapuolten välinen osallistava yhteistyö ja vesivarojen hallinta tällaisten vesistöalueiden valtioiden kesken lieventää läpinäkymättömyyttä jatkuvissa hydrologisissa prosesseissa, pienentää negatiivisia vaikutuksia vesistöjen alapuolisilla alueilla sekä parantaa kestävyyttä
The methodological aspects of constructing a high-resolution DEM of large territories using low-cost UAVs on the example of the Sarycum Aeolian complex, Dagestan, Russia
No full textUnmanned aerial vehicles (UAV) have long been well established as a reliable way to construct highly accurate, up-to-date digital elevation models (DEM). However, the territories which were modeled by the results of UAV surveys can be characterized as very local. This paper presents the results of surveying the Sarycum area of the Dagestan Nature Reserve of Russia with an area of 15 sq. km using a DJI Phantom 4 UAV, as well as the methodological recommendations for conducting work on such a large territory. As a result of this work, a DEM with 0.5 m resolution as well as an ultrahigh resolution orthophotoplane were obtained for the first time for this territory, which make it possible to assess the dynamics of aeolian processes at a qualitatively different level
Double-Binding Botulinum Molecule with Reduced Muscle Paralysis: Evaluation in In Vitro and In Vivo Models of Migraine
No full text© 2020, The Author(s). With a prevalence of 15%, migraine is the most common neurological disorder and among the most disabling diseases, taking into account years lived with disability. Current oral medications for migraine show variable effects and are frequently associated with intolerable side effects, leading to the dissatisfaction of both patients and doctors. Injectable therapeutics, which include calcitonin gene–related peptide–targeting monoclonal antibodies and botulinum neurotoxin A (BoNT/A), provide a new paradigm for treatment of chronic migraine but are effective only in approximately 50% of subjects. Here, we investigated a novel engineered botulinum molecule with markedly reduced muscle paralyzing properties which could be beneficial for the treatment of migraine. This stapled botulinum molecule with duplicated binding domain—binary toxin-AA (BiTox/AA)—cleaves synaptosomal-associated protein 25 with a similar efficacy to BoNT/A in neurons; however, the paralyzing effect of BiTox/AA was 100 times less when compared to native BoNT/A following muscle injection. The performance of BiTox/AA was evaluated in cellular and animal models of migraine. BiTox/AA inhibited electrical nerve fiber activity in rat meningeal preparations while, in the trigeminovascular model, BiTox/AA raised electrical and mechanical stimulation thresholds in Aδ- and C-fiber nociceptors. In the rat glyceryl trinitrate (GTN) model, BiTox/AA proved effective in inhibiting GTN-induced hyperalgesia in the orofacial formalin test. We conclude that the engineered botulinum molecule provides a useful prototype for designing advanced future therapeutics for an improved efficacy in the treatment of migraine
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