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Behavioral predictability in a lynx spider is interactively influenced by mean behavior, prey density,and an insecticide

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Current Zoology 2018年第6期64卷 713-720页

作者： radek michalko Ondrej KOSULIC Thitiya PUNG Patchanee VICHITBANDHA Department of Forest Ecology Faculty of Forestry and Wood TechnologyMendel University in BrnoZemedelska 3Brno61300Czech Republic Department of Forest Protection and Wildlife Management Faculty of Forestry and Wood TechnologyMendel University in BrnoZemedelska 3Brno61300Czech Republic Department of Chemistry Kasetsart UniversityKamphaeng SaenNakhon Pathom73140Thailand Department of Science Faculty of Liberal Arts and ScienceKasetsart UniversityKamphaeng SaenNakhon Pathom73140Thailand

Behavioral predictability,i.e.,short-term intra-individual variability under relatively constant environmental conditions,has only recently begun to gain attention.It is unknown,however,whether predictability of individuals with distinct mean behavior changes differently as a response to ecological factors such as resource availability.Moreover,the response might be affected by anthropogenic contaminants that are ubiquitous in the environment and that can affect animals' variability in behavior.Here,we investigated the relationship between mean predatory activity and predictability in predatory activity along a prey density gradient in the lynx spider Oxyopes lineatipes.We further examined how this relationship is influenced by insecticides,azadirachtin,and a plant extract from Embelia ribes.We found that all studied variables affected the predictability.In the control and Embelia treatments,that did not differ significantly,the predictability decreased with increasing prey density in a mean behavior-specific way.Individuals with low mean predatory activity were relatively less predictable than were those with high activity from low to moderate prey densities but more predictable at high prey densities.Azadirachtin altered this pattern and the individuals with low predatory activity were less predictable than were those with high predatory activity along the whole gradient of prey density.Our results show that predictability can change along an environmental gradient depending on a mean behavior.The relative predictability of the individuals with distinct mean behavior can depend on the value of the environmental gradient. In addition,this relationship can be affected by anthropogenic contaminants such as pesticides.

关键词： azadirachtin environmental gradient intra-individual variability pesticide resource availability

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斑驳旧影卡罗维发利某公寓

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室内设计与装修 2024年第1期 134-137页

作者： [捷克]Plus One Architects 秋落(编译) radek Ulehla(摄影) Plus One Architects

该公寓位于捷克卡罗维发利(Karlovy Vary)风景如画的磨坊柱廊(MI nska kolonada/Mill Colonnade)和世界著名国际电影节所在地温泉酒店附近,由当地设计事务所Plus One Architects设计完成。公寓所在的建筑历史悠久,它建于20世纪初,至今... 详细信息

该公寓位于捷克卡罗维发利(Karlovy Vary)风景如画的磨坊柱廊(MI nska kolonada/Mill Colonnade)和世界著名国际电影节所在地温泉酒店附近,由当地设计事务所Plus One Architects设计完成。公寓所在的建筑历史悠久,它建于20世纪初,至今仍保存不少完整的历史构件,这些元素不禁让人联想到建筑当年最迷人与辉煌的时刻。

关键词：设计事务所柱廊公寓旧影国际电影节温泉酒店风景如画

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非洲最恐怖的生命禁区一百年里无人敢涉足的达洛尔

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环球人文地理 2024年第5期 96-103页

作者：李理 Vladislav radek Benot ArtushFoto Lina dinozzaver Michail mikhail cheremkin flickr

色彩绚丽的盐层让人称奇，强酸性的湖水腐蚀鞋子，车队时不时被袭击，还曾有游客被撕票……达洛尔深刻诠释了什么叫“越迷人的越危险”。达洛尔位于埃塞俄比亚东北角，被描述成一个鬼城。因为2005年之后，埃塞俄比亚中央统计局的人口报...

色彩绚丽的盐层让人称奇，强酸性的湖水腐蚀鞋子，车队时不时被袭击，还曾有游客被撕票……达洛尔深刻诠释了什么叫“越迷人的越危险”。达洛尔位于埃塞俄比亚东北角，被描述成一个鬼城。因为2005年之后，埃塞俄比亚中央统计局的人口报告中，就已没有此地区的人口数据。这里是世界上最遥远、最荒芜的地区之一，没有学校、没有商店，也没有公路，对外交通除了游客车队，就只有前往该地收集盐块的阿法尔人与骆驼。然而，这里的形势极不稳定，

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Combining High-Resolution Imaging,Deep Learning,and Dynamic Modeling to Separate Disease and Senescence in Wheat Canopies

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Plant Phenomics 2023年第2期5卷 295-312页

作者： Jonas Anderegg radek Zenkl Achim Walter Andreas Hund Plant Pathology Group Institute of Integrative BiologyETH ZurichZurichSwitzerland Crop Science Group Institute of Agricultural SciencesETH ZurichZurichSwitzerland

Maintenance of sufficiently healthy green leaf area after anthesis is key to ensuring an adequate assimilate supply for grain filling.Tightly regulated age-related physiological senescence and various biotic and abiotic stressors drive overall greenness decay dynamics under field conditions.Besides direct effects on green leaf area in terms of leaf damage,stressors often anticipate or accelerate physiological senescence,which may multiply their negative impact on grain filling.Here,we present an image processing methodology that enables the monitoring of chlorosis and necrosis separately for ears and shoots(stems+leaves)based on deep learning models for semantic segmentation and color properties of vegetation.A vegetation segmentation model was trained using semisynthetic training data generated using image composition and generative adversarial neural networks,which greatly reduced the risk of annotation uncertainties and annotation effort.Application of the models to image time series revealed temporal patterns of greenness decay as well as the relative contributions of chlorosis and necrosis.Image-based estimation of greenness decay dynamics was highly correlated with scoring-based estimations(r≈0.9).Contrasting patterns were observed for plots with different levels of foliar diseases,particularly septoria tritici blotch.Our results suggest that tracking the chlorotic and necrotic fractions separately may enable(a)a separate quantification of the contribution of biotic stress and physiological senescence on overall green leaf area dynamics and(b)investigation of interactions between biotic stress and physiological senescence.The high-throughput nature of our methodology paves the way to conducting genetic studies of disease resistance and tolerance.

关键词： Wheat enable image

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Natural regeneration in logging gaps of different sizes in Subri River Forest Reserve(Ghana)

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Journal of Forestry Research 2022年第4期33卷 1157-1174页

作者： Maame Esi Hammond radek Pokorný Simon Abugre Augustine Gyedu Department of Silviculture Faculty of Forestry and Wood TechnologyMendel University in BrnoZemědělská361300 BrnoCzech Republic Department of Forest Science School of Natural ResourcesUniversity of Energy and Natural ResourcesP.O.Box 214SunyaniGhana Forest Services Division(FSD) P.M.BKumasiGhana

Subri River Forest Reserve(SR)is the most extensive forest area in Ghana with an accompanying rich floral species.Over the years,logging from both legally prescribed and illegal operations remain the predominant forest disturbance in SR.Gap creation following logging is crucial in determining tree species composition and diversity.Hence,the study evaluated the composition and diversity of naturally regenerated tree species in logging gaps of different sizes and,again examined the roles of these tree species in fulfilling the economic and ecological agenda of sustainable forest management after logging in SR.Twelve gaps were randomly selected:4 each were grouped into small size(≤200 m^(2)),medium size(201–300 m^(2)),and large size(≥300 m^(2)).Data were gathered from 1 m^(2) circular area at gap centres and repeatedly inside 1 m width strip along 20 m individual N-S-E-W transects.Species diversity differed significantly between gap sizes.Higher diversity indices were measured in large size gaps.Gap sizes shared similar species.There were significant differences among various height groupings of tree species across all three gap sizes.Pioneers preferred medium to large size gaps,while shadetolerant tree species preferred small size gaps for their abundance.Vulnerable and Lower Risk Near Threatened tree species under Conservation Status and,Premium and Commercial tree species under Utilisation Status preferred small size gaps for their proliferation and conservation.Therefore,we recommend the single tree-based selective logging for ensuring creations of small to medium size(200–300 m^(2))gaps through adjustments to the logging permit process,revision of Allocation Quota Permit,strict adherence to the 40-year polycyclic selection system,along with more dedicated enforcement and monitoring.Changes along these protocols would tremendously facilitate natural regeneration of different suites of timber species resulting in the improvement of the overall biodiversity conservation associated with the forest,more sustainable

关键词： Conservation status Gap size Diversity Logging Natural regeneration Subri River Forest Reserve Tree species Utilisation status

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Data-Driven Self-Learning Controller for Power-Aware Mobile Monitoring IoT Devices

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Computers, Materials & Continua 2022年第2期70卷 2601-2618页

作者： Michal Prauzek Tereza Paterova Jaromir Konecny radek Martinek VSB–Technical University of Ostrava Department of Cybernetics and Biomedical EngineeringOstrava70800Czech Republic

Nowadays,there is a significant need for maintenance free modern Internet of things(IoT)devices which can monitor an environment.IoT devices such as these are mobile embedded devices which provide data to the internet via Low Power Wide Area Network(LPWAN).LPWAN is a promising communications technology which allows machine to machine(M2M)communication and is suitable for smallmobile embedded devices.The paper presents a novel data-driven self-learning(DDSL)controller algorithm which is dedicated to controlling small mobile maintenance-free embedded IoT devices.The DDSL algorithm is based on a modified Q-learning algorithm which allows energy efficient data-driven behavior of mobile embedded IoT devices.The aim of the DDSL algorithm is to dynamically set operation duty cycles according to the estimation of future collected data values,leading to effective operation of power-aware systems.The presented novel solution was tested on a historical data set and compared with a fixed duty cycle reference algorithm.The root mean square error(RMSE)and measurements parameters considered for the DDSL algorithm were compared to a reference algorithm and two independent criteria(the performance score parameter and normalized geometric distance)were used for overall evaluation and comparison.The experiments showed that the novel DDSL method reaches significantly lowerRMSE while the number of transmitted data count is less than or equal to the fixed duty cycle algorithm.The overall criteria performance score is 40%higher than the reference algorithm base on static confirmation settings.

关键词： 5G and beyond wireless IoT LPWAN M2M Q-learning

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Small-extracellular vesicles and their microRNA cargo from porcine follicular fluids:the potential association with oocyte quality

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Journal of Animal Science and Biotechnology 2022年第6期13卷 1727-1742页

作者： Ahmed Gad Matej Murin Alexandra Bartkova Veronika Kinterova Katerina Marcollova Jozef Laurincik radek Prochazka Institute of Animal Physiology and Genetics Czech Academy of Sciences27721 LiběchovCzech Republic Department of Animal Production Faculty of AgricultureCairo UniversityGiza 12613Egypt Department of Botany and Genetics Faculty of Natural SciencesConstantine the Philosopher University in Nitra94901 NitraSlovakia.

Background: Ovarian follicular fluids(FFs) contain several kinds of regulatory factors that maintain a suitable micro-environment for oocyte development. Extracellular vesicles(EVs) are among the factors that play essential roles in regulating follicle and oocyte development through their cargo molecules that include microRNAs(miRNAs). This study aimed to investigate small-EV(s-EV) miRNAs in porcine FFs and their potential association with oocyte quality.Methods: Individual aspirated oocytes were stained with lissamine green B stain(LB), a vital stain for oocyte quality, and each oocyte was classified as high-quality(unstained;HQ) or low-quality(stained;LQ). FFs corresponding to oocytes were pooled together into HQ and LQ groups. Small-EVs were isolated from FFs, characterized, and their miRNA cargo was identified using the Illumina Nova Seq sequencing platform. Additionally, s-EVs from the HQ and LQ groups were utilized to investigate their effect on oocyte development after co-incubation during in vitro maturation.Results: A total of 19 miRNAs(including miR-125b, miR-193a-5p, and miR-320) were significantly upregulated, while 23(including miR-9, miR-206, and miR-6516) were downregulated in the HQ compared to the LQ group. Apoptosis, p53 signaling, and cAMP signaling were among the top pathways targeted by the elevated miRNAs in the HQ group while oocyte meiosis, gap junction, and TGF-beta signaling were among the top pathways targeted by the elevated miRNAs in the LQ group. The supplementation of small-EVs during maturation does not affect the oocyte developmental rates. However, LQ s-EVs increase the proportion of oocytes with homogeneous mitochondrial distribution and decrease the proportion of heterogeneous distribution.Conclusion: Our findings indicated that FF-EVs contain different miRNA cargos associated with oocyte quality and could affect the mitochondrial distribution patterns during oocyte maturation.

关键词： Extracellular vesicles Follicular fuids MiRNA Oocyte quality Porcine

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Hybrid In-Vehicle Background Noise Reduction for Robust Speech Recognition:The Possibilities of Next Generation 5G Data Networks

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Computers, Materials & Continua 2022年第6期71卷 4659-4676页

作者： radek Martinek Jan Baros Rene Jaros Lukas Danys Jan Nedoma VSB-Technical University of Ostrava Faculty of Electrical Engineering and Computer ScienceDepartment of Cybernetics and Biomedical Engineering70800Ostrava-PorubaCzechia VSB Technical University of Ostrava Faculty of Electrical Engineering and Computer ScienceDepartment of Telecommunications70800Ostrava-PorubaCzechia

This pilot study focuses on employment of hybrid LMS-ICA system for in-vehicle background noise reduction.Modern vehicles are nowadays increasingly supporting voice commands,which are one of the pillars of autonomous and SMART vehicles.Robust speaker recognition for context-aware in-vehicle applications is limited to a certain extent by in-vehicle back-ground noise.This article presents the new concept of a hybrid system which is implemented as a virtual instrument.The highly modular concept of the virtual car used in combination with real recordings of various driving scenarios enables effective testing of the investigated methods of in-vehicle background noise reduction.The study also presents a unique concept of an adaptive system using intelligent clusters of distributed next generation 5G data networks,which allows the exchange of interference information and/or optimal hybrid algorithm settings between individual vehicles.On average,the unfiltered voice commands were successfully recognized in 29.34%of all scenarios,while the LMS reached up to 71.81%,and LMS-ICA hybrid improved the performance further to 73.03%.

关键词： 5G noise reduction hybrid algorithms speech recognition 5G data networks in-vehicle background noise

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Automated decision support for Hallux Valgus treatment options using anteroposterior foot radiographs

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World Journal of Orthopedics 2023年第11期14卷 800-812页

作者： Konrad Kwolek Artur Gądek Kamil Kwolek radek Kolecki Henryk Liszka Department of Orthopedics and Traumatology University HospitalKraków 30-688MałopolskaPoland Department of Orthopedics and Physiotherapy Jagiellonian University Collegium MedicumKraków 30-688MałopolskaPoland Department of Spine Disorders and Orthopedics Gruca Orthopedic and Trauma Teaching HospitalOtwock 05-400Poland

BACKGROUND Assessment of the potential utility of deep learning with subsequent image analysis to automate the measurement of hallux valgus and intermetatarsal angles from radiographs to serve as a preoperative aid in establishing hallux valgus severity for clinical decision-making.AIM To investigate the accuracy of automated measurements of angles of hallux valgus from radiographs for further integration with the preoperative planning process.METHODS The data comprises 265 consecutive digital anteroposterior weightbearing foot radiographs.181 radiographs were utilized for training(161)and validating(20)a U-Net neural network to achieve a mean Sørensen–Dice index>97%on bone segmentation.84 test radiographs were used for manual(computer assisted)and automated measurements of hallux valgus severity determined by hallux valgus(HVA)and intermetatarsal angles(IMA).The reliability of manual and computerbased measurements was calculated using the interclass correlation coefficient(ICC)and standard error of measurement(SEM).Inter-and intraobserver reliability coefficients were also compared.An operative treatment recommendation was then applied to compare results between automated and manual angle measurements.RESULTS Very high reliability was achieved for HVA and IMA between the manual measurements of three independent clinicians.For HVA,the ICC between manual measurements was 0.96-0.99.For IMA,ICC was 0.78-0.95.Comparing manual against automated computer measurement,the reliability was high as well.For HVA,absolute agreement ICC and consistency ICC were 0.97,and SEM was 0.32.For IMA,absolute agreement ICC was 0.75,consistency ICC was 0.89,and SEM was 0.21.Additionally,a strong correlation(0.80)was observed between our approach and traditional clinical adjudication for preoperative planning of hallux valgus,according to an operative treatment algorithm proposed by EFORT.CONCLUSION The proposed automated,artificial intelligence assisted determination of hallux valgus angles based on deep learning holds great potential as

关键词： Computer-aided diagnosis Artificial intelligence in orthopedics Automated preoperative decision support Deep learning Medical imaging

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Thermoelectric energy harvesting for internet of things devices using machine learning:A review

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CAAI Transactions on Intelligence Technology 2023年第3期8卷 680-700页

作者： Tereza Kucova Michal Prauzek Jaromir Konecny Darius Andriukaitis Mindaugas Zilys radek Martinek VSB-Technical University of Ostrava OstravaCzech Republic Kaunas University of Technology KaunasLithuania

Initiatives to minimise battery use,address sustainability,and reduce regular maintenance have driven the challenge to use alternative power sources to supply energy to devices deployed in Internet of Things(IoT)networks.As a key pillar of fifth generation(5G)and beyond 5G networks,IoT is estimated to reach 42 billion devices by the year 2025.Thermoelectric generators(TEGs)are solid state energy harvesters which reliably and renewably convert thermal energy into electrical energy.These devices are able to recover lost thermal energy,produce energy in extreme environments,generate electric power in remote areas,and power micro‐sensors.Applying the state of the art,the authorspresent a comprehensive review of machine learning(ML)approaches applied in combination with TEG‐powered IoT devices to manage and predict available energy.The application areas of TEG‐driven IoT devices that exploit as a heat source the temperature differences found in the environment,biological structures,machines,and other technologies are summarised.Based on detailed research of the state of the art in TEG‐powered devices,the authors investigated the research challenges,applied algorithms and application areas of this technology.The aims of the research were to devise new energy prediction and energy management systems based on ML methods,create supervised algorithms which better estimate incoming energy,and develop unsupervised and semi‐supervised ap-proaches which provide adaptive and dynamic operation.The review results indicate that TEGs are a suitable energy harvesting technology for low‐power applications through their scalability,usability in ubiquitous temperature difference scenarios,and long oper-ating lifetime.However,TEGs also have low energy efficiency(around 10%)and require a relatively constant heat source.

关键词： adaptive systems intelligent embedded systems internet of things machine learning sensors

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