An improved FSM method is used in geophysical and environmental corrections to enhance the final product of the along track Jason-2 SLA data and extend it near the Red Sea borders. In this study the ionospheric correction range, wet tropospheric correction range, sea state bias correction range and dry tropospheric correction range are enhanced and improved using FSM01, which helped to retrieve three more tracks (106, 170 and 234), earlier neglected by the distribution centers, and extend the tracks towards the coast. The FSM01 SLA is compared with Jason-2 SLA and AVISO SLA for the available 5 tracks, in which the FSM01 SLA show a good agreement and higher correlation with the Jason-2 SLA compared with that of AVISO, in addition to that it fills the gaps in the times series of all tracks. The new retrieved tracks also compared with those retrieved by AVISO, both data show similar variability, with FSM01 SLA show no gaps in the time series. The FSM01 SLA also extended towards the coast and show high correlation with the coastal tide measurements.

At an energy flux imbalance of about 1 W m–2, the ocean is storing 90 % of the heat accumulating by global warming. However, neither the causes nor the responsible geophysical processes are sufficiently well understood. More detailed investigations of the different phenomena contributing to the oceanic energy balance are warranted. Here, the role of low-level marine clouds in the air-sea interaction is analysed. TEOS-10, the International Thermodynamic Equation of State of Seawater – 2010, is exploited for a rigorous thermodynamic description of the climatic trend of the Lifted Condensation Level (LCL) of the marine troposphere. Rising Sea-Surface Temperature (SST) at constant Relative Humidity (RH) is elevating marine clouds, cooling the cloud base and reducing the downward thermal radiation. This LCL feedback effect is negative and counteracting ocean warming. At the current global SST of about 292 K, the net radiative heat flux from the ocean surface to the LCL cloud base is estimated to 24 W m–2. Per degree of SST increase, this net flux is expected to be enhanced by almost 0.5 W m–2. The climatic LCL feedback effect is relevant for the ocean’s energy balance and may thermodynamically rigorously be modelled in terms of TEOS-10 equations. LCL height may serve as a remotely measured, sensitive estimate for sea surface relative fugacity, or conventional relative humidity.

Many reported sea level records are often not a single measurement. They are then a composition of different records from several tide gauges. Sometimes, they are from the same tide gauge, but the tide gauge stability has been affected by earthquakes. This is the case of Guam as discussed in the present manuscript. The claimed sea level acceleration of Guam is only the result of two earthquakes that have compromised the stability of the tide gauge.

The hyper-arid environment is characterized by high inter-annual climatic fluctuations. The yearly average rainfall can change substantially for several years, forming wet or dry sub-periods. Observing rainfall trends over a sub-period can lead to a false perception of a change in the trend but may in fact represent a periodic cycle when examined on a larger time scale. We aimed to better characterize the rainfall regime prevailing in the hyper-arid Arava Valley (Israel/Jordan), and to examine the response of vegetation to annual rainfall. We hypothesized that annual and perennial vegetation would respond differently to wet and dry sub-periods, and that grazing activities will impact vegetation growth. We used a time series of monthly rainfall, from which we calculated Standard Precipitation Index (SPI), and calculated proxies of perennial and annual vegetation over the last four decades using Landsat-derived Normalized Difference Vegetation Index (NDVI). We found no clear trend in rainfall amounts during this period, however we did identify wet and dry sub-periods which were statistically distinct in rain and in vegetation patterns from each other. The highest levels of correlation between rainfall and the NDVI derived proxies were found when examining average rainfall over a period of two- three years for the annual vegetation and over four years for perennial vegetation. Using the Mann-Kendall test, we identified a time lag of two to four years, with the proxies of annual vegetation responding faster than the proxies of perennial vegetation, to shifts between wet and dry sub-period. In addition, we found a consistent difference between natural vegetation cover in Jordan (grazed) and Israel (non-grazed), favoring the development of natural vegetation on the Israeli side. We conclude that integrating between long-term remote sensing satellite imagery and climatic records revealed the greater resilience of perennial vegetation in the hyper-arid region to climatic fluctuation, and enabled us to identify the vegetation’s sensitivity to anthropogenic impact.

This study presents a world review as well as new additional data in form of submerged speleothems that are used for paleo sea level reconstructions. Speleothems significantly contributed to the understanding of the global and regional sea level variations during the Middle and Late Quaternary. The studied speleothems cover the last 1.4 Myr and are focused mainly on Marine Isotope Stages (MIS) 1, 2, 3, 5.1, 5.3, 5.5, 7.1, 7.2, 7.3 and 7.5. Results reveal that submerged speleothems represent extraordinary archives providing detailed information on former sea level changes. We present also new results from stalactites collected in central Mediterranean sea, at Favignana and Ustica islands (Sicily, Italy), both characterized by continental, phreatic or marine layers. The study and analysis of the latter speleothems provide results of great interest for relative sea level changes over the last 1000 years.

Approximately 20% of the world's population resides in coastal areas at risk of rising sea levels due to climate change. This could lead to severe weather events and ecological impact on critical infrastructure. To combat the effects of water-based environmental forces on the land, hard engineering methods such as seawalls have been utilized. However, a more sustainable solution is emerging in nature-based engineering, which involves growing vegetation. This approach can help extend the lifespan of existing technology and provide climate change adaptation and resilience for coastal and riverine communities. The following paper will review seaweed farms as an advanced nature-based mitigation approach. The paper also outlines the findings of experiments at RWTH Aachen University's hydraulic laboratory. These tests involved using seaweed as a natural solution to dampen waves in a model system to test a hypothesis. One result involving a system with two lines of seaweed revealed 15 percent wave damping. Using seaweed as a nature-based solution, a gentle engineering approach to designing future vegetated protection systems can extend the design life of existing coastal infrastructure. This can also provide protection against climate-induced sea level rise, promote coastal risk mitigation, restore ecosystems, and foster blue bio-economic development.

The Wester coastal Delta zone of the Netherlands, the relatively more crowded area of the country hosting ten out of seventeen heading to twenty million people, asks for a growing alertness on the topic of resiliency the light of climate-change, like many coastal urban areas do. Approaches of resiliency are often pre-dominated by governmental rescue planning and mobilization of technology innovative solutions. By comparing the float disasters of the 2015 Katrina and 2012 Sandy thunder-storms that hit respectively New Orleans and New York we can learn that the behaviour of people can make the difference in overcoming climate change impact disasters. Post-PhD research with focus on the Dutch Zaanstreek-Waterland area north of the city of Amsterdam, where in 1916 a severe flood took place, proved such. The outcome from focus group sessions is that the alertness and availability of the people to act in case of disaster urgency makes a positive difference, if the memory of the area residents to previous disaster handling is kept alert. This Zaanstreek-Waterland research showed that the disaster from 100 year before still kept the residents alert into resiliency. With that result the defi-nition of resilience should be improved by adding: ‘the interplay in a triangular relationship of civil servants, technicians and residents’.

Multimission satellite altimetry (e.g. ERS, Envisat, TOPEX/Poseidon, Jason) data have enabled a synoptic view of ocean variations in the past decades, including sea-level rise and mesoscale circulations. Since 2016, the Sentinel-3 mission has provided better spatial and temporal sampling compared with its predecessors. The Sentinel-3 Ku/C Radar Altimeter (SRAL) is one of the synthetic aperture radar altimeters (SAR Altimeter) which is more precise in coastal and lake observations. In this study, we validate Sentinel-3 Level-2 products in Baltic Sea coastal areas and two lakes in Estonia. Moreover, the Copernicus Marine Environment Monitoring Service (CMEMS) Level-3 sea-level anomaly data and the Nucleus for European Modelling of the Ocean (NEMO) reanalysis model outcomes are compared with measurements from a tide gauge network. A dense in situ water level network deployed along the coast for geodetic observation was utilised to provide ground truths for validating altimetry results. Three validation methods were used for Level-2 data: (i) collocated Sentinel-3 and GNSS ship measurements; (ii) a national geoid model (EST-GEOID2017) with sea-level anomaly correction; (iii) collocated Sentinel-3 and buoy measurements. The validations were carried out in seven Sentinel-3A/B overpasses in 2019. Our results show that the uncertainty of the Sentinel-3 Level-2 altimetry product is below decimetre level on the Estonian coast and the targeted lakes. Results from CMEMS Level-3 showed a correlation of 0.8 (RMSE 0.19 m) and 0.91 (RMSE 0.27 m) when compared against tide gauge measurements and NEMO model, respectively.

Accompanied by increasing population growth and urban sprawl, most coastal cities are unprecedentedly vulnerable to climate change and its impacts, such as sea level rise, increasing extreme storm events, and coastal flooding. Coastal resilience and sustainable development are antidotes to vulnerability; they aim to enhance the adaptive capability of absorbing disturbances and resisting uncertainty. This study explores building a quantitative assessment framework to measure resilience and provide an objective and comparable method to understand the strengths and weaknesses in a given region. The proposed 25 resilience indicators incorporate the aspects of essential livelihood protection, infrastructure and natural resource maintenance, emergency facilities and institutions, floodplain management regulations, and adaptive planning process. Each indicator is assigned the resilience quality that includes robustness, resourcefulness, redundancy, and rapidity. The aggregated resilience quality scoring reflects the systematic performance of the city to cope with the coastal hazards. The innovative part of this framework is combining hazard mitigation measures, climate adaptation strategies, and sustainable development goals together to achieve a comprehensive assessment method. In the case of New Haven, the resilience assessment is taken as a practical monitoring tool and decision-making support.

A study is made of the progressive ‘decoherence’ of cosmic ray extensive air shower particle detector signals in small air showers through measurements of coincidence rates for pairs of detectors versus the detector separation. Measurements are made both when only the two separated detectors themselves trigger in coincidence, and when that coincidence trigger also requires the detection of a local air shower by a small external air shower array. The addition of the explicit air shower trigger ensures that the latter data correspond to showers of a larger particle size, and triggering by very localised shower cores is then unlikely. When including a shower trigger, the decoherence results appear substantially different in form. The coincidence rate between two detectors only can be approximated by a power law variation with separation distance. When triggering involves an air shower array, the variation becomes close to an exponential form with characteristic exponent distances varying systematically with increasing detector and air shower size thresholds. A result is that one can see that small air showers will exhibit clear non-Poissonian density fluctuations near their cores out to distances of ~5m, or at shower energies below ~0.05 PeV. These ideas can be helpful in understanding the statistical properties of signals when using large detectors in air shower arrays.

This study introduces a technique to uncover concealed patterns in satellite altimetry data, reflecting sea level variations in inland water bodies. We applied the methodology to the Caspian Sea, using altimetry data from four satellite missions over 27 years (1993-2020): TOPEX/Poseidon, Jason1, Jason2, and Jason3. The approach involves two steps: estimating sea level trends and identifying breakpoints that indicate trend shifts; and leveraging time lags between breakpoints at various locations to classify water drainage areas according to their degree of influence on each period. By revealing concealed patterns in the altimetry data, this technique can provide insights to understand the impacts of global warming and local climate changes on sea level fluctuations in the Caspian Sea. We hope that our study can facilitate subsequent interdisciplinary research on the intricate interplay between climate change and hydrological processes in inland water bodies.

One-fifth of the world’s population and critical infrastructures are near the coast and regions at high risk of sea level elevation. Climate change is expected to increase coastal extreme events, rising sea levels, and impact on ecosystem. This paper reviews coastal physical processes, wave and impacts, and the introduction of nature-based models for the mitigation of flooding, erosion, and recovery. Hard engineering like seawalls has been used to prevent, protect, and control water-based environmental forces with an extended impact on the land. A nature-based engineering solution, such as growing vegetation, is being adopted as a sustainable solution to help make existing technology live its design life and provide climate change adaptation and resilience for coastal and riverine communities. This paper presents applications of seaweed farms as an advanced nature-based mitigation approach. The result of the experiments conducted at RWTH Aachen University on wave damping of seaweed types and farming structures to validate the hypothesis will be presented in part B. A soft engineering approach to designing future vegetated protection systems using seaweed as a nature-based solution can help existing coastal infrastructure design life and protect against climate-induced SLR rise and adaptation, coastal risk mitigation, ecosystem restoration, and blue bio-economic development.

This paper introduces a new destriping algorithm for remote sensing data. The method is based on combined Haar Stationary Wavelet transform and Fourier filtering. State-of-the-Art methods based on the discrete wavelet transform (DWT) may not always be effective and may cause different artifacts. Our contribution is three-fold: i) we propose to use the Undecimated Wavelet transform (UWT) to avoid as much as possible shortcomings of the classical DWT; ii) we combine a spectral filtering and UWT using the simplest possible wavelet, the Haar basis, for a computational efficiency; iii) we handle 2D fields with missing data, as commonly observed in ocean remote sensing data due to atmospheric conditions (e.g., cloud contamination). The performances of the proposed filter are tested and validated on the suppression of horizontal strip artifacts in cloudy L2 Sea Surface Temperature (SST) and ocean color snapshots.

In the paper the verification of forecasts of precipitation conditions measured by the standardized precipitation index SPI is presented. For the verification of categorical forecasts a contingency table was used. Standard verification measures were used for the SPI value forecast. The 30 day SPI moved every 10 days by 10 days was calculated in 2013-2015 from April to September on the basis of precipitation data from 35 meteorological stations in Poland. Predictions of the 30 day SPI were created in which precipitation was forecasted in the next 10 days (the SPI 10-day forecast) and 20 days (the SPI 20-day forecast). Both for the 10 and 20 days, the forecasts were skewed towards drier categories at the expense of wet categories. There was a good agreement between observed and 10-day forecast categories of precipitation. Less agreement is obtained for 20-day forecasts – these forecasts evidently “over-dry” the assessment of precipitation anomalies. The 10-day SPI value forecast accuracy is acceptable, whereas for the 20-day forecast is unsatisfactory. Both for the SPI categorical and the SPI value forecast, the 10-day SPI forecast is reliable and the 20-day forecast should be accepted with reservation and used with caution.

Sea surface wind forecasts in the Adriatic Sea often suffer for unadequate modelling, especially for the wind speed. This has detrimental effects on the accuracy of sea level and storm surge predictions. We present a numerical method to reduce the bias between the sea surface wind observed by the scatterometers and that supplied by the European Centre for Medium-Range Weather Forecasts (ECMWF) global atmospheric model, for storm surge forecasting applications. The method, called “wind bias mitigation”, relies on scatterometer observations to determine a multiplicative factor ∆ws which modulates the standard model wind in order to decrease the bias between scatterometer and model. We compare four different mathematical approaches to this method, for a total of eight different formulations of the multiplicative factor ∆ws. Four datasets are used for the assessment of the eight different bias mitigation methods: a collection of 29 Storm Surge Events (SEVs) cases in the years 2004-2014, a collection of 48 SEVs in the years 2013-2016, a collection of 364 cases of random sea level conditions in the same period, and a collection of the seven SEVs in 2012-2016 that were worst predicted by the Centro Previsioni e Segnalazioni Maree, Comune di Venezia (Tide Forecast and Early Warning Centre of the Venice Municipality - CPSM). The statistical analysis shows that the bias mitigation procedures supplies a mean wind speed more accurate than the standard forecast, when compared with scatterometer observations, in more than 70% of the analyzed cases.

The investigation of submerged speleothems for sea level studies has made significant contributions to the understanding of the global and regional sea level variations during the Middle and Late Quateranry. This has been especially the case for the Mediterranean Sea, where more than 300 submerged speleothems sampled in 32 caves have been analysed so far. Here, we present a comprehensive review of the results obtained from the study of submerged speleothems since 1978. The studied speleothems cover the last 1.4 Ma and are focused mainly on Marine Isotope Stages (MIS) 1, 2, 3, 5.1, 5.3, 5.5, 7.1, 7.2, 7.3 and 7.5. Results reveal that submerged speleothems represent extraordinary archives providing accurate information on former sea level changes, also considering that the Mediterranean Sea is devoid of any tropical corals since the Miocene. New results from a stalagmite collected at Palinuro (Campania, Italy) characterized by marine overgrowth are also reported. The measured elevations of speleothems are contaminated by the local response to glacial- and hydro-isostatic adjustment (GIA), and thus might significantly deviate from the global eustatic signal. Age and altitude comparation between Mediterranean speleothems, flowstone from Bahamas with local GIA provide a new scenario for MIS 5 and 7 sea level reconstrutions.

The national study analyzes sea level rise (SLR) impacts based on 36 different SLR and storm surge scenarios across 5.7 million geographic locations and 3 time periods. Taking an approach based on engineering design guidelines and current cost estimates, the study details projected cost impacts for states, counties, and cities. These impacts are presented from multiple perspectives including total cost, cost per-capita, and cost per-square mile. The purpose of the study is to identify specific locations where infrastructure is vulnerable to rising sea levels. The study finds that Sea Level Rise (SLR) and minimal storm surge is a $400 billion threat to the United States by 2040 that includes a need for at least 50,000 miles of protective barriers. The research is limited in its scope to protecting coastal infrastructure with sea walls. Additional methods exist and may be appropriate in individual situations. The study is original in that it is a national effort to identify infrastructure that is vulnerable as well as the cost associated with protecting this infrastructure.

AbstractLocal estimates of coastal flood risk are required for coastal planning and development, including the location and design of sea-defences and coastal buildings, such as harbours and associated infrastructure. This paper discusses the use of three parameters associated with estimating such risks; the flood return period, the instantaneous flood probability and the flood design risk, and it describes the mathematical background for their derivation. The discussion is extended to include the effects of sea level rise and how it can be incorporated into the calculations. Flood height can vary quite rapidly with distance along the coast, being affected by coastal topology, which may magnify or diminish the tidal and surge effects. Similarly land heave influences the local effects of sea level rise and can be influenced by water extraction, tectonic movements and melting ice. Tide gauge measurements provide a local historical record from which the various parameters can be retrieved. This paper discusses the algorithms used to derive these measures from tide-gauge records. The figures have been derived for four tide gauges located on the UK east coast.

This bibliographic study addresses key aspects related to fishing, product safety, and climate change in the Adriatic Sea region. The examination of product safety focuses on the assessment of contaminants originating from human activities such as industry, mining, agriculture, and household waste disposal. The contamination of the aquatic environment has emerged as a pressing global concern, extending to the Adriatic subbasin. Aquatic organisms, including fish, are prone to accumulating pollutants directly from polluted water sources and indirectly through the food web. The bio-accumulation of potentially hazardous substances, particularly heavy metals, pesticides, PCBs, PAHs, and antibiotic resistance in aquatic organisms, poses a significant threat to human health. Climate change effects will deplete our seafood supply in terms of quantity and safety owing to negative consequences such as higher levels of pollution, parasites, viruses, infections, acidification, and toxicities such as shellfish poisoning. Global food safety strategies should be developed to reduce greenhouse gas emissions and promote environmentally friendly technology, which indirectly affects seafood quality and microbiological safety, especially for Adriatic Sea, which is part of the Mediterranean Sea, characterized by the most polluted waters in the world.

The Baltic Sea coast is one of the geographical areas of Europe most threatened by the current climate change. This issue concerns erosional processes, groundwater flows affecting sea-shore settings. Long term hydrological investigations within the western sector of the Łeba Barrier, northern Poland, based on a network of monitoring stations, coupled with the meteorology records of the past 12 years, revealed significant fluctuations in ground water fluxes from the Baltic Sea on the adjoining mainland. Groundwater resources are negatively affected by sea-level drops in the coastal areas along the southern Baltic coast. The low sea levels strengthen the effects of low precipitation and high temperatures, causing altogether the occurrence of hydrogeological drought in the coastal sandbar areas. Drought periods are characterized by lowering of water-tables in the Slowinski National Park. The obtained results have direct relevance to a long-term assessment of water resources, environmental protection of the climatically most susceptible ecosystems, as well as the tourism management of this popular visitors’ destination.

Sea level rise (SLR) is a critical consequence of climate change, posing significant threats to coastal regions worldwide. Accurate and efficient assessment of potential inundation areas is crucial for effective coastal planning and adaptation strategies. This study aims to explore the utility of free and open source software for geospatial (FOSS4G) tools for mapping SLR inundation, providing cost-effective solutions that are accessible to researchers and policymakers. We employed a combination of geospatial data, including high-resolution elevation models, tidal data, and projected SLR scenarios. Utilizing widely available FOSS4G tools like QGIS, GDAL/OGR and GRASS GIS we developed an integrated workflow to map inundation extents using a passive bathtub approach for various SLR scenarios. Our findings demonstrate that FOSS4G tools offer reliable and accessible means to map SLR inundation, empowering stakeholders to assess coastal vulnerabilities and devise sustainable adaptation measures. The open-source approach facilitates collaboration and reproducibility, fostering a comprehensive understanding of the potential impacts of SLR on coastal ecosystems and communities.

Mediterranean Sea are dynamic habitats in which human activities have been conducted for centuries and which feature micro-tidal environments with about 0.40 m of range. For this reason, human settlements are still concentrated along a narrow coastline strip, where any change in the sea level and coastal dynamics may impact anthropic activities. We analyzed light detection and ranging (LiDAR) and Copernicus Earth Observation data. Aim of this research is to provide estimates and detailed maps (in three coastal plain of Sardinia (Italy) and in the Pontina Plain (southern Latium, Italy) of: i) the past marine trasgression occurred during MIS 5.5 higstand 119 kyrss BP; ii) the coastline regression occurred during the last glacial maximum MIS 2 (21.5 krs cal BP) and iii) the potential marine submersion for 2100 and 2300. The objective of this multidisciplinary study is to provide maps of sea-level rise future scenarios using the IPCC RCP 8.5 2019 [1]projections and glacio-hydro-isostatic movements for the above selected coastal zones, which are the locations of touristic resorts, railways, and heritage sites. We estimated a potential loss of land for the above areas of between about 146 km² (IPCC 2019-RCP8.5 scenario [1]) and 637 km² along a coastline length of about 268 km.

Barchan dune fields are a dominant landscape feature in SE Qatar and a key element of the peninsula’s geodiversity. The migration of barchan dunes is mainly controlled by dune size, wind pattern, vegetation cover and human impact. We investigate the variability of dune migration in Qatar over a time period of 50 years using high-resolution satellite and aerial imagery and explore its relation to the regional Shamal wind system, teleconnection patterns, and limitations in sand supply associated with the transgression of the Arabian Gulf. We detect strong size-dependent differences in migration rates of individual dunes as well as significant decadal variability on a dune-field scale, which was found to be correlating with the intensity of the North Atlantic Oscillation (NAO) and the Indian Summer Monsoon (ISM). High uncertainties associated with the extrapolation of migration rates back into the Holocene, however, do not permit to further specify the timing of the loss of sand supply and the onset of the mid-Holocene relative sea-level (RSL) highstand. For the youngest phase considered in this study (2006–2015), human impact is anticipated to have accelerated dune migration under a weakening Shamal regime through sand mining and excessive vehicle frequentation upwind of the core study area.

Investigation of sea-level positions during the highly-dynamic Marine Isotope Stage 3 (MIS 3: 29-61 kyrs BP) proves difficult because: i) in stable and subsiding areas, coeval coastal sediments are currently submerged at depths of few to several tens of meters below present sea level; ii) in uplifting areas, the preservation of geomorphic features and sedimentary records is limited due to the erosion occurred during the Last Glacial Maximum (LGM) with sea level at depth of -130 m, followed by marine transgression that determined the development of ravinement surfaces. This study discusses previous research in the Mediterranean and elsewhere, and describes new fossiliferous marine deposits laying on metamorphic bedrock of Cannitello (Calabria, Italy). Radiocarbon ages of marine shells (about 43 kyrs cal BP) indicate that these deposits, presently between 28 and 30 meters above sea level, formed during MIS 3.1. Elevation correction of the Cannitello outcrops (considered in an intermediate-to-far-field position with respect to the ice sheet) with the local vertical tectonic rate and Glacial Isostatic Adjustment (GIA) rate allows to propose a revision of the eustatic depth for this highstand. Our results are consistent with recently proposed estimates based on a novel ice sheet modelling technique.

Frequent flooding from sea-level rise (SLR) is one of the immediate climate change impacts affecting low-lying and exposed coastal communities. These communities rely upon the delivery of three-waters services for wastewater, stormwater and water supply. Due to ongoing SLR, managing these networks will increasingly be a challenge. This raises the issue of how local government can reconcile maintaining levels of service as the impacts of climate change and their uncertainties worsen over the coming decades (and beyond). Can they be adapted over time to retain levels of service or will they eventually require retreat and if so at what adaptation threshold? This paper explores managed retreat of two-waters infrastructure (wastewater and stormwater) as an adaptation option using a Dynamic Adaptive Pathway Planning (DAPP) approach. In the study, we use DAPP to frame the retreat of two-water networks, developing a combination of an area specific retreat strategy, pathway portfolios, retreat phases, land use change signaling and identify pathway conflicts and synergies. Repurposing retreated areas by utilizing Water Sensitive Urban Design (WSUD) options was found to extend retreat thresholds for adjacent areas. A systematic ’routine’ developed in this study provides a structured approach for managed retreat of two-water infrastructure with the aim to reduce future disruption from flooding, signal land use changes early and allow for gradual budget adjustments by the agencies to manage expenditure over time. This approach helps inform and improve the decision-making process for the agencies and the communities they serve, by providing a stepwise process that can be communicated spatially and visually, thereby making a retreat adaptation option more manageable.

We present an attempt to estimate the long-term changes of Relative Sea Level (RSL) and the different factors contributing to such trends on a local and regional scale using a statistical linear model. The time series analysed correspond to 17 tide-gauges grouped in three different areas: the northern and western Atlantic coasts of the Iberian Peninsula, the Canary Islands, and the southern and eastern coasts of the Iberian Peninsula and Balearic Islands. The analysis was performed for two periods: 1948-2019, using tide-gauge data, and 1993-2019, using both tide-gauge and altimetry data for comparison. The trends for the period 1948-2019 ranged between 1.09 0.14 (Canary Islands) and 2.05 0.21 mm/yr, for the northern and western Atlantic Iberian Peninsula. Altimetry data during the period 1993-2019 yielded quite homogeneous results for all the locations and regions, ranging between 2.7 0.4 and 3.0 0.3 mm/yr. On the contrary, the results obtained from tide-gauge data for this recent period showed a large dispersion, very likely due to local effects or even levelling or instrumental errors. Nevertheless, when the results were averaged for each area, the observed trends were comparable to the altimetry results, with values of 2.3 0.8, 2.7 0.5 and 2.8 0.8 mm/yr for the three regions of study. A stepwise forward linear regression has been used to relate the observed RSL variability to the atmospheric forcing and the thermosteric and halosteric components of the sea level. Surprisingly, the thermosteric and halosteric contributions were not significantly correlated to the observed RSL in many cases, and consequently the steric, the total addition of mass, the mass of salt, and the freshwater contributions to the observed sea level trends could not be reliably estimated. This result seems to be the consequence of the scarcity of temperature and salinity data. This hypothesis is confirmed by the exception of L’Estartit tide-gauge. This location is close to a well sampled region. In this case, the atmospheric variables and the thermosteric and halosteric terms explained the 80 % of the observed RSL variance and the contributions of these terms could be estimated. The freshwater contribution for this location was between 1.3 and 1.4 mm/yr, consistent with recent estimations of the contributions of glaciers and Greenland and Antarctica Ice Sheets. These results highlight the importance of monitoring programs and routine sampling for the determination of the different factors contributing to the sea level variability.

Data collected at the Cascais tide gauge, located on the west coast of Portugal Mainland, have been analyzed and sea level rise rates have been updated. Based on a bootstrapping linear regression model and on polynomial adjustments, time series are used to calculate different empirical projections for the 21^st century sea level rise, by estimating the initial velocity and its corresponding acceleration. The results are consistent to an accelerated sea level rise, showing evidence of a faster rise than previous century estimates. Based on different numerical methods of second order polynomial fitting, it is possible to build a set of projection models of relative sea level rise. Appling the same methods to regional sea level anomaly from satellite altimetry, additional projections are also built with good consistency. Both data sets, tide gauge and satellite altimetry data, enabled the development of an ensemble of projection models. The relative sea level rise projections are crucial for national coastal planning and management since extreme sea level scenarios can potentially cause erosion and flooding. Based on absolute vertical velocities obtained by integrating global sea level models, neo-tectonic studies and permanent Global Positioning System (GPS) station time series, it is possible to transform relative into absolute sea level rise scenarios, and vice-versa, allowing the generation of absolute sea level rise projection curves and its comparison with already established global projections. The sea level rise observed at the Cascais tide gauge has always shown a significant correlation with global sea level rise observations, evidencing relatively low rates of composed vertical land velocity from tectonic and post-glacial isostatic adjustment, and residual synoptic regional dynamic effects rather than a trend. An ensemble of sea level projection models for the 21st century is proposed with its corresponding probability density function, both for relative and absolute sea level rise for the west coast of Portugal Mainland.

Several disruptive attacks against companies in the maritime industry have led experts to consider the increased risk imposed by cyber threats as a major obstacle to undergoing digitization. The industry is heading toward increased automation and connectivity, leading to reduced human involvement in the different navigational functions and increased reliance on sensor data and software for more autonomous modes of operations. To meet the objectives of increased automation under the threat of cyber attacks, the different software modules that are expected to be involved in different navigational functions need to be prepared to detect such attacks utilizing suitable detection techniques. Therefore, we propose a systematic approach for analyzing the navigational NMEA messages carrying the data of the different sensors, their possible anomalies, malicious causes of such anomalies as well as the appropriate detection algorithms. The proposed approach is evaluated through two use cases, traditional Integrated Navigation System (INS) and Autonomous Passenger Ship (APS). The results reflect the utility of specification and frequency-based detection in detecting the identified anomalies with high confidence. Also, the analysis is found to facilitate the communication of threats through indicating the possible impact of the identified anomalies against the navigational operations. Moreover, we have developed a testing environment that facilitates conducting the analysis. The environment includes a developed tool, NMEA−Manipulator that enables the invocation of the identified anomalies through a group of cyber attacks on sensor data. Our work paves the way for future work in the analysis of NMEA anomalies toward the development of an NMEA intrusion detection system.

Sea level rise is one of the main factor of risk for the preservation of cultural heritage sites located along the coasts of the Mediterranean basin. Coastal retreat, erosion and storm surges are yet posing serious threats to archaeological and historical structures built along the coastal zones of this region. In order to assess the coastal changes by the end of 2100 under an expected sea level rise of about 1 m, a detailed determination of the current coastline position and the availability of high resolution DSM, is needed. This paper focuses on the use of very high-resolution UAV imagery for the generation of ultra-high resolution mapping of the coastal archaeological area of Pyrgi, near Rome (Italy). The processing of the UAV imagery resulted in the generation of a DSM and an orthophoto, with an accuracy of 1.94 cm/pixel. The integration of topographic data with two sea level rise projections in the IPCC AR5 2.6 and 8.5 climatic scenarios for this area of the Mediterranean, were used to map sea level rise scenarios for 2050 and 2100. The effects of the Vertical Land Motion (VLM) as estimated from two nearby continuous GPS stations located as much as close to the coastline, were included in the analysis. Relative sea level rise projections provide values at 0.30±0.15 cm by 2050 and 0.56±0.22 by 2100, for the IPCC AR5 8.5 scenarios and at 0.13±0.05 cm by 2050 and 0.17±0.22 by 2100, for the IPCC AR5 2.6 scenario. These values of rise will correspond to a potential beach loss between 12.6% and 23.5% in 2100 for RCP 2.6 and 8.5 scenarios, respectively, while during the highest tides the beach will be reduced up to 46.4%. With these sea level rise scenarios, Pyrgi with its nearby Etruscan temples and the medieval castle of Santa Severa will be soon exposed to high risk of marine flooding, especially during storm surges, thus requiring suitable adaptation strategies.

Depth-sensing technology has led to broad applications of inexpensive depth cameras that can capture human motion and scenes in 3D space. Background subtraction algorithms can be improved by fusing color and depth cues, thereby allowing many issues encountered in classical color segmentation to be solved. In this paper, we propose a new fusion method that combines depth and color information for foreground segmentation based on an advanced color-based algorithm. First, a background model and a depth model are developed. Then, based on these models, we propose a new updating strategy that can eliminate ghosting and black shadows almost completely. Extensive experiments have been performed to compare the proposed algorithm with other, conventional RGB-D algorithms. The experimental results suggest that our method extracts foregrounds with higher effectiveness and efficiency.

Climate simulations in West Africa have been attributed with large uncertainties. Global climate projections are not consistent with changes in observations at the regional or local level of the Niger basin, making management of hydrological projects in the basin uncertain. This study evaluates the potential of using the quantile mapping bias correction to improve the Coupled Model Intercomparison Project (CMIP5) outputs for use in hydrology impact studies. Rainfall and temperature projections from 8 CMIP5 Global Climate Models (GCM) were bias corrected using the quantile mapping approach. Impacts of climate change was evaluated with bias corrected rainfall, temperature and potential evapotranspiration (PET). The IHACRES hydrological model was adapted to the Niger basin and used to simulate impacts of climate change on discharge under present and future conditions. Bias correction significantly improved the accuracy of rainfall and temperature simulations compared to observations. Nash coefficient (NSE) for monthly rainfall comparisons of 8 GCMs to the observed was improved by bias correction from 0.69 to 0.84. The standard deviations among the 8 GCM rainfall data were significantly reduced from 0.13 to 0.03. Increasing rainfall, temperature, PET and river discharge were projected for all GCMs used in this study under the RCP8.5 scenario. These results will help improving projections and contribute to the development of sustainable climate change adaptation strategies.

The Covid-19 pandemic was a challenge for the whole world and it had major secondary effects on humans and environmental health. The viral infection induced, in many situations, secondary bacterial infections, especially enteric infections by destabilizing the balance of the gastrointestinal microbiota. The large-scale use of antibiotics and biocides both for curative and preventive purposes has determined an increase in bacterial resistance and at the same time the possibility of pathogenic microorganisms’ multiplication and their dissemination to natural environments. Wastewater is the main vector of faecal microorganisms that favour their dissemination into natural aquatic ecosystems. The present paper aimed to analyze the effect of Covid-19 pandemic on the microbiological quality of wastewater from sewage treatment plants in Romania and its impact on receiving rivers. In order to highlight different and important areas of Romania, 3 cities from the east, center and west were selected for a microbiological evaluation of their WWTPs influent and effluent between ante-Covid-19 and Covid-19 pandemic peak period, when the Covid-19 pandemic had a direct impact on WWTPs microbial composition. Our study showed a higher contamination with fecal bacteria linked to a higher Covid-19 incidence. The increased usage of pharmaceutical compounds, at their turn, increased the number of resistant bacteria reaching the environment via WWTP effluents.

Two coupled climate models which participated the CMIP6 project (Coupled Model Intercomparison Project Phase 6), the Earth System Model of Chinese Academy of Sciences version 2 (CAS-ESM2-0) and the Nanjing University of Information Science and Technology Earth System Model version 3 (NESM3), were assessed in terms of the impact of four new sea ice parameterization schemes. These four new schemes are related to air-sea heat flux, radiation penetration and absorption, melt ponds, ice-ocean flux, respectively. To evaluate the effectiveness of these schemes, key sea ice variables with and without these new schemes, such as sea ice concentration (SIC) and sea ice thickness (SIT), were compared against observation and reanalysis product from 1980 to 2014. The simulations followed the design of historical experiments within the CMIP6 framework. The results revealed that both models demonstrated improvements in simulating Arctic SIC and SIT when the new parameterization schemes were implemented. The model bias of SIC in some marginal sea ice zones of Arctic was reduced, especially during March. The SIT was increased and the transpolar gradient of SIT was reproduced. The changes of spatial pattern of SIC and SIT after adding new schemes bear similarities between the two coupled models. This suggests that the new schemes have potential for broad application in climate models, especially for those with underestimated SIT.

Coastal flooding exacerbated by climate change is recognised as a major global threat which is expected to impact more than a quarter of people currently residing in Pacific Islands countries. While most research in the last decade has focused on understanding the dynamics and impacts of future coastal flooding from extreme sea levels, relative sea level rise (RSLR) effects on tsunami hazards are not well understood. Far-field or distant-sourced tsunamis tend to have relatively lower impacts in Pacific Island states compared with locally sourced events, but there is limited understanding on how the impact of far-field tsunamis changes over time due to RSLR. Using the hydrodynamics software BG-Flood, we modelled the Tōhoku tsunami from propagation to inundation in Samoa under incremental SLR to examine the effects that RSLR has on changing the exposure of the built environment (e.g., buildings) to a far-field tsunami. Outputs of maximum tsunami inundation and flow depth intensities which incorporate incremental SLR were then combined with digital representations of buildings and depth-damage functions in the RiskScape multi-hazard risk modelling software to assess the changes in building exposure over time. Results indicate that present day buildings exposure in Samoa to a Tōhoku-oki type far-field tsunami will increase by approx. 600% with 1 m RSLR by 2080–2130, and approx. 2,350% with 2 m RSLR by 2130–2140. These findings provide a useful baseline for tsunami hazard risk assessment under changing sea level conditions in analogous island environments.

X-band marine radar captures signal reflected from the sea surface. Theoretical studies indicate that the initial unfiltered signal contains meaningful information including parameters related to wind waves. Traditional methods for estimating significant wave height (SWH) rely on the physical laws describing signal reflection from rough surfaces. However, recent studies suggest the feasibility of employing artificial neural networks (ANNs) for SWH approximation. Both classical and ANN-based approaches necessitate costly in situ data. In this study, we propose generating synthetic radar images with specified wind wave parameters using Fourier-based approach and Pierson–Moskowitz wave spectrum as a viable alternative. We generate synthetic images and use them for unsupervised learning approach to train a convolutional component of an ANN. After that, we train a regression ANN based on the previous convolutional component to obtain SWH back from synthetic images. Then, we apply preliminary trained weights for the same regression model to train SWH approximation on the dataset of real sea clutter images. In this study, we demonstrate the increase in the accuracy of SWH estimation from radar images with the preliminary training on synthetic data.

We have repeated the experiment performed recently by Krasznahorkay et al., (Phys. Rev. Lett. 116, 042501 (2016)), which may indicate a new particle called X17 in the literature. In order to get a reliable, and independent result, we used a different type of electron-positron pair spectrometer which have a more simple acceptance/efficiency as a function of the correlation angle, but the other conditions of the experiment were very similar to the published ones. We could confirm the presence of the anomaly measured at the Ex=18.15 MeV resonance, and also confirm their absence at the Ex=17.6 MeV resonance, and at Ep= 800 keV off resonance energies.

We have carried out a systematic review of the scientific literature published on PM and, more specifically, on its presence in fish and seafood of interest to the population from Mediterranean area. Given its preference and wide acceptability in the scientific community, we followed the PRISMA. The potential functional and clinical toxicity in humans due to MP in fish must be considered by the chemical composition of the microplastics the presence of PM and its additives, associated contaminants and pathogens in fish and shellfish, as well as the possible effects on human health, requires improving legislation on evaluation and providing a basis for consumer protection.

Recent studies have elucidated the diversity of genes encoding venom in sea anemones. However, most of those genes are yet to be explored in an evolutionary context. Insulin is a common peptide across metazoans and has been coopted into a predatory venom in many venomous lineages. In this study, we focus on the diversity of insulin-derived venoms in sea anemones and on elucidating their evolutionary history. We sourced data for 34 species of sea anemones and found sequences belonging to two venom families that have Insulin PFAM annotations (Cono-Insulin, VP302). Our findings show that both families have undergone duplication events. Members of each of the independently evolving clades have consistent predicted protein structures and distinct dN/dS values. Our work also shows that VP302 is part of a multidomain venom contig and has experienced a secondary gain into the venom system of cuticulate sea anemones.

The sea level rise, a consequence of climate change, is one of the biggest challenges that countries and regions with coastal lowland areas will face in the medium term. This study proposes a methodology for assessing the vulnerability to sea level rise (SLR) on the Atlantic coast of Portugal mainland. Some scenarios of extreme sea level for different return periods and extreme flooding events were estimated for 2050 and 2100, as proposed by the European Union Directive 2007/60/EC. A set of physical parameters are considered for the multi-attribute analysis technique implemented by the Analytic Hierarchy Process, in order to define a Physical Vulnerability Index fundamental to assess coastal vulnerability. For each SLR scenario, coastal vulnerability maps, with spatial resolution of 20 m, are produced at national scale to identify areas most at risk of SLR, constituting key documents for triggering adaptation plans for such vulnerable regions. For 2050 and 2100, it is estimated 903 km² and 1146 km² of vulnerable area, respectively, being the district of Lisbon the most vulnerable district in both scenarios. Results are available through a Web Map Service, for Portuguese public entities, and through a web map viewer for public and communities in general.

Sea cucumber peptides are small molecular peptides with biological function obtained from fresh sea cucumber as feedstock, which were hydrolyzed by protease, separated ,and purified. As the main product of sea cucumber refinement, it more accurately expresses the nutritional effect of sea cucumber and has a wide range of potential applications. It has been demonstrated that sea cucumber has a certain value-added effect on bone mineral density regulation, but the differences in bone mineral density regulation of extracts from different parts of sea cucumber and the techniques contributing to the efficient isolation of bone sea cucumber peptides have not been reported. Firstly, the aim of this study was to reveal the differences in osteogenic activities of enzymatic peptides from three major parts of sea cucumber: the body wall, the egg protein, and the intestinal protein. Then, the bone-enhancing sea cucumber peptides were isolated based on the characteristics of the dif-ferences in molecular weight distribution, hydrophilicity, and ionisation degree of sea cucumber, and to explore the possible mechanisms of their action. The findings indicated that sea cucumber peptides extracted from three distinct sites exhibited a dose-dependent enhancement of osteoblast proliferation and differentiation. Upon evaluating the three methods of sea cucumber peptide separation based on molecular weight distribution, hydrophilicity, and ionization, it was observed that the fraction isolated by hydrophobicity and

European strategic policy directions towards a sustainable blue economy have strengthened interest in maritime investments, thus increasing sectoral competition for marine space. Emerging repercussions out of such a rising interest need to be handled by Marine Spatial Planning (MSP) as a means for: properly allocating marine space to diverse uses; managing conflicts and promoting synergies among them; and pursuing a multi-use perspective of this space. A critical stage of each MSP exercise is the exploration of land-based spatial/developmental directions and their current or potential repercussions in the marine environment in order for constraints but also perspectives in policy choices/maritime uses in the MSP context to be featured; and attainment of a successful coherence or coordination between land and marine spatial/developmental policies to be ensured, by analyzing Land-Sea-Interaction (LSI) and embedding it in the MSP. The latter is the focus of this paper aiming to illuminate critical issues emerging from land-based (terrestrial) policy frameworks for feeding MSP endeavors; and is accomplished in the Northern Aegean Sea, Greece. Towards this end, a multilevel approach is adopted, delving into diverse policy contexts at the local, regional and national/European level and aiming to attain more informed MSP decisions in the specific study region.

Overexpression of a constitutively active truncated form of OsCDPK1 (OEtr) in rice produced smaller seeds, but a double-stranded RNA gene-silenced form of OsCDPK1 (Ri) yielded large seeds, suggesting that OsCDPK1 plays a functional role in rice seed development. In the study presented here, we propose a model in which OsCDPK1 plays key roles in negatively controlling the grain size, amylose content, and endosperm appearance, and also affects the physicochemical properties of the starch. The dehulled transgenic OEtr grains were smaller than the dehulled wild-type grains, and the OEtr endosperm was opaque and had a low amylose content and numerous small loosely packed polyhedral starch granules. However, the OEtr grain sizes and endosperm appearances were not affected by the temperature being either optimal (25 °C ) or low (22 °C) or high (31 °C) during the grain-filling phase. In contrast, the transgenic Ri grains were larger, had higher amylose contents, and had more transparent endosperms filled with tightly packed polyhedral starch granules. This demonstrates that OsCDPK1 plays a novel functional role in starch biosynthesis during seed development and affects the transparent appearance of the endosperm. These results improve our understanding of the molecular mechanisms through which the grain filling process occurs in rice.

Background and Objectives: Knee osteoarthritis (KOA) is a degenerative disease that is targeting continuously people of different ages, but especially the elderly population, the number of which tends to increase continuously at the global level. Apart from age, excess weight can influence the evolution of the disease, with obesity being associated with a weak inflammation stage, and an imbalance between pro-inflammatory and anti-inflammatory cytokines. The present work aimed to identify specific biomarkers alike ACRP-30, IL-10, TNF-α, and IL-6 in synovial fluid and correlate them with patients’ clinical data. Materials and Methods: 24 subjects with KOA and over 50 years of age are involved in the present study. All patients performed a supine X-ray at the target knee. Ultrasound was performed using an ESAOTE MyLab Omega (ESAOTE Spa, Italy) ultrasound device and a 6-19 MHz linear array transducer. Results: The obtained results highlighted a significant correlation between age and adiponectin level (p=0.0451, r=-0.412). Also, the IL-10 values are lower in cases where the intensity of the pain is more pronounced (p=0.0405, r=-0.421). In addition, analyzing the data by gender, it was observed that in the case of males, stiffness is more related to age (p=0.0079, r=0.7993), compared to women (p=0.0203, r=0.6223). In the case of women, the progression of the disease tends to increase more intensively total WOMAC score’s values (p=0.00031, r=0.8342), compared with men (p=0.0289, r=7013). Regarding interleukins and BMI, significant correlations were observed only in the case of men. Conclusions: A significant correlation between age and adiponectin, and adiponectin and IL-6, suggests that advanced age may contribute to adiponectin reduction. Comparing men with women, it was observed that men's age is more related to rigidity, and IL-6 and IL-10 are directly correlated to BMI and women seem to be more sensitive to pain and stiffness.

Unmanned Aerial Vehicles (UAVs) image object detection has great application value in military and civilian fields. However, the objects in the captured images from UAVs have problems of large scale variation, complex backgrounds, and a large proportion of small objects. To resolve these problems, a multi-scale object detector based on coordinate and global information aggregation is proposed, named CGMDet. Firstly, a Coordinate and Global Information Aggregation Module (CGAM) is designed by aggregating local, coordinate, and global information, which can obtain features with richer context information. Secondly, a Multi-Feature Fusion Pyramid Network (MF-FPN) is proposed, which can better fuse features of different scales and obtain features containing more context information through repeated use of feature maps, to better detect multi-scale targets. Moreover, more location information of low-level feature maps is integrated to improve the detection results of small targets. Furthermore, we modified the bounding box regression loss of the model to make the model more accurately regress the bounding box and faster convergence. Finally, the proposed CGMDet was tested on VisDrone and UAVDT datasets and mAP0.5 of 50.9% and 48% was obtained, respectively. At the same time, our detector achieved the best results compared to other detectors.

Portugal Mainland has hundreds of thousands of people living in the Atlantic coastal zone, with numerous high economic value activities and a high number of infrastructures that must be protected from natural coastal hazard, namely extreme storms and sea level rise (SLR). In the context of climate change adaptation strategies, a reliable and accurate assessment of the physical vulnerability to SLR is crucial. This study is a contribution to the implementation of flooding standards imposed by the European Directive 2007/60/EC, which requires each member state to assess the risk associated to SLR and floods caused by extreme events. Therefore, coastal hazard in the Continental Atlantic coast of Portugal Mainland was evaluated for 2025, 2050 and 2100 in the whole coastal extension with different sea level scenarios for different extreme event return periods and due to SLR. A coastal flooding probabilistic map was produced based on the developed methodology using Geographic Information Systems (GIS) technology. The Extreme Flood Hazard Index (EFHI) was determined on flood probabilistic bases through five probability intervals of 20% of amplitude. For a given SLR scenario, the EFHI is expressed, on the probabilistic flooding maps for an extreme tidal maximum level, by five hazard classes ranging from 1 (Very Low) to 5 (Extreme).

Sea level rise due to global warming is a continuing and, disappointingly, accelerating process, which has already affected and will further impact coastal lowlands and the social and economic activities in these areas. Delos Island, situated in the middle of the Cyclades in the Aegean Sea, was considered the most sacred of all islands in ancient Greek culture and a trading hub for the entire eastern Mediterranean. Uninhabited since the 7th century AD, and consistently the focus of research and touristic attention, the island is designated as an archaeological site and inscribed on the Unesco World Heritage List. Previous studies on the relative sea level (rsl) changes suggest a steadily rsl rise during the last 6,300 years, starting from a sea level at -4.80±0.20m in the Late Neolithic. The seafront of the ancient city of Delos is subject to the effects of the rsl rise, which have caused significant coastline retreat and exposure to the northerly winds and waves, whereas parts of the coastal lowland where the remains of the ancient city lie are inundated, forming extended wetlands. The future impacts of rsl rise on the seafront of ancient Delos are illustrated on very high-resolution digital surface models, evaluating both the flooding risk under different climatic projections, as provided by the IPCC AR6 report, and the ongoing land subsidence, as recorded by GNSS data. An rsl rise ranging from 87cm (SSP1-2.6 scenario) to 148cm (SSP5-8.5 scenario) is anticipated by 2150, requiring both resilience strategies and adaptation solutions as well as mitigation policies to cope with the effects of climate change.

A power factor correction (PFC) rectifier is compulsory for any electronic circuitry connected to the electrical grid. The majority of PFCs that are utilized are based on a step-up converter that supplies high DC voltage at its output terminal. Electric vehicle battery voltage varies from a few hundred to almost a thousand volts. Thus, an additional conversion step is obligatory when designing an all-brand universal charger. This paper presents a new type of front-end versatile step up / down three voltage level PFC. The rectifier can operate in continuous conduction mode or discontinuous conduction mode. First, the rectifier’s principle of operation is described, and then an innovative rectifier is analyzed for continuous and discontinuous conduction modes. Finally, the proposed theory is experimentally validated in a multiplier-less dual loop mode at discontinuous conduction modes. It was shown that although there is no multiplier use in the control circuitry, the power factor is near unity. It was revealed that the rectifier could swing the output voltage from 50 V to 900 V while the input voltage was 230 Vrms. The innovative topology suits any standard PFC rectifier, three-level voltage supplement, and dual-stage low-voltage power supply.

This study presents the synthesis and electrochemical evaluation of nitrogen-doped vanadium oxide (N-V2O3/C) as a cathode material for aqueous zinc-ion batteries (AZIBs), using a hydrothermal method. Compared to undoped V2O3/C, N-V2O3/C exhibits enhanced electrical conductivity, capacity, and electrochemical kinetics, attributed to the incorporation of pyridinic and pyrrolic nitrogen. Initial charge-discharge cycles indicate phase transitions to amorphous vanadium oxides, enhancing conductivity. N-V2O3/C shows a high specific capacity of 168.4 mAh g−1 at 10 A g−1 and remarkable reversibility, highlighted by the transient existence of intermediate species during cycling. Optimal electrochemical performance is achieved with a vanadium to nitrogen molar ratio of 2:3, indicating the significant impact of nitrogen doping concentration on the material’s efficiency. This work underscores the potential of N-V2O3/C as a superior cathode material for AZIBs.

This work assesses sea-level rise using three different models created on Free and Open-Source Software for Geographic Information System (FOSS4GIS). Based on regional projections of Special Report on Climate Change and Oceans and Cryosphere (SROCC) of the Intergovernmental Panel on Climate Change (IPCC), the models were applied to a case of study on Rio Grande do Sul coast – Brazil under different sea-level rise scenarios by the end of this century. The End Point Rate for QGIS (EPR4Q), calculates a shoreline projection using End Point Rate method. The Uncertainty Bathtub Model (uBTM), analyses the sea-level rise impact by the uncertainty of sea-level projec-tions and vertical error of the Digital Elevation/Terrain Model (DEM/DTM). The Bruun Rule for Google Earth Engine Model (BRGM) predicts the shoreline position with sea-level rise, using topographic and bathymetric data from Unmanned Aerial Vehicles (UAV) and Coastal Modelling System (SMC – Brazil), respectively. The results indicated a maximum shoreline retreat for 2100 of -502 m and -1727 m using EPR4Q and BRGM, correspondingly. The uBTM using the land-use of Mapbiomas showed a maximum of 44.57 km2 of urban area impacted by the sea-level flood. This research highlights the possibility of performing coastal management analysis in GIS environ-ment using non-commercial software.

This paper investigates the impact of price and real exchange rate volatility on Foreign Direct Investment (FDI) inflows in a panel of 10 Latin American and Caribbean countries, observed between 1990 and 2012. Both price and exchange rate volatility series are estimated through the Generalized Autoregressive Conditional Heteroscedasticity model (GARCH). Our results, obtained employing the Fixed Effects estimator, confirm the theory of hysteresis and option value, in so far it is found a statistically significant negative effect of exchange rate volatility on FDI. Price volatility, instead, turns out to be positive but insignificant. Moreover, we show that human capital and trade openness are key for attracting foreign capital. From the policy perspective, our analysis suggests the importance of stabilization policies as well as the one of government credibility in promoting trade openness and human capital formation.

Cardiovascular diseases are a broadly understood concept focusing on vascular and heart dys-function. Lack of physical exercise, type 2 diabetes, obesity, hypertension, dyslipidemia, throm-boembolism, kidney, and lung diseases all contribute to the development of heart and blood vessel dysfunction. Although effective and important, traditional treatment with diuretics, statins, beta–blockers, calcium inhibitors, ACE inhibitors, and anti–platelet drugs remain a second–line treatment after dietary intervention and lifestyle changes. Scientists worldwide are still looking for an herbal product that would be quite effective and free from side effects, either taken together or before the standard pharmacological intervention. Such herbal–originated medication therapy may include Morus alba L. (white mulberry), Elaeagnus rhamnoides (L.) A. Nelson (sea–buckthorn), Allium sativum L. (garlic), Convallaria majalis L. (lily of the valley), Leonurus cardiaca L. (mother-wort), and Crataegus spp. (hawthorn). Valuable herbal raw materials include leaves, fruits, seeds, and even thorns. This short review focuses on six herbs that can constitute an interesting and po-tential therapeutic option in the management of cardiovascular disorders.

The anomaly detection task is very important in computer science. And there are a lot of anomaly detection methods. Different from some thresholding methods, some unsupervised methods could make us get more accurate and faster result, which is the object of the project. In this paper, I tried to use EWMA and some other methods in two datasets: Webank time consuming indicators dataset and AIOps Challenge dataset. The paper consists nine parts: background of the project, related work, description of algorithms, implementation details, experimental setup and data sets used, experimental results and discussion, future directions, reference and meeting notes.

Anomaly detection and segmentation aim at distinguishing abnormal images from normal images and further localizing the anomalous regions. Feature reconstruction based method has become one of the mainstream methods for this task. This kind of method has two assumptions: (1) The features extracted by neural network is a good representation of the image. (2) The autoencoder solely trained on the features of normal images cannot reconstruct the features of anomalous regions well. But these two assumptions are hard to meet. In this paper, we propose a new anomaly segmentation method based on feature reconstruction. Our approach mainly consists of two parts: (1) We use a pretrained vision transformer (ViT) to extract the features of the input image. (2) We design a self-attention autoencoder to reconstruct the features. We regard that the self-attention operation which has a global receptive field is beneficial to the methods based on feature reconstruction both in feature extraction and reconstruction. The experiments show that our method outperforms the state-of-the-art approaches for anomaly segmentation on the MVTec dataset. It is both effective and time-efficient.

This study addresses the critical challenge of Speech Enhancement (SE) in noisy environments, where the deployment of Deep Neural Networksolutions on microcontrollers is hindered by their extensive computational demands. Focusing on this gap, our research introduces a novel SE method optimized for MCUs, employing a 2-layer GRU model that capitalizes on perceptual speech properties and innovative training methodologies. By incorporating self-reference signals and a dual strategy of compression and recovery based on the Mel scale, we develop an efficient model tailored for low-latency applications. Our GRU-2L-128 model demonstrates a significant reduction in size and computational requirements, achieving a 14.2× decrease in model size and a 409.1× reduction in operations compared to traditional DNN methods like DCCRN, without compromising performance. This advancement offers a promising solution for enhancing speech intelligibility in resource-constrained devices, marking a pivotal step in SE research and application.

Evidence suggests that COVID-19 infection can cause lasting health consequences. Multidiscipli-nary rehabilitation services have been recommended to reduce the sequalae. However, the effec-tiveness of physical exercise interventions remains insufficiently documented. The aim of this study was to develop and implement a specific and well tolerated protocol-based, intervention to reduce muscle weakness in older adults impacted by COVID-19. Forty six older adults random-ized into Intervention and Control Groups. Isometric and isokinetic strength assessments were conducted for selected muscle groups using JBA Staniak® torquemeter and Biodex System 3 dy-namometer. Functional abilities were evaluated with the Time Up and Go test and Chair Stand Tests. Men in intervention group demonstrated a significant improvement in static conditions for knee flexors (KF), trunk extensors (TE) and trunk flexors (TF) and in dynamic conditions for knee extensors (KE). Women in intervention group showed significant improvement in static condition for EF, KF, TE and TF and in dynamic for a KE and for a KF. The interaction GROUP x TESTING SESSION was significant for Chair test (s) and Chair test (n). Our results demonstrate the effec-tiveness of a well-tolerated, protocol-based approach that can be used to diminish long lasting functional deficits in post-covid survivors.

By shifting the focus from aggregate-level analysis to individual-level analysis, we believe that the DAD model can contribute to a more comprehensive understanding of driving behavior. Combing DAD with a conflict identification (CIM) model can potentially enhance the effectiveness of Advanced Driver Assistance Systems (ADAS) in terms of crash evasion capabilities. This paper is part of our research titled Automatic Safety Diagnosis in Connected Vehicle Environment, which received funding from the Southeastern Transportation Research, Innovation, Development, and Education Center.

Soil organic matter (SOM) is important for the global carbon cycle, and hyperspectral remote sensing has proven a promising method for fast SOM content estimation. However, soil physical properties significantly affect the sensitivity of satellite hyperspectral imaging to SOM, leading to poor generalization ability of the estimation model. This study aims to improve the spatiotemporal transferability of the SOM prediction model by alleviating the coupling effect of soil physical properties on the spectra. Based on satellite hyperspectral images and soil physical variables, including soil moisture (SM), soil surface roughness (root mean squared height, RMSH), and soil bulk weight (SBW), a soil spectral correction strategy was established based on the information unmixing method. Two important grain-producing areas in Northeast China were selected as study areas to verify the performance and transferability of the spectral correction model and SOM content prediction model. The results showed that soil spectral corrections based on fourth-order polynomials and the XG-Boost algorithm had excellent accuracy and generalization ability, with residual predictive deviations (RPD) exceeding 1.4 in almost all bands. In addition, when the soil spectral correction strategy was adopted, the accuracy of the SOM prediction model and the generalization ability after model migration were significantly improved. The SOM prediction accuracy based on the XG-Boost corrected spectrum was the highest, with a coefficient of determination (R2) of 0.76, root mean square error (RMSE) of 5.74 g/kg, and RPD of 1.68. The prediction accuracy, R2, RMSE, and RPD of the model after migration were 0.72, 6.71 g/kg, and 1.53, respectively. Compared with the direct migration prediction of the model, adopting the soil spectral correction strategy based on fourth-order polynomials and XG-Boost reduced the RMSE of the SOM prediction results by 57.90% and 60.27%, respectively. The performance comparison highlighted the advantages of considering soil physical properties in regional-scale SOM prediction.

Predicting anomalies in manufacturing assembly lines is crucial for reducing time and labor costs and improving processes. For instance, in rocket assembly, premature part failures can lead to significant financial losses and labor inefficiencies. With the abundance of sensor data in the Industry 4.0 era, machine learning (ML) offers potential for early anomaly detection. However, current ML methods for anomaly prediction have limitations, with F1-measure scores of only 50% and 66% for prediction and detection, respectively. This is due to challenges like the rarity of anomalous events, scarcity of high-fidelity simulation data (actual data is expensive), and the complex relationships between anomalies not easily captured by traditional ML approaches. Specifically, these challenges relate to two dimensions of anomaly prediction: predicting when anomalies will occur and understanding the dependencies between them. This paper introduces a new method called Robust and Interpretable 2D Anomaly Prediction (RI2AP) designed to address both dimensions effectively. RI2AP is demonstrated on a rocket assembly simulation, showing up to a 30-point improvement in F1 measure compared to current ML methods. This highlights its potential to enhance automated anomaly prediction in manufacturing. Additionally, RI2AP includes a novel interpretation mechanism inspired by a causal-influence framework, providing domain experts with valuable insights into sensor readings and their impact on predictions. Finally, the RI2AP model is deployed in a real manufacturing setting for assembling rocket parts. Results and insights from this deployment demonstrate the promise of RI2AP for anomaly prediction in manufacturing assembly pipelines.

Glaucoma secondary to Peters' anomaly (PA) is an important factor affecting visual prognosis, but there are few reports on it. This study aimed to investigate the characteristics of glaucoma asso-ciated with PA and glaucoma surgery outcomes. This retrospective study included 31 eyes of 20 patients with PA. PA was classified into three stages: Stage 1, with a posterior corneal defect only; Stage 2, a corneal defect with iridocorneal adhesion; and Stage 3, a corneal defect with lens ab-normalities. The associations between glaucoma and anterior segment dysgenesis severity, visual prognosis, and glaucoma surgery outcomes were analyzed. Sixteen eyes of 10 patients developed glaucoma. Stage 1 PA had no glaucoma, 52% of Stage 2 had glaucoma, and 75% of Stage 3 had glaucoma. Of the 16 eyes with glaucoma, 11 underwent surgery. Eight of these 11 eyes achieved intraocular pressure (IOP) control. Five of the nine eyes that underwent trabeculotomy (TLO) succeeded, and none had corneal staphyloma. Three of the four eyes for which TLO was ineffective had corneal staphyloma (P=0.0331). Patients with PA are more likely to develop glaucoma as anterior segment dysgenesis progresses, and the effect of TLO is limited if corneal staphyloma is present.

The properties of the dark matter, dark energy, graviton and photon are discussed in terms of the new three-dimensional quantized space model. Three new particles (bastons) with the electric charges (EC) are proposed as the dark matters. It is proposed that the EC, LC and CC charges are aligned along the time axes but not along the space axes. The photon is confined on its corresponding three-dimensional quantized space. However, the graviton can be evaporated into other three-dimensional quantized spaces. The rest mass of the electron neutrino (n_e) of 3.494·10⁻³ eV/c² is obtained from the experimental vacuum energy density in terms of quantum field theory (QFT). The rest mass and force range of the massive g(0,0,0) graviton with the Planck size are m_g = 3.1872·10⁻³¹ eV/c² and x_r = 3.0955·10²³ m = 10.0 Mpc, respectively, based on the experimental rest mass and rms charge radius of the proton. The possible diameter (10 Mpc) of the largest galaxy cluster is remarkably consistent with the gravitational force range (10 Mpc). Then, the diameter of the largest dark matter distribution related to the largest galaxy cluster is 9.2865·10²³ m = 30 Mpc equal to the force range of the massive g(0) graviton with the rest mass of 1.0624·10⁻³¹ eV/c². Because of the huge number (N) of the evaporated gravitons, the very small Coulomb’s constant of about 10⁻⁴⁸k and large gravitation constant of 10⁶G_N are expected for the charged dark matters. Therefore, F_c(mm) > F_g(dd) > F_g(mm) > F_g(dm) > F_c(dd) > F_c(dm) = 0 for the proton-like particle. The proposed weak gravitational force between the dark matters and normal matters explains the observed dark matter distributions of the bullet cluster, Abell 1689 cluster and Abell 520 cluster. The transition from the galaxy without the dark matters to the galaxy with the dark matters are explained. Also, the accelerated space expansion is caused by the new space quanta created by the evaporated gravitons into the x1x2x3 space and repulsive electromagnetic force between dark matters corresponding to the dark energy. The decreasing coupling constant of the strong force, neutron lifetime anomaly and the pressure distribution inside the proton are explained by the unobservable proton and hadronization. And the rest mass of 1.4 TeV/c² is assigned to the Le particle with the EC charge of −2e. The proposed rest mass (26.12 eV/c²) of the B1 dark matter is indirectly confirmed from the supernova 1987A data. Also, the gravitation constant has been changing with the time because of the graviton evaporation.

A recent research has identified an inverse amplitude link between obliquity damping and short eccentricity amplification during Mid-Late Pleistocene based on LR04 δ18O and equatorial Pacific Site 846 sea surface temperature records, which is associated with the Earth's long-term cooling. In the present study, new evidence of this anticorrelation is presented from Antarctic D-CO2-CH4 records, global benthic-planktic δ18O, and regional (Atlantic, Pacific, Mediterranean, and Indian) climate-related proxies. Based on a critical review of theoretical constrains (Earth's oblateness changes and ice-volume phase lag in the obliquity band <5.0 kyr), this widespread and symmetric (bipolar) obliquity response damping has been interpreted as an effect of the obliquity-oblateness feedback, which could be the latent physical mechanism at the origin of the Mid-Pleistocene Transition (MPT). Indeed, results and considerations of the present work suggest that fast and positive/negative net variation of the Earth's oblateness in the obliquity band was controlled by dominant glacio-eustatic water mass component and, assuming a rapid response of the ice-volume to surface temperature changes, the mean obliquity lag response is estimated to be <5.0 kyr over the past 800 kyr. These elements may explain the interglacial/glacial damping observed in the obliquity response. The consolidation of the Earth's long-term icy-state in the subtrend IV, culminated with the post-MPT obliquity damping, might have contributed to the strengthening of the short eccentricity response by mitigating the obliquity ‘ice-killing’ during obliquity maxima (interglacials), favouring the obliquity-cycle skipping and a feedback amplified ice-growth in the short eccentricity band (obliquity damping hypothesis). This could suggest a different impact of the climate friction than what is generally believed, which is presumably the latent physical mechanism that triggers transient ‘competitive’ interaction between obliquity and short eccentricity started early during the Piacenzian.

Flood disasters have become an annual occurrence along the East Coast of North Sumatra (ECNS) and Medan City. Numerous studies have examined the causal factors flood disasters in this area, such as changes in land use, drainage issues, and population growth, but the intensity of flood disasters continues to rise. In this research, we focused on evaluating the unexplored factors of land subsidence and rising sea levels in this region. Our investigation uses multi-sensor satellite data and field surveys to conduct an assessment of the rate of land subsidence and sea level rise and assess their implications on flood disasters. This study highlights the potential of multi-sensor satellite data as a primary resource for assessing flood disasters in coastal areas and densely populated large cities. Furthermore, we propose a solution to reduce the negative impact of flood disasters by by engaging local communities and environmental enthusiasts in mangrove reforestation along the coastline.

Neonatal mortality remains a public health concern and timely identification and prompt intervention in the delivery room could improve the outcome in neonatal morbidity and mortality. We performed a retrospective study to help us finding the profile of the high-risk newborn regarding the need for NICU admission, necessary to establish a protocol regarding the mobilization of human and material resources for the urgent management of these situations. All deliveries and neonates born in Maternity of Bihor County Emergency Clinical Hospital from Romania were reviewed, including relevant maternal, foetal and perinatal data to identify risk factors that result in birth of newborns needing resuscitation at birth between 2012-2019. The number of births showed a downward trend but around 90% of the births were on term. The incidence of neonatal emergencies is increasing, the most frequently were respiratory, sepsis and other infections, cardiovascular emergencies, and extreme prematurity. It is noted that in 2021 the incidence of these neurological emergencies increased by three times compared to previous years. The neonatal and maternal risk factors associated with admission to the NICU were mother's age over 35 years, low level of education, second/more gestation, weight under 2500 g, Apgar score under 7, premature rupture of membranes, macrosomia and mother's infectious pathology. Accurate identification of risk factors and anticipation at the birth of a high-risk neonate would result in adequate preparation and prompt resuscitation of neonates who need some level of intervention and thus, reducing neonatal morbidity and mortality.

The rapid evolution of information and communication technologies, notably the Internet, has yielded substantial benefits while posing challenges to information system security. With an increasing frequency of cyber threats—from unauthorized access to data breaches—the digital landscape's vulnerability is evident. Addressing the financial impact of cybercrime, this study delves into the role of Artificial Intelligence (AI) and Machine Learning (ML) technologies in cybersecurity. Analyzing advancements and outcomes, the research explores practical techniques for anomaly detection, threat prediction, and automated response. By investigating prior research and real-world implementations, the study provides valuable insights into the potential of AI/ML, uncovering current trends, challenges, and prospects in enhancing cybersecurity tactics amid a dynamically changing threat landscape.

Foundational and state-of-the-art anomaly detection methods through power system state estimation are reviewed. The traditional components for bad data detection such as chi-square testing, residual-based methods, and hypothesis testing are discussed to explain the motivations for recent anomaly detection methods given the increasing complexity of power grids, energy management systems, and cyber-threats. In particular, state estimation anomaly detection based on data-driven quickest change detection and artificial intelligence are discussed and directions for research are suggested with particular emphasis on considerations of the future smart grid.

In recent years, video anomaly detection technology , which can intelligently analyze massive video and quickly find abnormal phenomena, has attracted extensive attention with the wide application of video surveillance technology . To address the complex and diverse problem of abnormal human behavior detection in surveillance videos, a surveillance video abnormal behavior detection and localization supervised method based on the deep network model and the traditional method is proposed. Specifically, we combined AGMM and YOLACT methods to obtain more accurate foreground information by fusing the foreground maps extracted by each technique. To further improve the accuracy , we use the PWC-Net technique to extract features of the foreground images and input them into an anomaly classification model for classification. The proposed method effectively detects and locates the abnormal behavior in the monitoring scene. In addition to the aforementioned methods, this paper also employs YOLOV5 and DeepSORT networks for object detection and tracking in the video, which allows us to track the detected objects for better understanding of the scene in the video. Experiments on the UCSD benchmark dataset and the comparison with state-of-the-art schemes prove the advantages of our method.

Wheat is widely grown in the Mediterranean region, where plants frequently face water shortages. Recently, more attention to rationalizing irrigation water has been increased due to the adoption of sustainable agricultural systems. Therefore, two field trials were conducted at the Agriculture Research Station of Nubaria, Egypt, in two consecutive seasons (2017/2018 and 2018/2019) to examine three irrigation regimes' effects, i.e., 60, 80, and 100% of crop evapotranspiration (ETc) combined with foliar ascorbic acid (AsA) or vitamin B2 (VB2) on yield and water productivity of wheat in two separate experiments. A split-plot design was used with three replicates. Irrigation treatments occupied the main plots, while the subplot in the first experiment was placed by ascorbic acid (AsA) and vitamin B2 in the second experiment. Results indicated that 100% ETc produced higher wheat yield and its attributes and insignificantly surpassed 80% ETc. Compared to the unsprayed plots, the foliar application of 6 mM ascorbic acid or 2.0 mM vitamin B2 significantly increased grain yield, components, and water use efficiency. The interaction between irrigation regimes (100% ETc or 80% ETc) and foliar application of 6.0 mM ascorbic acid or 2.0 mM vitamin B2 resulted in the highest values with no significant difference. Foliar application of ascorbic acid or vitamin B2 saved 20% of irrigation water while yielding a reduction that did not exceed 10%. The biplot graph of treatments x traits (TT) was a useful statistical tool for examining the impact of treatments on yield and its characteristics and identifying the links between these factors. Results suggest that riboflavin or ascorbic acid may have a potential role as an effective antioxidant for enhancing the tolerance for drought stress in wheat.

Hyperspectral anomaly detection is to recognize anomalies from complex scene in an unsupervised way. Currently, many spectral-spatial detection methods have been proposed with a cascaded manner. However, they often neglect complementary characteristics between spectral and spatial dimensions, which easily leads to yield high false alarm rate. To alleviate this issue, a spectral-spatial information fusion (SSIF) method is designed for hyperspectral anomaly detection. First, an isolation forest is exploited to obtain spectral anomaly map, in which the object-level feature is constructed with entropy rate segmentation algorithm. Then, a local spatial saliency detection scheme is proposed to produce spatial anomaly result. Finally, the spectral and spatial anomaly scores are integrated together followed by a domain transform recursive filtering to generate the final detection result. Experiments on five hyperspectral datasets prove that the proposed SSIF produces superior detection result over other state-of-the-art detection techniques.

There are many applications of anomaly detection in IoT domain. IoT technology consists of large number of interconnecting digital devices not only generating huge data continuously but also making real-time computations. Since IoT devices are highly exposed due to Internet, they frequently meet with the challenges of illegitimate accesses in the form of intrusions, anomaly, fraud, etc. Identifying these illegitimate accesses in IoT domain can be an exciting research problem. In numerous applications fuzzy clustering and rough set theory have been successfully employed. As the data generated in IoT domains are high-dimensional, the clustering methods used for lower dimensional data cannot be applied efficiently. In this article, mixed approaches consisting of nano topology and fuzzy clustering techniques are proposed for anomaly detection. First of all, the nano topology is generated to find lower dimensional apace and then a couple of well-known fuzzy clustering techniques are employed on it for the efficient anomaly detection. The effectiveness of the proposed approaches is evaluated using time-complexity analysis, experimental studies with a synthetic dataset and a real-life dataset along with comparative studies with traditional fuzzy clustering approaches namely fuzzy c-means clustering (FCM) algorithm, Gustafson-Kessel (GK) Algorithm, Gath-Geva (GG) Algorithm, Mahalanobis Distance based Fuzzy C-Means algorithm (M-FCM), and Common Mahalanobis Distance based Fuzzy C-Means algorithm (CM-FCM). Experimentally, it has been found that the proposed approaches outperform the aforesaid algorithms in terms of detection rates, accuracy rates, false alarm rates and computational times.

Log collection and storage is a crucial process for enterprises around the globe. Log analysis helps identify potential security breaches and, in some cases, is required by law for compliance. However, enterprises often delegate these responsibilities to third-party Cloud Service Providers (CSPs), where the logs are collected and processed for anomaly detection and stored in a data warehouse for archiving. Prevalent schemes rely on plain (unencrypted) data for anomaly detection. More often, these logs can reveal sensitive information about an organization or the customers of that organization. Hence, it is best to keep it encrypted at all times. This paper presents "SigML++," an extension of work done in "SigML." We utilize Fully Homomorphic Encryption (FHE) with the Cheon-Kim-Kim-Song (CKKS) scheme for supervised log anomaly detection on encrypted data. We use an Artificial Neural Network (ANN) based probabilistic polynomial approximations using a Perceptron with linear activation. We probabilistically approximate the Sigmoid activation function (σ(x)) in the encrypted domain for the intervals [−10,10] and [−50,50]. Experiments show better approximations for Logistic Regression (LR) and Support Vector Machine (SVM) for low-order polynomials.

Pipeline integrity is an important area of concern for the oil and gas, refining, chemical, hydrogen, carbon sequestration, and electric-power industries, due to the safety risks associated with pipeline failures. Regular monitoring, inspection, and maintenance of these facilities is therefore required for safe operation. Large standoff magnetometry (LSM) is a non-intrusive, passive magnetometer-based mea- surement technology that has shown promise in detecting defects (anomalies) in regions of elevated mechanical stresses. However, analyzing the noisy multi-sensor LSM data to clearly identify regions of anomalies is a significant challenge. This is mainly due to the high frequency of the data collection, mis-alignment between consecutive inspections and sensors, as well as the number of sensor measurements recorded. In this paper we present LSM defect identification approach based on ma- chine learning (ML). We show that this ML approach is able to successfully detect anomalous readings using a series of methods with increasing model complexity and capacity. The methods start from unsupervised learning with "point" methods and eventually increase complexity to supervised learning with sequence methods and multi-output predictions. We observe data leakage issues for some methods with randomized train/test splitting and resolve them by specific non-randomized splitting of training and validation data. We also achieve a 200x acceleration of support-vector classifier (SVC) method by porting computations from CPU to GPU leveraging the cuML RAPIDS AI library. For sequence methods, we develop a customized Convolutional Neural Network (CNN) architecture based on 1D convolutional filters to identify and characterize multiple properties of these defects. In the end, we report the scalability of the best-performing methods and compare them, for viability in field trials.

In this study, a commercial sodium butyrate protected by a new buffer salt solution (NSB) was tested to determine whether it can be used as an antibiotic alternative in broiler production. 192 1-day-old broilers were randomly allocated to three dietary treatments: soybean meal diet (CON), antibiotic diet (ANT, basal diet + 100 mg/kg aureomycin), and NSB (basal diet + 800 mg/kg NSB). The growth performance, serum anti-inflammatory cytokines, intestinal morphology, gut barrier function, antioxidative parameters, SCFAs content, and cecal microbiota were analyzed. The result showed that NSB significantly improved ADFI, ADG (p < 0.01), and decreased FCR (p < 0.01). Serum anti-inflammatory cytokine IL-10 was up-regulated (p < 0.01), and pro-inflammatory TNF-α was down-regulated (p < 0.05) by NSB supplementation. H&E results showed that VH and VH: CD ratio significantly increased (p < 0.05) in the jejunum and ileum in the NSB group. Furthermore, ZO-1 (p < 0.01), claudin-1 (p < 0.01), and occludin (p < 0.05) in the jejunum and claudin-1 (p < 0.01) and mucin-2 (p < 0.05) in the ileum significantly up-regulated in the NSB group, respectively. Additionally, SOD (p < 0.05) and T-AOC/MDA ratio (p < 0.01) in the jejunum and SOD in the ileum were significantly increased (p < 0.05) in the NSB group. The MDA level also significantly increased (p < 0.01) in the ANT group in the jejunum. Propionic acid (p < 0.05) and butyric acid (p < 0.01) content significantly increased in the NSB group in the jejunum and ileum segments. The 16S rRNA sequencing results showed no significant difference (p > 0.05) in alpha and beta diversity among the groups. LEFSe analysis also indicated that Peptostreptococcaceae, Colidextribacter, Firmicutes, Oscillospira, and Erysipelatoclostridiaceae, which promote SCFA production (p < 0.05), were identified as dominant taxon-enriched bacterial genera in the NSB group. The Spearman correlation analysis revealed that Colidextribacter with ADFI, ADG, VH, claudin-1 (p < 0.05), and unclassified_f__Peptostreptococcaceae with ADFI, IL-10, and ZO-1 were positively correlated (p < 0.05). Furthermore, ADFI and ADG with IL-10, claudin-1, SOD, T-AOC, and butyric acid (p < 0.05), and similarly, ADG with VH (p < 0.05) showed a positive correlation. In conclusion, NSB enhanced the growth performance by improving jejunum and ileum morphology, serum anti-inflammatory cytokines, regulating intestinal barrier function and antioxidant capacity, SCFAs content, and cecum microbiota, showing its potential use as an alternative to antibiotics in poultry nutrition.

Proteins play a pivotal role in supporting athletes by promoting muscle hypertrophy and minimizing protein catabolism during exercise. They stimulate muscle protein synthesis, aiding in the repair of exercise-induced muscle damage and serving as an energy source, particularly for post-exercise glycogen replenishment. In individuals with phenylketonuria (PKU), for whom protein intake is limited to essential phenylalanine (Phe), supplementation with Phe-free amino acids or glycomacropeptides is necessary. Tailored for macronutrient and micronutrient content, these protein substitutes fulfill most protein requirements, but lack Phe, unlike dietary sources of protein. Protein requirements for athletes with PKU exceed general recommendations due to limited availability in supplements and potential catabolism mitigation. Various factors influence tolerated dietary Phe in PKU, including protein substitute quantity, distribution, pharmacological treatment, age, growth rate, pregnancy, catabolic states, and physical activity. While general protein requirements for PKU surpass those of the general population, adherence to supplement regimens is crucial for athletes and patients with PKU. Strategically timed protein substitutes enhance muscle protein synthesis, body composition, and sports performance within safety limits. Individualized intake is essential for optimal outcomes, as proteins have a multifaceted role in optimizing athletic performance and addressing challenges in PKU.

Background: One challenge in implant dentistry is to reduce marginal bone resorption to avoid long-term esthetic and biological complications. Sub-gingival implant placement has been suggested as a suitable strategy. Methods: 38 healthy patients were enrolled to receive bone level (BL – Control group) or 2 mm sub-crestal (SC - Test group) conical connection, platform-switched implants. An immediate tissue level abutment was used in the Test group by following a One Time Abutment (OTA) restorative procedure. Marginal Bone Modification (MBM) was calcu-latied on standardized radiographs at surgery (T0), loading (T1), and 6 months (T2) and 12 months after loading (T3), and cathegorised in Bone Loss (BL) if the resorption occurred below the implant neck and Bone Remodeling (BR) if above. Pocket probing depth (PPD), Bleeding on probing (BoP) and Plaque Index (PI) were also collected. Results: The mean MBM in the test group after 12 months was 0.61mm, compared to 0.52mm in the control group. However, no BL was observed in the test group, as the MBM completely occurred above the implant neck as BR, whilst in the control group MBM corresponded in total to BL. The average PPD was 2.38mm in the test group and 3.04mm in the control group after 12 months, BoP 50% and 43%, PI 33% and 19.44% respectively. Conclusions: Sub-crestal conical connection, platform-switched implants could be a suitable clinical choice to avoid BL with exposure of the treated implants surfaces.

Conditions under which compositions of component systems form a well-defined system-of-systems here are formulated at a fundamental level. Statement of what defines a well-defined composition and sufficient conditions guaranteeing such a result offers insight into exemplars that can be found in special cases such as differential equation and discrete event systems. For any given global state of a composition, two requirements can be stated informally as: 1) the system can leave this state, i.e., there is at least one trajectory defined that starts from the state, and 2) the trajectory evolves over time without getting stuck at a point in time. Considered for every global state, these conditions determine whether the resultant is a well-defined system and if so, whether it is non-deterministic or deterministic. We formulate these questions within the framework of iterative specifications for mathematical system models that are shown to be behaviorally equivalent to the Discrete Event System Specification (DEVS) formalism. This formalization supports definitions and proofs of the afore-mentioned conditions. Implications are drawn at the fundamental level of existence where the emergence of a system from an assemblage of components can be characterized. We focus on systems with feedback coupling where existence and uniqueness of solutions is problematic.

Cutaneous melanoma (CM) is a candidate for screening programs, since its prognosis is excellent when diagnosed in an early disease stage. Targeted screening of those at high risk for developing CM, a cost-effective alternative to population-wide screening, requires valid procedures to identify the high-risk group. Self-assessment of the number of nevi has been suggested as a component of such procedures, but its validity has not yet been established. We analyzed the level of agreement between self-assessments and expert assessments of the number of melanocytic nevi in the area between the wrist and the shoulder of both arms based on 4548 study subjects in whom mutually blinded double counting of nevi was performed. Nevus counting followed the IARC protocol. Study subjects received written instructions, photographs, a mirror, and a “nevometer” to support self-assessment of nevi larger than 2 mm. Nevus counts were categorized based on the quintiles of the distribution into five levels defining a nevus score. Cohen’s weighted kappa-coefficient was estimated to measure the level of agreement. In the total sample, agreement between self-assessments and expert-assessments was moderate (raw agreement: 50.29%, weighted κ=0.596). Self-assessed nevus counts were higher than those determined by experts (mean differ-ence: 3.33 nevi). The level of agreement was independent of sociodemographic and cutaneous factors; however, participants’ eye color had a significant impact on the level of agreement. Our findings show that even with comprehensive guidance, only a moderate level of agreement between self-assessed and expert-assessed nevus counts can be achieved. Self-assessed nevus information does not appear to be reliable enough to be used in individual risk assessment to target screening activities.

In Indonesia, types of meat are still identified manually due to increasing prices for beef and buf-falo ahead of Idul Fitri. Traders mix beef with pork to thwart the beef scam. Haar wavelet and GLCM, GLCM with angles of 0°, 45°, 90°, and 135°, as well as matrices using contrast, correlation, energy, homogeneity, and entropy, are used in feature extraction. The following are the results of meat image classification testing using k-NN, Haar wavelet, and GLCM: The k-NN algorithm shows superiority in identifying fresh (99%), frozen (99%), and rotten (96%) meat texture images with the most fantastic accuracy results in every situation. Meanwhile, GLCM routinely provides good results, especially regarding the texture image of fresh meat (183.21) and rotten meat (115.79). However, despite delivering less throughput than k-NN and GLCM, Haar wavelets are still helpful, especially when dealing with fresh meat texture images (89,96).

Anomaly Detection in real time data is accepted as a vital research area. Clustering has effectively been tried for this purpose. As the datasets are real time, the time of generating of the data is also important. In this article, we introduce a mixture of partitioning and agglomerative hierarchical approach to detect anomalies from such datasets. It is a two-phase method which follows partitioning approach first and then agglomerative hierarchical approach. The dataset can have mixed attributes. In phase-1, a unified metric defined on mixed attributes is used. The same is also used for merging of similar clusters in phase-2. Also, we have kept the track of time attribute of each data instance which produces the clusters with their lifetimes in phase-1. Then in phase-2, we merge the similar clusters. While merging, the similar clusters, the lifetimes of the corresponding clusters with overlapping cores are to be superimposed producing fuzzy time intervals. This way, each cluster will have an associated fuzzy lifetime. The data instances either belonging sparse clusters or not belonging to any of the clusters can be treated as anomalies. The efficacy of the algorithms can be established using both complexity analysis as well as experimental studies.

Anomaly detection is an active area of research with numerous methods and applications. This survey reviews the state-of-the-art of data-driven anomaly detection techniques and their application to the the aviation domain. After a brief introduction to the main traditional data-driven methods for anomaly detection, we review the recent advances in the area of neural networks, deep learning and temporal-logic based learning. We cover especially unsupervised techniques applicable to time series data because of their relevance to the aviation domain, where the lack of labeled data is the most usual case, and the nature of flight trajectories and sensor data is sequential, or temporal. The advantages and disadvantages of each method are presented in terms of computational efficiency and detection efficacy. The second part of the survey explores the application of anomaly detection techniques to aviation and their contributions to the improvement of the safety and performance of flight operations and aviation systems. As far as we know, some of the presented methods have not yet found an application in the aviation domain. We review applications ranging from the identification of significant operational events in air traffic operations to the prediction of potential aviation system failures for predictive maintenance.

This paper proposes a novel single-stage buck-boost three-Level neutral-point-clamped (NPC) inverter with two independent dc sources coupled for the grid-tied photovoltaic (PV) application, which can effectively solve the unbalanced operational conditions generally appeared between two coupled independent PV sources induced by the unequal irradiation and temperature distribution. The proposed control scheme can simultaneously guarantee the maximum power point (MPP) operation of both PV sources and maintain the output waveform quality. Compared to the traditional two-stage PV inverter, the proposed NPC inverter could reduce the PV array voltage requirement and dc-link capacitors’ voltage rating, meanwhile show the advantage in operational efficiency. MATLAB simulations and the captured experimental results are presented to show the performance of the proposed three-level inverter.

Wave buoy measurements were carried out near the north-eastern Black Sea coast at the natural reserve Utrish in 2020-2021. In total, about 11 months of data records were collected during two stages of the experiment at 600 and 1500 meters offshore and depths of 18 and 42 meters. The measured waves propagate almost exclusively from the seaward directions. Generally, the waves do not follow the local wind directions, thus, implying a mixed sea state. Nevertheless, dimensionless wave heights and periods appears to be quite close to the previously established empirical laws for the wind-driven seas. The results of the wave turbulence theory are applied for estimates of spectral energy fluxes and their correspondence to the energy flux from the turbulent wind pulsations. These estimates are consistent with today's understanding of wind-wave interaction. It is shown that the main fraction of the wind energy flux goes to the direct Kolmogorov-Zakharov cascade to high wave frequencies and then to dissipation in small scales. Less than 1% of the wind energy flux is directed to low frequency band (the so-called inverse Kolmogorov-Zakharov cascade), thus, providing wave energy growth.

Due to the long duration and high cost of traditional pumping tests, the response of well water level to seismic wave, earth tide, and barometric pressure provides a feasible method for determining continuous changes in aquifer hydraulic parameters. Aiming at the problem that the response phase shift of well BB water level to tidal tide M2 is greater than that of tide O1, this paper preferentially calculated the time series changes of S, Be, n and BKu based on the response mechanism of well water level to barometric pressure and earth tide and with the help of smooth moving method. Then by using the mixed flow model, the variation of transmissivity and leakage coefficient over time were simultaneously obtained, and the evolution process and characteristics of aquifer parameters near well BB caused by the Wenchuan earthquake were ultimately discussed. The calculation results are basically consistent with the previous pumping test, and have been verified and compared by using other scholars' methods. This solving process avoids the problems such as excessive dependence on initial values, multiple solutions, and unstable tide O1, which has a promoting effect on the study of the impact of seismic activity on aquifer systems.

The recent global calamity of sea level rise and flooding is a major disaster. Some settlements on Java Island, Indonesia, have been reported to be sinking. Bedono Village is one village on the Central Java Coast that is vulnerable to sea level rise. Aside from the possibility of sea level rise, the calamity in this village is linked to prior mangrove degradation and fish pond expansion. This tragedy has displaced 78.63% of the population over a decade, and the remaining population is still surviving in the remaining lands. This study aims to investigate the effects of sea-level rise-related permanent inundation on the lives of the remaining residents in Bedono Village, with a focus on their adaptation and resilience methods. The results disclosed that remaining populations have taken active initiation by adopting multiple strategies that include structural prevention measures and nature based solution through mangrove reforestations. As a result based on mangrove assessment using Geographic Information Systems, mangrove cover increased from 1 ha in 2010 to 9.5 ha in 2012. This achievement has provided an alternative income through mangrove ecotourism. Women and multi stakeholder participation have also contributed to the implementation of adaptation strategy. Despite those significant achievements, the adaptation and resilience was challenged by lack of education and awareness towards sea level rise issues and their confounding disasters. The nature-based solution and local wisdom in Bedono Village is an example that is in line with GPDRR UNDRR 2022, the Sendai Framework, and The Bali Agenda for Resilience that ensures a whole-of-society approach to sea-level rise by ensuring that no one is left behind, creating an inclusive environment, and promoting gender.

It is shown that the Lambda component in the cosmological Lambda-CDM model can be conceived as vacuum energy, consisting of gravitational particles subject to Heisenberg’s energy-time uncertainty. These particles can be modelled as elementary polarisable Dirac-type dipoles (“darks”) in a fluidal space at thermodynamic equilibrium, with spins that are subject to the Bekenstein-Hawking entropy. Around the baryonic kernels, uniformly distributed in the universe, the spins are polarized, thereby invoking an increase of the effective gravitational strength of the kernels. It explains the dark matter effect of galaxies to the extent that a numerical value of Milgrom’s acceleration constant can be assigned by theory. Non-polarized vacuum particles beyond the baryonic kernels compose the dark energy at the cosmological level. The result is an interpretation of gravity at the quantum level in terms of quantitatively established shares in baryonic matter, dark matter and dark energy, which correspond with the values of the Lambda-CDM model.

Lower cutoff levels in screening programs have led to an increase in the proportion of detected cases with transient hypothyroidism, leading to increase of the overall incidence of primary congenital hypothyroidism (CH) in several countries. We have performed retrospective evaluation on the data from 251,008 (96.72%) neonates screened for thyroid-stimulating hormone (TSH) level in dried blood spot specimens taken 48 hours after birth, between 2002 and 2015, using DELFIA method. A TSH value of 15 mIU/L was used as the cutoff point until 2010 and 10 mIU/L thereafter. Primary CH was detected in 127 newborns (1/1976) of which 81.1% had permanent and 18.9% had transient CH. The incidence of primary CH was increased from 1/2489 until to 2010 to 1/1585 thereafter (p=0.131). However, the incidence of permanent CH was slightly increased (p=0.922), while the transient CH incidence had 8-fold increasing after lowering the TSH cutoff level (p<0.001). In cases with permanent CH, we observed lower frequency for thyroid dysgenesis (82.7 vs. 66.7%) and higher frequency for normal in-situ thyroid gland (17.3 vs. 33.3%), for the period with reduced TSH cutoff value. Our findings support the impact of lower TSH cutoff on the increasing incidence of congenital hypothyroidism.

Data from NASA’s Soil Moisture Active Passive Mission (SMAP) and from the California Cooperative Oceanic Fisheries Investigations (CalCOFI) were used to examine the freshening that occurred during 2015-2016 in the Southern California Current System. Overall the freshening was found to be related to the 2014-2016 Northeast Pacific Warm Anomaly. The primary goal was to determine the feasibility of using SMAP data to observe the surface salinity signal associated with the warming. As a first step direct comparisons were done with salinity from the CalCOFI data at one-meter depth. During 2015 SMAP was saltier than CalCOFI by 0.5 PSU, but biases were reduced to < 0.1 PSU during 2016. South of 33°N, and within 100 km of the coast, SMAP was fresher in 2015 by almost 0.2 PSU. CalCOFI showed freshening of 0.1 PSU. North of 33°N SMAP and CalCOFI saw significant freshening in 2016, SMAP by 0.4 PSU and CalCOFI by 0.2 PSU. Differences between SMAP and CalCOFI are consistent with the increased stratification in 2015 and changes in the mixed layer depth.

Purpose: to assess any differences on marginal bone loss between bone-level or tissue-level dental implants through a review of literature until September 2019. Materials and methods: MEDLINE, Embase and other database were searched by two independent authors. The search was limited to articles in English. Results: The search provided 1028 records and, after removing the duplicates through titles and abstracts screening, 45 full-text articles were assessed for eligibility. For qualitative analysis 20 articles were included, 17 articles of them for quantitative analysis. A total of 1161 patients (mean age 54,4 years) and 2933 implants were observed, 1427 (Tissue-level) and 1506 (Bone-level). The survival rate and the success rate were more than 90%, except for 2 studies with a success rate of 88% and 86.2%. No studies reported any differences between groups in term of success and survival rates. Three studies showed that BL-implants had statistically less marginal bone loss (P< 0.05). Only one study reported statistically less marginal bone loss in TL-implants (P< 0.05). In the most part of the studies, differences between implant types in marginal bone loss were not statistically significant. Conclusion: Despite to the peri-implant tissue around transmucosal implants has been reported to be inflammation-free because of the absence of bacterial infiltration in the micro-gap between the fixture and abutment, no clinical and radiological differences were highlighted between groups from the included studies after a variable period of follow-up ranged between 1 to 5 years.

Ebstein anomaly, a congenital heart condition, can manifest across a broad age spectrum, including adulthood. We present a unique case of a 67-year-old woman with no significant medical history who sought medical attention for severe headaches, vomiting, and respiratory distress, accompanied by elevated blood pressure and rapid pulse. Initial investigations, including troponin T levels, yielded unremarkable results. However, an electrocardiogram unveiled a right bundle branch block (RBBB) pattern, prompting further assessment. A subsequent chest X-ray disclosed an enlarged heart, indicating cardiomegaly, prompting echocardiography. The echocardiogram confirmed the diagnosis of Ebstein anomaly, revealing a 26 mm apical displacement of the tricuspid valve (TV). The patient was managed conservatively with supplemental oxygen and exhibited gradual improvement. This case underscores the infrequent occurrence of Ebstein anomaly in adulthood and underscores the necessity for thorough evaluation in cases of unexplained cardiopulmonary symptoms.

Finding anomalies in the real-time system is recognized as one of most challenging study in information security. It has so many applications like IoT, and Stock-Market. In any IoT system the data generated are real-time, and temporal in nature. Since due to the extreme exposure to Internet and interconnectivity of devices, the IoT systems often face issues like fraud, anomalies, intrusions etc. Discovering anomaly in such domain can be interesting. Clustering and rough set theory have been tried in many cases. Considering the time-stamp associated with IoT data, time-dependent patterns like periodic clusters can be generated which could be helpful for the efficient detection of anomalies by providing more in-depth analysis of the system. In this paper, a mixed method comprising of nano topology, a modified k-means clustering and an interval superimposition technique is used for finding fuzzy periodic clusters in the subspace generated by the nano topology. For every clusters there will be an associated sequence of time-intervals where it exists. The sequence time-intervals accompanying with each clusters may exhibit some remarkable patterns. For example, there may exist different types of periodicity namely yearly, monthly, daily, and hourly etc. For finding such fuzzy periodicity, an operation called interval-superimposition has been used. The time-intervals associated with each cluster are superimposed if they have reasonable overlapping. Each superimposed time-interval generates a fuzzy time-interval. The data instances are thought to be anomalous if they either belong to sparse clusters or don't belong to any clusters. The efficacy of the method can be assessed by means of both time-complexity analysis and comparative studies with existing clustering-based anomaly detection algorithms with a real-life and a synthetic dataset. It can been found experimentally that our method can extract anomaly with 98% of accuracy and it runs cubic time approximately.

The challenging issues of Computer Network and Databases are not only the intrusion detection but also the reduction of false positive and increase of detection rate. In any intrusion detection system, anomaly detection mainly focuses on modeling the normal behavior of the users and detecting the deviations from normal behavior which are assumed to be potential intrusions or treat. Several techniques have already been successfully tried for this purpose. However, the normal and suspicious behavior are hard to predict as there is no precise boundary differentiat-ing one from another. Here rough set theory and fuzzy set theory come into the picture. In this article, a hybrid approach based on rough set theory and intuitionistic fuzzy set theory is pro-posed for the detection of anomaly. The proposed approach is a classification approach which takes the advantages of softness properties both rough and fuzzy set theory to deal with uncer-tainty in the dataset. The algorithm classifies the data instances in such a way that they can be expressed using natural language. The experimental results with a real world dataset and a syn-thetic dataset show that the proposed algorithm has normal true positive rates of 91.989% and 96.99% and attack true positive rates of 91.289% and 96.29% respectively

Machine learning methods have widely been applied to detect anomalies in machine and cutting tool behavior during lathe or milling. However, detecting anomalies in the workpiece itself did not get the same attention by researchers. That is why in this article, the authors present a pub-licly available multivariate time series dataset which was recorded during milling of 16MnCr5. Due to artificially introduced, though realistic anomalies in the workpiece the dataset can be ap-plied for anomaly detection. By using a convolutional autoencoder as a first model good results in detecting the location of the anomalies in the workpiece were achieved. Furthermore, milling tools with two different diameters where used which led to a dataset eligible for transfer learn-ing. The objective of this article is to provide researchers with a real-world time series dataset of the milling process which is suitable for modern machine learning research topics like anomaly detection and transfer learning.

The rapid industrial growth in solar energy is gaining increasing interest in renewable power from smart grids and plants. Anomaly detection in photovoltaic (PV) systems is a demanding task. In this sense, it is vital to utilize recent advances in machine learning to accurately and timely detect different anomalies and condition monitoring. This paper addresses this issue by evaluating different machine learning techniques and schemes and showing how to apply these approaches to solve anomaly detection and detect faults on photovoltaic components. For this, we apply distinct state-of-the-art machine learning techniques (AutoEncoder Long Short-Term Memory (AE-LSTM), Facebook-Prophet, and Isolation Forest) to detect faults/anomalies and evaluate their performance. These models shall identify the PV system's healthy and abnormal actual behaviors. Our results provide clear insights to make an informed decision, especially with experimental trade-offs for such complex solution space.

Time series data are significant and are derived from temporal data, which involve real numbers representing values collected regularly over time. Time series have a great impact on many types of data. However, time series have anomalies. We introduce hybrid algorithm named novel matrix profile (NMP) to solve the all-pairs similarity search problem for time series data. The proposed NMP inherits the features from two state-of-the art algorithms: similarity time-series automatic multivariate prediction (STAMP), and short text online microblogging protocol (STOMP). The proposed algorithm caches the output in an easy-to-access fashion for single- and multidimensional data. The proposed NMP algorithm can be used on large data sets and generates approximate solutions of high quality in a reasonable time. The proposed NMP can also handle several data mining tasks. It is implemented on a Python platform. To determine its effectiveness, it is compared with the state-of-the-art matrix profile algorithms i.e., STAMP and STOMP. The results confirm that the proposed NMP provides higher accuracy than the compared algorithms.

Critical Infrastructures and associated real time Informational systems need some security protection mechanisms that will be able to detect and respond to possible attacks. For this reason, Anomaly Detection Systems (ADS), as part of a Security Information and Event Management (SIEM) system, are needed for constantly monitoring and identifying potential threats inside an Information Technology (IT) System. Typically, ADS collect information from various sources within a CI system using security sensors or agents and correlate those information so as to identify anomaly events. Such sensors though in a CI setting (factories, power plants, remote locations) may be placed in open areas and left unattended thus becoming targets themselves of security attacks. They can be tampering and malicious manipulated so that they provide false data that may lead an ADS or SIEM system to falsely comprehend the CI current security status. In this paper, we describe existing approaches on security monitoring in critical infrastructures and focus on how to collect security sensor - agent information in a secure and trusted way. We then introduce the concept of hardware assisted security sensor information collection that improve the level if trust (by hardware means) and also increase the responsiveness of the sensor. Thus, we propose a Hardware Security Token (HST) that when connected to a CI Host, it acts as a secure anchor for security agent information collection. We describe the HST functionality, its association with a host device, its expected role and its log monitoring mechanism. We also provide information on how security can be established between the Host device and the HST.Then, we introduce and describe the necessary Host components that need to be established in order to guarantee a high security level and correct HST functionality. We, also provide a realization-implementation of the HST overall concept in a FPGA SoC evaluation board and describe how the HST implementation can controlled. Finally, we provide indicative use case scenarios of how the HST can be used in practice to provide a variety of different security services beyond acting as a secure ADS sensor.

Nowadays, unmanned vehicles are widely used in various fields. However, the safety of unmanned vehicles is directly determined by the autopilot systems, and sensor faults are a very critical factor that affects the normal operation of the autopilot systems. To efficiently and comprehensively test unmanned systems for sensor faults, this paper proposes a chip-level testing platform for unmanned vehicle autopilot systems based on FPGA hardware-in-the-loop simulation. Unlike existing testing platforms, the introduction of FPGA technology allows us to achieve nanosecond real-time simulation frequency, thus realizing chip-level simulation of sensors and black-box automated testing. Firstly, we propose a set of general platform and modeling methods, and the paper presents a detailed application of the four most common necessary sensors in autopilot systems as examples. Secondly, we propose for the first time a chip-level fault testing method for unmanned systems, and build a test platform with a quadrotor vehicle, the PX4 software platform, and the Pixhawk4 hardware platform as examples. Finally, the experimental results verify that the testing platform proposed in this paper can adapt to various autopilot systems and has high simulation test reliability.

Within the eastern part of the Black Sea and Sea of Azov, the vegetation of charophytes was studied using the Brown-Blanquet approach. For the first time, five communities are described, including three associations and two subassociations from the alliance Charion canescentis Krausch 1964, the order Charetalia intermediae Sauer 1937, and the class Charetea intermediae F. Fukarek 1961. Diagnoses of the described syntaxa are given. A comparison with the Baltic and Mediterranean communities of charophytes is made. It is shown that all the Azov-Black Sea communities with the dominance of charophytes, in contrast to Western European ones, include Lamprothamnium papulosum with high constancy. At the same time, the communities of the southern seas of Russia are characterized by a lower occurrence and abundance of higher aquatic plants (except for Nanozostera noltei) and a slightly higher occurrence of red and green algae. An indirect ordination analysis showed the ecological isolation of the described phytocenoses and revealed two main factors influencing the floristic composition of communities – the granulometric composition of bottom sediments and water eutrophication.

Traditional ways of validating satellite-derived sea surface temperature (SST) and sea surface salinity (SSS) products, using comparisons with buoy measurements, do not allow for evaluating the impact of mesoscale to submesoscale variability. Here we present the validation of remotely-sensed SST and SSS data against the unmanned surface vehicle (USV) – Saildrone – measurements from the Spring 2018 Baja deployment. More specifically, biases and root mean square differences (RMSD) were calculated between USV-derived SST and SSS values, and six satellite-derived SST (MUR, OSTIA, CMC, K10, REMSS, and DMI) and three SSS (JPLSMAP, RSS40, RSS70) products. Biases between the USV SST and OSTIA/CMC/DMI were approximately zero while MUR showed a bias of 0.2C. OSTIA showed the smallest RMSD of 0.36C while DMI had the largest RMSD of 0.5C. An RMSD of 0.4C between Saildrone SST and the satellite-derived products could be explained by the daily variability in USV SST which currently cannot be resolved by remote sensing measurements. For SSS, values from the JPLSMAP product showed saltier biases of 0.2 PSU, while RSS40 and RSS70 showed fresh biases of 0.3 PSU. An RMSD of 0.4 PSU could not be explained solely by the daily variability of the USV-derived SSS. Coherences were significant at the longer wavelengths, with a local maximum at 100 km that is most likely associated with the mesoscale turbulence in the California Current System.

The log data generated during the operation of a software system contains information about the system, and using logs for anomaly detection can detect system failures in a timely manner. Most of the existing log anomaly detection methods are specific to a particular system, have cold start problems, and are sensitive to updates in log format. In this paper, we propose a log anomaly detection method LogBD based on pre-training model and domain adaptation, which uses the pre-training model BERT to learn the semantic information of logs, and can solve the problems caused by multiple meaning words and log statement updates. An anomaly detection method LogBD based on distance to determine anomalies is constructed based on domain adaptation, using TCN networks to extract common features of different system logs and map them to the same hypersphere space. Finally, experiments are conducted on two publicly available datasets to evaluate the method, and the experimental results show that the method can better solve the log instability problem and has some improvement in the cross-system log anomaly detection effect.

This paper addresses the difficulty of balancing real-time response and low power consumption in intelligent manhole cover application scenarios. It proposes a method to distinguish normal and abnormal events by segmenting the boundary where the acceleration of the intelligent manhole cover deviates from a set threshold and lasts for a certain period, based on the difference in vibration patterns of the intelligent manhole cover when a normal event and an abnormal event occur. This paper uses digital output motion sensor data autonomous data fusion to implement the pattern mentioned above recognition algorithm, which reduces the MCU computing and working time and the overall power consumption of the system while meeting the real-time response requirements. The test results demonstrate that the method has a high rate of anomaly recognition accuracy. The method ensures the system's real-time response capability, and the actual low power consumption test demonstrates that the device can operate continuously for 9.5 years. The low power consumption index exceeds the requirements of the existing national standard, thereby resolving the issue that it is challenging to balance intelligent manhole cover abnormality recognition and low power consumption.

Context: Anomaly detection in a data center is a challenging task, having to consider different services on various resources. Current literature shows the application of artificial intelligence techniques to either log files or monitoring data: the former created by services at run time, while the latter produced by specific sensors directly on the physical or virtual machine. Objectives: We propose a model that exploits information both in log files and monitoring data to identify patterns and detect anomalies over time. Methods: The key idea is to use on one side natural language processing solutions to detect problems at service level, extracting words that represent anomalies. Clustering and topic modeling techniques have been used to identify patterns and group them with respect to topics. On the other side time series anomaly detection technique has been applied to sensors data in order to combine problems found in the log files with problems stored in the monitoring data. Results: We have tested our approach on a real data center equipped with log files and monitoring data that can characterize the behaviour of physical and virtual resources in production. We have observed a correspondence between anomalies in log files and monitoring data, e.g. an increase in memory usage or machine load. The results are extremely promising. Conclusion: Our model requires to integrate site administrators' expertise in order to consider all critical scenario in the data center and understand results properly.