What You See Is Not What You Know: Deepfake Image Manipulation

Research indicates that deceitful videos tend to spread rapidly online and influence people’s opinions and ideas. Because of this, video misinformation via deepfake video manipulation poses a significant online threat. This study aims to discover what factors can influence viewers’ capability of distinguishing deepfake videos from genuine video footage. This work focuses on exploring deepfake videos’ potential use for deception and misinformation by exploring people’s ability to determine whether videos are deepfakes in a survey consisting of deepfake videos and original unedited videos. The participants viewed a set of four videos and were asked to judge whether the videos shown were deepfakes or originals. The survey varied the familiarity that the viewers had with the subjects of the videos. Also, the number of videos shown at one time was manipulated. This survey showed that familiarity of subjects has a statistically significant impact on how well people can determine a deepfake. Notably, however, almost two thirds of study participants (102 out of 154, or 66.23%) were unable to correctly identify a sequence of just four videos as either genuine or deepfake. Overall, the study provides insights into possible methods for countering disinformation and deception resulting from the misuse of deepfakes

What You See Is Not What You Know: Studying Deception in Deepfake Video Manipulation

Research indicates that deceitful videos tend to spread rapidly online and influence people’s opinions and ideas. Because of this, video misinformation via deepfake video manipulation poses a significant online threat. This study aims to discover what factors can influence viewers’ capability to distinguish deepfake videos from genuine video footage. This work focuses on exploring deepfake videos’ potential use for deception and misinformation by exploring people’s ability to determine whether videos are deepfakes in a survey consisting of deepfake videos and original unedited videos. The participants viewed a set of four videos and were asked to judge whether the videos shown were deepfakes or originals. The survey varied the familiarity that the viewers had with the subjects of the videos. Also, the number of videos shown at one time was manipulated. This survey showed that familiarity with subjects has a statistically significant impact on how well people can determine a deepfake. Notably, however, almost two-thirds of study participants (102 out of 154, or 66.23%) were unable to correctly identify a sequence of just four videos as either genuine or deepfake. This study provides insights into possible considerations for countering disinformation and deception resulting from the misuse of deepfakes

Subseasonal-to-interdecadal variability of the Australian monsoon over North Queensland

Daily rainfall occurrence and amount at 11 stations over North Queensland are examined for summers 1958–1998, using a Hidden Markov Model (HMM). Daily rainfall variability is described in terms of the occurrence of five discrete ‘weather states’, identified by the HMM. Three states are characterized respectively by very wet, moderately wet, and dry conditions at most stations; two states have enhanced rainfall along the coast and dry conditions inland. Each HMM rainfall state is associated with a distinct atmospheric circulation regime. The two wet states are accompanied by monsoonal circulation patterns with large-scale ascent, low-level inflow from the north-west, and a phase reversal with height; the dry state is characterized by circulation anomalies of the opposite sense. Two of the states show significant associations with midlatitude synoptic waves. Variability of the monsoon on time-scales from subseasonal to interdecadal is interpreted in terms of changes in the frequency of occurrence of the five HMM rainfall states. Large subseasonal variability is identified in terms of active and break phases, and a highly variable monsoon onset date. The occurrence of the very wet and dry states is somewhat modulated by the Madden–Julian oscillation. On interannual time-scales, there are clear relationships with the El Niño–Southern Oscillation and Indian Ocean sea surface temperatures (SSTs). Interdecadal monsoonal variability is characterized by stronger monsoons during the 1970s, and weaker monsoons plus an increased prevalence of drier states in the later part of the record. Stochastic simulations of daily rainfall occurrence and amount at the 11 stations are generated by introducing predictors based on large-scale precipitation from (a) reanalysis data, (b) an atmospheric general circulation model (GCM) run with observed SST forcing and (c) antecedent June–August Pacific SST anomalies. The reanalysis large-scale precipitation yields relatively accurate station-level simulations of the interannual variability of daily rainfall amount and occurrence, with rainfall intensity less well simulated. At some stations, interannual variations in 10-day dry-spell frequency are also simulated reasonably well. The interannual quality of the simulations is markedly degraded when the GCM simulations are used as inputs, while antecedent Pacific SST inputs yield an anomaly correlation skill comparable to that of the GCM

Global Wave Energy Testing Sites : Seafloor Bathymetry and Slope

A review of global marine energy test sites and associated bathymetric condition

Wave Energy Converter Archetypes and Power Performance Representation

There is a continuing and increasing need to develop renewable energy technologies that are efficient, cost-effective and produce usable forms of energy. Wave energy converters (WECs) have an opportunity to play a key and significant role in the integration of renewable energy technologies on a commercial scale. It is estimated that waves off the United States coast could provide ~64% of U.S electricity generation in 2018 [1]. A priority requirement to assist marine energy development is a better understanding what types of WECs are currently being developed and their current associated power production performance estimates. This report explains the key differences between different WECs and the various power take-off (PTO) systems within then. Overviews of how the WEC operates, basic dimensions of the WEC, and a performance matrix are provided for each WEC archetype. The performance matrices illustrate the amount of power generated based on the significant wave height and the wave energy period, as per the International Electrotechnical Commission TC-114 Technical Specifications

Subseasonal-to-interdecadal variability of the Australian monsoon over North Queensland

Daily rainfall occurrence and amount at 11 stations over North Queensland are examined for summers 1958–1998, using a Hidden Markov Model (HMM). Daily rainfall variability is described in terms of the occurrence of five discrete ‘weather states’, identified by the HMM. Three states are characterized respectively by very wet, moderately wet, and dry conditions at most stations; two states have enhanced rainfall along the coast and dry conditions inland. Each HMM rainfall state is associated with a distinct atmospheric circulation regime. The two wet states are accompanied by monsoonal circulation patterns with large-scale ascent, low-level inflow from the north-west, and a phase reversal with height; the dry state is characterized by circulation anomalies of the opposite sense. Two of the states show significant associations with midlatitude synoptic waves. Variability of the monsoon on time-scales from subseasonal to interdecadal is interpreted in terms of changes in the frequency of occurrence of the five HMM rainfall states. Large subseasonal variability is identified in terms of active and break phases, and a highly variable monsoon onset date. The occurrence of the very wet and dry states is somewhat modulated by the Madden–Julian oscillation. On interannual time-scales, there are clear relationships with the El Niño–Southern Oscillation and Indian Ocean sea surface temperatures (SSTs). Interdecadal monsoonal variability is characterized by stronger monsoons during the 1970s, and weaker monsoons plus an increased prevalence of drier states in the later part of the record. Stochastic simulations of daily rainfall occurrence and amount at the 11 stations are generated by introducing predictors based on large-scale precipitation from (a) reanalysis data, (b) an atmospheric general circulation model (GCM) run with observed SST forcing and (c) antecedent June–August Pacific SST anomalies. The reanalysis large-scale precipitation yields relatively accurate station-level simulations of the interannual variability of daily rainfall amount and occurrence, with rainfall intensity less well simulated. At some stations, interannual variations in 10-day dry-spell frequency are also simulated reasonably well. The interannual quality of the simulations is markedly degraded when the GCM simulations are used as inputs, while antecedent Pacific SST inputs yield an anomaly correlation skill comparable to that of the GCM

Cumulative and temporal associations between antimicrobial prescribing and community-associated <i>Clostridium difficile</i> infection:population-based case-control study using administrative data

Background. Community-associated (CA) Clostridium difficile infection (CDI) is a major public health problem. This study estimates the magnitude of the association between temporal and cumulative prescription of antimicrobials in primary care and CA-CDI. CA-CDI is defined as cases without prior hospitalisation in the previous 12 weeks who were either tested outside of hospital or tested within 2 days of admission to hospital. Methods. Three National patient level datasets –covering CDI cases, community prescriptions and hospitalisations were linked by the NHS Scotland unique patient identifier, the community health index, CHI. All validated cases of CDI from August 2010 to July 2013 were extracted and up to six population-based controls were matched to each case from the CHI register for Scotland. Statistical analysis used conditional logistic regression. Results. 1446 unique cases of CA-CDI were linked with 7964 age, sex and location matched controls. Cumulative exposure to any antimicrobial in the previous 6 months has a monotonic dose-response association with CA-CDI. Individuals with excess of 28 defined daily doses (DDD) to any antimicrobial (19.9% of cases) had an odds ratio (OR)=4.4 (95% CI:3.4-5.6) compared to those unexposed. Individuals exposed to 29+ DDD of high risk antimicrobials (cephalosporins, clindamycin co-amoxiclav, or fluoroquinolones) had an OR=17.9 (95% CI:7.6-42.2). Elevated CA-CDI risk following high risk antimicrobial exposure was greatest in the first month (OR=12.5 (8.9-17.4)) but was still present 4-6 months later (OR=2.6 (1.7-3.9)). Cases exposed to 29+DDD had prescription patterns more consistent with repeated therapeutic courses, using different antimicrobials, than long term prophylactic use. Conclusions. This analysis demonstrated temporal and dose-response associations between CA-CDI risk and antimicrobials with an impact of exposure to high risk antimicrobials remaining 4-6 months later

Residual effect of community antimicrobial exposure on risk of hospital onset healthcare associated Clostridioides difficile infection:a case-control study using national linked data

Background: Associations between antimicrobial exposure in the community and community-associated Clostridioides difficile infection (CA-CDI) are well documented but associations with healthcare-associated CDI (HA-CDI) are less clear. This study estimates the association between antimicrobial prescribing in the community and HA-CDI. Methods: A matched case–control study was conducted by linking three national patient level datasets covering CDI cases, community prescriptions and hospitalizations. All validated cases of HA-CDI (August 2010 to July 2013) were extracted and up to three hospital-based controls were matched to each case on the basis of gender, age, hospital and date of admission. Conditional logistic regression was applied to estimate the association between antimicrobial prescribing in the community and HA-CDI. A sensitivity analysis was conducted to consider the impact of unmeasured hospital antimicrobial prescribing. Results: Nine-hundred and thirty unique cases of HA-CDI with onset in hospital and no hospital discharge in the 12 weeks prior to index admission were linked with 1810 matched controls. Individuals with prior prescription of any antimicrobial in the community had an odds ratio (OR) = 1.41 (95% confidence interval (CI) 1.13–1.75) for HA-CDI compared with those without. Individuals exposed to high-risk antimicrobials (cephalosporins, clindamycin, co-amoxiclav or fluoroquinolones) had an OR = 1.86 (95% CI: 1.33–2.59). After accounting for the likely impact of unmeasured hospital prescribing, the community exposure, particulary to high-risk antimicrobials, was still associated with elevated HA-CDI risk. Conclusions: Community antimicrobial exposure is an independent risk factor for HA-CDI and should be considered as part of the risk assessment of patients developing diarrhoea in hospital

A classification system for global wave energy resources based on multivariate clustering

Better understanding of the global wave climate is required to inform wave energy device design and large-scale deployment. Spatial variability in the global wave climate is analysed here to provide a range of characteristic design wave climates. K-means clustering was used to split the global wave resource into 6 classes in a device agnostic, data-driven method using data from the ECMWF ERA5 reanalysis product. Classification using two sets of input data were considered: a simple set (based on significant wave height and peak wave period) and a comprehensive set including a wide range of relevant wave climate parameters. Both classifications gave resource classes with similar characteristics; 55% of tested locations were assigned to the same class. Two classes were low energy, found in enclosed seas and sheltered regions. Two classes were moderate wave energy classes; one swell dominated and the other in areas with wave action often generated by more local storms. Of the two higher energy classes; one was more often found in the northern hemisphere and the other, most energetic, predominantly on the tips of continents in the southern hemisphere. These classes match existing regional understanding of resource. Consideration of publicly available device power matrices showed good performance was primarily realised for the two highest energy resource classes (25–30% of potential deployment locations); it is suggested that effort should focus on optimising devices for additional resource classes. The authors hypothesise that the low-risk, low variability, swell dominated moderate wave energy class would be most suitable for future exploitation

PacWave Wave Resource Assessment

The Pacific Northwest of the United States is characterized by one of the greatest annual mean wave power resources in the world. As a result, the wave energy resource offshore of Oregon has been characterized, through hindcast models and physical buoy data, throughout the past decade. Over the past 8 years, Oregon State University (OSU) has been developing an open-ocean wave energy test facility, PacWave, which is affiliated with the Pacific Marine Energy Center (PMEC). The facility consists of north and south test sites off the coast of Newport, Oregon. This report contains detailed analysis of wave characteristics at both the north and south sites based on a newly available 32-year SWAN hindcast simulation and follows the recommendations issued by the International Electrotechnical Commission (IEC) technical specification (TS) 62600-101 for wave energy resource assessments. This assessment aims to build upon the previous wave energy characterizations in the region and provide the most up-to-date characterization of the wave energy resource at PacWave