The Community Mental Health Education and Detection (CMED) tool was designed and validated for community health workers (CHWs) in South Africa to promote mental health education, detection and linkage to care for adults at risk of mental health conditions. This study evaluated CMED scale-up using implementation research to understand reach and adoption.Routinely collected CHW data from three scale-up community areas were analysed over six months. Using the Reach and Adoption components of the RE-AIM framework, data included the (i) number of CMED administrations; (ii) proportion of identified presumptive cases; and (iii) proportion of referred cases who received care. These data identified high-and low-adopting CHW teams. Observations and repeated group discussions explored factors influencing adoption. CHWs completed 2,135 CMED administrations. Seventeen percent screened positive and were referred for further assessment at PHC facilities; 62% of those referred presented for assessment, diagnosis, and management. Adoption varied across teams. Barriers included poor data systems and inconsistent supply of mental health services. Supportive leadership and supervision were strong facilitators of adoption. Policy uptake signalled maintenance. Findings suggest the CHW-delivered CMED tool is viable and useful for narrowing the treatment gap by strengthening demand for and access to mental health services.

Greenwashing poses a significant challenge to the fight against climate change by undermining trust in corporate sustainability claims. This study introduced the greenwashing tendency score (GTS), an automatable method designed to detect greenwashing tendencies in corporate sustainability reports. By leveraging textual sentiment and alignment analysis techniques in conjunction with environmental, social, and governance ratings, the GTS quantifies discrepancies between communicated and actual sustainability performance. We applied our methodology to 36 German stock index companies during the years from 2020 to 2022. Our key findings reveal substantial variations in greenwashing tendencies among these companies, emphasizing the need for more transparent and reliable sustainability reporting. The GTS emerged as a scalable, reproducible, and objective tool that can aid, for example, investors, regulators, and Non-government organizations in identifying greenwashing practices. This research contributed to the sustainable finance literature by introducing a neutral and open measure to assess firms’ greenwashing tendency, summarizing implications for policymaking and regulatory authorities and discussing its potential for long-term accountability and integrity in corporate sustainability communications.

The Puerto Rico Plain Pigeon Patagioenas inornata wetmorei suffered a severe population decline after hurricanes Irma and Maria in September 2017. We used distance sampling to estimate abundance (density and population size) in April–June 1986−2024, accounting for changes in detection probability. We used the distance-sampling abundance estimates to populate a Bayesian state–space logistic model and update posterior estimates of population carrying capacity, maximum population growth rate, population recovery time, and predicted abundance in April–June 2025−2034, accounting for observation and process variances. In addition, we used predicted abundance to assess potential extinction risk (probability Pr[N2025−2034 = 0|data]), population self-sustainability above 5,000 individuals (Pr[N2025−2034 >5,000|data]), and population surpassing the 2.5th percentile of carrying capacity (Pr[N2025−2034 >30,000|data]). The population has not recovered from the hurricanes, with estimated density averaging 0.0015 individuals/ha (bootstrapped standard error [SE] = 0.0006) and population size averaging 1,097 individuals (SE = 455) at the 749,000-ha survey region in April–June 2018−2024. Posterior mean estimates were 41,580 individuals (Markov Chain Monte Carlo standard deviation [SD] = 8,052) for population carrying capacity, 0.183 (SD = 0.056) for maximum population growth rate, six years (SD = 2) for recovery time, and 7,173 individuals (SD = 12,309) for predicted abundance in April–June 2025−2034. The population may reach self-sustainability levels (range Pr[N2025−2034 >5,000|data] = 0.326−0.631) but currently is undergoing a prolonged bottleneck and may become extinct (range Pr[N2025−2034 = 0|data] = 0.199−0.332), particularly if reproduction continues to be mostly unsuccessful, anthropogenic disturbances remain unabated, and on top of that another devastating hurricane makes landfall during the next 10 years. The Puerto Rico Plain Pigeon subspecies is in urgent need of management aiming to increase and maintain abundance above 5,000 individuals but preferably surpassing the 2.5th percentile of population carrying capacity as in the late 1990s (range Pr[N2025−2034 >30,000|data] = 0.000−0.181).

The key requirement for GMO authorisation is the submission of analytical methods for the detection, identification and quantification (DIQ), which has proven challenging in the case of New Genomic Techniques (NGTs). Currently available non-analytical approaches, such as blockchain traceability and probabilistic analysis, while potentially useful for monitoring, are insufficient for authorisation purposes. The lack of reliable DIQ methods hinders the authorisation of NGT products and raises concerns for both organic and conventional agriculture, where the presence of NGT products goes undetected. Therefore, the existing GMO regulatory framework requires reevaluation to address the challenges posed by NGTs while ensuring compliance with the broader EU food law framework.

This paper examines the effects of state capacity on the reported Covid-19 infection (and mortality) rate and its policy implications. We analyse two dimensions of state capacity which were critical during the pandemic. The healthcare capacity acted to contain the virus outbreak (an effect we call containment). The information capacity acted to detect contagious yet asymptomatic cases (an effect we call detection). We argue that containment pushes down the reported infection rate. In contrast, detection pushes it up, thus generating a non-linear combined effect that we estimate systematically using Colombian municipality-level as well as country-level data, different data sources, and various empirical strategies. Our findings indicate that the infection (and mortality) rates were likely under-reported, especially in areas with a low state capacity level, due to their poor capabilities to detect the virus. Our study put the emphasis on the many facets of state capacity, each affecting in complex ways our understanding of important phenomena, such as the Covid-19 outbreak.

Automatic license plate recognition (ALPR) systems are increasingly used to solve issues related to surveillance and security. However, these systems assume constrained recognition scenarios, thereby restricting their practical use. Therefore, we address in this article the challenge of recognizing vehicle license plates (LPs) from the video feeds of a mobile security robot by proposing an efficient two-stage ALPR system. Our ALPR system combines the on-the-shelf YOLOv7x model with a novel LP recognition model, called vision transformer-based LP recognizer (ViTLPR). ViTLPR is based on the self-attention mechanism to read character sequences on LPs. To ease the deployment of our ALPR system on mobile security robots and improve its inference speed, we also propose an optimization strategy. As an additional contribution, we provide an ALPR dataset, named PGTLP-v2, collected from surveillance robots patrolling several plants. The PGTLP-v2 dataset has multiple features to cover chiefly the in-the-wild scenario. To evaluate the effectiveness of our ALPR system, experiments are carried out on the PGTLP-v2 dataset and five benchmark ALPR datasets collected from different countries. Extensive experiments demonstrate that our proposed ALPR system outperforms state-of-the-art baselines.

Oral cancer survival rates have seen little improvement over the past few decades. This is mainly due to late detection and a lack of reliable markers to predict disease progression in oral potentially malignant disorders (OPMDs). There is a need for highly specific and sensitive screening tools to enable early detection of malignant transformation. Biochemical alterations to tissues occur as an early response to pathological processes; manifesting as modifications to molecular structure, concentration or conformation. Raman spectroscopy is a powerful analytical technique that can probe these biochemical changes and can be exploited for the generation of novel disease-specific biomarkers. Therefore, Raman spectroscopy has the potential as an adjunct tool that can assist in the early diagnosis of oral cancer and the detection of disease progression in OPMDs. This review describes the use of Raman spectroscopy for the diagnosis of oral cancer and OPMDs based on ex vivo and liquid biopsies as well as in vivo applications that show the potential of this powerful tool to progress from benchtop to chairside.

This paper discusses the challenges and opportunities in accessing data to improve workplace relations law enforcement, with reference to minimum employment standards such as wages and working hours regulation. Our paper highlights some innovative examples of government and trade union efforts to collect and use data to improve the detection of noncompliance. These examples reveal the potential of data science as a compliance tool but also suggest the importance of realizing a data ecosystem that is capable of being utilized by machine learning applications. The effectiveness of using data and data science tools to improve workplace law enforcement is impacted by the ability of regulatory actors to access useful data they do not collect or hold themselves. Under “open data” principles, government data is increasingly made available to the public so that it can be combined with nongovernment data to generate value. Through mapping and analysis of the Australian workplace relations data ecosystem, we show that data availability relevant to workplace law compliance falls well short of open data principles. However, we argue that with the right protocols in place, improved data collection and sharing will assist regulatory actors in the effective enforcement of workplace laws.

Although fluorescence detection is a sensitive method in the field of pollutant analysis, its application is restricted due to the fluorescence shown by organic material being quenched after aggregation and to low photo-thermal stability. To address these issues, a novel mineral/dye composite material was prepared by intercalating a fluorescence molecule, Rhodamine (R6G), into the interlayer space of montmorillonite (Mnt). This composite material greatly enhanced the light stability and efficiency of R6G. After enhancement, the fluorescence lifetime of R6G-Mnt was eight times longer than originally and the luminous intensity was 20 times greater. Chromium at the mmol/L (mM) level can be detected by the naked eye when its enhanced fluorescent property is fabricated into a solid test paper, even though a fluorescence spectrophotometer should be used for detection at the 0.01 μmol/L level in the sensing range 0.01 μmol/L to 100 mmol/L. These results can provide new avenues as well as a theoretical and experimental foundation for the development of novel supramolecular luminescent material.

For many deaths associated with influenza and Omicron infections, those viruses are not detected. We applied previously developed methodology to estimate the contribution of influenza and Omicron infections to all-cause mortality in France for the 2014–2015 through the 2018–2019 influenza seasons, and the period between week 33, 2022 and week 12, 2023. For the 2014–2015 through the 2018–2019 seasons, influenza was associated with annual average of 15,654 (95% CI (13,013, 18,340)) deaths, while between week 33, 2022 and week 12, 2023, we estimated 7,851 (5,213, 10,463) influenza-associated deaths and 32,607 (20,794, 44,496) SARS-CoV-2 associated deaths. For many Omicron-associated deaths for cardiac disease, mental&behavioural disorders, and other causes, Omicron infections are not characterised as a contributing cause of death – for example, between weeks 33–52 in 2022, we estimated 23,983 (15,307, 32,620) SARS-CoV-2-associated deaths in France, compared with 12,811 deaths with COVID-19 listed on death certificate. Our results suggest the need for boosting influenza vaccination coverage in different population groups in France, and for wider detection of influenza infections in respiratory illness episodes (including pneumonia) in combination with the use of antiviral medications. For Omicron epidemics, wider detection of Omicron infections in persons with underlying health conditions is needed.