Academic literature on the topic 'Ecocrime'

Directive 2008/99/EC, on the protection of the environment through criminal law, was approved after fierce dispute among European institutions. Its aim was to harmonise the definition of environmental offences in the EU and to ensure that they would be punished with ‘proportionate, effective and dissuasive’ criminal sanction. The authors, after having outlined the notion of environmental crime that the harmonisation instrument wishes to combat, critically assess its policy objectives and investigate the legal and practical outcomes of its implementation. In the final paragraph, they discuss the challenges faced by national authorities when trying to translate such supranational legislation into court practices.

Over time, ecocide was considered mainly through the praxiology of command. This paper will demonstrate that actions aiming to prevent ecocide are hindered by the existing reference point in the construction of environmental standards. In order to build ecological safety, the illusory and short-term nature of environmental projects should be neutralised. Ecocide is a threat that requires solutions both at the level of national law and a forward-oriented global strategy. The discussion will show that ecocide is a virulent and pejorative phenomenon that currently goes beyond classically understood ecocrime. Preventing ecocide is an urgent necessity of a civilisational nature. The intergeneration component will be highlighted in considerations, which should encourage the updating and implementation of a strategy with a view to ensuring ecological safety on a continuous basis. The formal-dogmatic and comparative method in interpretation legal acts of international law with legal documents in the field of environmental protection has been applied in order to justify the hypothesis. Scientific research are accompanied by an analyze the relationship between the nature and human world.

Addressing environmental crimes is an illegal behavior which means that by doing this, it is possible to affect our individual lives. This study explored the environmental crime in the province of Misamis Occidental in the past five years. The descriptive-correlational design was the research design used in the study. The data that was acquired from the DENR office. In order to help the competent authorities create inventive strategies, the trend of environmental crimes in the province of Misamis Occidental during the last five years has been examined. The data shows that environmental crimes have significantly increased over the previous five years, with illegal logging and unlawful disposal of hazardous waste being the most common infractions. The study also demonstrates that the absence of enforcement and regulation contributes to the persistence of these crimes. To address these problems, the authority may think about enacting more severe fines and punishments for the environment to encourage responsible environmental activities, crimes must be reduced, enforcement must be strengthened, and awareness and education initiatives must be launched. These tactics might contribute to decreasing environmental crimes and increasing the province's environmental sustainability. In conclusion, conducting a comprehensive analysis of the five-year trends of environmental crimes in Misamis Occidental is essential and urgent. The insights gained from this study will play a pivotal role in formulating evidence-based intervention plans, empowering the authority to combat environmental crimes effectively and safeguard the province's natural resources for future generations. This study will serve as a crucial basis for crafting effective intervention plans by the authority. By understanding the patterns and dynamics of environmental crimes over a significant period, the authority will be equipped with invaluable insights to develop targeted prevention, enforcement, and restoration strategies.

This paper is an ethnographic case study of COVID-19 emergence in Santo Tomé (South America, NE Argentina, ≂ 25,000 inhabitants). Based on interviews with healthcare personnel, we describe local containment and prevention policies in a context of national lockdown measures. We reconstruct a tree diagram of infections, index cases and close contacts that spread infection locally. In parallel, fieldwork in a sample of impoverished subsistence agricultures and fishermen allows us to describe drought and fresh food production decline during confinement as convergent ecocrises (pluralea interactions) with the SARS-CoV-2 outbreak. The core idea of the article, which emerged from ethnographic fieldwork evidence, is that in the context of climate change, the sudden onset of an infectious disease interacts with convergent ecocrises.

Abstract In 2016, over 50,000 people died in the US from heroin and prescription opioid related overdoses. Current standard of care is not sufficient. Therapeutic vaccines against opioids are safe, long-lasting, and cost-effective alternatives to opioid-based pharmacotherapy. Opioid vaccines elicit opioid-specific antibodies that prevent opioid distribution to the brain and opioid-induced behavior and toxicity. To ensure translation, we have developed candidate vaccines that include components suitable for good manufacturing practices and have the ability to be characterized according to FDA guidelines. In this study, we synthesized three oxycodone-based (OXY) haptens to assess linker and bioconjugation chemistry in vaccine efficacy against oxycodone in mice. Then, we tested efficacy of lead OXY haptens conjugated to novel carrier proteins consisting of E. coli-expressed diphtheria cross-reactive material (EcoCRM™) and nontoxic tetanus toxin fragment (rTTHc). Haptens using tetraglycine linkers coupled with carbodiimide chemistry elicited the greatest oxycodone specific serum IgG antibody titers and significantly reduced oxycodone distribution to the brain. Oxycodone vaccines containing EcoCRM™ and rTTHc, adsorbed on alum adjuvant, were effective in inducing expansion of oxycodone-specific plasma and germinal center B cells, blocking oxycodone distribution to the brain, and were easily characterized by mass spectrometry. The OXY-EcoCRM™ and OXY-rTTHc were equivalent, or superior, to vaccines containing keyhole limpet hemocyanin protein, tetanus toxoid and CRM197, making them candidate immunogens for further development.

Background/Objectives: Rapid development of vaccines against SARS-CoV-2 was pivotal to controlling the COVID-19 pandemic. The emergency also provided a rare opportunity to test novel vaccine platforms such as mRNA in large clinical trials. Most of the early vaccines used SARS-CoV-2 Spike protein as the target antigen. Nevertheless, subsequent studies have shown that Receptor Binding Domain (RBD) of Spike also can yield efficacious vaccines, and we previously demonstrated that chemical conjugation of RBD to a carrier protein, EcoCRM®, enhanced antibody responses and induced strong virus neutralization activity in mice. Methods: Here, we compared the immunogenicity of this conjugate to that of an approved mRNA vaccine from Pfizer/BioNTech in rhesus macaques over a period of nine months. Results: AS01-adjuvanted RBD conjugate induced a similar or better antibody response, receptor binding inhibition, and virus neutralization activity against different variants of SARS-CoV-2, compared to mRNA. IgG subclass profiles induced by conjugate and mRNA vaccines were initially dominated by IgG1 and IgG3 then switched to IgG2 and IgG4 dominant profiles during the subsequent six-month period. Polyclonal immune sera from the conjugate and mRNA had similar antibody avidity at multiple time points. Conclusions: In summary, antibody responses in rhesus macaques induced by the RBD-EcoCRM conjugate and the Spike mRNA vaccine are very similar. These results demonstrate the potential for the RBD-EcoCRM conjugate as a vaccine against SARS-CoV-2.

Ekonomisk brottslighet är mer lukrativt jämfört med annan brottslighet som narkotika, häleri och människohandel. Tidiga åtgärder som försvårar att kriminella kan använda företag för brottsliga syften gör att stora kostnader för samhället kan undvikas. En genomgång av litteraturen visade också att det finns stora brister i samarbetet mellan svenska myndigheter för att upptäcka grov ekonomisk brottslighet. Idag uppdagas brotten först ofta efter att en konkurs inletts. I studier har maskininlärningsmodeller prövats för att kunna upptäcka ekonomisk brottslighet och några svenska myndigheter använder maskininlärningsmodeller för att upptäcka brott men mer avancerade metoder används idag av danska myndigheter. Bolagsverket har idag ett omfattande register för bolag i Sverige och denna studie syftar till att undersöka om maskininlärning kan användas för att identifiera misstänkta bolag, genom att använda digitalt inlämnade årsredovisningar och information ur bolagsverkets register för att kunna träna klassificeringsmodeller att identifiera misstänkta bolag. För att träna modellen så har stämningsansökningar inhämtats från Ekobrottsmyndigheten som kunnat kopplas till specifika bolag av de inlämnade årsredovisningar. Principalkomponentanalys används för att visuellt visa på skillnader mellan grupperna misstänkta och icke misstänkta bolag och analyserna visade på ett överlapp mellan grupperna och ingen tydlig klustring av grupperna. Data var obalanserat med 38 misstänkta bolag av totalt 1009 bolag och därför användes översamplingstekniken SMOTE för att skapa mer syntetiskt data och för att öka antalet i gruppen misstänkta. Två maskininlärningsmodeller Random Forest och Stödvektormaskin (SVM) jämfördes i en 10 fold korsvalidering. Där båda uppvisade en recall på runt 0.91 men där Random Forest hade en mycket högre precision och med högre accuracy. Random Forest valdes och tränades på nytt och uppvisades en recall på 0.75 när den testades på osett data bestående av 8 misstänkta av 202 bolag. Ett sänkt tröskelvärde resulterade i en högre recall men med en större antal felklassificerade bolag. Studien visar tydligt problemet med obalans i data och de utmaningar man ställs inför med mindre data. Ett större data hade möjligjort ett strängare urval på brottstyper som hade kunnat ge en mer robust modell som skulle kunna användas av bolagsverket för att lättare kunna identifiera misstänkta bolag i deras register.<br>Economic crimes are more lucrative compared to other crimes as drugs, selling of stolen gods, trafficing. Early preventions that make it more difficult for criminals to use companies for criminal purposes can reduce large costs for sociaty. A litterature study showed that there are large weaknesses in the collaboration between Swedish authorities to detect serious economic crimes.Today most crimes among companies that commit fraud are found after a company has declared bancruptcy. In studies, machine learning models have been tested to detect economic crimes and some swedish authorites are now using machine learning methods to detect different crimes and more advanced methods are used by the danish authorites. Bolagsverket has a large register of companies in Sweden and the aim of this study is to investigate if machinelearning can be used to detect on annual reports that have been digitaly submited and information in Bolagsverket’s register to be able to train classificationsmodels and identify companies that are suspicious. To be able to train the model lawsuits have been collected from the Swedish Economic Crime Authority that can be connected to specific companies through their digitally submited annual report. Principal component analysis is used to visually show differences between the groups suspect companies and not suspected companies and the analysis show that there is an overlap between the groups and no clear clustering between the groups. Because the dataset was unbalanced with 38 suspicious companies out of 1009 companies the oversampling tecnique SMOTE was used to create more synthethic data and more suspects in the dataset. The two machinelearnings models Random Forest and support vector machine (SVM) was compared in a 10 fold crossvalidation. Both models showed a recall on around 0.91 but Random Forest had a much higher precision with a higher accuracy. Random Forest was chosen and was trained again and showed a recall on 0.75 when it was tested on unseen data with 8 suspects out of 202 companies. Lowering the treshold resulted in a higher recall but with a larger portion of wrongly classfied companies. The study shows clearly the problem with an unbalanced dataset and the challanges with a small dataset. A larger dataset could have made it possible to make a more selective selection of certain crimes that could have resulted in a more robust model that could be used by Bolagsverket to easier identify suspicous companies in their register.