scholarly journals Medicinal Cannabis: Policy, Patients, and Providers

2021 ◽  
pp. 152715442198960
Author(s):  
Jennie E. Ryan ◽  
Sean Esteban McCabe ◽  
Carol J. Boyd
Keyword(s):  
District Of Columbia ◽  
State Policies ◽  
Policy Changes ◽  
State Laws ◽  
Federal Policies ◽  
Health Delivery ◽  
Medicinal Cannabis ◽  
Cannabis Policy ◽  
The Impact

Medicinal cannabis is legal in some form in 47 states, 3 United States territories, and the District of Columbia. An estimated three million Americans use cannabis for relief of a variety of illnesses, and this figure is expected to grow based on policy changes. However, cannabis remains illegal at the federal level as a Schedule I drug under the 1970 Controlled Substances Act. Schedule I classification of cannabis has impeded the advancement of research, leaving providers with little evidence-based information to educate their patients. Furthermore, the disparities in individual state laws create significant social and health inequities in gaining access to medicinal cannabis. Conflicting state and federal policies regarding medicinal cannabis create logistical and ethical dilemmas, and all U.S. stakeholders—patients, providers, and health delivery systems—may be impacted by conflicting federal and state policies. This brief addresses the impact of conflicting cannabis policies.

ON THE CLASSIFICATION OF DIGITAL TECHNOLOGIES IN CIVIL PROCESS

2019 ◽  
pp. 27-35
Author(s):  
Alexandr Neznamov
Keyword(s):  
Information Processing ◽  
Scientific Community ◽  
Digital Technologies ◽  
Significant Feature ◽  
Legal Proceedings ◽  
Civil Process ◽  
Civil Proceedings ◽  
Processing Information ◽  
The Impact

Digital technologies are no longer the future but are the present of civil proceedings. That is why any research in this direction seems to be relevant. At the same time, some of the fundamental problems remain unattended by the scientific community. One of these problems is the problem of classification of digital technologies in civil proceedings. On the basis of instrumental and genetic approaches to the understanding of digital technologies, it is concluded that their most significant feature is the ability to mediate the interaction of participants in legal proceedings with information; their differentiating feature is the function performed by a particular technology in the interaction with information. On this basis, it is proposed to distinguish the following groups of digital technologies in civil proceedings: a) technologies of recording, storing and displaying (reproducing) information, b) technologies of transferring information, c) technologies of processing information. A brief description is given to each of the groups. Presented classification could serve as a basis for a more systematic discussion of the impact of digital technologies on the essence of civil proceedings. Particularly, it is pointed out that issues of recording, storing, reproducing and transferring information are traditionally more «technological» for civil process, while issues of information processing are more conceptual.

Influence of internal and external risks on sustainability of financial system of Аltai krai

2018 ◽  
Vol 35 (4) ◽  
pp. 133-136
Author(s):  
R. N. Ibragimov
Keyword(s):  
Sustainable Development ◽  
Financial Stability ◽  
Management Strategy ◽  
Financial System ◽  
Risk Management Strategy ◽  
The Sustainable Development ◽  
The Stability ◽  
The Impact

The article examines the impact of internal and external risks on the stability of the financial system of the Altai Territory. Classification of internal and external risks of decline, affecting the sustainable development of the financial system, is presented. A risk management strategy is proposed that will allow monitoring of risks, thereby these measures will help reduce the loss of financial stability and ensure the long-term development of the economy of the region.

Fish diversity

2017 ◽  
Author(s):  
Derek Burton ◽  
Margaret Burton
Keyword(s):  
Life Style ◽  
Fish Diversity ◽  
Additional Species ◽  
The Impact ◽  
Current Species

Fish diversity is considered in terms of variety of their morphological, taxonomic, habitat and population attributes. Fish, with over 30, 000 current species, represent the largest group of vertebrates. The complexity of classification of a group of this size and antiquity, together with recognition of additional species, demands continuous ongoing revision. The impact of the recent fundamental changes in fish classification in 2016 is discussed. Life in water involves adaptations to widely different habitats which can result in physiological morphological and life-style variations which are reviewed.

scholarly journals Formation of an Export-Oriented Agricultural Economy and Regional Open Innovations

2021 ◽  
Vol 7 (1) ◽  
pp. 32
Author(s):  
Victor L. Shabanov ◽  
Marianna Ya Vasilchenko ◽  
Elena A. Derunova ◽  
Andrey P. Potapov
Keyword(s):  
Agricultural Economy ◽  
Institutional Management ◽  
Agricultural Exports ◽  
Modeling Methodology ◽  
Fixed Assets ◽  
Analysis Application ◽  
The Impact ◽  
The Relationship

The aim of the work is to find relevant indicators for assessing the relationship between investments in fixed assets in agriculture, gross output of the industry, and agricultural exports using tools for modeling the impact of innovation and investment development on increasing production and export potential in the context of the formation of an export-oriented agricultural economy. The modeling methodology and the proposed estimating and forecasting tools for diagnosing and monitoring the state of sectoral and regional innovative agricultural systems are used to analyze the relationship between investments in fixed assets in agriculture, gross output of the industry, and agricultural exports based on the construction of the classification of Russian regions by factors that aggregate these features to diagnose incongruence problems and to improve institutional management in regional innovative export-oriented agrosystems. Based on the results of the factor analysis application, an underestimated role of indicators of investment in agriculture, the intensity and efficiency of agricultural production, were established. Based on the results of the cluster analysis, the established five groups of regions were identified, with significant differences in the level of investment in agriculture, the volume of production of the main types of agricultural products, and the export and exported food. The research results are of practical value for use in improving institutional management when planning reforms and transformations of regional innovative agrosystems.

scholarly journals Foreign investment in emerging legal medicinal cannabis markets: the Jamaica case study

2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Marta Rychert ◽  
Machel Anthony Emanuel ◽  
Chris Wilkins
Keyword(s):  
Civil Society ◽  
Foreign Investment ◽  
Production Efficiency ◽  
Production Quality ◽  
Small Scale ◽  
Organic Cultivation ◽  
Enhanced Production ◽  
Political Interference ◽  
Medicinal Cannabis ◽  
The Impact

Abstract Introduction The establishment of a legal market for medicinal cannabis under the Dangerous Drugs Amendment Act 2015 has positioned Jamaica at the forefront of cannabis law reform in the developing world. Many local cannabis businesses have attracted investment from overseas, including from Canada, US and Europe. Aim To explore the opportunities and risks of foreign investment in an emerging domestic legal cannabis market in a developing country. Methods Thematic analysis of semi-structured face-to-face interviews with 22 key informants (KIs) from the Jamaican government, local cannabis industry, academia and civil society, and field observations of legal and illegal cannabis cultivators. Results KIs from the Jamaican public agencies and domestic cannabis entrepreneurs saw foreign investment as an essential source of capital to finance the start-up costs of legal cannabis businesses. Local cannabis entrepreneurs prioritised investors with the greatest financial resources, brand reputation and export networks. They also considered how allied an investor was with their business vision (e.g., organic cultivation, medical vs. recreational). The key benefits of partnering with a foreign investor included transfer of technical knowledge and financial capital, which enhanced production, quality assurance and seed-to-sale tracking. Some KIs expressed concern over investors’ focus on increasing production efficiency and scale at the expense of funding research and development (R&D) and clinical trials. KIs from the local industry, government agencies and civil society highlighted the risks of ‘predatory’ shareholder agreements and domestic political interference. Concerns were raised about the impact of foreign investment on the diversity of the domestic cannabis sector in Jamaica, including the commitment to transition traditional illegal small-scale cannabis cultivators to the legal sector. Conclusion While foreign investment has facilitated the commercialisation of the cannabis sector in Jamaica, regulatory measures are also needed to protect the domestic industry and support the transition of small-scale illegal cultivators to the legal regime. Foreign investments may alter the economic, social and political determinants of health in transitioning from illegal to legal cannabis market economy.

scholarly journals Minimization of the Line Resistance Impact on Memdiode-Based Simulations of Multilayer Perceptron Arrays Applied to Pattern Recognition

2021 ◽  
Vol 11 (1) ◽  
pp. 9
Author(s):  
Fernando Leonel Aguirre ◽  
Nicolás M. Gomez ◽  
Sebastián Matías Pazos ◽  
Félix Palumbo ◽  
Jordi Suñé ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Ex Situ ◽  
Multilayer Perceptrons ◽  
Spice Simulation ◽  
Large Dataset ◽  
High Line ◽  
Calibration Algorithm ◽  
The Impact ◽  
Line Resistance

In this paper, we extend the application of the Quasi-Static Memdiode model to the realistic SPICE simulation of memristor-based single (SLPs) and multilayer perceptrons (MLPs) intended for large dataset pattern recognition. By considering ex-situ training and the classification of the hand-written characters of the MNIST database, we evaluate the degradation of the inference accuracy due to the interconnection resistances for MLPs involving up to three hidden neural layers. Two approaches to reduce the impact of the line resistance are considered and implemented in our simulations, they are the inclusion of an iterative calibration algorithm and the partitioning of the synaptic layers into smaller blocks. The obtained results indicate that MLPs are more sensitive to the line resistance effect than SLPs and that partitioning is the most effective way to minimize the impact of high line resistance values.

scholarly journals A Phantom Study to Investigate Robustness and Reproducibility of Grey Level Co-Occurrence Matrix (GLCM)-Based Radiomics Features for PET

2021 ◽  
Vol 11 (2) ◽  
pp. 535
Author(s):  
Mahbubunnabi Tamal
Keyword(s):  
Textural Features ◽  
Strongly Correlated ◽  
Segmentation Method ◽  
Diagnosis And Prognosis ◽  
Radiotracer Uptake ◽  
Positron Emission ◽  
Occurrence Matrix ◽  
The Impact ◽  
Robust To Noise

Quantification and classification of heterogeneous radiotracer uptake in Positron Emission Tomography (PET) using textural features (termed as radiomics) and artificial intelligence (AI) has the potential to be used as a biomarker of diagnosis and prognosis. However, textural features have been predicted to be strongly correlated with volume, segmentation and quantization, while the impact of image contrast and noise has not been assessed systematically. Further continuous investigations are required to update the existing standardization initiatives. This study aimed to investigate the relationships between textural features and these factors with 18F filled torso NEMA phantom to yield different contrasts and reconstructed with different durations to represent varying levels of noise. The phantom was also scanned with heterogeneous spherical inserts fabricated with 3D printing technology. All spheres were delineated using: (1) the exact boundaries based on their known diameters; (2) 40% fixed; and (3) adaptive threshold. Six textural features were derived from the gray level co-occurrence matrix (GLCM) using different quantization levels. The results indicate that homogeneity and dissimilarity are the most suitable for measuring PET tumor heterogeneity with quantization 64 provided that the segmentation method is robust to noise and contrast variations. To use these textural features as prognostic biomarkers, changes in textural features between baseline and treatment scans should always be reported along with the changes in volumes.

scholarly journals Automated classification of clinical trial eligibility criteria text based on ensemble learning and metric learning

2021 ◽  
Vol 21 (S2) ◽  
Author(s):  
Kun Zeng ◽  
Yibin Xu ◽  
Ge Lin ◽  
Likeng Liang ◽  
Tianyong Hao
Keyword(s):  
Clinical Trial ◽  
Ensemble Learning ◽  
Metric Learning ◽  
Classification Performance ◽  
Ensemble Model ◽  
Automated Classification ◽  
Eligibility Criteria ◽  
Data Imbalance ◽  
The Impact

Abstract Background Eligibility criteria are the primary strategy for screening the target participants of a clinical trial. Automated classification of clinical trial eligibility criteria text by using machine learning methods improves recruitment efficiency to reduce the cost of clinical research. However, existing methods suffer from poor classification performance due to the complexity and imbalance of eligibility criteria text data. Methods An ensemble learning-based model with metric learning is proposed for eligibility criteria classification. The model integrates a set of pre-trained models including Bidirectional Encoder Representations from Transformers (BERT), A Robustly Optimized BERT Pretraining Approach (RoBERTa), XLNet, Pre-training Text Encoders as Discriminators Rather Than Generators (ELECTRA), and Enhanced Representation through Knowledge Integration (ERNIE). Focal Loss is used as a loss function to address the data imbalance problem. Metric learning is employed to train the embedding of each base model for feature distinguish. Soft Voting is applied to achieve final classification of the ensemble model. The dataset is from the standard evaluation task 3 of 5th China Health Information Processing Conference containing 38,341 eligibility criteria text in 44 categories. Results Our ensemble method had an accuracy of 0.8497, a precision of 0.8229, and a recall of 0.8216 on the dataset. The macro F1-score was 0.8169, outperforming state-of-the-art baseline methods by 0.84% improvement on average. In addition, the performance improvement had a p-value of 2.152e-07 with a standard t-test, indicating that our model achieved a significant improvement. Conclusions A model for classifying eligibility criteria text of clinical trials based on multi-model ensemble learning and metric learning was proposed. The experiments demonstrated that the classification performance was improved by our ensemble model significantly. In addition, metric learning was able to improve word embedding representation and the focal loss reduced the impact of data imbalance to model performance.

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