A system dynamics approach to food security: The case of Turkey

Predicting a sustainable food safety policy for the near future is among Turkey's priority problems. In this context, this study aims to predict Turkey's sustainable food safety policies. For this reason, the system dynamics model, which is a dynamic cycle-based method with stock and flow diagrams, is used in this paper. This study supposed the six different scenarios for 2020 and 2050. Data were selected as population, productivity rate, arable land fertility rate, and annual food consumption (per capita). The purpose of creating these scenarios; To determine the most appropriate policy to ensure food safety in Turkey. In the first scenario, we assumed that the current situation continues. In the second scenario, the average productivity rate was increased by 1.5%. The third scenario assumes that annual per capita food consumption rises to 1.2 tonnes per year. In the fourth scenario, the total fertility rate is accelerated by 2%. In the fifth scenario, we assumed that the arable land loss rate decreased by 1/3. Finally, we assumed that the sixth scenario covers all the second, third, fourth, and fifth scenarios and that 2 points reduce food losses. In conclusion, the findings show that food security responds positively in scenarios 2 and 6. However, in other scenarios, food security is negatively affected. The findings show that the sixth scenario is the best-case scenario. To ensure food security, it is necessary to reduce arable land losses and food waste. Training farmers and control of the food supply chain will be beneficial for sustainable food security in Turkey. We recommend that policymakers consider these recommendations.

Exploring the Energy Saving Potential in Private, Public and Non-Motorized Transport for Ten Swedish Cities

Transport energy conservation research in urban transport systems dates back principally to the Organization of the Petroleum Exporting Countries’ (OPEC) “Arab Oil Embargo” (1973–1974) and the Iranian revolution (1979), when global oil supplies became threatened and costs rose steeply. Two subsequent Gulf Wars (1991 and 2003) highlighted the dangerous geo-political dimensions of Middle-Eastern oil. In latter times, the urgency to reduce global CO2 output to avoid catastrophic climate change has achieved great prominence. How to reduce passenger transport energy use therefore remains an important goal, which this paper pursues in ten Swedish cities, based on five scenarios: (1) increasing the relatively low public transport (PT) seat occupancy in each Swedish city to average European levels (buses 35%, light rail 48%, metro 60% and suburban rail 35%); (2) doubling existing PT seat occupancy in each Swedish city; (3) increasing existing car occupancy in each Swedish city by 10%; (4) decreasing existing energy use per car vehicle kilometer by 15%; (5) increasing existing modal split for daily trips by non-motorized modes to 50% in each city. A sixth “best-case scenario” is also explored by simultaneously combining scenarios 2 to 5. The data used in the paper come from systematic empirical research on each of the ten Swedish cities. When applied individually, scenario 2 is the most successful for reducing passenger transport energy use, scenarios 1 and 4 are next in magnitude and produce approximately equal energy savings, followed by scenario 5, with scenario 3 being the least successful. The best-case, combined scenario could save 1183 million liters of gasoline equivalent in the ten cities, representing almost a 60% saving over their existing 2015 total private passenger transport energy use and equivalent to the combined 2015 total annual private transport energy use of Stockholm, Malmö and Jönköping. Such findings also have important positive implications for the de-carbonization of cities. The policy implications of these findings and the strategies for increasing public transport, walking and cycling, boosting car occupancy and decreasing vehicular fuel consumption in Swedish cities are discussed.

Electric and biomethane-fuelled urban buses: comparison of environmental performance of different powertrains

Purpose The paper aims to promote the transition to low/zero emission of the local public transport, particularly, urban buses are taken into account. Method The life cycle assessment of electric and biomethane-fuelled urban buses is performed by exploiting SimaPro commercial software (v.9.1.1.). Attention is focused on powertrains. Both midpoint and endpoint analyses are performed. Referring to environmental impact, the best compressed biomethane gas (CBG) powertrain was compared to the best electric one. Additionally, the worst-case scenario has been considered for both CBG and electric powertrains. Results CBG powertrain outperforms the electric one if overall greenhouse gas emissions are considered. However, the electric powertrain seems promising for human health and ecosystem. Conclusions The environmental performance of the two powertrains is good. Both of the two technologies have strength and weak points that anyhow make them good candidates for a clean local public transport of the future. The analysis performed in the paper suggests a future investigation on hybrid electric-CBG powertrain. Actually, such a solution could benefit from both the strengths of the biomethane and the electric powertrain.

Analysis of Voltage Rise Phenomena in Electrical Power Network With High Concentration of Renewable Distributed Generations

The increasing penetration levels of Renewable Distributed Generation (RDG) into power system have proven to bring both positive and negative impacts. The occurrence of under voltage at the far end of a conventional Distribution Network (DN) may not raise concern anymore with RDGs integration into the power system. However, a high penetration of RDG into power system may cause problems such as voltage rise or over-voltage and reverse power flows at the Point of Common Coupling (PCC) between RDG and DN. This research paper presents the voltage rise and reverse power flow effects in power system with high concentration of RDG. The analysis is conducted on a sample DN, i.e., IEEE 13-bus test system, with RDG by considering the most critical scenario such as low power demand and peak power injection to DN from RDG. The Simulations are carried out using MATLAB/Simulink software, a mathematical model of a distribution grid, integrating RDG is developed for studying the effects of voltage rise and bidirectional flow of power. Furthermore, a control strategy is proposed to be installed at PCC of the DN to control/or mitigate the voltage rise effects and to limit the reverse power flow when operating in a worst critical scenario of minimum load and maximum generation from RDG. The proposed control strategy also mitigates the voltage-current harmonic signals, improve the power factor, and voltage stability at PCC. Finally, recommendations are provided for the utility and independent power producer to counteract the effects of voltage rise at PCC. The study demonstrated that, PCC voltage can be sustained with a high concentration of RDG during a worst-case scenario without a reverse power flow and voltage rise beyond grid code limits.

Insurance payment for artificial intelligence technology: Methods used by a stroke artificial intelligence system and strategies to qualify for the new technology add-on payment

The first ever insurance reimbursement for an artificial intelligence (AI) system, which expedites triage of acute stroke, occurred in 2020 when the Centers for Medicare and Medicaid Services (CMS) granted approval for a New Technology Add-on Payment (NTAP). Key aspects of the AI system that led to its approval by the CMS included its unique mechanism of action, use of robotic process automation, and clear linkage of the system’s output to clinical outcomes. The specific strategies employed encompass a first-case scenario of proving reimbursable value for improved stroke outcomes using AI. Given the rapid change in utilization of AI technology in stroke care, we describe the economic drivers of stroke AI systems in healthcare, focusing on concepts of reimbursement for value added by AI to the stroke care system. This report reviews (1) the successful approach used by the first NTAP-approved AI system, (2) economic variables in insurance reimbursement for AI, and (3) resultant strategies that may be utilized to facilitate qualification for NTAP reimbursement, which may be adopted by other AI systems used in stroke care.

Feasibility of Using Wearable EMG Armbands combined with Unsupervised Transfer Learning for Seamless Myoelectric Control

Myoelectric control uses electromyography (EMG) signals as human-originated input to enable intuitive interfaces with machines. As such, recent rehabilitation robotics employs myoelectric control to autonomously classify user intent or operation mode using machine learning. However, performance in such applications inherently suffers from the non-stationarity of EMG signals across measurement conditions. Current laboratory-based solutions rely on careful, time-consuming control of the recordings or periodic recalibration, impeding real-world deployment. We propose that robust yet seamless myoelectric control can be achieved using a low-end, easy-to-don and doff wearable EMG sensor combined with unsupervised transfer learning. Here, we test the feasibility of one such application using a consumer-grade sensor (Myo armband, 8 EMG channels @ 200 Hz) for gesture classification across measurement conditions using an existing dataset: 5 users x 10 days x 3 sensor locations. Specifically, we first train a deep neural network using Temporal-Spatial Descriptors (TSD) with labeled source data from any particular user, day, or location. We then apply the Self-Calibrating Asynchronous Domain Adversarial Neural Network (SCADANN), which automatically adjusts the trained TSD to improve classification performance for unlabeled target data from a different user, day, or sensor location. Compared to the original TSD, SCADANN improves accuracy by 12±5.2% (avg±sd), 9.6±5.0%, and 8.6±3.3% across all possible user-to-user, day-to-day, and location-to-location cases, respectively. In one best-case scenario, accuracy improves by 26% (from 67% to 93%), whereas sometimes the gain is modest (e.g., from 76% to 78%). We also show that the performance of transfer learning can be improved by using a better model trained with good (e.g., incremental) source data. We postulate that the proposed approach is feasible and promising and can be further tailored for seamless myoelectric control of powered prosthetics or exoskeletons.

TorchEsegeta: Framework for Interpretability and Explainability of Image-based Deep Learning Models

Clinicians are often very sceptical about applying automatic image processing approaches, especially deep learning based methods, in practice. One main reason for this is the black-box nature of these approaches and the inherent problem of missing insights of the automatically derived decisions. In order to increase trust in these methods, this paper presents approaches that help to interpret and explain the results of deep learning algorithms by depicting the anatomical areas which influence the decision of the algorithm most. Moreover, this research presents a unified framework, TorchEsegeta, for applying various interpretability and explainability techniques for deep learning models and generate visual interpretations and explanations for clinicians to corroborate their clinical findings. In addition, this will aid in gaining confidence in such methods. The framework builds on existing interpretability and explainability techniques that are currently focusing on classification models, extending them to segmentation tasks. In addition, these methods have been adapted to 3D models for volumetric analysis. The proposed framework provides methods to quantitatively compare visual explanations using infidelity and sensitivity metrics. This framework can be used by data scientists to perform post-hoc interpretations and explanations of their models, develop more explainable tools and present the findings to clinicians to increase their faith in such models. The proposed framework was evaluated based on a use case scenario of vessel segmentation models trained on Time-of-fight (TOF) Magnetic Resonance Angiogram (MRA) images of the human brain. Quantitative and qualitative results of a comparative study of different models and interpretability methods are presented. Furthermore, this paper provides an extensive overview of several existing interpretability and explainability methods.

PFDA-FMEA-VSM Application for Outsourcing Risk Analysis

Manufacturing corporations has the acceptance of the Outsourcing Process (OP) to improve industrial activities as well as to archive the revenue objectives, and with this, Risk Analysis (RA) tools are constantly used to assure expected results. Failure Mode and Effect Analysis (FMEA) is one of preferred RA tools, moreover, it is proven that FMEA adds uncertainty because of the human participation at the RA, afterward it is demonstrated that Pythagorean Fuzzy Dimensional Analysis – FMEA – Value Stream Mapping (PFDA-FMEA-VSM) method removes the uncertainty in RA, likewise it aids to the stakeholders for decision making, giving more advantages improving the use of the resources on the project. This document exhibits a real case scenario in a manufacturing firm applying PFDA-FMEA-VSM method adapted for manufacturing OP. The application of PFDA-FMEA-VSM shows solid RA results, removing the human intervention uncertainty added to the risk ranking, gives advantages to the stakeholders for visualize the main risks in detailed diagram, as well as make easier to take better decisions on where to apply resources and mitigate risks during OP.

Bond parameters for peeling and debonding in thin plated RC beams subjected to mixed mode loading – Framework

Studies have primarily focussed on predicting mode-II debonding failure; whereas, in real-case-scenario, flexurally strengthened reinforced concrete (RC) beams observe premature failure mechanisms under mixed-mode loading conditions engaging geometrical and material variations. Peeling is a consequence of flexural crack as debonding is of interfacial shear crack. Under bending, peeling failure is considerably catastrophic over debonding due to the nature of crack formation; therefore, this needs to be distinguished in predictive analysis. In this paper, a new numerical modeling methodology is approached using eXtended finite element method (xFEM) for flexural cracks and Cohesive Zone Model (CZM) for shear cracks without predefining crack locations. The parameters of the constitutive models are identified through comparing finite element results with the experimental data. These parameters are related to key material properties. Based on proposed framework, the models provide a good estimation of plate strain distribution, cracks and failure type, in terms of mode and load of failure. Bilinear bond-slip curve is a closer match over exponential crack evolution at interface.

Ethical Standards for a Post-COVID-19 World

The cataclysmic events of 2020 created an urgent need for mental health counseling to help individuals, families, and communities deal with grief, loss, and trauma. The sheer magnitude of the challenges has highlighted the necessity for collective interventions, as the need for help far surpasses what can be met through traditional individual or family counseling. Clinical mental health counselors must be prepared to respond to the new challenges in creative, culturally responsive, and ethical ways. The authors discuss the limitations of the prevailing codes of ethics, which are grounded in principle ethics, and propose that virtue ethics and relational ethics perspectives can be incorporated into ethical reasoning to make the process more responsive to collective interventions. A case scenario is presented and analyzed to illustrate this broader and more inclusive approach to ethical decision-making in a situation that calls for a collective intervention.