Deep learning techniques have been widely applied in wireless communication systems to enhance resilience and reduce computational complexity. This paper investigates both traditional and deep learning-based approaches for real-time relay selection in a cooperative communication system with multiple energy-harvesting relays and signal space diversity. The assumed relay decoding scheme is decode-and-forward (DF), with selection criteria based on successful decoding from the source, sufficient energy availability, and the best channel to the destination. The system performance is evaluated in terms of outage probability. Monte Carlo simulations are used to determine the exact outage probability of the system and to generate datasets for training machine learning models. The traditional machine learning models implemented include Decision Tree (DT), Logistic Regression (LR), K-Nearest Neighbor (KNN), and Support Vector Machines (SVMs). The deep learning-based method used is the deep neural network (DNN). Two datasets-one with six features and another with nine features-were used for training and testing. The 6-feature datasets are comparatively less random and complex than the 9-feature datasets. The results indicate that among traditional models KNN achieves the highest accuracy and is thus used as a benchmark to compare against DNN performance. For the 9-feature datasets, both KNN and DNN struggle to accurately approximate the exact outage probability, suggesting that the 9-feature datasets are too complex and noisy for effective modeling. However, on the 6-feature datasets, KNN achieves 77% accuracy, while DNN achieves a significantly higher accuracy of 99%. Due to its high accuracy, the DNN model closely approximates the exact outage probability while offering greater computational efficiency compared to the KNN model. These results underscore the potential of deep learning in optimizing real-time relay selection for energy-harvesting cooperative communication systems.
I defend conscientious objection (CO) in the medical profession by refuting the incompatibility thesis (IT). IT maintains that CO is incompatible with fulfilling the objectives and obligations of the medical profession and thus impermissible for medical professionals. To refute IT, I construct a case of conscience-based refusal in parallel with a case of profession-based refusal, such that both cases fulfill the primary objective and core obligation of the medical profession, thereby directly demonstrating the compatibility of CO with fulfilling obligations in the medical profession. I conclude that IT is false and thus that CO is permissible for medical professionals. I then defend my argument against potential objections. My argument strategy is two-fold. First, to avoid begging the question, I follow a neutral definition of CO that is compatible with IT. Second, to make the argument as logically strong as possible, I assume only minimal claims about the primary objective and core obligation of the medical profession that should be acceptable to (at least some) defenders of IT.
Concerns regarding academic dishonesty are a persistent problem in higher education, including health professions and pharmacy education. Students may respond to academic pressures and new policy or procedure restrictions with innovative ways to gain an unfair advantage through technological advances and old-school methods. Recent shifts to online and hybrid modalities, as well as advances in artificial intelligence, smartphones, watches, and other technologies, continue to escalate the arms race between educational programs and those seeking to circumvent the system. This manuscript seeks to remind faculty and administrators of common cheating modalities that students may use (including high- and low-technology approaches) while calling on members of the Academy to refrain from merely discussing academic integrity issues but rather to actively seek to minimize and address these concerns. This requires increased awareness of various types of cheating and academic integrity matters, understanding of cheating approaches, intentional reflection on academic integrity policies, and implementation of related risk-reduction strategies. It is our responsibility as educators to prevent and address developing complications and be aware of advances in our field. One of the best ways to address advancing concerns of cheating, plagiarism, and academic integrity is by informing ourselves of recent developments relative to academic integrity, as well as peer-reviewed testing strategies to reduce cheating and other types of academic integrity issues.
Humanity must increase agricultural output to feed its increasingly large and often affluent population. Currently, industrialized agriculture can meet these demands, but it is at the expense of water quantity and quality. Existing research has successfully applied evolutionary multiobjective optimization (EMO) to optimize field-scale irrigation schedules, significantly reducing water use while maintaining yield. However, a lack of trust between farmers and optimization software stands in the way of adoption of such tools, and the optimization runs infeasibly slow. In this research, we apply progressively-interactive EMO (PI-EMO) to incorporate human decision-making into the optimization process to make the algorithm more trustworthy and faster. To do so, we implemented an improved publicly available version of the PI-NSGA-II algorithm for use in irrigation management. We then compared the performance of NSGA-II (a non-interactive EMO) and PI-NSGA-II against 900 different irrigation optimization scenarios. Across these scenarios, the overall performance of PI-NSGA-II was comparable to that of NSGA-II while running significantly faster with half the function evaluation. In doing so, we were able to: (1) successfully incorporate interactivity into the agricultural optimization problem, and (2) reduce the number of function evaluations in half. This had a median runtime improvement of 23% between the two algorithms. In other words, PI-EMO-VF makes EMO agricultural optimization more trustworthy and faster than current state-of-the-art EMO irrigation platforms. This study demonstrates the feasibility of future optimization decision support tools, which will empower farmers to make decisions that are not only productive, but maintain the agro-ecosystem supporting humanity.
Priority encoders are typically considered expensive hardware components in terms of complexity, especially at high bit precisions or input lengths (e.g., above 512 bits). However, if the complexity can be reduced, priority encoders can feasibly accelerate a variety of key applications, such as high-precision integer arithmetic and content-addressable memory. We propose a new paradigm for constructing priority encoders by generalizing the previously proposed two-level priority encoder structure. We extend this concept to three and four levels using two techniques – cascading and composition – and discuss further generalization. We then analyze the complexity and delay of new and existing priority encoder designs as a function of input length, for both FPGA and ASIC implementation technologies. In particular, we compare the multi-level structure to the traditional single-level priority encoder structure, a tree-based design, a recursive design, and the two-level structure. We find that the two-level architecture provides balanced performance – reducing complexity by around half, but at the cost of a corresponding increase in delay. Additional levels have diminishing returns, highlighting a tradeoff between complexity and delay. Meanwhile, the tree and recursive designs are generally faster, but are more complex than the two-level and multi-level structures. We explore several characteristics and patterns of the designs across a wide range of input lengths. We then provide recommendations on which architecture to use for a given input length and implementation technology, based on which design factors – such as complexity or delay – are most important to the hardware designer. With this overview and analysis of various priority encoder architectures, we provide a priority encoder toolkit to assist hardware designers in creating the most optimal design.
