Search Results (22)

Genome-wide association studies (GWASs) play a central role in precision medicine, powering a range of clinical applications from pharmacogenomics to disease risk prediction. A critical component of GWASs is genotype imputation, a computational method used to infer untyped genetic variants. While imputation increases variant coverage by estimating genotypes at untyped loci, this expanded coverage can enhance the ability to detect genetic associations in some cases. However, imputation also introduces biases, particularly for rare variants and underrepresented populations, which may compromise clinical accuracy. This review examines the challenges and clinical implications of genotype imputation errors, including their impact on therapeutic decisions and predictive models, like polygenic risk scores (PRSs). In particular, the sources of imputation errors have been deeply explored, emphasizing the disparities in performance across ancestral populations and downstream effects on healthcare equity and addressing ethical considerations surrounding the access to equitable genomic resources. Based on the above, we propose evidence-based best practices for clinical GWAS implementation, including the direct genotyping of clinically actionable variants, the cross-population validation of imputation models, the transparent reporting of imputation quality metrics, and the use of ancestry-matched reference panels. As genomic data becomes increasingly adopted in healthcare systems worldwide, ensuring the accuracy and inclusivity of GWAS-derived insights is paramount. Here, we suggest a framework for the responsible clinical integration of imputed genetic data, paving the way for more reliable and equitable personalized medicine. Full article

The question of whether ethics is possible without God is a non-issue. While many believe that without God, morality collapses, I contend that the existence or non-existence of God has no bearing on whether ethics is possible, whether moral truths exist, or whether ethical inquiry is viable. Ethics is no more secure within a theistic framework than an atheistic one. I establish this by critically examining Divine Command Theory (DCT) and its variants, including Divine Nature Theory, demonstrating that they fail to provide truthmakers for moral statements, explain moral truths, generate moral knowledge, or serve as a practical guide for ethical decision making. If one seeks a way to justify ethical principles or resolve moral dilemmas, appealing to God does not improve the situation; supernatural explanations, including those invoking divine commands or nature, fail to meet the criteria of explanatory adequacy. I conclude by suggesting a secular approach to ethics—drawing from Ted Schick’s inference to the best action—that does not depend on God’s existence. Ultimately, if moral nihilism is a concern, God’s existence offers no solution. If ethics is possible at all, it is possible regardless of whether God exists. Full article

This study aims to develop an integrated framework that combines an attention-enhanced adaptive neuro-fuzzy inference system (ANFIS) with multi-objective optimization to address the challenges of subjective indicator weight allocation and insufficient nonlinear relationship modeling in environmental performance evaluation of water infrastructure. Drawing on the tri-dimensional theory of performance evaluation—success, results, and actions—the framework organizes environmental performance indicators into five primary and nine secondary dimensions. Through empirical analysis across China’s five major river basins (Yangtze, Yellow, Pearl, Huai, and Hai Rivers), our model demonstrates comprehensive superiority with faster convergence (46 iterations) and superior accuracy (R² = 0.915), significantly outperforming basic attention (62 iterations, R² = 0.862) and traditional ANFIS (85 iterations, R² = 0.828) models across all metrics. There is a significant gradient difference in environmental performance scores across the five major river basins: the Yangtze River Basin performs the best (0.99), followed by the Yellow River Basin (0.98), with the Hai River (0.92) and Huai River (0.86) in the middle, and the Pearl River Basin scoring the lowest (0.77). This disparity reflects the differences in basin characteristics and governance. Full article

Wireless sensor networks (WSNs) are a cornerstone of modern Internet of Things (IoT) infrastructure, enabling seamless data collection and communication for many IoT applications. However, the deployment of WSNs in remote or inaccessible locations poses significant challenges in terms of energy efficiency and secure communication. Sensor nodes, with their limited battery capacities, require innovative strategies to minimize energy consumption while maintaining robust network performance. Additionally, ensuring secure data transmission is critical for safeguarding the integrity and confidentiality of IoT systems. Despite various advancements, existing methods often fail to strike an optimal balance between energy efficiency and quality of service (QoS), either depleting limited energy resources or compromising network performance. This paper introduces a novel framework that integrates double reconfigurable intelligent surfaces (RISs) into WSNs to enhance energy efficiency while ensuring secure communication. To jointly optimize both RIS phase shift matrices, we employ a fuzzy deep reinforcement learning (FDRL) framework that integrates reinforcement learning (RL) with fuzzy logic and long short-term memory (LSTM)-based architecture. The RL component learns optimal actions by iteratively interacting with the environment and updating Q-values based on a reward function that prioritizes both energy efficiency and secure communication. The LSTM captures temporal dependencies in the system state, allowing the model to make more informed predictions about future network conditions, while the fuzzy logic layer manages uncertainties by using optimized membership functions and rule-based inference. To explore the search space efficiently and identify optimal parameter configurations, we use the advantage of the multi-objective artificial bee colony (MOABC) algorithm as an optimization strategy to fine-tune the hyperparameters of the FDRL framework while simultaneously optimizing the membership functions of the fuzzy logic system to improve decision-making accuracy under uncertain conditions. The MOABC algorithm enhances convergence speed and ensures the adaptability of the proposed framework in dynamically changing environments. This framework dynamically adjusts the RIS phase shift matrices, ensuring robust adaptability under varying environmental conditions and maximizing energy efficiency and secure data throughput. Simulation results validate the effectiveness of the proposed FDRL-based double RIS framework under different system configurations, demonstrating significant improvements in energy efficiency and secrecy rate compared to existing methods. Specifically, quantitative analysis demonstrates that the FDRL framework improves energy efficiency by 35.4%, the secrecy rate by 29.7%, and RSMA by 27.5%, compared to the second-best approach. Additionally, the model achieves an R² score improvement of 12.3%, confirming its superior predictive accuracy. Full article

Graph convolution networks (GCNs) have been extensively researched for action recognition by estimating human skeletons from video clips. However, their image sampling methods are not practical because they require video-length information for sampling images. In this study, we propose an Auxiliary Task Graph Convolution Network (AT-GCN) with low and high-frame pathways while supporting a new sampling method. AT-GCN learns actions at a defined frame rate in the defined range with three losses: fuse, slow, and fast losses. AT-GCN handles the slow and fast losses in two auxiliary tasks, while the mainstream handles the fuse loss. AT-GCN outperforms the original State-of-the-Art model on the NTU RGB+D, NTU RGB+D 120, and NW-UCLA datasets while maintaining the same inference time. AT-GCN shows the best performance on the NTU RGB+D dataset at 90.3% from subjects, 95.2 from view benchmarks, on the NTU RGB+D 120 dataset at 86.5% from subjects, 87.6% from set benchmarks, and at 93.5% on the NW-UCLA dataset as top-1 accuracy. Full article

This paper proposes a novel approach to personalizing robot behavior using Model Predictive Control (MPC). Social humanoid robots, equipped with advanced sensors and human-like capabilities, are increasingly integrated into human environments, necessitating adaptable and intuitive communication interfaces. Our approach enables the design of adaptive interfaces that support fluid, personalized human–robot interactions. In the proposed framework, a user model is applied to predict responses to potential robot actions. Initially, this model represents an average user; however, it is updated as the robot gathers new observations, leading to increasingly personalized decisions. Experiments assessed the performance of five machine-learning algorithms generating user models in a simulated environment, with the Light Gradient Boosting Machine (LGBM) achieving the best results, closely followed by Random Forest (RF). A comparison of the inference time showed that LGBM is more than four times faster than RF. Outlier-removal techniques showed a modest performance improvement over models without outlier removal. Additionally, robot adaptation was tested in the experiments, showing an increase in the average reward over time, although with a relatively high standard deviation. The results suggest that the proposed approach for robot behavior adaptation based on MPC works well, and the recommended algorithm for the user model is LGBM. Full article

The journey towards realizing real-time AI-driven IoT applications is facing a significant hurdle caused by the limited resources of IoT devices. Particularly for battery-powered edge devices, the decision between performing task inference locally or by offloading to edge servers, all while ensuring timely results and conserving energy, is a critical issue. This problem is further complicated when an edge device houses multiple local inference models. The challenge of effectively allocating inference models to tasks between local models and edge server models under strict time and energy constraints while maximizing overall accuracy is recognized as a strongly NP-hard problem and has not been addressed in the literature. Therefore, in this work we propose MASITO, a novel multi-agent deep reinforcement learning framework designed to address this intricate problem. By dividing the problem into two sub-problems namely task scheduling and edge server selection we propose a cooperative multi-agent system for addressing each sub-problem. MASITO’s design allows for faster training and more robust schedules using cooperative behavior where agents compensate for each other’s sub-optimal actions. Moreover, MASITO dynamically adapts to different network configurations which allows for high-mobility edge computing applications. Experiments on the ImageNet-mini dataset demonstrate the framework’s efficacy, outperforming genetic algorithms (GAs), simulated annealing (SA), and particle swarm optimization (PSO) in scheduling times by providing lower times ranging from 60% up to 90% while maintaining comparable average accuracy in worst-case scenarios and superior accuracy in best-case scenarios. Full article

Candida albicans is the most critical fungus causing oral mycosis. Many mouthwashes contain antimicrobial substances, including antifungal agents. This study aimed to investigate the in vitro activity of 15 commercial mouthwashes against 12 strains of C. albicans. The minimal inhibitory concentrations (MICs), minimal fungicidal concentrations (MFCs), and anti-biofilm activity were studied. MICs were determined by the micro-dilution method using 96-well plates, and MFCs were determined by culturing MIC suspensions on Sabouraud dextrose agar. Anti-biofilm activity was evaluated using the crystal violet method. The mouthwashes containing octenidine dihydrochloride (OCT; mean MICs 0.09–0.1%), chlorhexidine digluconate (CHX; MIC 0.12%), and CHX with cetylpyridinium chloride (CPC; MIC 0.13%) exhibited the best activity against C. albicans. The active compound antifungal concentrations were 0.5–0.9 µg/mL for OCT products and 1.1–2.4 µg/mL for CHX rinses. For mouthwashes with CHX + CPC, concentrations were 1.56 µg/mL and 0.65 µg/mL, respectively. Products with polyaminopropyl biguanide (polyhexanide, PHMB; MIC 1.89%) or benzalkonium chloride (BAC; MIC 6.38%) also showed good anti-Candida action. In biofilm reduction studies, mouthwashes with OCT demonstrated the most substantial effect (47–51.1%). Products with CHX (32.1–41.7%), PHMB (38.6%), BAC (35.7%), Scutellaria extract (35.6%), and fluorides + essential oils (33.2%) exhibited moderate antibiofilm activity. The paper also provides an overview of the side effects of CHX, CPC, and OCT. Considering the in vitro activity against Candida albicans, it can be inferred that, clinically, mouthwashes containing OCT are likely to offer the highest effectiveness. Meanwhile, products containing CHX, PHMB, or BAC can be considered as promising alternatives. Full article

In our daily lives, we are often faced with the need to explain various phenomena, but we do not always select the most accurate explanation. For example, let us consider a “toxic” relationship with physical and psychological abuse, where one of the partners is reluctant to end it. Explanations for this situation can range from emotional or economic dependency to irrational hypotheses such as witchcraft. Surprisingly, some people may turn to the latter explanation and consequently seek ineffective solutions, such as visiting a witch doctor instead of a psychologist. This choice of an inappropriate explanation can lead to actions that are not only ineffective but potentially harmful. This example underscores the importance of inference to the best explanation (IBE) in everyday decision making. IBE involves selecting the hypothesis that would best explain the available body of data or evidence, a process that is crucial to making sound decisions but is also vulnerable to bias and errors of judgment. Within this context, the purpose of our article is to explore how the IBE process and the selection of appropriate explanations impact decision making and problem solving in real life. To this end, we systematically analyze the role of IBE in the ARDESOS-DIAPROVE program, evaluating how this approach can enhance the teaching and practice of critical thinking. Full article

In this study, we propose a population-based, data-driven intelligent controller that leverages neural-network-based digital twins for hypothesis testing. Initially, a diverse set of control laws is generated using genetic programming with the digital twin of the system, facilitating a robust response to unknown disturbances. During inference, the trained digital twin is utilized to virtually test alternative control actions for a multi-objective optimization task associated with each control action. Subsequently, the best policy is applied to the system. To evaluate the proposed model predictive control pipeline, experiments are conducted on a multi-mode heat transfer test rig. The objective is to achieve homogeneous cooling over the surface, minimizing the occurrence of hot spots and energy consumption. The measured variable vector comprises high dimensional infrared camera measurements arranged as a sequence (655,360 inputs), while the control variable includes power settings for fans responsible for convective cooling (3 outputs). Disturbances are induced by randomly altering the local heat loads. The findings reveal that by utilizing an evolutionary algorithm on measured data, a population of control laws can be effectively learned in the virtual space. This empowers the system to deliver robust performance. Significantly, the digital twin-assisted, population-based model predictive control (MPC) pipeline emerges as a superior approach compared to individual control models, especially when facing sudden and random changes in local heat loads. Leveraging the digital twin to virtually test alternative control policies leads to substantial improvements in the controller’s performance, even with limited training data. Full article

In this work, we propose a novel data-driven approach to recover missing or corrupted motion capture data, either in the form of 3D skeleton joints or 3D marker trajectories. We construct a knowledge-base that contains prior existing knowledge, which helps us to make it possible to infer missing or corrupted information of the motion capture data. We then build a kd-tree in parallel fashion on the GPU for fast search and retrieval of this already available knowledge in the form of nearest neighbors from the knowledge-base efficiently. We exploit the concept of histograms to organize the data and use an off-the-shelf radix sort algorithm to sort the keys within a single processor of GPU. We query the motion missing joints or markers, and as a result, we fetch a fixed number of nearest neighbors for the given input query motion. We employ an objective function with multiple error terms that substantially recover 3D joints or marker trajectories in parallel on the GPU. We perform comprehensive experiments to evaluate our approach quantitatively and qualitatively on publicly available motion capture datasets, namely CMU and HDM05. From the results, it is observed that the recovery of boxing, jumptwist, run, martial arts, salsa, and acrobatic motion sequences works best, while the recovery of motion sequences of kicking and jumping results in slightly larger errors. However, on average, our approach executes outstanding results. Generally, our approach outperforms all the competing state-of-the-art methods in the most test cases with different action sequences and executes reliable results with minimal errors and without any user interaction. Full article

Aberrant miRNA expression has been associated with a large number of human diseases. Therefore, targeting miRNAs to regulate their expression levels has become an important therapy against diseases that stem from the dysfunction of pathways regulated by miRNAs. In recent years, small molecules have demonstrated enormous potential as drugs to regulate miRNA expression (i.e., SM-miR). A clear understanding of the mechanism of action of small molecules on the upregulation and downregulation of miRNA expression allows precise diagnosis and treatment of oncogenic pathways. However, outside of a slow and costly process of experimental determination, computational strategies to assist this on an ad hoc basis have yet to be formulated. In this work, we developed, to the best of our knowledge, the first cross-platform prediction tool, DeepsmirUD, to infer small-molecule-mediated regulatory effects on miRNA expression (i.e., upregulation or downregulation). This method is powered by 12 cutting-edge deep-learning frameworks and achieved AUC values of 0.843/0.984 and AUCPR values of 0.866/0.992 on two independent test datasets. With a complementarily constructed network inference approach based on similarity, we report a significantly improved accuracy of 0.813 in determining the regulatory effects of nearly 650 associated SM-miR relations, each formed with either novel small molecule or novel miRNA. By further integrating miRNA–cancer relationships, we established a database of potential pharmaceutical drugs from 1343 small molecules for 107 cancer diseases to understand the drug mechanisms of action and offer novel insight into drug repositioning. Furthermore, we have employed DeepsmirUD to predict the regulatory effects of a large number of high-confidence associated SM-miR relations. Taken together, our method shows promise to accelerate the development of potential miRNA targets and small molecule drugs. Full article

James Sterba argues that a good God is not logically possible. He argues that what he calls the Pauline Principle, which says that we should never do evil that good may come of it, implies that a good God would prevent horrendous evil consequences of immoral actions. However, there are plenty of examples of such actions in our world. So, a good God does not exist. I offer an example from Derek Parfit, and one of my own, that calls the Pauline Principle into question. Sterba believes that what he calls Moral Evil Prevention Requirements (MEPRs) follow from the Pauline Principle, and that they are necessary truths which imply that a good God would prevent horrendous evil consequences of immoral actions. Whether these (MEPRs) follow from the Pauline Principle or do not, they may be necessary truths that could form the basis of Sterba’s argument. However, I argue that they are not necessary truths. If modified to become such, Sterba faces a challenge from the Skeptical Theists that can only be met by turning his argument into an evidential version of the problem of evil. I compare Sterba’s argument with my version of the evidential argument from evil that says that if God exists, there is not excessive, unnecessary suffering and whose second premise says there is. I argue that it is easier to establish that there is excessive, unnecessary suffering than to establish Sterba’s second premise (once his principles are modified). That second premise will say that there are no goods that logically require God to allow immoral actions that have horrendous evil consequences. Sterba faces a dilemma: either he has an unsound logical argument or a weak evidential argument for the non-existence of God. In either case, he does not have a good logical argument for atheism. Full article

This article proposes the use of reinforcement learning (RL) algorithms to control the position of a simulated Kephera IV mobile robot in a virtual environment. The simulated environment uses the OpenAI Gym library in conjunction with CoppeliaSim, a 3D simulation platform, to perform the experiments and control the position of the robot. The RL agents used correspond to the deep deterministic policy gradient (DDPG) and deep Q network (DQN), and their results are compared with two control algorithms called Villela and IPC. The results obtained from the experiments in environments with and without obstacles show that DDPG and DQN manage to learn and infer the best actions in the environment, allowing us to effectively perform the position control of different target points and obtain the best results based on different metrics and indices. Full article

The understanding of the effects of multidirectional loadings imposed on major load bearing elements such as reinforced concrete (RC) columns by seismic actions for collapse prevention is of utmost importance, and a few simplified models are available in the literature. In this study, the distinguishing features of two machine-learning (ML) methods, namely, multi expression programming (MEP) and adaptive neuro-fuzzy inference system (ANFIS) are exploited for the first time to develop eight novel prediction models (M1-to M4-MEP and M1-to M4-ANFIS) with different combinations of input parameters to predict the biaxial shear strength of RC columns (V). The performance of the developed models was assessed using various statistical indicators and by comparing them with the experimental values. Based on the statistical analysis of the developed models, M1-ANFIS and M1-MEP performed very well and exhibited the best overall efficiency of the studied ML methods. Simple mathematical formulations were also provided by the MEP algorithm for the prediction of V, using which the M1-MEP model was finalized based on its performance, accuracy, and generalization capability. A parametric analysis was also performed for the model to show that the mathematical formulation provided by MEP accurately represents the system under consideration and is imperative for prediction purposes. Based on its performance, the model can thus be recommended to update the current code provisions and engineering practices. Full article