Recently, there has been a lot of research in Quantum Cellular Automata (QCA) technology because it promises low power consumption, low complexity, low latency, and compact space. Simultaneously, approximate arithmetic, a new paradigm in computing, streamlines the computational process and emerges as a low-power, high-performance design approach for arithmetic circuits. Furthermore, the XOR gate has been widely used in digital design and is a basic building block that can be used in many upcoming technologies. The full adder (FA) circuit is a key component of QCA technology and is utilized in arithmetic logic operations including subtraction, multiplication, and division. A great deal of research has been done on the design of approximate FA, full subtractor (FS), full adder/subtractor (FA/S), and 4-bit ripple carry adder (RCA) based on XOR logic, establishing them as essential components in the creation of QCA-based arithmetic circuits. This study presents three new and effective QCA-based circuits, based on XOR logic: an approximate FA, an approximate FS, an approximate FA/S, and an approximate 4-bit ripple carry adder (RCA). Interestingly, some designs have inputs on one side and outputs on the other, making it easier to reach the components without being encircled by other cells and leading to a more effective circuit design. In particular, a delay of 0.5 clock phases, an area of 0.01 μm2, and implementation utilizing just 11 cells was accomplished in the approximate FA and subtractor designs. In a similar vein, the estimated FA/S designs showed 0.5 clock phase delay, 0.01 μm2 area, and 12 cells used for implementation. An approximate 4-bit RCA is proposed using 64 QCA cells. The effectiveness of these designs is evaluated through functional verification with the QCADesigner program. According to simulation results, these proposed solutions not only function well but significantly outperform previous ideas in terms of speed and space. The proposed FA, FS, and RCA designs surpassed the previous best designs by 21%, 21%, and 43%, respectively, in terms of cell count.

Mode awareness is important for the safe use of automated vehicles, yet drivers\' understanding of mode transitions has not been sufficiently investigated. In this study, we administered an online survey to 838 respondents to examine their understanding of control responsibilities in partial and conditional driving automation with four types of interventions (brake pedal, steering wheel, gas pedal, and take-over request). Results show that most drivers understand that they are responsible for speed and distance control after brake pedal interventions and steering control after steering wheel interventions. However, drivers have mixed responses regarding the responsibility for speed and distance control after steering wheel interventions and the responsibility for steering control after gas pedal interventions. With a higher automation level (conditional driving automation), drivers expect automation to remain responsible more often compared to a lower automation level (partial driving automation). Regarding Hands-on requirements, more than 99% of respondents answered that drivers would keep their hands on the steering wheel after all intervention types in partial automation, while 60-95% would place their hands on the wheel after various intervention types in conditional automation. A misalignment between actual logic and drivers\' expectations regarding control responsibilities is observed by comparing survey responses to the mode transition logic of commercial partially automated vehicles. To resolve confusion about control responsibilities and ensure consistent expectations, we propose implementing a consistent mode design and providing enhanced information to drivers.

BACKGROUND: Logic models graphically present the socio-technical components of a variety of \'programs\' such as educational programs. They show the underlying logic and assumptions of how a program is supposed to work. We suggest that they can be used to describe the mechanisms of complex socio-technical health IT interventions.
OBJECTIVE: To assess the suitability of logic models to describe cause-effect chains of health IT.
RESULTS: We are currently conducting an integrative review of the impact of patient portals on patient outcomes. We extracted the following elements of logic models from the found publications: resources, activities, output, outcome, and impact. These factors are then used to populate the logic model and form a structured graphical representation of the evidence. Until now, all the evidence we found could be fit into the logic model. The logic model was able to accommodate diverse types of evidence.
CONCLUSIONS: Logic models seem to be suitable for representing evidence on the impact of health IT.

Quantified modal inferences interest logicians, linguists, and computer scientists, but no previous psychological study of them appears to be in the literature. Here is an example of one: All those artists are businessmen. Paulo is possibly one of the artists. What follows? People tend to conclude: Paulo is possibly a businessman (Experiment 1). It seems plausible, and it follows from an intuitive mental model in which Paulo is one of a set of artists who are businessmen. Further deliberation can yield a model of an alternative possibility in which Paulo is not one of the artists, which confirms that the conclusion is only a possibility. The snag is that standard modal logics, which deal with possibilities, cannot yield a particular conclusion to any premises: Infinitely many follow validly (from any premises) but they do not include the present conclusion. Yet, further experiments corroborated a new mental model theory\'s predictions for various inferences (Experiment 2), for the occurrence of factual conclusions drawn from premises about possibilities (Experiment 3) and for inferences from premises of modal syllogisms (Experiment 4). The theory is therefore plausible, but we explore the feasibility of a cognitive theory based on modifications to modal logic.

In this paper we investigate the notion of silence using different tools, in particular the hexagon of oppositions.

So far, biocomputation strictly follows traditional design principles of digital electronics, which could reach their limits when assembling gene circuits of higher complexity. Here, by creating genetic variants of tristate buffers instead of using conventional logic gates as basic signal processing units, we introduce a tristate-based logic synthesis (TriLoS) framework for resource-efficient design of multi-layered gene networks capable of performing complex Boolean calculus within single-cell populations. This sets the stage for simple, modular, and low-interference mapping of various arithmetic logics of interest and an effectively enlarged engineering space within single cells. We not only construct computational gene networks running full adder and full subtractor operations at a cellular level but also describe a treatment paradigm building on programmable cell-based therapeutics, allowing for adjustable and disease-specific drug secretion logics in vivo. This work could foster the evolution of modern biocomputers to progress toward unexplored applications in precision medicine.

Any experiment brings about results and conclusions that necessarily have a component of uncertainty. Many factors influence the degree of this uncertainty, yet they can be overlooked when drawing conclusions from a body of research. Here, we showcase how subjective logic could be employed as a complementary tool to meta-analysis to incorporate the chosen sources of uncertainty into the answer that researchers seek to provide to their research question. We illustrate this approach by focusing on a body of research already meta-analyzed, whose overall aim was to assess if human infants prefer prosocial agents over antisocial agents. We show how each finding can be encoded as a subjective opinion, and how findings can be aggregated to produce an answer that explicitly incorporates uncertainty. We argue that a core feature and strength of this approach is its transparency in the process of factoring in uncertainty and reasoning about research findings. Subjective logic promises to be a powerful complementary tool to incorporate uncertainty explicitly and transparently in the evaluation of research.

There is great interest in fabricating devices that can detect and remove water pollutants, especially heavy metal ions and dyes from wastewater, to promote sustainable water use. In this study, an extract of Borassus flabellifer leaves (BoF-LE) was used to synthesize silver nanoparticles (BoF-AgNPs), with the BoF-LE serving as a reducing and capping agent. The sensitivity and selectivity of BoF-AgNPs for Mn(II) ions were tested by comparing with the control sample and other competent metal ions. Our results showed that BoF-AgNPs are extremely sensitive and selective in detecting Mn(II) ions, with a detection limit of 0.3 ppb. HR-TEM, UV-Vis spectroscopy, and DLS investigations were used to confirm that BoF-AgNPs detect Mn(II) ions by an aggregation-based mechanism. Additionally, it was found that BoF-AgNPs are effective in rapidly decolorizing MB dye, as demonstrated by their ability to decolorize MB by 92.66% within 7 min. This study is the first to report successful synthesis of BoF-AgNPs and their two applications, which are enabled with an Inhibit-AND logic gate. Using BoF-AgNPs to detect and degrade water pollutants may promote sustainable water use.

The finding that people tend to prefer logically valid conclusions over invalid ones is known in the literature as the logic-liking effect and has traditionally been interpreted as evidence for the notion of so-called logical intuitions. Results of more recent empirical studies investigating conditional and categorical syllogisms suggest, however, that previous instances of the logic-liking effect can be accounted for by a confound in terms of surface-feature atmosphere. But the true nature of this atmosphere effect has so far remained largely elusive. Here, we address this issue and introduce two variants of disjunctive syllogisms that enable us to deconfound validity, possibility of the conclusion, and surface-feature atmosphere, which has been impossible with simple disjunctive syllogisms used in earlier studies. Three experiments, in which participants were asked to provide liking and logic ratings for these arguments, revealed that the logic-liking effect in disjunctive syllogisms can be explained by an atmosphere confound in combination with implied demand to consider logicality when judging likability. We also observed a strong atmosphere effect in logic ratings over and above an effect of logical validity per se. Furthermore, atmosphere effects appear to be induced only by specific surface features, namely those that are ecologically valid, if fallible, predictors for logicality. We conclude that acquired atmosphere heuristics provide proxies for logical validity that reasoners often take at face value. A comparison of the present results with previous findings from experiments that focused on conditional and categorical syllogisms additionally indicates that these atmosphere heuristics are used irrespective of an argument\'s complexity.

reasoning is a key ability for an intelligent system. Large language models (LMs) achieve above-chance performance on abstract reasoning tasks but exhibit many imperfections. However, human abstract reasoning is also imperfect. Human reasoning is affected by our real-world knowledge and beliefs, and shows notable \"content effects\"; humans reason more reliably when the semantic content of a problem supports the correct logical inferences. These content-entangled reasoning patterns are central to debates about the fundamental nature of human intelligence. Here, we investigate whether language models-whose prior expectations capture some aspects of human knowledge-similarly mix content into their answers to logic problems. We explored this question across three logical reasoning tasks: natural language inference, judging the logical validity of syllogisms, and the Wason selection task. We evaluate state of the art LMs, as well as humans, and find that the LMs reflect many of the same qualitative human patterns on these tasks-like humans, models answer more accurately when the semantic content of a task supports the logical inferences. These parallels are reflected in accuracy patterns, and in some lower-level features like the relationship between LM confidence over possible answers and human response times. However, in some cases the humans and models behave differently-particularly on the Wason task, where humans perform much worse than large models, and exhibit a distinct error pattern. Our findings have implications for understanding possible contributors to these human cognitive effects, as well as the factors that influence language model performance.