Good air quality is a critical determinant of public health, influencing life expectancy, respiratory health, work productivity, and the prevention of chronic diseases. This study presents a novel approach to classifying the Air Quality Index (AQI) using deep learning techniques, specifically convolutional neural networks (CNNs). We collected and curated a dataset comprising 11,000 digital images from three distinct regions in Indonesia—Jakarta, Malang, and Semarang—ensuring uniformity through standardized acquisition settings. The images were categorized into four air quality classes: good, moderate, unhealthy for sensitive groups, and unhealthy. We designed and implemented a CNN architecture optimized for AQI classification. The model achieved an impressive accuracy of 99.81% using K-fold cross-validation. In addition, the model’s interpretative capabilities were examined using techniques such as Grad-CAM, providing valuable insights into how the CNN identifies and classifies air quality conditions based on image features. These findings underscore the effectiveness of CNNs for AQI classification and highlight the potential for future work to incorporate a more diverse set of digital images captured from various perspectives to enhance dataset complexity and model robustness. The dataset is publicly accessible at https://doi.org/10.5281/zenodo.15727522.

Constraint Programming developed within Logic Programming in the Eighties; nowadays all Prolog systems encompass modules capable of handling constraint programming on finite domains demanding their solution to a constraint solver. This work focuses on a specific form of constraint, the so-called table constraint, used to specify conditions on the values of variables as an enumeration of alternative options. Since every condition on a set of finite domain variables can be ultimately expressed as a finite set of cases, Table can, in principle, simulate any other constraint. These characteristics make Table one of the most studied constraints ever, leading to a series of increasingly efficient propagation algorithms. Despite this, it is not uncommon to encounter real-world problems with hundreds or thousands of valid cases that are simply too many to be handled effectively with standard CPU-based approaches. In this paper, we deal with the Compact-Table (CT) algorithm, the state-of-the-art propagation algorithms for Table. We describe how CT can be enhanced by exploiting the massive computational power offered by modern Graphics Processing Units (GPUs) to handle large Table constraints. In particular, we report on the design and implementation of GPU-accelerated CT, on its integration into an existing constraint solver, and on an experimental validation performed on a significant set of instances.

While the existence of a stable matching for the stable roommates problem possibly with incomplete preference lists (SRI) can be decided in polynomial time, SRI problems with some fairness criteria are intractable. Egalitarian SRI that tries to maximize the total satisfaction of agents if a stable matching exists, is such a hard variant of SRI. For experimental evaluations of methods to solve these hard variants of SRI, several well-known algorithms have been used to randomly generate benchmark instances. However, these benchmark instances are not always satisfiable and usually have a small number of stable matchings if one exists. For such SRI instances, despite the NP-hardness of Egalitarian SRI, it is practical to find an egalitarian stable matching by enumerating all stable matchings. In this study, we introduce a novel algorithm to generate benchmark instances for SRI that have very large numbers of solutions, and for which it is hard to find an egalitarian stable matching by enumerating all stable matchings.

We investigate the expressive power of Higher-Order $Datalog^\neg$ under both the well-founded and the stable model semantics, establishing tight connections with complexity classes. We prove that under the well-founded semantics, for all $k\geq 1$, $(k+1)$-Order $Datalog^\neg$ captures $k-\textsf {EXP}$, a result that holds without explicit ordering of the input database. The proof of this fact can be performed either by using the powerful existential predicate variables of the language or by using partially applied relations and relation enumeration. Furthermore, we demonstrate that this expressive power is retained within a stratified fragment of the language. Under the stable model semantics, we show that $(k+1)$-Order $Datalog^\neg$ captures $\textsf {co}-(k-\textsf {NEXP})$ using cautious reasoning and $k-\textsf {NEXP}$ using brave reasoning, again with analogous results for the stratified fragment augmented with choice rules. Our results establish a hierarchy of expressive power, highlighting an interesting trade-off between order and non-determinism in the context of higher-order logic programing: increasing the order of programs under the well-founded semantics can surpass the expressive power of lower-order programs under the stable model semantics.

Capturing dynamic targets is particularly challenging for either rigid or soft grippers, as impact buffering should be completed in a short time to ensure the reliability of the robotic system. At collision onset, to deal with relatively low contact forces, adopting low stiffness and damping can effectively mitigate the rebound of the dynamic targets. As the contact area and forces increase, employing high stiffness and damping becomes necessary for absorbing high energy. This paper proposed a novel robotic gripper whose stiffness and damping follow a predefined profile “low stiffness and damping for low impact and high stiffness and damping for high impact.” The variable effects of impact buffering and energy dissipation in a collision process were modeled and analyzed. Then, a passive variable stiffness and damping regulator (P-VSDR) was developed where tendons and pulleys are used to generate a nonlinear motion from a linear spring-damper unit. The contact dynamics model of the robotic gripper equipped with P-VSDR was established. Simulated and experimental results show that this gripper enables reliable capture of dynamic targets with different velocities.

While LGBTQIA+ identities are already mostly invisible in the Italian education system, the current anti-gender policies proposed by right-wing and far-right politicians risk further hindering an inclusive education. However, recent Italian graphic novels pave the way for a multifaceted representation of the LGBTQIA+ community and an alternative form of education. For instance, Nicoz Balboa’s Play with Fire (2020) and Alec Trenta’s Barba (2022) are two autofictional graphic novels that depict the authors’ discovery of their trans identity and their experiences in the cis-heteronormative society. The article argues that the two works by Balboa and Trenta are not just examples of autofiction but also constitute an archive of memory and activism. First, the article traces the damaging effects of a lack of education around LGBTQIA+ themes. Then, it explores how Balboa and Trenta understand their lives by reading LGBTQIA+ stories and histories. Crucially, the article investigates how both authors become a point of reference themselves by representing their own bodies and including explanations about gender and sexuality topics. Documenting the way Balboa and Trenta build a counter-educational space in their graphic novels and chart a literary queer and trans genealogy, the article ultimately suggests that their works are a form of activist practice.

The effectiveness of robotic grippers is critical for the secure and damage-free manipulation of objects with diverse geometries and material properties. This paper presents the design, analysis, and experimental evaluation of a novel reconfigurable four-finger robotic gripper. The proposed design incorporates two stationary fingers fixed to a circular base and two movable fingers repositioned and reoriented via a face gear mechanism, enabling multiple finger configurations to enhance adaptability. A single geared motor drives the opening and closing motions of all four fingers, simplifying the actuation mechanism. The robotic gripper was fabricated using 3D printing technology, ensuring cost-effective and precise manufacturing. Experimental tests were conducted to evaluate the robotic gripper’s reconfigurability and grasping performance across a range of objects, demonstrating its effectiveness in various configurations. Additionally, a closed-loop force control system was implemented to assess the grasping performance of a soft reconfigurable variant. Grasping force measurements were performed on three distinct objects, yielding a grasping curve that confirmed successful adaptation and secure handling. While the results validate the robotic gripper’s performance, further refinement of the control algorithm is recommended to optimize its capabilities. Compared to conventional three-finger designs, the proposed robotic gripper offers superior reconfigurability and adaptability, making it suitable for a broader range of industrial and research applications. The innovative face gear mechanism and modular design expand the robotic gripper’s functionality, positioning it as a versatile tool for advanced robotic manipulation tasks.