I've been reviewing smart lighting for years. Here are tips on how to prep, what to choose, and the smart lights that work best in each room. The importance of light in our daily lives is often underestimated. It can motivate and make you feel energized or create a cozy feel and help you relax. We've never had more choice or control over the light in our homes, so if you're still using regular bulbs and lamps, it's time to switch to smart lighting. I've been testing and reviewing smart lighting for years, and I've learned loads about what works and what doesn't.

This paper presents a novel approach to automated stripboard circuit layout design using Answer Set Programming (ASP). The work formulates the layout problem as both a synthesis and multi-objective optimization task that simultaneously generates viable layouts while minimizing board area and component strip crossing. By leveraging ASP's declarative nature, this work expresses complex geometric and electrical constraints in a natural and concise manner. The two-phase solving methodology first ensures feasibility before optimizing layout quality. Experimental results demonstrate that this approach generates compact, manufacturable layouts for a range of circuit complexities. This work represents a significant advancement in automated stripboard layout, offering a practical tool for electronics prototyping and education while showcasing the power of declarative programming for solving complex design automation problems.

Machine learning, and in particular its modern incarnation of deep learning (DL) [1, 2], has become an indispensable tool in particle physics, a field that routinely handles vast datasets and nonlinear relationships among observables [3-6]. In recent years, advances in DL have expanded the scope of data-driven progress across the energy, intensity, accelerator, and cosmic frontiers [7, 8]. Despite remarkable advancements, most current DL applications in particle physics are supervised, relying either on Monte Carlo (MC) simulations or on labeled experimental data. However, because simulations cannot fully capture the complexity of the real world, a persistent gap between MC and Data leads to training bias. Direct training on real data, in turn, demands extensive human labeling, which is labor-intensive and hard to scale [9]. For this reason, the development of unsupervised DL [10, 11] is integral for particle physics. Unsupervised learning has achieved remarkable success in tasks such as clustering [12, 13], anomaly detection [14, 15], and learning representations [16, 17].

We show that \emph{Sorting by Strip Swaps} (SbSS) is NP-hard by a polynomial reduction of \emph{Block Sorting}. The key idea is a local gadget, a \emph{cage}, that replaces every decreasing adjacency $(a_i,a_{i+1})$ by a guarded triple $a_i,m_i,a_{i+1}$ enclosed by guards $L_i,U_i$, so the only decreasing adjacencies are the two inside the cage. Small \emph{hinge} gadgets couple adjacent cages that share an element and enforce that a strip swap that removes exactly two adjacencies corresponds bijectively to a block move that removes exactly one decreasing adjacency in the source permutation. This yields a clean equivalence between exact SbSS schedules and perfect block schedules, establishing NP-hardness.

We present a method to fabricate double shell structures printed in trans-versal directions using multi-axis fused-deposition-modeling (FDM) robot-ic 3D printing. Shell structures, characterized by lightweight, thin walls, fast buildup, and minimal material usage, find diverse applications in pro-totyping and architecture for uses such as fa\c{c}ade panels, molds for concrete casting, or full-scale pavilions. We leverage an underlying representation of transversal strip networks generated using existing methods and propose a methodology for converting them into printable partitions. Each partition is printed separately and assembled into a double-shell structure. We out-line the specifications and workflow that make the printing of each piece and the subsequent assembly process feasible. The versatility and robust-ness of our method are demonstrated with both digital and fabricated re-sults on surfaces of different scales and geometric complexity.

Power strips are essential for any workstation because you have so many devices to keep plugged in, from your computer to your monitors to your chargers and everything else. But power strips get in the way. Unless you have this desk-clamping power strip with surge protection, which is down to just 25 right now from its original 59. This versatile power station securely mounts to the side or back of your desk using an edge-mount bracket, freeing your workspace of clutter while keeping all your devices accessible within arm's reach. It has three power outlets, two 20W USB-C PD ports, and two USB-A ports, so you can fast charge multiple devices at once while also powering your computer, monitors, and accessories.

Determining whether two STRIPS planning instances are isomorphic is the simplest form of comparison between planning instances. It is also a particular case of the problem concerned with finding an isomorphism between a planning instance $P$ and a sub-instance of another instance $P_0$ . One application of such a mapping is to efficiently produce a compiled form containing all solutions to P from a compiled form containing all solutions to $P_0$. We also introduce the notion of embedding from an instance $P$ to another instance $P_0$, which allows us to deduce that $P_0$ has no solution-plan if $P$ is unsolvable. In this paper, we study the complexity of these problems. We show that the first is GI-complete, and can thus be solved, in theory, in quasi-polynomial time. While we prove the remaining problems to be NP-complete, we propose an algorithm to build an isomorphism, when possible. We report extensive experimental trials on benchmark problems which demonstrate conclusively that applying constraint propagation in preprocessing can greatly improve the efficiency of a SAT solver.

A soft robot shaped like a Möbius strip can move when activated by light, and could be used to transport medicine and collect samples inside the body. Zi Liang Wu at Zhejiang University in China and his colleagues created their strips from thin sheets of hydrogels, materials made from long chain-like polymer molecules linked together by water molecules. They made the strips into unusual shapes, such as a Möbius strip – a one-sided surface that results from a band with a twist in it – or a similar curved surface called a Seifert ribbon. Why giving AI a robot body could make its'brain' more human-like

Agents act to bring about a state of the world that is more compatible with their personal or institutional values. To formalise this intuition, the paper proposes an action framework based on the STRIPS formalisation. Technically, the contribution expresses actions in terms of Value-based Formal Reasoning (VFR), which provides a set of propositions derived from an Agent's value profile and the Agent's assessment of propositions with respect to the profile. Conceptually, the contribution provides a computational framework for a form of consequentialist ethics which is satisficing, pluralistic, act-based, and preferential.

Artificial intelligence (AI) has immense potential in time series prediction, but most explainable tools have limited capabilities in providing a systematic understanding of important features over time. These tools typically rely on evaluating a single time point, overlook the time ordering of inputs, and neglect the time-sensitive nature of time series applications. These factors make it difficult for users, particularly those without domain knowledge, to comprehend AI model decisions and obtain meaningful explanations. We propose CGS-Mask, a post-hoc and model-agnostic cellular genetic strip mask-based saliency approach to address these challenges. CGS-Mask uses consecutive time steps as a cohesive entity to evaluate the impact of features on the final prediction, providing binary and sustained feature importance scores over time. Our algorithm optimizes the mask population iteratively to obtain the optimal mask in a reasonable time. We evaluated CGS-Mask on synthetic and real-world datasets, and it outperformed state-of-the-art methods in elucidating the importance of features over time. According to our pilot user study via a questionnaire survey, CGS-Mask is the most effective approach in presenting easily understandable time series prediction results, enabling users to comprehend the decision-making process of AI models with ease.