Search Results (3,476)

The celebrated question of whether $P=NP$ continues to define the boundary between the feasible and the intractable in computer science. In this paper, we revisit the problem from two complementary angles: Time-Relative Description Complexity and automated discovery, adopting an epistemic rather than ontological perspective. Even if polynomial-time algorithms for NP-complete problems do exist, their minimal descriptions may have very high Kolmogorov complexity. This creates what we call an epistemic barrier, making such algorithms effectively undiscoverable by unaided human reasoning. A series of structural results—relativization, Natural Proofs, and the Probabilistically Checkable Proofs (PCPs) theorem—already indicate that classical proof techniques are unlikely to resolve the question, which motivates a more pragmatic shift in emphasis. We therefore ask a different, more practical question: what can systematic computational search achieve within these limits? We propose a certificate-first workflow for algorithmic discovery, in which candidate algorithms are considered scientifically credible only when accompanied by machine-checkable evidence. Examples include Deletion/Resolution Asymmetric Tautology (DRAT)/Flexible RAT (FRAT) proof logs for SAT, Linear Programming (LP)/Semidefinite Programming (SDP) dual bounds for optimization, and other forms of independently verifiable certificates. Within this framework, high-capacity search and learning systems can explore algorithmic spaces far beyond manual (human) design, yet still produce artifacts that are auditable and reproducible. Empirical motivation comes from large language models and other scalable learning systems, where increasing capacity often yields new emergent behaviors even though internal representations remain opaque. This paper is best described as a position and expository essay that synthesizes insights from complexity theory, Kolmogorov complexity, and automated algorithm discovery, using Time-Relative Description Complexity as an organising lens and outlining a pragmatic research direction grounded in verifiable computation. We argue for a shift in emphasis from the elusive search for polynomial-time solutions to the constructive pursuit of high-performance heuristics and approximation methods grounded in verifiable evidence. The overarching message is that capacity plus certification offers a principled path toward better algorithms and clearer scientific limits without presuming a final resolution of $P\stackrel{?}{=}NP$. Full article

High-resolution Digital Terrain Models (DTMs) are essential for precise terrain analysis, yet their production remains constrained by the high cost and limited coverage of LiDAR surveys. This study introduces a deep learning framework based on a modified Residual Channel Attention Network (RCAN) to super-resolve 10 m DTMs to 1 m resolution. The model was trained and validated on a 568 km² LiDAR-derived dataset using custom elevation-aware loss functions that integrate elevation accuracy (L1), slope gradients, and multi-scale structural components to preserve terrain realism and vertical precision. Performance was evaluated across 257 independent test tiles representing flat, hilly, and mountainous terrains. A balanced loss configuration (α = 0.5, γ = 0.5) achieved the best results, yielding Mean Absolute Error (MAE) as low as 0.83 m and Root Mean Square Error (RMSE) of 1.14–1.15 m, with near-zero bias (−0.04 m). Errors increased moderately in mountainous areas (MAE = 1.29–1.41 m, RMSE = 1.84 m), confirming the greater difficulty of rugged terrain. Overall, the approach demonstrates strong potential for operational applications in geomorphology, hydrology, and landscape monitoring, offering an effective solution for high-resolution DTM generation where LiDAR data are unavailable. Full article

A systematic experimental study of the positive-tone resist ELP-20 was conducted, covering its structural properties, film-formation behavior, and response to electron-beam exposure. Raman spectroscopy demonstrated the methacrylate nature of the resist and its spectral correspondence to poly(methyl methacrylate) PMMA, which enabled direct comparison both with PMMA itself and with existing methacrylate-based resists. Spin-coated films prepared from 3–11 wt.% solutions exhibited a robust power-law dependence of thickness on spin speed, h_∞ ∝ ω^{−0.48 ± 0.01}, and showed high thickness uniformity. The concentration dependence of the film thickness at a fixed spin speed allowed identification of the polymer–coil overlap region and enabled estimation of the effective molecular weight of the polymer base, M_eff = (25 ± 7) kg/mol. Lithographic characterization indicated a decrease in sensitivity with increasing electron energy, with a sensitivity of approximately 40 μC/cm² at 25 keV. A depth-dependent dose-distribution model provided an energy-independent average contrast value of γ ≈ 1.67. The results present a coherent and systematic description of ELP-20 behavior under electron-beam exposure and establish a basis for its further use in lithographic processing. Full article

This study proposes a unified deep-learning framework for the concurrent classification of Chronic Obstructive Pulmonary Disease (COPD) severity and regression of the FEV1/FVC ratio from chest X-ray (CXR) images. We integrated a ConvNeXt-Large backbone with a Slot Attention mechanism to effectively disentangle and refine disease-relevant features for multi-task learning. Evaluation on a clinical dataset demonstrated that the proposed model with a 5-slot configuration achieved superior performance compared to standard CNN and Vision Transformer baselines. On the independent test set, the model attained an Accuracy of 0.9107, Sensitivity of 0.8603, and Specificity of 0.9324 for three-class severity stratification. Simultaneously, it achieved a Mean Absolute Error (MAE) of 8.2649 and a Mean Squared Error (MSE) of 151.4704, and an ${\mathrm{R}}^2$ of 0.7591 for FEV1/FVC ratio estimation. Qualitative analysis using saliency maps also suggested that the slot-based approach contributes to attention patterns that are more constrained to clinically relevant pulmonary structures. These results suggest that our slot-attention-based multi-task model offers a robust solution for automated COPD assessment from standard radiographs. Full article

Many engineering design problems are formulated as constrained optimization tasks that are nonlinear and nonconvex, and often treated as black boxes. In such cases, metaheuristic algorithms are attractive because they can search complex design spaces without requiring gradient information. In this work, we propose an Enhanced Henry Gas Solubility Optimization (eHGSO) algorithm, which is an improved version of the physics-inspired HGSO method. The enhanced variant introduces six main contributions: (i) a more diverse, population-wide initialization strategy to cover the design space more thoroughly; (ii) adaptive temperature/pressure control parameters that automatically shift the search from global exploration to local refinement; (iii) an elitist archive with differential perturbation that accelerates exploitation around high-quality candidate designs; (iv) a simple combination of the global HGSO search moves with a lightweight gradient-free local search to refine promising solutions; (v) a constraint-handling mechanism that explicitly prioritizes feasible solutions while still allowing exploration near the constraint boundaries; and (vi) a complexity and ablation analysis that quantifies the impact of each mechanism and confirms that they introduce only modest computational overhead. We evaluate eHGSO on four classical constrained engineering design problems: the stepped cantilever beam, the tension/compression spring, the welded beam, and the three-bar truss. Its performance is compared with seventeen recent metaheuristic optimizers over multiple independent runs. eHGSO achieves the best average objective value on the cantilever, spring, and welded-beam problems and shares the best average result on the three-bar truss. Compared to the second-best method, the mean objective is improved by about $0.84\%$ for the cantilever beam and $0.35\%$ for the welded beam, while the spring and truss results are essentially equivalent at four significant figures. Convergence and robustness analyses show that eHGSO reaches high-quality solutions quickly and consistently. Overall, the proposed eHGSO algorithm appears to be a competitive and practical tool for constrained engineering design problems. Full article

This case study is an overview of architectural design solutions implemented in the construction of farming facilities and the technological processes necessary to support a sustainable farm that runs with nearly zero waste in a closed-loop system that functions with full energy independence. The research will thoroughly investigate the specific location and configuration of the farm units in the target area—providing an extensive description of all necessary building typologies and infrastructures. The text will provide a summary of the agricultural solutions implemented at the farm, which is located in the region of Vojvodina in the Republic of Serbia. This region consists mainly of fertile agricultural land and could be a template for further designs and innovations in sustainable farming. This case study concerns the design of a resilient and self-reliant farm complex that consists of multiple animal species (broilers, pigs, and cattle), including a biogas station. The study is meant to show that adjustments made in architectural design, variations in building typology, and smart urban planning can contribute significantly to the improvement of sustainability in agricultural practices. This case study demonstrates that investments in sustainable solutions not only benefit the environment but can also deliver significant economic returns for investors—thereby further stimulating growth and development in the field of sustainable agriculture. Full article

Given the pressing need to strengthen operational reliability in electrical distribution networks, this study proposes an optimization methodology based on the Teaching–Learning-Based Optimization (TLBO) algorithm for the strategic location of redundant lines. The model is validated on the “MV Distribution Network—Base Model” test system, considering the combination of the MTBF (Mean Time Between Failures) and MTTR (Mean Time To Repair) indicators as the objective function. After 500 independent runs, it is determined that the configuration with three redundant lines identified as LN_1011, LN_1058, and LN_0871 offers the most stable solution. Specifically, this topology increases the MTBF from 403.64 h to 409.42 h and reduces the MTTR from 2.351 h to 2.306 h. In addition, significant improvements are observed in the voltage profile and angle, along with a more balanced redistribution of active and reactive power, more efficient use of existing lines, and an overall reduction in energy losses. Full article

Limited workspace and singularities are major challenges for parallel mechanisms. This article addresses these issues for a 4-DOF 1-SPS/3-RRRRR parallel mechanism with a circular rail, proposed in our prior work. The mechanism has a 3R1T motion type with a movable center of spherical motion. The paper begins with a detailed description of the mechanism design. A closed-form solution of the inverse kinematics follows next, which computes the active joint coordinates and determines the spatial positions of all joints and links. Based on this solution, an iterative approach is applied to analyze the workspace for three different heights of the spherical motion center. The analysis reveals the regions of a full twist about the platform symmetry axis, bounded by maximum tilt angles of 51°, 38°, and 23°, respectively. Introducing joint constraints significantly reduces the workspace, limiting the tilt angles to 21°, 26°, and 0° at the same heights. Subsequently, screw theory is applied to identify serial, parallel, and constraint singularities, and an iterative approach is used to find the boundary of the singularity-free workspace. The analysis shows that the full-twist tilt angles are limited to 33°, a value determined solely on the platform geometry and independent of the spherical motion center height. These results establish a foundation for the design optimization and prototyping of the mechanism. Full article

This paper proposes a novel fractional-order African vulture optimization algorithm (FO-AVOA) for solving the optimal reactive power dispatch (ORPD) problem. By integrating fractional calculus into the conventional AVOA framework, the proposed method enhances the exploration–exploitation balance, accelerates convergence, and improves solution robustness. The ORPD problem is formulated as a constrained optimization task with the objective of minimizing real power losses while satisfying generator voltage limits, transformer tap ratios, and reactive power compensator constraints. The general optimization capability of the FO-AVOA is verified using the CEC 2017, 2020, and 2022 benchmark functions. In addition, the method is applied to the IEEE 30-bus and IEEE 57-bus test systems. The results demonstrate significant power loss reductions of up to 15.888% and 24.39% for the IEEE 30-bus and IEEE 57-bus systems, respectively, compared with the conventional AVOA and other state-of-the-art optimization algorithms, along with strong robustness and stability across independent runs. These findings confirm the effectiveness of the FO-AVOA as a reliable optimization tool for modern power system applications. Full article

A promising approach to advancing sustainable urban mobility is the increased use of light electric vehicles, such as e-cycles and their cargo-carrying variants: e-cargo cycles. These micromobility vehicles fall between e-cycles and conventional vehicles in terms of transport capacity, range, and cost. A key advantage of e-cargo cycles over their non-electrified counterparts is the electric powertrain, which enables them to carry heavier payloads, travel longer distances, and reduce driver fatigue. Since the primary use of e-cargo cycles is urban parchment deliveries, trip efficiency plays a critical role in their effectiveness within urban logistics. This efficiency is influenced by factors such as travel distance, traffic density, and the weight and volume of the delivery payload. While higher delivery capacity generally enhances efficiency, studies have shown that as the drop size increases, the efficiency of e-cargo cycle delivery trips tends to decline. A practical way to address this limitation is the use of cargo attachments, such as trailers. These micromobility solutions are already widely implemented globally and significantly enhance transport capacity. This paper reports the process of designing and testing the control algorithm of an electrical system for an experimental attachment demonstrator that can be used to convert most cycle vehicles into cargo variants. The system integrates two 250 W BLDC hub motors, two 576 Wh lithium-ion batteries, dual load-cell sensing in the coupling element, and an STM32-based controller to provide independent propulsion and synchronization with the leading cycle. The force-based control strategy enables automatic adaptation to varying payloads typically encountered in urban logistics, which is supported by the variable storage volume capable of transporting payloads of up to 200 kg. Full article

Background/Objectives: The further application of high-coverage whole genome sequencing in fields such as paleogenomics, forensic investigations, and conservation genomics is impeded by two major barriers: extremely high costs and stringent sample requirements. Utilizing low-coverage sequencing offers a practical solution to these constraints; however, this approach introduces a primary challenge—the necessity to reconstruct distorted genomic information for downstream analysis. Methods: Analytical experiments conducted on low- to medium-coverage sequencing data confirmed the accuracy of several existing methods for inferring relationships up to the third degree and distinguishing unrelated individuals. Subsequently, efforts were made to evaluate allele-frequency-independent methods within animal genomics, where analyses are likely to encounter challenges such as uncertain allele frequencies, diverse sample types, and suboptimal sample quality. Kinship inference was performed on a total of 33 pairs of animal samples across three species, comprising nine parent–offspring pairs and four full-sibling pairs. Results: The analysis revealed that two efficient algorithm implementations (READ and KIN) successfully identified all unrelated pairs. Notably, among the various algorithms utilized, only KIN exhibited confusion between first- and second-degree relationships when subjected to. Conclusions: This study has filled a critical gap in the existing literature by conducting a comprehensive evaluation of various algorithms on low-coverage sequencing data derived from authentic human and animal samples, accompanied by detailed ground truth—a vital task that has been overlooked. Full article

Bacteria encode a broad range of survival and defence systems, including CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats)-Cas systems, restriction-modification systems, and toxin–antitoxin (TA) systems, which are involved in bacterial regulation and immunity. The traditional view holds that CRISPR-Cas systems and TA systems are two independent defense lines in prokaryotes. However, groundbreaking studies in recent years have revealed multi-level functional coupling between them. This review systematically elaborates on this mechanism, focusing on three types of TA systems that mediate the core correlation of CRISPR-Cas systems: CreTA maintains the evolutionary stability of CRISPR-Cas systems through an addiction mechanism; CreR enables self-regulation of CRISPR-Cas expression; and CrePA provides herd immunity by triggering abortive infection after the CRISPR-Cas system has been destroyed by Anti-CRISPRS protein. Additionally, we discuss the evolutionary homology between the type III toxin AbiF and the type VI CRISPR effector Cas13, offering a new perspective for understanding the origin of CRISPR-Cas systems. These findings not only reveal the functional coupling of prokaryotic defense systems but also provide a powerful theoretical framework and practical solutions for addressing stability challenges in CRISPR technology applications. Full article

The research process was based on an analysis of an existing building equipped with a heat pump on which photovoltaic panels were installed; then, based on energy consumption, the investment profitability was evaluated. In this research, using the available data, the coefficient of self-consumption of energy from the PV installation, the potential index of the installation’s own needs coverage, and the index of energy use from photovoltaic modules were determined, which in practice is equated with the energy efficiency of the PV installation. The entire investment was subjected to simulation and field tests to determine the energy demand of a single-family building. The main aim of this work was to check whether a system equipped with a heat pump combined with a PV installation is an effective technical solution in the analysed climatic conditions in one of the countries of Central and Eastern Europe. In addition, both positive and negative aspects of renewable energy sources were analysed, including long-term financial savings, energy independence, and reductions in greenhouse gas emissions. It has been shown that the described solution is characterised by high initial costs depending on weather conditions. The installation presented would allow us to avoid 1891 kg/year of CO₂ emissions, which means that with this solution, we contribute to environmental protection activities. Full article

Precision agriculture technologies are widely recognized as a key pathway to achieving agricultural sustainable intensification. However, empirical research on their farm-level economic benefits and environmental gains has yielded inconclusive and hotly debated results. This study employs a meta-analysis to systematically integrate 85 empirical studies from around the world, comprising 1472 independent farm observations. This approach aims to quantify the average effects of precision agriculture technologies (PATs) and explore the sources of heterogeneity. The results indicate that: (1) Overall, the adoption of precision agriculture technologies generates significant economic benefits, increasing the average return on investment by 22.3% and net profit by 18.5%; (2) Environmentally, technology adoption significantly improves nitrogen use efficiency (average increase of 15.1%), reduces pesticide application (average reduction of 12.8%), and decreases greenhouse gas emissions (average reduction of 9.4%); (3) Moderating effect analysis reveals that technology type, farm size, region, and development level are key factors causing effect heterogeneity. Variable rate technology and auto-guidance systems show the most pronounced benefits in large-scale grain farms, whereas benefits are relatively weaker and less stable in small-scale farms and developing countries. The findings of this study emphasize that the realization of precision agriculture’s benefits is highly context-dependent. Therefore, policy formulation and technology promotion should abandon the “one-size-fits-all” model and adopt differentiated strategies. These strategies should focus on lowering application barriers for smallholders and developing low-cost, locally adapted technical solutions. This approach is essential to maximize the sustainability potential of the technologies. Full article

This paper describes a compact low-cost telemetry system featuring ready-made sensors and an acquisition unit based on the ESP32, which makes use of the LoRa/Wi-Fi wireless standard for communication, and autonomous fallback logging to guarantee data recovery during communication loss. Ensuring safe atmospheric re-entry requires reliable onboard monitoring of capsule conditions during descent. The system is intended for sub-orbital, low-cost educational capsules and experimental atmospheric descent missions rather than full orbital re-entry at hypersonic speeds, where the environmental loads and communication constraints differ significantly. The novelty of this work is the development of a fully self-contained telemetry system that ensures continuous monitoring and fallback logging without external infrastructure, bridging the gap in compact solutions for CubeSat-scale capsules. In contrast to existing approaches built around UAVs or radar, the proposed design is entirely self-contained, lightweight, and tailored to CubeSat-class and academic missions, where costs and infrastructure are limited. Ground test validation consisted of vertical drop tests, wind tunnel runs, and hardware-in-the-loop simulations. In addition, high-temperature thermal cycling tests were performed to assess system reliability under rapid temperature transitions between −20 °C and +110 °C, confirming stable operation and data integrity under thermal stress. Results showed over 95% real-time packet success with full data recovery in blackout events, while acceleration profiling confirmed resilience to peak decelerations of ~9 g. To complement telemetry, the TeleCapsNet dataset was introduced, facilitating a CNN recognition of descent states via 87% mean Average Precision, and an F1-score of 0.82, which attests to feasibility under constrained computational power. The novelty of this work is twofold: having reliable dual-path telemetry in real-time with full post-mission recovery and producing a scalable platform that explicitly addresses the lack of compact, infrastructure-independent proposals found in the existing literature. Results show an independent and cost-effective system for small re-entry capsule experimenters with reliable data integrity (without external infrastructure). Future work will explore AI systems deployment as a means to prolong the onboard autonomy, as well as to broaden the applicability of the presented approach into academic and low-resource re- entry investigations. Full article