Search Results (298)

For the purpose of fulfilling the dual requirements of persistent cellular network connectivity and flight safety for cellular-connected Unmanned Aerial Vehicles (UAVs) operating in dense urban airspace, this paper presents an A*-oriented comprehensive path-planning scheme for multiple connected UAVs that integrates a radio map and complex network. Existing research often lacks rigorous processing of environmental map data, while the traditional A* algorithm struggles to simultaneously handle constraints such as obstacle avoidance, flight maneuverability, and multi-UAV path conflicts. To overcome these limitations, this study first constructs a path-planning model based on complex-network theory using environmental data and the radio map, clarifying the separation of responsibilities between environment representation and algorithmic search. On this basis, we proposed an improved A* algorithm for multi-UAV scenarios termed MURM-A*. Simulation results demonstrate that the proposed algorithm effectively avoids collisions with obstacles, adheres to UAV flight dynamics, and prevents spatial conflicts between multi-UAV paths, while achieving a joint optimization between path efficiency and radio quality. In terms of performance comparison, the proposed algorithm shows a marginal difference but ensures operational validity compared to traditional A*, exhibits a slightly increase in flight time but achieves a substantial reduction in radio-outage time compared to the Deep Reinforcement Learning (DRL) method. Furthermore, employing the path-planning model enables the algorithm to more accurately identify environmental information compared to directly using raw environmental maps. The modeling time is also notably shorter than the training time required for DRL methods. This study provides a well-structured and extensible systematic framework for reliable path planning of multiple cellular-connected UAVs in complex radio environments. Full article

Background: Many probiotic strains have been studied in relation to irritable bowel syndrome (IBS). The aim of this study was to identify probiotic strains demonstrating efficacy in the management of IBS based on meta-analyses of randomized placebo-controlled trials (RPCTs). Methods: This systematic review was registered in the PROSPERO database (CRD420251047092). Searches were conducted in PubMed and Scopus on 8 April 2025. Additional completed studies with available results were identified through ClinicalTrials.gov. An additional search of the Cochrane Central Register of Controlled Trials (CENTRAL), including records indexed in EMBASE, was conducted in December 2025 and did not identify any additional studies. RPCTs were included if they evaluated single-strain probiotics without additional active components compared with a placebo in patients with IBS. Studies whose results could not be meta-analyzed were excluded. Results: A total of 2643 records were identified; 32 articles evaluating 10 probiotic strains were included in the meta-analyses. Meta-analyses demonstrated the efficacy of Bifidobacterium longum (formerly Bifidobacterium infantis) 35624, Lactobacillus rhamnosus GG, Lactiplantibacillus plantarum 299v (DSM 9843), Saccharomyces cerevisiae CNCM I-3856, and Bacillus coagulans Unique IS2 (MTCC 5260) in improving key IBS symptoms. Meta-analyses also demonstrated that Bacillus coagulans MTCC 5856 improved quality of life for those with IBS. Conflicting results were observed for Saccharomyces boulardii CNCM I-745. Meta-analyses did not demonstrate the efficacy of Escherichia coli Nissle 1917, Lactobacillus gasseri BNR17, or Lactobacillus casei Shirota. Conclusions: Several probiotic strains demonstrated efficacy in the treatment of IBS in meta-analyses of RPCTs. Full article

Objectives: Artificial intelligence (AI) is a transformative development in the field of medicine. In the field of musculoskeletal radiology, agentic AI is a technology that could flourish, but currently, the limited evidence base is fragmented and sparse, and we present a scoping review of it. Methods: Parallel searches were conducted in four databases: PubMed, Embase, Scopus, and Web of Science. Search terms included all agentic AI and autonomous AI agents, as well as radiology. All papers underwent screening by two independent reviewers, with conflicts resolved through consensus. Initially, inclusion criteria involved all papers on general radiology, which were later stratified for musculoskeletal radiology and applicable papers to ensure inclusion of all suitable studies. A thematic analysis was undertaken by two independent reviewers. Results: Eleven studies met the inclusion criteria, comprising two MSK (musculoskeletal)-specific and nine general radiology papers applicable to MSK workflows. Four key themes emerged. Agentic decision support was demonstrated across five studies, showing improved diagnostic coordination, pathway navigation, and reduced clinician workload. Workflow optimisation was highlighted in four studies, with agentic systems enhancing administrative efficiency, modality selection, and overall radiology throughput. Image analysis and reconstruction were improved in three studies, with multi-agent systems enabling enhanced image quality and automated interpretation. Finally, four studies addressed conceptual, ethical, and governance considerations, emphasising the need for transparency, safety frameworks, and clinician oversight. Conclusion: Agentic AI shows considerable promise for enhancing MSK radiology through improved decision support, image analysis, and workflow efficiency; however, the current evidence remains limited and largely theoretical. Full article

Multi-platform aerial remote sensing supports critical applications including wide-area surveillance, traffic monitoring, maritime security, and search and rescue. However, constrained by observation altitude and sensor resolution, targets inherently exhibit small-scale characteristics, making small object detection a fundamental bottleneck. Aerial remote sensing faces three unique challenges: (1) spatial heterogeneity of modality reliability due to scene diversity and illumination dynamics; (2) conflict between precise localization requirements and progressive spatial information degradation; (3) annotation ambiguity from imaging physics conflicting with IoU-based training. This paper proposes RAPT-Net with three core modules: MRAAF achieves scene-adaptive modality integration through two-stage progressive fusion; CMFE-SRP employs hierarchy-specific processing to balance spatial details and semantic enhancement; DS-STD increases positive sample coverage to 4× through spatial tolerance expansion. Experiments on VEDAI (satellite) and RGBT-Tiny (UAV) demonstrate mAP values of 62.22% and 18.52%, improving over the state of the art by 4.3% and 10.3%, with a 17.3% improvement on extremely tiny targets. Full article

Purpose: Proton beam therapy (PBT) offers superior dosimetric sparing of organs at risk compared to photon radiotherapy for non-small cell lung cancer (NSCLC); however, comparative clinical evidence regarding survival benefits remains conflicting. This systematic review and meta-analysis aimed to evaluate the clinical outcomes and toxicity profiles of PBT versus photon radiotherapy, with a specific focus on time-dependent survival patterns. Methods: We searched PubMed, EMBASE, and Cochrane CENTRAL databases for comparative studies published up to 10 October 2025. Primary outcomes were overall survival (OS), progression-free survival (PFS), and local progression-free survival (LPFS). Individual patient data (IPD) were reconstructed from Kaplan–Meier curves when hazard ratios (HRs) were not reported. Odds ratios (ORs) were calculated for survival at fixed time points (1, 3, and 5 years) and for toxicity endpoints. Results: Seven studies comprising 244,604 patients were included, encompassing retrospective cohorts, multi-institutional datasets, and one randomized trial. In the overall pooled analysis, PBT showed no statistically significant superiority over photon radiotherapy for OS (HR = 0.91, 95% CI: 0.69–1.19, p = 0.483), PFS (HR = 1.09, 95% CI: 0.81–1.47, p = 0.572), or LPFS (HR = 0.89, 95% CI: 0.47–1.69, p = 0.732). Sensitivity and subgroup analyses restricted to Stage I and Stage I–II NSCLC similarly failed to demonstrate significant differences in survival outcomes. However, exploratory time point analysis utilizing ORs revealed a distinct temporal pattern: PBT was associated with improved odds of all-cause mortality at 1 year (OR = 0.60, 95% CI: 0.49–0.73, p < 0.001). This survival advantage dissipated over time, with no significant differences observed at 3 years or 5 years. Regarding safety, PBT did not significantly reduce the odds of grade ≥ 2 radiation pneumonitis (OR = 0.98, 95% CI: 0.41–2.33, p = 0.967) or grade ≥ 3 events (OR = 1.40, p = 0.540) compared to photons. Conclusions: While long-term oncologic control appears comparable between proton and photon radiotherapy, exploratory analyses suggest that PBT is associated with improved odds of 1-year overall survival. This potential early benefit, observed in retrospective cohorts, likely reflects the mitigation of acute treatment-related mortality. These findings are hypothesis-generating and support the use of PBT for patients at high risk of toxicity and advocate for a model-based approach to patient selection. Full article

Path planning for multiple four-way shuttles in high-density warehousing is frequently hampered by efficiency-degrading conflicts, particularly head-on deadlocks. To address this challenge, this paper proposes a multi-agent reinforcement learning (MARL) framework based on Proximal Policy Optimization (PPO). The core of our approach is a novel Cooperative Avoidance Reward Mechanism (CARM), which employs a dual-component reward structure. This structure integrates a distance-guided reward to ensure efficient navigation towards targets and a cooperative avoidance reward that uses both immediate and delayed returns to incentivize implicit collaboration. This design effectively resolves conflicts and mitigates the policy instability often caused by traditional collision penalties. Experiments in a 20 × 20 grid simulation environment demonstrated that, compared to a rule-based A* and Conflict-Based Search (CBS) algorithms, the proposed method reduced the average travel distance and total time by 35.8% and 31.5%, respectively, while increasing system throughput by 49.7% and maintaining a task success rate of over 95%. Ablation studies further confirmed the critical role of CARM in achieving stable multi-agent collaboration. This work offers a scalable and efficient data-driven solution for real-time path planning in complex automated warehousing systems. Full article

Modern power distribution networks are increasingly challenged with nonlinear operating conditions, the high penetration of distributed energy resources, and conflicting operational objectives such as loss minimization and voltage regulation. Existing load-flow optimization approaches often suffer from slow convergence, premature stagnation in non-convex search spaces, and limited robustness when handling conflicting multi-objective performance criteria under fixed network constraints. To address these challenges, this paper proposes a Fractional Multi-Objective Load Flow Optimizer (FMOLFO), which integrates a fractional-order numerical regularization mechanism with an adaptive Pareto-based Differential Evolution framework. The fractional-order formulation employed in FMOLFO operates over an auxiliary iteration domain and serves as a numerical regularization strategy to improve the sensitivity conditioning and convergence stability of the load-flow solution, rather than modeling the physical time dynamics or memory effects of the power system. The optimization framework simultaneously minimizes physically consistent active power loss and voltage deviation within existing network operating constraints. Extensive simulations on IEEE 33-bus and 69-bus benchmark distribution systems demonstrate that FMOLFO achieves an up to 27% reduction in active power loss, improved voltage profile uniformity, and faster convergence compared with classical Newton–Raphson and metaheuristic baselines evaluated under identical conditions. The proposed framework is intended as a numerically enhanced, optimization-driven load-flow analysis tool, rather than a control- or dispatch-oriented optimal power flow formulation. Full article

Background/Objectives: Triple-negative breast cancer (TNBC) is an aggressive subtype, with limited diagnostic options and no targeted early detection tools. Liquid biopsy represents a minimally invasive approach for detecting tumor-derived molecular alterations in body fluids. This scoping review aimed to comprehensively synthesize all liquid biopsy-derived molecular biomarkers evaluated for the diagnosis of TNBC in adults. Methods: This review followed the Arksey and O’Malley framework and PRISMA-ScR guidelines. Systematic searches of PubMed, Scopus, Embase, and Web of Science identified primary human studies evaluating circulating molecular biomarkers for TNBC diagnosis. Non-TNBC, non-human, hereditary, treatment-response, and nonmolecular studies were excluded. Data on study design, patient characteristics, biospecimen type, analytical platforms, biomarker class, and diagnostic performance were extracted and synthesized descriptively by biomolecule class. Results: Thirty-two studies met the inclusion criteria, comprising 15 protein-based, 12 RNA-based, and 6 DNA-based studies (one reporting both protein and RNA). In total, 1532 TNBC cases and 3137 participants in the comparator group were analyzed. Protein biomarkers were the most frequently studied, although only APOA4 appeared in more than one study, with conflicting results. RNA-based biomarkers identified promising candidates, particularly miR-21, but validation cohorts were scarce. DNA methylation markers showed promising diagnostic accuracy yet lacked replication. Most studies were small retrospective case–control designs with heterogeneous comparators and inconsistent diagnostic reporting. Conclusions: Evidence for liquid biopsy-derived biomarkers in TNBC remains limited, heterogeneous, and insufficiently validated. No biomarker currently shows reproducibility suitable for clinical implementation. Robust, prospective, and standardized studies are needed to advance liquid biopsy-based diagnostics in TNBC. Full article

The rapidly evolving business landscape, driven by stringent energy conservation policies, compels construction firms to adopt energy-efficient project-centric structures, particularly in modern construction projects. These firms face a complex, multi-mode, resource-constrained, multi-project scheduling problem characterized by dynamic project arrivals and multiple resource constraints, including global, local, and non-renewable capacities. This environment pressures managers to simultaneously optimize the conflicting objectives of minimizing total project duration and total energy consumption. To address this challenge, we propose a novel multi-objective Smart Raccoon Family Optimization (SRFO) algorithm. The SRFO, a hybrid evolutionary approach, is designed to enhance global exploration and local exploitation. Its performance is boosted by integrating a non-dominated sorting mechanism, a dedicated energy-efficient search strategy, and enhanced genetic operators. The SRFO simultaneously optimizes two conflicting objectives: minimizing the total project duration and total energy consumption. This approach effectively integrates the unique constraint of off-site component production and on-site assembly within an intelligent scheduling framework. Empirical validation across benchmark problems and a real-world case study is conducted, comparing the SRFO with existing multi-objective approaches, such as NSGA-III, MOABC, and MOSMO. Performance is assessed using convergence and distribution metrics, augmented by TOPSIS-based multi-criteria decision-making. Results conclusively demonstrate that the proposed SRFO significantly outperforms existing approaches and offers a robust, high-quality solution for project management in energy-constrained environments. Full article

Background: Common polymorphisms in the MTHFR gene, C677T and A1298C, have been associated with increased risk for psychiatric neurodevelopmental disorders, including autism spectrum disorder (ASD). However, studies provide conflicting evidence for the strength of the association with ASD based on both the allelic variant and population structure of the cohorts studied. Methods: Using systematic literature search and selection criteria, we calculated ASD-associated odds ratios for the two most-reported MTHFR variants. Twenty-two articles reported the association between MTHFR C677T and ASD, including 13913 subjects (4391 cases, 9522 controls). Nine articles, including 3009 subjects (1462 cases, 1547 controls), evaluated the link between MTHFR A1298C and ASD susceptibility. Results: We identified a statistical association between ASD and the MTHFR C677T variant, regardless of race or ethnicity. However, there was no statistical support for an association between ASD and the MTHFR A1298C variant. In both cases, substantial-to-considerable residual heterogeneity remained (I² ~67% and 73%, respectively). Exploring the heterogeneity by meta-regression on race/ethnicity, the African (Egyptian) cohort with MTHFR C677T variants had a higher ASD susceptibility than Asian or European cohorts in most models, though this susceptibility difference was not observed between Africans and Europeans for the homozygous case (TT vs. CC). Similarly, the African (Egyptian) cohort with MTHFR A1298C variants also had a higher ASD susceptibility than Asian or European cohorts in most models, though this susceptibility difference was not observed between Africans and Asians for the homozygous case (CC vs. AA). Conclusions: Our findings support previous analyses that identified a statistical association between ASD and the MTHFR C677T variant but none between ASD and the MTHFR A1298C variant. We also reveal a greater potential for these variants to exacerbate ASD phenotypes in an African (Egyptian) cohort. Future studies should assess the mechanistic contribution of these variants to MTHFR function, especially potential hypomorphic sensitivity in individuals with African (Egyptian) ancestry. Full article

Background/Objective: Systematic reviews (SRs) with network meta-analysis (NMA) support evidence-based decision-making by enabling both direct and indirect comparisons across multiple interventions. Given the expanding use of Janus kinase (JAK) inhibitors in rheumatoid arthritis (RA), the methodological rigor of SRs with NMA is essential for trustworthy conclusions. This study is aimed at evaluating the methodological quality of SRs with NMA assessing the efficacy and/or safety of JAK inhibitors in RA. Methods: PubMed and Embase were searched for full-text SRs with NMAs evaluating JAK inhibitors as a therapeutic class in RA. Eligible publications were English-language articles reporting efficacy and/or safety outcomes. Narrative reviews, letters, duplicates, reviews focused on a single JAK inhibitor, and reviews without quantitative synthesis were excluded. Three independent reviewers assessed methodological quality using AMSTAR 2. Descriptive statistics were used to summarize findings. Results: Of the 222 records identified, 18 SRs with NMA met the inclusion criteria: 5 focused on efficacy, 5 on safety, and 8 assessed both. The most consistently fulfilled AMSTAR 2 items were a clearly defined PICO question (100%), duplicate study selection (100%), and reporting of conflicts of interest (100%). Common shortcomings included lack of protocol registration (44%), incomplete reporting of the search strategy (39%), and absence of publication bias assessment (50%). Risk-of-bias assessment varied by review focus: all safety reviews complied (100%), compared with 20% of efficacy reviews and 37% of mixed reviews. Conclusions: Most SRs with NMA of JAK inhibitors in RA present relevant methodological limitations, particularly in protocol registration, search reporting, and risk-of-bias assessment. Methodological standards were generally higher in safety-focused reviews, underscoring the need for more consistent and rigorous conduct and reporting, especially in efficacy and mixed reviews, to strengthen confidence in NMA-derived conclusions. Full article

In modern financial markets, decision-makers increasingly rely on quantitative methods to navigate complex trade-offs among multiple, often conflicting objectives. This paper addresses constrained multi-objective optimization (MOO) with an application to portfolio optimization for minimizing risk and maximizing return. To this end, and to address existing gaps, we propose a novel reinforcement learning (RL)-guided non-dominated sorting genetic algorithm II (NSGA-II) enhanced with gray relational coefficients (GRC), termed RL-NSGA-II-GRC, which combines an RL agent controller and GRC-based selection to improve the convergence and diversity of the Pareto-optimal fronts. The agent adapts key evolutionary parameters online using population-level metrics of hypervolume, feasibility, and diversity, while the GRC-enhanced tournament operator ranks parents via a unified score simultaneously considering dominance rank, crowding distance, and geometric proximity to ideal reference. We evaluate the framework on the Kursawe and CONSTR benchmark problems and on a NASDAQ portfolio optimization application. On the benchmarks, RL-NSGA-II-GRC achieves convergence metric improvements of about 5.8% and 4.4% over the original NSGA-II, while preserving a well-distributed set of non-dominated solutions. In the portfolio application, the method produces a smooth and densely populated efficient frontier that supports the identification of the maximum Sharpe ratio portfolio (with annualized Sharpe ratio = 1.92), as well as utility-optimal portfolios for different risk-aversion levels. The main contributions of this work are three-fold: (1) we propose an RL-NSGA-II-GRC method that integrates an RL agent into the evolutionary framework to adaptively control key parameters using generational feedback; (2) we design a GRC-enhanced binary tournament selection operator that provides a comprehensive performance indicator to efficiently guide the search toward the Pareto-optimal front; (3) we demonstrate, on benchmark MOO problems and a NASDAQ portfolio case study, that the proposed method delivers improved convergence and well-populated efficient frontiers that support actionable investment insights. Full article

With the development of manufacturing industry, traditional fixed process processing methods cannot adapt to the changes in workshop operations and the demand for small batches and multiple orders. Therefore, it is necessary to introduce multiple robots to provide a more flexible production mode. Currently, some Job Shop Scheduling Problems with Transportation (JSP-T) only consider job scheduling and vehicle task allocation, and does not focus on the problem of collision free paths between vehicles. This article proposes a novel solution framework that integrates workshop scheduling, material handling robot task allocation, and conflict free path planning between robots. With the goal of minimizing the maximum completion time (Makespan) that includes handling, this paper first establishes an extended JSP-T problem model that integrates handling time and robot paths, and provides the corresponding workshop layout map. Secondly, in the scheduling layer, an improved Deep Q-Network (DQN) method is used for dynamic scheduling to generate a feasible and optimal machining scheduling scheme. Subsequently, considering the robot’s position information, the task sequence is assigned to the robot path execution layer. Finally, at the path execution layer, the Priority Based Search (PBS) algorithm is applied to solve conflict free paths for the handling robot. The optimized solution for obtaining the maximum completion time of all jobs under the condition of conflict free path handling. The experimental results show that compared with algorithms such as PPO, the scheduling algorithm proposed in this paper has improved performance by 9.7% in Makespan, and the PBS algorithm can obtain optimized paths for multiple handling robots under conflict free conditions. The framework can handle scheduling, task allocation, and conflict-free path planning in a unified optimization process, which can adapt well to job changes and then flexible manufacturing. Full article

Emerging multi-unmanned aerial vehicle (multi-UAV) applications in smart cities must navigate cluttered airspace while meeting tight mobile edge computing (MEC) deadlines. Classical grid planners, including A-star (A*), D-star Lite (D* Lite), and conflict-based search with D-star Lite (CBS-D*) and metaheuristics such asparticle swarm optimization (PSO), either replan too slowly in dynamic scenes or waste energy on long detours. This paper presents PPO-adjusted incremental refinement (PAIR), a decentralized hybrid planner that couples an A* global backbone with a continuous PPO refinement module for multi-UAV navigation on two-dimensional (2-D) urban grids. A* produces feasible waypoint routes, while a shared risk-aware PPO policy applies local offsets from a compact state encoding. MEC tasks are allocated by a separate heterogeneous scheduler; PPO optimizes geometric objectives (path length, risk, and a normalized propulsion-energy surrogate). Across nine benchmark scenarios with static and Markovian dynamic obstacles, PAIR achieves 100% mission success (matching the strongest baselines) while delivering the best energy surrogate (104.9 normalized units) and shortest mean travel time (207.8 s) on a reproducible $100\times 100$ grid at fixed UAV speed. Relative to the strongest non-learning baseline (PSO), PAIR reduces energy by about 4% and travel time by about 3%, and yields roughly 10–20% gains over the remaining planners. An obstacle-density sweep with 5–30 moving obstacles further shows that PAIR maintains shorter paths and the lowest cumulative replanning time, supporting real-time multi-UAV navigation in dynamic urban MEC environments. Full article

Multi-objective optimization (MOO) plays a critical role in mechanical and industrial engineering, where conflicting design goals must be balanced under complex constraints. In this study, we introduce the Multi-Objective Giant Trevally Optimizer (MOGTO), a novel extension of the Giant Trevally Optimizer inspired by predatory foraging dynamics. MOGTO integrates predation-regime switching into a Pareto-based framework, enhanced with feasibility-aware archiving, knee-biased selection, and adaptive constraint handling. We benchmark MOGTO against established algorithms—NSGA-II, SPEA2, MOEA/D, and ParetoSearch—using synthetic test suites (ZDT1–3, DTLZ2) and classical engineering problems (welded beam, spring, and pressure vessel). Performance was assessed with Hypervolume (HV), Inverted Generational Distance (IGD), Spacing, and coverage metrics across 30 independent runs. The results demonstrate that MOGTO consistently achieves competitive or superior HV and IGD, maintains more uniform spacing, and generates larger feasible archives than the baselines. Particularly on constrained engineering problems, MOGTO yields more feasible non-dominated solutions, confirming its robustness and industrial applicability. These findings establish MOGTO as a reliable and general-purpose metaheuristic for multi-objective optimization in engineering design. Full article