Search Results (171)

It is well-known that activation functions are crucial in determining spectral expressiveness, training dynamics, and reconstruction accuracy in implicit neural representations (INRs), which employ coordinate-based multilayer perceptrons to represent continuous signals. Despite showing excellent performance, sinusoidal activations, for example SIREN, are limited in their adaptability to diverse signal types due to their fixed harmonic structure. In this paper, we propose two novel periodic activation functions for INRs. (1) Harmonic generalizes sinusoidal activations by combining the fundamental frequency with learned second and third harmonics through per-neuron trainable amplitude coefficients, resulting in a richer spectral basis within the SIREN initialization framework. (2) PM-FINER (Phase-Modulated FINER) extends the variable-periodic FINER activation by embedding frequency modulation synthesis directly into the instantaneous phase, enabling data-driven phase distortion via a learnable modulation index and carrier ratio. We conducted comprehensive experiments spanning nine architectural configurations (including SIREN, WIRE, FINER, Gaussian, Harmonic, PM-FINER, and an additional direct comparison against the Subtractive Modulative Network (SMN)), using six natural images, three learning rate schedulers, and three random seeds, totaling 486 main training runs (534 runs total including an ${\omega }_0$ sensitivity sweep). Our evaluation combined peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and rigorous statistical analysis, such as paired t-tests, Wilcoxon signed-rank tests, Cohen’s d effect sizes, and Friedman rank tests. Under cosine annealing, Harmonic achieves a mean PSNR gain of 6.08 dB over SIREN and 2.57 dB over FINER (both $p<0.001$, Cohen’s $d>3.7$), while PM-FINER ranks statistically on par with Harmonic (mean difference $0.17$ dB, $p=0.36$), outperforming all of the other baselines. Compared with SMN, Harmonic outperforms it by $+7.94$ dB under cosine annealing (Bonferroni-adjusted $p<{10}^{-5}$, Cohen’s $d=12.3$), winning on all six images. Additionally, the Friedman ranking across the six images confirmed Harmonic (with mean rank $=1.33$) and PM-FINER (with mean rank $=1.67$), being the top two methods under cosine annealing. Our results establish interpretable multi-harmonic and phase-modulated activations as real alternatives to the existing INR activation functions. Full article

Traditional job shop scheduling research primarily focuses on regular performance measures such as makespan. However, in a Just-in-Time (JIT) production environment, the objective shifts toward minimizing non-regular measures, specifically the weighted sum of earliness and tardiness (E/T) penalties, as excessive earliness leads to increased work-in-process inventory costs. Addressing the JIT scheduling problem in Assembly Job-shop Scheduling Problem (AJSP) is challenging, as traditional genetic algorithms (GAs) often suffer from premature convergence due to the randomness of initial populations. This paper proposes an Improved Genetic Algorithm (IGA) based on a multi-rule collaborative initialization mechanism. The algorithm explicitly incorporates assembly tree structure constraints during the encoding phase. For population initialization, a hybrid strategy is designed by integrating forward scheduling, backward scheduling, and forward-scheduling-based delay adjustment rules to ensure both the quality and diversity of the initial solutions. Simulation experiments and ablation studies demonstrate that the proposed IGA consistently achieves lower total weighted costs across various problem scales compared to standard algorithms. The results validate that the collaborative initialization strategy effectively balances global exploration and local exploitation, providing a robust solution for AJSP under JIT constraints. Full article

To scientifically explore the vehicle capacity characteristics of circular earthwork transportation routes in water conservancy projects, this paper takes the second-phase project of the Huaihe River Sea Entrance Channel as the research background. Key influencing factors such as road conditions, vehicle performance parameters, safe car-following distance, and earthwork loading–unloading duration are comprehensively considered, and a cellular automaton simulation model is constructed. Horizontal comparative verification is carried out with the Intelligent Driver Model, System Dynamics model, and field measured data to verify model accuracy. The results reveal that the cellular automaton (CA) model yields a total vehicle transport trip count of 606, with a MAPE of 0.66% when compared against the field-measured average of 602 trips. The simulated average travel speed reaches 16.71 km/h, corresponding to a MAPE of 2.89% relative to the field measurement of 16.24 km/h. The error metrics of these two indicators are markedly lower than those derived from alternative models. Due to differences in modeling paradigms and applicable mechanisms, the three models exhibit distinct characteristics in simulation performance. Among them, the cellular automaton model is more suitable for the circular earthwork transportation scenario of this study, which can accurately reflect the coupling characteristics of microscopic traffic behaviors such as multi-route confluence and node queuing, and has high consistency with actual engineering operation. Sensitivity analysis indicates that improving earth loading efficiency and reasonably arranging excavator quantity can significantly enhance the overall transportation efficiency. The modeling ideas and simulation analysis method adopted in this paper are not only applicable to the specific engineering scenario, but also can be extended to similar water conservancy earthwork transportation and large-scale engineering logistics transportation fields. It can provide theoretical basis and engineering reference for earthwork scheduling optimization and quantitative calculation of traffic capacity in water conservancy projects. Full article

Background: The International Agency for Research on Cancer estimated around 3197 annual deaths along with 5008 newly diagnosed cases of cervical cancer in Pakistan. Worldwide, introduced in 164 WHO member states, the HPV vaccine provides over ninety percent (90%) protection from human papillomavirus (16 & 18 types) infections. This article intended to document the vaccine (HPV) introduction in a low-middle-income country through the lens of EPI preparedness, vaccination coverage achieved, community acceptance, and implementation challenges during Phase I. Methodology: The research applied a qualitative and quantitative mix method to review the intricate procedure of new vaccine rollout within the national context. A qualitative participant observation approach assessed the planning, approval, and implementation phases of the HPV vaccine. Quantitative data statistics were evaluated for national & regional vaccination coverages, rapid convenience assessment findings, and adverse events reports. Results: The overall reported administrative HPV campaign coverage was 75%, with the maximum regional coverage of 81% by the Punjab, followed by 66% of the Sindh, 43% by the Azad Jammu & Kashmir, and 38% by the Islamabad. Rapid Convenience Assessment findings highlighted the main reasons for refusal (71%), with unavailable girls during the campaign (22%) for non-HPV vaccination. Community acceptance varied across the regions, with notable challenges in implementation being observed. Discussion & Way Forward: Initial phase campaign coverage (70.6%) was greater than the worldwide reported first dose mean coverage (61.6%) for the same multi-age cohort, indicative of an encouraging start in resource limited setting. Documented coverage was below the high-performing countries but comparable to multiple low and middle-income countries. Federal Directorate of Immunization, in collaboration with provincial EPI stakeholders, should prioritize including the newly introduced HPV vaccine in the routine immunization schedule of the Phase I regions and should also implement the lessons learned in the subsequent rollout phases in 2026 in Khyber Pakhtunkhwa and 2027 in Balochistan & Gilgit Baltistan. Expanding fixed EPI sites for HPV vaccination, promoting school-centered vaccination, rationalizing outreach in marginalized areas, sustaining the cold chain system, implementing a culturally acceptable communication plan, and resolving internet connectivity challenges are the key strategies to address implementation challenges. Full article

Higher turbine inlet temperatures improve cycle efficiency but intensify blade thermal loading, so internal passages rely on turbulators that raise convection within coolant pressure budgets. Streamwise sinusoidal ribs introduce curvature and spanwise phasing beyond straight transverse bars, yet reconciled multi-row thermal–hydraulic data for such layouts in high-aspect-ratio blade-cooling analogues remain scarce. Steady three-dimensional computational fluid dynamics (CFD) of turbulent airflow in a 4:1 rectangular channel with uniform heat flux on one ribbed wall are applied to compare nine parametric sinusoidal-rib layouts and one transverse baseline at bulk Reynolds numbers from 20,000 to 90,000. The normalized Nusselt number ($Nu/N{u}_0$), Fanning friction factor ($f/f_0$), and composite thermal–hydraulic performance indices quantify the trade-off. Several layouts outperform the transverse baseline; a streamwise-increasing rib-height schedule achieves the highest pressure-drop-weighted index, whereas a large-amplitude uniform waviness gives the best heat-transfer-dominated index. The parametric matrix indicates when streamwise waviness merits further study in ribbed passage design. Full article

Front-End Planning (FEP) is the very early stage of delivering megaprojects, during which strategic decisions are made that can dictate the project’s overall costs and timelines over the lifetime of the project. While existing FEP frameworks provide valuable insights into managing FEPs in Western nations, they have never been validated by reviewing Saudi Arabia’s unique environment, where large-scale projects are characterized by complicated multi-agency coordination systems and rapid timelines due to Vision 2030 initiatives. This exploratory research examines which FEP activities are most strongly associated with cost and schedule variances in Saudi megaprojects. A quantitative survey was administered to 35 respondents who have experience working on projects with a minimum of SAR 1 billion in capital cost to evaluate the quality of 33 FEP activities organized into five domains. Cronbach’s alpha validated domain composite scores, with four of five domains demonstrating good to excellent reliability (α = 0.73 to 0.91); the Technical Planning domain (α = 0.583) was excluded from regression analysis due to insufficient internal consistency. Multiple regression analysis examined associations between domain composites and project outcomes. Schedule performance was significantly associated with FEP quality (R² = 0.34, p = 0.012), with Project Planning showing a large negative association (β = −0.80, p = 0.002) and Business Planning showing a significant positive association (β = 0.75, p = 0.031). However, sensitivity analysis revealed that the Project Planning finding was substantially dependent on a single influential observation, while the Business Planning association remained robust across model specifications. The cost model did not reach statistical significance (p = 0.305), attributable in part to insufficient statistical power (achieved power = 0.40). Ownership type was not significant after controlling for FEP quality. The findings suggest that Project Planning activities, including scope compilation, preliminary execution planning, cost estimation, and master scheduling, may be associated with reduced schedule variance, though this association requires confirmation with larger samples. A preliminary four-tier prioritized framework is proposed to guide resource allocation during front-end phases while acknowledging the exploratory nature of the evidence base. Full article

This paper proposes a Multi-strategy Improved Love Evolution Algorithm, named MSILEA, to overcome the limitations of the original Love Evolution Algorithm (LEA) in complex optimization tasks. Although LEA has a distinctive stimulus–value–role interaction mechanism, its linear search-radius control, distance-dominated behavioral decision rule, and weak directional learning in the value phase make it prone to insufficient exploitation, ineffective behavioral switching, and local optimum trapping on rotated, hybrid, and composition functions. To address these issues, MSILEA introduces three complementary strategies: a nonlinear two-stage search radius regulation strategy, a quality–distance joint decision strategy, and a winner-direction differential learning strategy. These strategies respectively improve stage-dependent search control, multi-criteria behavioral selection, and directional learning ability. From the perspective of the symmetry concept, the proposed MSILEA can be regarded as an optimization framework that dynamically regulates the symmetry and asymmetry of population interactions. The encounter and role mechanisms preserve paired interaction symmetry among candidate solutions, whereas the quality–distance joint decision and winner-direction differential learning strategies introduce controlled symmetry breaking to guide the population toward higher-quality regions of the search space. MSILEA is evaluated on the CEC2017 and CEC2022 benchmark suites and compared with nine representative classical and advanced metaheuristic algorithms. On the 30-dimensional CEC2017 suite, MSILEA achieves the best Friedman mean rank of 1.93, outperforming the original LEA with a mean rank of 4.60. On the CEC2022 suite, MSILEA also obtains the best mean ranks of 2.50 and 2.00 in the 10-dimensional and 20-dimensional cases, respectively. In the microgrid day-ahead optimal scheduling problem, MSILEA obtains the lowest mean operating cost of 1.23 × 10⁶ CNY and reduces the cost by approximately 50.80% compared with LEA. The average CPU time of MSILEA is 18.47 s, which is comparable to LEA and lower than several improved competitors. These results indicate that MSILEA can improve optimization accuracy, convergence robustness, and engineering feasibility without increasing the theoretical computational complexity. Full article

To achieve an optimal trade-off between production efficiency and energy benefits in complex manufacturing environments, this paper addresses the Green Flexible Job Shop Scheduling Problem (GFJSP) by establishing a multi-objective mathematical model that minimizes both makespan and total energy consumption. An Improved Non-dominated Sorting Genetic Algorithm II (INSGA-II) is proposed to solve this model. In the population initialization phase, chaotic mapping is integrated with multiple heuristic rules to generate a high-quality and uniformly distributed initial population. Furthermore, an enhanced elite selection mechanism is employed to effectively prevent premature convergence. Subsequently, adaptive crossover and mutation operators are designed to enable differentiated evolution across sub-populations, effectively coordinating global exploration and local exploitation. Finally, experimental results on the Brandimarte and Hurink benchmark datasets demonstrate the superiority of the proposed algorithm in terms of convergence and diversity, providing a robust solution for optimizing green industrial production scheduling. Full article

Cooperative multi-UAV path planning under dynamic and adversarial conditions demands simultaneous satisfaction of safety, efficiency, and coordination constraints, yet existing swarm-intelligence and RL–swarm hybrids rely on deterministic switching rules, tabular states, and ad hoc training schedules. This paper proposes RL-JSO, a hybrid framework in which a dueling double deep Q-network with prioritized experience replay adaptively selects among the drift, passive, and active phases of a jellyfish search optimizer, replacing the deterministic time-control rule with a learned policy. The framework integrates a five-layer hierarchical safety control mechanism, a mastery-gated nine-stage curriculum, and a shared reward module that architecturally enforces fairness between RL-JSO and a paired RL-PSO counterpart. Evaluation across four progressive campaigns with 160 independent runs per algorithm shows that, within the evaluated JSO/PSO family, RL-JSO is the only method that sustains a 100% collision-free rate across all four progressive difficulty campaigns, its Cliff’s delta over standard JSO grows monotonically with difficulty from medium to large, and under a composite cooperation metric its coordination score remains nearly invariant while comparators degrade by 17–23%. A paired inference-time ablation on the trained checkpoint provides controlled inference-time evidence that adaptive phase switching is a principal contributor to the observed test-time performance within the trained system, rather than the heuristic fallback layers. Full article

Cascade hydropower scheduling is a high-dimensional, tightly constrained multi-objective optimization problem in which classical genetic and evolutionary algorithms struggle to find feasible solutions. Under random initialization, algorithms such as the Non-dominated Sorting Genetic Algorithm II (NSGA-II), the Non-dominated Sorting Genetic Algorithm III (NSGA-III), and the Constrained Two-Archive Evolutionary Algorithm (C-TAEA) rarely produce any feasible solution when the feasible region occupies a vanishingly small fraction of the search space. This paper presents a three-phase framework that combines physics-guided deep learning with evolutionary computation to accelerate both NSGA-II and NSGA-III. The method trains a Physics-Embedded Temporal Convolutional Network (PeTCN) as a differentiable surrogate model that explicitly incorporates physical constraints, applies gradient-based inverse optimization to obtain a feasible or near-feasible solution of high quality, and warm-starts NSGA-II or NSGA-III with that solution for efficient Pareto front exploration. Experiments on a real-world six-station cascade system show that, under a 1500 s fixed-time budget across 20 independent runs, Boosted NSGA-II and Boosted NSGA-III both find feasible solutions in all runs. Boosted NSGA-II and Boosted NSGA-III both reach the first feasible solution within roughly 50–60 generations of Phase 3 search on average, whereas standard NSGA-II produces no feasible run within the same budget and standard NSGA-III requires thousands of generations among its successful runs. The mean final hypervolume reaches $43.84\times {10}^6$ for Boosted NSGA-II and $46.52\times {10}^6$ for Boosted NSGA-III, and both boosted algorithms reach a target hypervolume of $35.00\times {10}^6$ in all 10 target-hypervolume runs. These results demonstrate that coupling physics-embedded surrogates with gradient-based initialization is an effective strategy for constrained multi-objective problems in which feasible solutions are extremely sparse. Full article

The large-scale integration of renewable energy and electric vehicles introduces grid-connected power fluctuations in microgrids. To address this, this paper proposes a novel two-stage optimization scheduling strategy that balances economic efficiency and grid compatibility. In the first stage, a multi-objective optimization model is formulated to minimize both operating costs and power fluctuations, and the Improved Multi-Objective Grey Wolf Optimization algorithm—incorporating the Bernoulli chaotic map—is employed to solve it efficiently. In the intra-day phase, a rolling tracking strategy based on model predictive control is proposed to address ultra-short-term forecasting errors, and a multi-unit hierarchical error compensation mechanism is designed. This mechanism prioritizes the use of supercapacitors to absorb high-frequency fluctuations, followed by the coordinated use of batteries, electric vehicle clusters, and micro gas turbines to mitigate residual deviations, thereby effectively reducing the operational burden on individual energy storage devices. Finally, a comparative analysis of six simulation cases was conducted using a weighted evaluation metric that integrates average power deviation values and interconnection line power fluctuations. The results confirm that this strategy not only significantly smooths grid-connected power fluctuations but also demonstrates exceptional robustness and adaptability under extreme forecast error scenarios. Full article

The global network of pipelines constitutes a strategic backbone for the world economy, enabling safe and efficient transportation of energy products. These pipelines serve distinct functions in the energy supply chain: gas pipelines support emerging cleaner energy carriers; multi-product pipelines provide versatility in transporting refined liquid fuels; and oil pipelines remain dominant for crude oil delivery. Energy management across the pipeline value chain emphasizes efficiency optimization, cost reduction, and sustainability through real-time monitoring, data analytics, integrated systems, and technological innovations spanning operations, maintenance, and emission control. Despite their critical role, petroleum depots remain relatively understudied, particularly in developing and Sub-Saharan African contexts. This review synthesizes insights from over 100 studies on energy-efficient pumping, predictive control, digitalization, and socio-technical energy management in depots. Analysis of these studies highlights recurring operational and infrastructural issues that constrain energy efficiency in depots. The challenges include irregular truck-loading schedules, frequent pump cycling, aging equipment, power-supply instability, manual operator interventions, and policy-driven constraints. The reviewed studies demonstrate that anticipatory, multi-layer control strategies integrating short-horizon flow forecasting, hybrid model predictive control, and cyber–physical–human–institutional system representations outperform reactive approaches in mitigating energy losses and operational variability. Site-specific calibration and phased deployment emerge as pragmatic pathways for implementing advanced energy optimization under the constrained conditions typical of real-world petroleum depots. Full article

To address the challenges of strong coupling between resource allocation and collaborative scheduling in vehicle–UAV cooperative inspections of power distribution lines, as well as the difficulty in balancing efficiency and stability, this paper proposes a path-scheduling feedback-based evolutionary cooperative optimization method. First, an integrated modeling framework for resource allocation and execution scheduling is constructed, incorporating vehicle path decisions and drone task scheduling into a unified optimization space. Next, a feedback-driven two-layer multi-objective evolutionary collaborative optimization algorithm (FB-MOC²) is introduced. The outer layer performs evolutionary search for adaptive resource allocation, while the inner layer solves path planning and collaborative scheduling, with dynamic resource adjustments achieved through execution-layer feedback, forming a data-driven adaptive optimization process. Subsequently, sensitivity analysis is conducted on resource deployment mechanisms, revealing phased evolutionary patterns between resource scale and system performance, and identifying the effective operational range for resource allocation. Finally, the algorithm’s robustness is validated under multiple failure scenarios. Simulation results demonstrate that the proposed method reduces total operation time from 412 min to 315 min, improves battery utilization to 78.5%, and maintains recovery costs within 1.65 times the baseline even under high drone failure rates, while ensuring full inspection coverage. This approach provides an effective bio-inspired and data-driven solution for adaptive resource allocation and robust scheduling in intelligent power distribution line inspections. Full article

Sustainable urban development depends not only on efficient design and construction but also on the long-term governance of built assets during their operational phase. However, Building Information Modeling (BIM) is still predominantly applied to design and delivery processes, with limited integration into structured maintenance and renewal planning. This study develops a BIM-enabled lifecycle governance methodology that integrates lifecycle cost modeling, service-life estimation, and time-based renewal scheduling into a unified digital asset environment. Rather than proposing a new theoretical model, the study focuses on the systematic integration and operationalization of these components into a reproducible and auditable workflow. The methodology is validated through an anonymized multi-asset industrial portfolio comprising buildings, technical infrastructure, and external works, modeled over a 30-year planning horizon using structured maintenance and renewal data. Comparative scenario analysis between reactive and planned lifecycle strategies evaluates expenditure distribution, capital concentration, and intervention synchronization. The results demonstrate that embedding structured lifecycle parameters within BIM improves the predictability of annual expenditures, reduces cost concentration in peak renewal years, and enhances transparency of long-term asset planning without significantly altering cumulative lifecycle costs. These outcomes support more structured financial planning and coordination of maintenance and renewal activities at the portfolio level. The study does not quantify environmental or social sustainability impacts; its contribution lies in providing a governance-oriented methodology that transforms BIM-based asset data into decision-support outputs for long-term lifecycle planning. Full article

Unlike reactive systems, mechanical ventilation controlled by CO₂ concentration operates at a target efficiency that dynamically increases whenever the target CO₂ level is exceeded. This approach eliminates the typical ‘dead-time’ and prevents air quality degradation by ensuring the system adjusts its performance immediately in response to concentration changes. In this work, the study focuses on the development and evaluation of data-driven predictive models for near-term indoor CO₂ forecasting that can be integrated into pre-occupancy ventilation strategies, rather than designing a complete control scheme. Experimental data were collected over four months in a 48 m² smart laboratory configured as an open-plan office, where a heterogeneous IoT sensing architecture logged synchronized time-series measurements of CO₂ and microclimate variables (temperature, relative humidity, PM_2.5, TVOCs), together with acoustic noise levels and appliance-level energy consumption used as indirect occupancy-related signals. Raw telemetry was transformed into a 22-feature state vector using a structured feature engineering method incorporating z-score standardization, cyclic time encodings, multi-horizon CO₂ lags, rolling statistics, momentum features, and non-linear interactions to represent temporal autocorrelation and daily periodicity. The study benchmarks multiple regression paradigms, including simple baselines and ensemble methods, and found that an automated multi-level stacked ensemble achieved the highest predictive fidelity for short-term forecasting, with an Mean Absolute Error (MAE) of 32.97 ppm across an observed CO₂ range of 403–2305 ppm, representing improvements of approximately 24% and 43% over Linear Regression and K-Nearest Neighbors (KNN), respectively. Temporal diagnostics showed strong phase alignment with observed CO₂ rises during occupancy transitions and statistically reliable prediction intervals. Five-fold walk-forward cross-validation confirmed the temporal stability of these results, with top models achieving consistent R² values of 0.93–0.95 across Folds 2–5. These results demonstrate that, within a single-room university laboratory setting, historical sensor data from low-cost IoT devices can support accurate short-term CO₂ forecasting, providing a predictive layer that could support future proactive ventilation scheduling aimed at reducing CO₂ lag at the start of occupancy while avoiding unnecessary ventilation runtime. Generalization to other building types and occupancy profiles requires further validation. Full article