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Generative models based on diffusion and flow matching have recently been applied to structure-based drug design, but their outputs often include unrealistic protein-ligand interactions that do not obey the laws of physics. We present an energy guidance framework that incorporates a molecular mechanics force field (MMFF94) directly into the sampling process. The method steers molecular generation toward more physically plausible and energetically stable conformations without retraining the underlying model. We evaluate this approach using two state-of-the-art architectures, SemlaFlow, a flow matching model and EDM, a diffusion model, on the PDBBind dataset. Across both models, energy guidance improves enthalpic interaction energy, improves strain energy by up to 75%, and generates over 1000 ligands with better docking scores than native ligands. These results demonstrate that lightweight, physics-based guidance can significantly enhance generative drug design while preserving chemical validity and diversity. SCIENTIFIC CONTRIBUTION: We introduce a novel, training-free force field guidance framework that steers ligand generation using empirical molecular mechanics (e.g., MMFF94) during diffusion or flow-based sampling-without modifying or retraining the base generative model (e.g., EDM or Semflaflow by [24]). Our method operates as a plug-in during inference time, leveraging energy feedback to generate poses with lower strain and having better predicted interactions with the protein structure. Our main contributions are as follows:Energy-based guidance without retraining: Unlike methods that require gradients from neural affinity predictors (e.g., BADGER [26]), our approach injects classical force field feedback (MMFF94) directly during the posterior sampling step.Improved docking and strain metrics: In benchmarks against unconditional EDM and Semflaflow, our guided inference yields consistently better AutoDock Vina scores and lower ligand strain energy, even after optimizing the final structures using the same force field.Compatibility and flexibility: Because the guidance module is external, it can be applied broadly to multiple generative backbones-without retraining or architecture modifications, and can be applied to arbitrary differentiable potential energy functions.Theoretical guarantee of stability. We demonstrate in Appendix B that the gradient correction step corresponds to a descent step on the energy under standard smoothness assumptions. While the full sampling update also includes model-driven (and, in the diffusion case, stochastic) components, this result formalizes how the guidance term locally biases the trajectory toward lower-energy regions and provides a principled justification for its stabilizing effect.

The minimization of power consumption poses a very important challenge to industries which aim at reducing power consumption and achieving their ecological sustainability objectives. The study proposes a superior decision-making model of the q-fractional fuzzy logic with reference to energy consumption in the industrial setting. The proposed model is the combination of multiple criteria including the power requirement of a machine, operating efficiency, temporal limitations and energy prices. The framework is concerned with uncertainties and imprecision in energy consumption data using the concepts of the q-fractional fuzzy aggregation operators, e.g., Weighted Average (WA), Weighted Geometric (WG), Ordered Weighted Average (OWA), Ordered Weighted Geometric (OWG), Hybrid Average (HA) and Hybrid Geometric (HG). The model will seek to attain high level of quality and cost efficient strategies in production due to energy saving. The result of its performance is confirmed by its case studies and simulations and has a huge decrease in energy consumption as compared to the traditional methods of optimization. These findings indicate how this other q-fractional fuzzy set can be a feasible and intelligent technology to manage energy during the industrial processes towards ecological sustainability, and it must be based on minimal human interjections as much as feasible. The empirical findings show that the advised q-fraction fuzzy verdict paradigm significantly reduces industrial power usage and outperforms the competitive CoCoSo mode in terms of reliability and effectiveness.

In nature, photosynthesis is driven by solar light and a large proportion of the visible spectrum is absorbed by the light harvesting complexes (LHCs), which then transfer the energy to the reaction center. Inspired by nature, we implemented a light harvesting energy transfer cascade within biomimetic lipid bilayers of liposomes built with DPPC (1,2-dipalmitoyl-sn-glycero-3-phosphocholine), using membrane-anchored fluorescein, 2-(3,6-dihydroxy-9H-xanthen-9-yl)-5-dodecanamidobenzoic acid (FlC12) as primary absorber and membrane anchored eosin Y, hexadecyl 2-(2,4,5,7-tetrabromo-3,6-dihydroxy-9H-xanthen-9-yl)benzoate (EYC16), as energy acceptor to sensitize oxygen and generate the reactive oxygen species 1O2. Finally, the model substrate nicotinamide adenine dinucleotide (NADH) is oxidized by 1O2 within the compartmentalizing liposome nanoreactors. It was observed that our metal-free LHC system has only a minor effect on the photooxidation rate of NADH when the nanoreactor membrane is functionalized symmetrically. By contrast, asymmetric membrane functionalization of the liposome nanoreactor membranes leads to acceleration by 16% to 27% when using multi-colored light emitting diodes (LED) or simulated solar light, respectively.

The aim of this study is to understand the factors contributing to patients' non-adherence to lifestyle modification plans among visitors of the Lifestyle Clinics in King Abdul-Aziz Medical City, Jeddah. Adherence to these plans is crucial for improving health outcomes and preventing chronic diseases. A cross-sectional study was conducted at the Lifestyle Clinics within the Primary Healthcare department of King Abdulaziz Medical City, Jeddah. Participants were adults referred for weight reduction. Data were collected using a questionnaire covering sociodemographic characteristics, adherence to lifestyle modifications, and barriers to adherence. The adherence level was assessed using a validated 13-item questionnaire, and the data were analyzed using IBM SPSS Statistics. A total of 380 participants were included, with a median age of 42 years (IQR: 32-50 years). Approximately 45.5% were adherent to the lifestyle modification plan, while 54.5% were non-adherent. Significant positive correlations were found between age and adherence (Correlation Coefficient=.205, p<.001), with healthcare workers showing higher adherence levels (p=0.027). Common barriers to adherence included lack of willpower (74.5%), energy (70.8%), and time (68.9%). Statistically significant associations were identified between lack of energy (p=0.019) or time (p=0.023) and non-adherence. This study identified key factors associated with non-adherence to lifestyle modification plans, particularly younger age, non-healthcare occupations, and perceived barriers such as lack of energy and lack of time. Despite high levels of knowledge regarding healthy lifestyle practices, adherence remained suboptimal, highlighting the gap between awareness and behavioral implementation. Addressing practical barriers through targeted, behavior-focused interventions may improve adherence and long-term health outcomes.

Heterogeneous briquettes made from rice husk-pine sawdust blend treated with sulfuric acid have the potential to satisfy the increasing global demand for sustainable energy. However, untreated blends are constrained by unfavorable thermochemical properties. In this study, a blend comprising 10 wt% rice husk and 90 wt% pine sawdust was subjected to H2SO4 treatment at 1%, 2%, 3%, 4%, and 5% (v/v) for 1-h at ambient temperature. Briquettes were subsequently formulated, and thermochemical properties were assessed to identify treatment conditions enhancing energy and combustion performance. The briquettes were analyzed for lignocellulose using acid detergent fiber and lignin, and neutral detergent fiber; higher heating value HHV using calorimetry; and thermal efficiency and emissions using a water boiling test (WBT) coupled with a portable emission monitoring system (PEMS) 4000-series sensor. The results demonstrated that sulfuric acid treatment increased briquettes' HHV from 18.21&#x2009;&#xb1;&#x2009;0.09&#xa0;MJ/kg (untreated) up to 18.82&#x2009;&#xb1;&#x2009;0.03&#xa0;MJ/kg. The 1% concentration yielded the highest thermal efficiency (71.49&#x2009;&#xb1;&#x2009;2.05%), accompanied by the lowest CO emissions (0.88&#x2009;&#xb1;&#x2009;0.03&#xa0;g/MJd), meeting the ISO 19,867-1:2018 Tier 5 limits, and the lowest PM2.5 emissions (100.67&#x2009;&#xb1;&#x2009;1.53&#xa0;mg/MJd), within the Tier 3 limit. Overall, the optimal sulfuric acid treatment condition for effective combustion performance and energy delivery was 1%. However, cost-benefit analysis should be incorporated into future studies for practical real-world application.

The corrosion inhibition behavior of carbon steel (CS) in 1.0 M HCl in the presence of 4-(2-(4-oxo-3-phenyl-3,4-dihydroquinazolin-2-yl) vinyl) phenyl benzenesulfonate (4-OPB) and4-(2-(3-(4-hydroxyphenyl)-4-oxo-3,4-dihydroquinazolin-2-yl)vinyl)phenyl benzenesulfonate (4-HPB) was investigated through chemical evaluation via weight loss (WL) measurements, as well as electrochemical techniques, including AC impedance (EIS), and potentiodynamic polarization (PDP). The inhibition efficiency (IE) increased progressively with higher concentrations of the tested compounds and with temperature elevation, reaching a maximum of 93.2% and 90.1% at 21&#x2009;&#xd7;&#x2009;10&#x207b;3 M of 4-HPB and 4-OPB, respectively from WL tests at 25C. In the other hand, it reached 96.5%, 95.9% for 4-HPB and 4-OPB at 45oCand the same concentration, respectively. The findings indicated that these compounds adhere on the CS surface and create a protective film whose formation conforms to the Langmuir adsorption isotherm, consistent with chemisorption, as supported by the relatively high adsorption energy values (&#x394;G&#xb0;ads&#x2009;<&#x2009;- 46 kJ mol&#x207b;1), rise in % inhibition by raising the temperature and the lowering in activation energy (E*) in presence of inhibitors than in its absence. These chemical compounds function as mixed-type inhibitors, according to PDP studies. Surface characterization of the inhibited CS using scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), and Fourier transform infrared spectroscopy (FTIR) demonstrated significant improvement in surface morphology. The collective results from all employed techniques exhibited strong agreement, validating the inhibitory performance of the studied compounds.

Starch serves as a vital energy reserve in plants. During its biosynthesis, malto-oligosaccharides (MOS) are essential primers. One of the key pathways for MOS production involves plastidial &#x3b1;-glucan phosphorylase (PHS1/Pho1) and disproportionating enzyme (DPE1). However, the functional relationship between these enzymes is unclear. Here, we demonstrate that rice PHS1 and DPE1 assemble into a multimeric complex. Cryo-EM structures of the PHS1-DPE1 complex reveal an assembly mechanism and suggest a potential substrate tunnel. Biochemical assays show the complex dramatically enhances catalytic efficiency over individual enzymes. Single-molecule fluorescence resonance energy transfer (smFRET) visualizes conformational dynamics, enabling rapid substrate transfer between the enzymes. We further identify the unique L80 loop in PHS1 as a potential regulator. Its deletion reduces catalytic efficiency and prolongs conformational state lifetimes during substrate transfer, thereby reducing the production of longer MOSs. Our findings establish that the PHS1-DPE1 complex facilitates efficient MOS primer synthesis through efficient substrate transfer or diffusion between the two enzymes, providing mechanistic insight into a critical step of starch biosynthesis with agronomic implications.

Seizure forecasting and affective state analysis using EEG-ECG data play a pivotal role in advancing neurological and mental health monitoring. However, existing methods such as Fed-Transformer, Res-1D CNN, and Fed-ESD suffer from privacy risks, inefficient feature extraction, and high computational overhead, limiting their effectiveness in real-world applications. To overcome these challenges, this study proposes NeuroFedSense, a novel Federated Learning-enabled Privacy-Preserving Framework that integrates a Temporal Convolutional Network (TCN) with an Attention Mechanism for accurate seizure forecasting and affective state analysis using EEG-ECG data, ensuring enhanced feature selection, interpretability and efficient decentralized training. The model leverages adaptive attention-based optimization and weighted feature selection to improve classification performance while ensuring data privacy. Implemented using TensorFlow, NeuroFedSense achieves 99.54% accuracy, 99.62% precision, 99.34% recall, and a 99.46% F1-score, outperforming Fed-Transformer (97.10% accuracy), Res-1D CNN (81.62% accuracy), and FML (99.10% accuracy). The ROC-AUC score of 0.99 further establishes its superiority over competing models. Additionally, the federated approach reduces energy consumption per node by 30% and optimizes communication efficiency by minimizing data transmission by 15% over 100 rounds. By ensuring high accuracy, improved privacy, reduced computational overhead, and enhanced energy efficiency, NeuroFedSense sets a new benchmark for decentralized, real-time seizure prediction and affective state monitoring. These findings underscore its potential for deployment in intelligent, privacy-preserving healthcare applications, addressing critical challenges in remote neurological monitoring.

The increasing penetration of photovoltaic distributed generation (PV-DG) in Radial Distribution Systems (RDSs) plays a vital role in achieving sustainable energy transition objectives; however, the inherent uncertainty associated with solar irradiance and load demand poses significant challenges to optimal planning and operation. This paper presents a stochastic optimization framework for PV-DG allocation in RDSs using the Barrel Theory-Based Optimizer (BTO). Uncertainties in solar irradiance and load demand are explicitly modeled using appropriate probability density functions and efficiently represented through a higher-order Point Estimate Method (PEM), which captures the essential statistical characteristics with a limited number of representative scenarios. The proposed framework simultaneously optimizes the location and capacity of PV-DG units to minimize real power losses and enhance voltage profile performance while ensuring system operational constraints are satisfied. The effectiveness of the proposed approach is validated on the 85-bus and the IEEE 118-bus RDSs, where the BTO exhibits superior convergence characteristics and enhanced solution robustness when compared with several benchmark optimization techniques, including the well-established Differential Evolution Algorithm (DEA), the recent Crocodile Ambush Optimization (CAO, 2025), and the Schr&#xf6;dinger Optimizer Algorithm (SOA, 2025). For the 85-bus RDS, the impact of integrating different numbers of PV units is systematically investigated. Simulation results confirm that the proposed BTO-based stochastic planning strategy significantly improves energy efficiency, voltage regulation, and loss reduction, thereby enhancing the overall sustainability of the RDS. For the 85-node RDS, the BTO achieves a noticeable reduction in average real power losses, outperforming DEA, CAO, and SOA by 2.55%, 4.10%, and 6.74%, respectively, when three PV units are installed. Additionally, for the case of four PV units, the proposed BTO yields even greater improvements, with loss reductions of 5.12%, 7.50%, and 14.12%, respectively, compared with the same benchmark algorithms. Furthermore, for five PV units, the BTO achieves much greater reduction, outperforming DEA, CAO, and SOA by 13.05%, 6.45%, and 32.31%, respectively, when three PV units are installed.

Accurate detection and segmentation of moving objects constitute a fundamental challenge in computer vision, particularly for intelligent video surveillance systems operating under variable illumination, dynamic backgrounds, and environmental noise. This paper presents a fully unsupervised dual-phase motion analysis framework that effectively combines statistical independence modeling and geometric contour evolution to achieve high-precision motion detection and segmentation. In the first phase, an enhanced Fast Independent Component Analysis (Fast-ICA) algorithm is employed to perform statistical decomposition of video sequences, exploiting temporal independence to distinguish moving foregrounds from static backgrounds. This process generates an initial motion mask with strong robustness to illumination variation and noise artifacts. In the second phase, a hybrid level set segmentation model integrating the global Chan-Vese formulation and a locally adaptive Yezzi-based energy function refines object boundaries through an adaptive energy minimization process. A stabilization term and a self-regulating convergence criterion are further incorporated to ensure contour smoothness, numerical stability, and resilience to topological changes. Comprehensive experiments conducted on the CDNet-2014 benchmark dataset demonstrate that the proposed method achieves an average recall of 0.9613, precision of 0.9089, and F-measure of 0.9310, outperforming several state-of-the-art supervised, semi-supervised and unsupervised background subtraction algorithms. The proposed Fast-ICA-Level Set fusion framework thus provides a robust, adaptive, and computationally efficient solution for real-world intelligent surveillance and autonomous visual monitoring applications.

Mitochondria represent central regulators of neuronal function, and their network is dynamically restructured via fission and fusion. The mitochondrial fission factor (MFF) serves as an adaptor protein that recruits and organizes the core fission machinery at the outer mitochondrial membrane. Here, we investigated the role of MFF in Agouti-related peptide (AgRP) neurons of the arcuate nucleus of the hypothalamus (ARC) in their regulation of systemic energy homeostasis. We demonstrated that mice lacking MFF in AgRP neurons exhibited increased mitochondrial size, both in AgRP neuron somata and their axonal compartments. This translated into increased mitochondrial Ca2+ uptake capacity, increased mitochondrial membrane potential, and a shift toward a more reduced mitochondrial NAD(P)H redox state. Ultimately, these changes resulted in increased neuronal excitability and neurotransmitter release to functionally enhance dynamic food intake during energy state transitions. Collectively, MFF-dependent mitochondrial fission links cell-type-specific neuronal mitochondrial dynamics via mitochondrial Ca2+ handling to control systemic metabolism.

We present a novel acceleration scheme capable of accelerating electrons and ions in an underdense plasma. Transversely Pumped Acceleration (TPA) uses multiple arrays of counter-propagating laser beamlets that focus onto a central acceleration axis. Tuning the injection timing and the spacing between the adjacent beamlets allows for precise control over the position and velocity of the intersection point of the counter-propagating beam arrays. This results in an accelerating structure that propagates orthogonal to the direction of laser propagation. We present the theory that sets the injection timing of the incoming pulses to accelerate electrons and ions with a tunable phase velocity plasma wave. Simulation results are also presented which demonstrate 1.12 GeV proton beams accelerated in 3.6 mm of plasma and electron acceleration gradients on the order of 1 TeV/m in a scheme that circumvents dephasing. This work has potential applications as a compact accelerator for medical physics and high energy physics colliders.