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Plantago lanceolata L. is increasingly incorporated in temperate pasture systems for its agronomic resilience and potential to reduce the environmental footprint of ruminant production through its specific secondary metabolites (SMs). However, how light intensity per se regulates P. lanceolata L. physiology, nutritive value and SM accumulation remains poorly understood due to confounding factors in field studies. This controlled-environment study evaluated the effects of three light intensities (200, 300, and 400 µmol photons m⁻² s⁻¹) on morphophysiological traits, forage quality, and SM concentrations in P. lanceolata L. cv. “Ceres Tonic”. Plants were grown in controlled-environment chambers under similar temperature, humidity and nutrient conditions. Morphological traits, biomass allocation, chlorophyll fluorescence, gas exchange, chemical composition, and root architecture were measured. Additionally, the most important secondary metabolites, aucubin, catalpol and acteoside, were also evaluated. Under the different light intensity treatments plants maintained stablephysiological parameters, total biomass production, leaf dimensions or root architecture. However, moderate light intensity (300 µmol photons m⁻² s⁻¹) optimized nutritive value by minimizing fiber concentrations and maximizing metabolizable energy. Acteoside concentration, as well as the iridoid glycosides aucubin and catalpol, were not affected by the different light intensities. These findings demonstrate that P. lanceolata L. maintains morphophysiological stability across the tested light intensity range studied, while selectively modulating forage quality. Full article

Communication latency is one of the main factors limiting the practical scalability of unmanned aerial vehicle (UAV) swarms operating with distributed formation control. In real-time UAV missions, such as coordinated swarm navigation, autonomous inspection, and aerial monitoring, delayed information exchange directly affects formation stability and operational safety. In practical aerial networks, inter-UAV communication latency is influenced by stochastic effects including jitter, burst delays, and multi-hop propagation, which are rarely captured by the simplified deterministic delay assumptions commonly adopted in analytical formation-control studies. This paper introduces a measurement-informed stochastic delay model and a communication–control delay-feasibility framework that jointly account for per-link latency behavior, multi-hop delay accumulation, and controller-level delay tolerance. The proposed framework is evaluated using an attractive–repulsive distance-based potential field (ARD–PF) formation controller, for which the maximum admissible end-to-end delay is quantified as a function of swarm size and inter-UAV separation. The delay model is calibrated and validated using more than 15,000 in-flight communication delay samples collected from a multi-UAV LoRa platform operating under realistic flight conditions. The results show that different mechanisms limit swarm operation under different operating scenarios. In some configurations, stochastic communication latency becomes the dominant constraint, whereas in others, formation geometry or network load determines the feasible operating region. Based on these elements, the proposed framework characterizes delay-feasible operating regions and predicts the maximum feasible swarm size under distributed formation control and realistic multi-hop communication latency. Full article

The transition toward sustainable and low-carbon power systems increasingly depends on the efficient accommodation of high shares of renewable energy while maintaining secure and reliable grid operation. In interconnected power systems, this challenge is intensified by strong cross-regional coupling, tie-line flow violation risks, and the high computational burden of fully coupled probabilistic assessments. To support the sustainable operation of renewable-rich interconnected systems, this paper proposes a probabilistic production simulation method that incorporates risk-aware tie-line exchange optimization. Sequential random sample paths are constructed by considering load fluctuations, renewable energy output uncertainty, and random outages of conventional units. Using cross-regional exchange power as coupling variables, a conditional value-at-risk (CVaR)-based pre-scheduling model is established to control tie-line and interface flow tail risks. Given the scheduled exchange power, cross-regional exchanges are transformed into regional boundary power injections, enabling decoupled sequential probabilistic production simulation for each region. The exchange schedule is then iteratively updated through marginal-value feedback. A four-region interconnected system is used for case-study validation. Results show that the proposed method improves renewable energy accommodation, reduces renewable curtailment, suppresses tie-line flow violation risk, and maintains high reliability assessment accuracy. Compared with the region-decoupled benchmark with fixed exchange power, the proposed method increases the renewable energy accommodation rate from 93.82% to 95.41% and reduces renewable curtailment from 312,162 MWh to 231,284 MWh, while also lowering expected energy not served and loss of load expectation. In addition, under the reported case-study setting, the proposed RC-IEF-PPS reduces the computation time from 5216.24 s for Full-PPS to 4074.63 s, i.e., by 21.9%, while maintaining comparable reliability assessment accuracy. These results indicate that the proposed framework can support the sustainable integration of high-penetration renewable energy by improving clean-energy utilization, operational reliability, and computational tractability in interconnected power systems. Full article

Background/Objectives: Glyphosate-based formulations are globally pervasive pollutants increasingly recognized as potential contributors to antimicrobial resistance (AMR) in environmental microbiomes. Although glyphosate is designed to inhibit plant 5-enolpyruvylshikimate-3-phosphate synthase, it also affects microbial metabolism, stress response, and genetic exchange. This review synthesizes the pathways through which glyphosate, its metabolite aminomethylphosphonic acid (AMPA), and commercial mixtures influence resistance-associated phenotypes and the dissemination of antibiotic resistance (ABR). Methods: A critical synthesis of the literature was conducted to evaluate the mechanistic and ecological interactions between glyphosate exposure and bacterial resistance in soil, aquatic, and host-associated microbiomes. Results: Experimental evidence showed that sublethal glyphosate exposure induced oxidative stress, altered membrane permeability, activated multidrug efflux pumps, and promoted tolerance phenotypes that could modify antibiotic susceptibility. It also enhances mutation rates and horizontal gene transfer processes associated with the emergence of resistance under controlled conditions. At the community level, glyphosate exposure is associated with microbiome restructuring and enrichment of resistance determinants, often without major shifts in overall diversity of the microbiome. These effects have been reported at environmentally relevant concentrations, although the evidence remains largely derived from laboratory and mesocosm studies. Conclusions: Glyphosate acts as both a biochemical modulator of resistance-related phenotypes and an environmental selective pressure that shapes microbial communities. Its widespread use and environmental persistence position it as a context-dependent contributor to the emergence and dissemination of AMR through interacting mechanistic and ecological pathways. Integrating AMR endpoints into pesticide risk assessments and surveillance frameworks is warranted, in addition to expanded field-based validation. Full article

This study presents an optimized and comparative investigation of four intelligent energy management strategies—Proportional–Integral–Derivative (PID), Fuzzy Logic Control (FLC), Equivalent Consumption Minimization Strategy (ECMS), and Artificial Neural Network (ANN)—applied to a photovoltaic–fuel cell–battery hybrid electric vehicle (PV–FC–HEV). A high-fidelity MATLAB/Simulink model integrates a 6 kW proton-exchange membrane fuel cell (PEMFC), a 500 W photovoltaic subsystem with MPPT, and a lithium-ion battery (LiB) pack. While 1000 W/m² represents Standard Test Conditions (STC), the level of 400 W/m² was specifically selected to simulate average cloudy conditions common in urban driving environments, rather than standard NOCT (800 W/m²), to test the EMS’s robustness under significantly reduced PV support and stressed battery conditions (initial SOC = 30%). While surface contamination and the resulting performance degradation significantly impact real-world results, this study assumes a clean surface to establish an idealized performance baseline for the control algorithms. However, the authors acknowledge that contaminant accumulation is a key factor; future work will incorporate a degradation factor (e.g., a 10–15% efficiency penalty) to evaluate the reliability of these EMS strategies under actual operating conditions. ECMS achieved the lowest hydrogen consumption, saving up to 10 L compared with PID, while ANN maintained the most stable state of charge (SOC > 80%), minimizing deep discharge cycles and improving operational stability. FLC provided balanced operation under fluctuating irradiance. Overall, ANN offered the most harmonized energy flow and dynamic stability, whereas ECMS maximized fuel economy. The findings provide practical guidance for designing sustainable and intelligent control systems in next-generation hybrid electric vehicles. Full article

Hybrid-electric regional aircraft require detailed thermal management digital twins to assess performance and feasibility while reducing physical test effort. The Functional Mock-Up Interface (FMI) enables partners to exchange subsystem models as Functional Mock-Up Units (FMUs) for gate-to-gate simulation while preserving intellectual property. However, FMU integration introduces numerical coupling challenges, interface overhead, and potential loss of accuracy depending on the integration method. Benchmarking against a DLR Thermofluid Stream (TFS) reference model showed that FMU-based co-simulation can significantly increase computational effort, specifically from 8 min up to 2.5 h. Control-based integration further implicates transient deviations due to filtering, although steady-state accuracy generally remains unchanged. Therefore, it is mandatory to evaluate and compare FMU integration strategies to show that digital twin performance targets remain achievable when design, solver settings, and filtering are only applied selectively and systematically. The results show clear design guidance: employ native fluid libraries when possible for speed and accuracy, use FMU paired with adapters and without filters for accuracy, and reserve filtering for numerical stabilization only. Using a control approach to integrate the FMU improves simulation speed compared to adapters but introduces a small error, which in turn reduces simulation accuracy. Full article

New approaches to improve organisational performance in firms are evolving in this data-driven age. However, there is lack of studies in examining the relationship between revenue, net profit, cash flow per share, and earnings per share. The energy sector remains under-researched regarding the multi-dimensional drivers of profitability. Existing research shows inconclusive evidence with studies predominantly examining revenue—performance relationship limiting to a single factor and not guiding potential investors regarding future earnings per share in the energy industry. This paper aims to bridge the gap in literature by proposing a data-driven approach to analyse the relationships between revenue, net profit, cash flow per share, and earnings per share. We examine these relationships by conducting an empirical analysis using secondary data derived from published annual reports of the energy firms listed on the Australian Securities Exchange (ASX). Our empirical study uses Pearson correlations and regression techniques to test the hypotheses on the relationships between revenue, net profit, cash flow per share, and earnings per share. Also, we use market capitalisation as a control variable and predictor of earnings per share in the energy industry. The data analysis results in four findings: (i) revenue positively influences earnings per share because higher revenue expands the firm’s earnings capacity within the financial performance, (ii) net profit has a strong positive effect on earnings per share, consistent with profitability theory and the direct derivation of EPS from net income, (iii) cash flow per share influences earnings per share because liquidity supports operational stability, investment decisions, and earnings sustainability (e.g., heavy capital expenditure contexts), and (iv) the combined effects of revenue, net profit, and cash flow per share provide a stronger and more holistic prediction of earnings per share than any single variable, consistent with multidimensional organisational performance theory (a more holistic valuation model than looking at single factors). In addition, the results indicate that market capitalisation (control variable) has both strong prediction of earnings per share and strong association with earnings per share. The results of this study can offer practitioners and investors in Australia and other countries for a better understanding of the relationships between revenue, net profit, cash flow per share, and earnings per share from energy companies. The data will help investors to make good investment data-driven decisions in the energy industry or other industries. It also motivates researchers to conduct similar studies in different contexts. We further provide recommendations, including a closed-loop Artificial Intelligence (AI) data-driven approach integrated into energy accounting and operational processes to enhance profitability. This approach operationalises the revenue and earnings-per-share (EPS) strategies identified in our empirical analysis, offering practical value for industry practitioners and guiding future research in this direction. Full article

Smart power management practices are needed for a sustainable EV charging infrastructure due to the fast use of renewable energy resources. An innovative power management structure for a small grid-connected solar PV system-based AC and DC charging station, combined with a backup purpose battery energy system (BESS), is demonstrated in this paper’s study. The sustainability transition is associated with integrating renewable energy resources with a battery storage system, providing a helpful solution for managing large power-demanding entities (EV, microgrid, etc.). In this study, a solar PV system takes 500 datasets (based on data availability or to prevent overfitting) of PV voltage, solar irradiance, and air temperature, and the performance of controlling for the maximum power point tracker by training these datasets using Levenberg–Marquardt (LM), which was implemented in the ANN toolbox and created this technique in MATLAB 2016 or Simulink. Also, using this technique for the estimation and forecasting of the datasets of solar PV systems and EVs obtains better results for achieving further targets. To enhance decision-making capability through optimized technique, we have to find it before forecasting PV power generation and EV datasets throughout the day (24 h). The optimized power flows among solar PV power generation, EV charging demand (including AC charging and DC fast charging), the BESS, and the utility/small grid under several priority operating scenarios. A famous technique for optimization, mixed-integer linear programming (MILP), is applied. In this technique, the objective function is used for the solution of problem formation and compliance with system constraints such as the power balancing equation, charging/discharging limits, SOC limits, and grid export/import exchange limits: basically, equality, inequality, and bounds limits. Optimized results show that the coordinated power flow operations are consented to by EV users, by prioritizing some key points, such as solar PV use at the maximum, reducing the grid power dependency, and the first power flow towards EV charging demand. The verified MILP-based solutions boost the maximum utilization of renewable energy resources, feasible EV charging demand, and scaling power flow among these entities. The key contribution of this study is suitable for different powered EV charging stations based on both AC and DC, with different ratings of EVs (including fast and slow charging). Most solar PV-based generation supports the EVCS and backup for ranking-wise BESS, and grid support for the EVCS. Also, the key contribution of hybrid techniques in this article is divided into two stages: in the first stage, an artificial neural network (ANN) is utilized for estimating the PV voltage at the maximum point and forecasting, while in the second stage, mixed-integer linear programming (MILP) employs optimal power management. Full article

Microplastics (MPs), particularly low-density polyethylene (LDPE), are widespread pollutants in aquatic environments and may affect cyanobacterial physiology. This study investigated the concentration-dependent effects of LDPE-MPs on the physiology and transcriptomic responses of Arthrospira platensis. Cultures were exposed to 10–5000 mg/L LDPE-MPs (nominal size ≤ 500 µm) for 16 days. Low to moderate concentrations (10–1000 mg/L) produced minimal effects on growth, biomass accumulation, or pigment contents. In contrast, higher concentrations (3000–5000 mg/L) were associated with reduced growth and biomass, accompanied by declines in chlorophyll a (Chl a) and phycobiliproteins over time. By day 16 at 5000 mg/L, biomass and Chl a decreased to 1.47 ± 0.03 g/L and 8.39 ± 0.24 µg/mL, respectively, compared with 1.64 ± 0.04 g/L and 10.81 ± 0.52 µg/mL in the control (p < 0.05). Accordingly, Chl a yield decreased by 13%. Field-emission scanning electron microscopy revealed adhesion of LDPE particles to filament surfaces and the formation of extracellular polymeric substance (EPS)-rich aggregates, which may influence light availability and nutrient exchange. Transcriptomic analysis indicated changes in several metabolic pathways, including nitrogen assimilation, photosynthetic electron transport, carbon metabolism, and metal homeostasis, together with differential expression of genes related to stress responses and EPS biosynthesis. Overall, these findings suggest that relatively high concentrations of LDPE microplastics may influence physiological and metabolic processes in A. platensis. Full article

Background/Objectives: This study addresses the need for scalable and predictive strategies linking mixing conditions to nanocarrier properties by developing and analyzing a coaxial jet antisolvent process for the continuous production of pharmaceutical nanocarriers. Methods: A single experimental platform was used to generate both curcumin-based nanoparticles and nanoliposomes, enabling direct comparison of how mixing regime and formulation variables influence product characteristics. Results: Fluid-dynamic behavior was first characterized using tracer and micromixing experiments, revealing a strong dependence of mixing time on flow conditions, with characteristic mixing times decreasing from >1000 ms under laminar conditions to approximately 10–30 ms in turbulent regimes. Nanoparticles and liposomes obtained under optimized conditions exhibited mean sizes in the range of 120–250 nm, with polydispersity indices typically below 0.2 under optimized turbulent conditions. To rationalize these observations, a computational framework was implemented, combining Reynolds-averaged computational fluid dynamics with a population balance formulation solved by the method of moments. The model provided spatially resolved insight into solvent exchange, supersaturation development, and nucleation–growth dynamics, showing good agreement with experimental trends and capturing the effect of mixing conditions on particle size across different regimes. Conclusions: Although simplified, the modeling approach establishes the basis for future extensions toward full population-balance distribution simulations capable of predicting complete particle size distributions, highlighting the ability of the coaxial jet mixer to control supersaturation and particle formation through tunable hydrodynamic conditions. This capability makes the system particularly attractive compared to conventional batch or less controllable mixing technologies, enabling a more rational and scalable design of pharmaceutical nanocarriers, with good encapsulation performance as discussed in the main text. Full article

The proliferation of Internet of Things (IoT) devices has created unprecedented challenges in cybersecurity, as billions of interconnected devices generate, process, and transmit sensitive data across diverse networks. This study addresses critical security vulnerabilities in IoT ecosystems, focusing on the development of a comprehensive security framework that encompasses device authentication, an attribute access control mechanism, and privacy preservation. This work introduces HADA, a proposed hybrid authentication method that combines the validation of unique credentials and trust value. For the authentication of the data owner and user, the following credentials are validated: identity, certificate, reconfigurable physical unclonable function (PUF), and trust. Differential privacy is used to secure the credentials during information exchange. Then, the newly developed dynamic attribute access control method selects the number of attributes and matches the attributes; these two processes are performed using the Bi-Fuzzy logic and graph neural network (GNN) algorithms, respectively. After matching the data, the user is allowed to access them from the cloud server. For data encryption, the lightweight SKINNY algorithm is implemented in Hyperledger Fabric blockchain. The proposed system performs better than existing methods in terms of throughput, latency, and resource utilization. Full article

The guava tree (Psidium guajava L.) is a tropical fruit tree of worldwide importance; however, the salinity of irrigation water severely limits its development in semi-arid regions. However, magnesium (Mg) can mitigate this stress by promoting plant photosynthetic activity. The objective was to evaluate the effect of foliar Mg in mitigating saline stress on photosynthesis and the growth of guava cultivar seedlings. The experiment was conducted in a randomized complete block design, in a 2 × 2 × 3 factorial scheme, with two guava cultivars (Kumagai and Paluma), two irrigation water salinity levels (a low-salinity control—0.5 dS m⁻¹, and salt stress—2.5 dS m⁻¹), and three doses of foliar Mg (0, 1, and 2 mL L⁻¹), and six replications. A salinity of 2.5 dS m⁻¹ reduced growth and gas exchange in both cultivars, with a reduction of approximately 30% in total dry mass, and 16% in CO₂ assimilation rate. Supplementation with 1 mL L⁻¹ of Mg attenuated the effects of stress, stimulating chlorophyll synthesis and gas exchange, reducing approximately leaf temperature in 3.5%, and vapor pressure deficit (VPD) in 12%. The Paluma cultivar was more responsive to Mg under salinity, with improved CO₂ assimilation rate, stomatal control, and water use efficiency. Kumagai showed greater growth in height and diameter with 1 mL L⁻¹ under stress. Foliar application of magnesium (1 mL L⁻¹) is a promising strategy to produce guava seedlings under saline stress. Full article

In the semi-arid Batna Belezma region of northeastern Algeria, groundwater is a vital resource for agriculture and drinking water. However, the climate leads to intense evaporation, which affects its quality. This study aims to identify the key hydrogeochemical processes that control groundwater composition in the Merouana Basin and to evaluate the predictive performance of machine learning (ML) models. A total of 30 groundwater samples were analyzed using multivariate statistical techniques, including Principal Component Analysis (PCA), and were modeled using PHREEQC to assess mineral saturation states. Additionally, ML-based regression models, including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGB),were employed to predict groundwater chemistry. The results indicate that the dominant ion distribution follows the following trend: Ca²⁺ > Mg²⁺ > Na⁺ and HCO₃⁻ > SO₄²⁻ > Cl⁻. Alkaline earth metals (Ca²⁺ and Mg²⁺) constitute the major fraction of total dissolved cations, reflecting carbonate equilibrium and dolomite dissolution processes. In contrast, Na⁺ represents a smaller proportion of the cationic load; however, its hydro-agronomic significance is substantial due to its influence on sodium adsorption ratio (SAR) and soil permeability. The PHREEQC modeling showed that calcite and dolomite precipitation promote evaporite dissolution, while most samples remain undersaturated with respect to gypsum. The PCA results reveal high positive loadings of Mg²⁺, Cl⁻, SO₄²⁻, HCO₃⁻, and EC, suggesting that ion exchange and seawater mixing are the primary controlling processes, with carbonate weathering playing a secondary role. To enhance predictive assessment, several supervised machine learning models were tested. Among them, the Random Forest model achieved the highest predictive performance (R² = 0.96) with low RMSE and MAE values, confirming its robustness and reliability. The results indicate that silicate weathering and mineral dissolution are the primary mechanisms governing groundwater chemistry. The integration of multivariate statistics and machine learning provides a comprehensive understanding of groundwater evolution and offers a reliable predictive framework for sustainable water resource management in semi-arid environments. Geochemical model performance showed a high global accuracy (GPI = 0.91), confirming a strong agreement between observed and simulated chemical data. However, the HH value (0.81) indicates some discrepancies, particularly for specific ions or extreme conditions. Full article

For the purpose of investigating the hydrochemical signatures and formation processes of mine water at the Feicheng Coal Mine, a total of 61 samples—including fresh mine water (FLW), old goaf water (OGW), and old lode water (OLW)—were collected and examined via statistical and hydrochemical approaches for the assessment of mine water suitability for irrigation employed sodium content (Na%), sodium adsorption ratio (SAR), permeability index (PI), and magnesium hazard ratio (MHR). The mine water proves slightly alkaline, featuring Na⁺ as the leading cation and SO₄²⁻/HCO₃⁻ as the leading anions. By average concentration, cations decrease in the order Na⁺ > Ca²⁺ > Mg²⁺, and anions decrease as SO₄²⁻ > HCO₃⁻ > Cl⁻. The hydrochemical types of OLW and FLW samples were primarily Ca-HCO₃ and Ca-Mg-Cl, whereas the OGW samples were predominantly of the Na-Cl-SO₄ and Na-HCO₃ types. Rock weathering serves as the main control on water chemistry, with hydrochemical components sourced largely from evaporite and carbonate dissolution. The sodium present in the water is likely attributable to silicate mineral dissolution or cation exchange processes. Cation exchange, with forward exchange dominant, is also a key hydrogeochemical process in the study area. SI results reveal that calcite and dolomite have reached saturation, while gypsum and halite remain undersaturated and tend to dissolve further. Irrigation suitability assessments indicate that most of the water quality in the Feicheng Coal Mine is excellent or good. A limited number of samples exhibited relatively high salinity, and most of them can be directly irrigated. To this end, this study proposes targeted treatment solutions, thus facilitating mine water development and utilization. Full article

This paper presents a lightweight MATLAB-based framework with a graphical interface for modeling, 3D simulation, trajectory generation, and experimental validation of a 6-DOF industrial robot. The platform integrates kinematic modeling using the rigidBodyTree structure, animated visualization, and both predefined and user-defined trajectory planning within a unified environment. A central aspect of the proposed approach is the implementation of a ROS-compatible TCP/IP communication protocol that avoids the need for a full ROS core installation while preserving compatibility with ROS-Industrial standards. This enables bidirectional data exchange between MATLAB and the robot controller within a simplified architecture. Communication performance tests indicate round-trip latency in the tens-of-milliseconds range and consistent StateServer update rates, supporting monitoring, trajectory execution, and digital twin synchronization in non-real-time conditions. Experiments conducted on an ABB IRB120 robot demonstrate a close correspondence between simulated and real motion, with RMSE below 0.0075 rad and MAE below 0.0065 rad across all joints. All data are stored in JSON format to support reproducibility and further analysis. By integrating simulation and real robot execution within a modular architecture, the proposed framework provides a practical tool for education, rapid prototyping, and experimental research in industrial robotics, while offering a basis for future extensions toward advanced control strategies and digital twin applications. Full article