Search Results (7,852)

Ammonia (NH₃) emissions from concentrated animal feeding operations (CAFOs) are recognized contributors to secondary fine particulate matter (PM_2.5) formation, yet empirically derived secondary PM_2.5 emission factors applicable to livestock operations remain limited. This study investigated NH₃-derived secondary PM_2.5 formation under controlled laboratory conditions using a PTFE flow reactor in which NH₃ was reacted with sulfur dioxide (SO₂) across ammonia-rich NH₃:SO₂ ratios, with and without zero air. The resulting aerosols were characterized using gravimetric analysis, elemental analysis, Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM/EDS), and particle size distribution (PSD) measurements. The recovered particles were dominated by inorganic ammonium–sulfur species, with FTIR and elemental trends indicating sulfite-related intermediates under no-zero-air conditions and more oxidized ammonium–sulfur products under oxygenated conditions. Accounting for both filter-collected and wall-deposited particles, unit particulate emission factors normalized to ammonia input were derived. Size-based apportionment using PSD data indicated that approximately 76.6% of the recovered particulate mass was within the PM_2.5 size range. Scaling the experimentally derived unit emission factors using literature-based ammonia emission rates yielded an estimated secondary PM_2.5 emission factor of 0.351 ± 0.084 g PM_2.5 per animal head per day for cattle feedlots, corresponding to approximately 3–4% of reported total PM_2.5 emissions. Because the experimental system isolates NH₃–SO₂ interactions under idealized conditions and does not represent full atmospheric chemistry, the derived values should be interpreted as screening-level estimates of NH₃-derived secondary PM_2.5 formation potential intended to support comparative air quality assessments of CAFOs rather than direct predictions of ambient PM_2.5 concentrations. Full article

This paper investigates the secrecy energy efficiency (SEE) maximization problem in a downlink multiple-input single-output (MISO) wireless communication system assisted by an intelligent reflecting surface with movable elements (ME-IRS). Unlike a conventional IRS, which has fixed-position elements, the proposed ME-IRS enables dynamic adjustment of element positions to exploit additional spatial degrees of freedom for performance enhancement. However, such flexibility introduces new challenges due to the strong coupling among transmit beamforming, IRS phase shifts, and element positions, as well as the additional power consumption caused by element movement. To address these issues, we formulate an SEE maximization problem by jointly optimizing the transmit beamforming, phase shift matrix, and element positions. The resulting problem is highly non-convex owing to the fractional objective function and coupled variables. To address this challenge, an efficient alternating optimization (AO) framework is developed by leveraging semidefinite relaxation (SDR), successive convex approximation (SCA), and gradient-based methods. Simulation results demonstrate that the proposed ME-IRS configuration significantly outperforms conventional fixed-position and discrete-position IRS configurations in terms of SEE, providing valuable insights into the impact of movable region size and system parameters. Full article

Aim: Conventional free-fall kinematic models applied to plyometric push-up assessment treat the upper body as a vertically translating point mass, ignoring the curvilinear trajectory imposed by the ankle pivot and systematically biasing flight-time and height estimates. Methods: A planar rigid-body pendulum pivoting about the ankle axis was formulated via two independent derivation pathways (static moment equilibrium and a gravitational-torque coordinate approach), yielding effective pendulum length L = (M_W/M) × L_OS. Closed-form expressions for flight time, arc displacement, maximum height, and mean mechanical power were derived analytically from energy conservation and compared against free-fall predictions across seven pendulum arm lengths (L_OW = 0.50–2.00 m) and 500 initial hand velocities per length, using adaptive Gauss–Kronrod quadrature (relative tolerance 10⁻¹⁰) with ODE cross-validation (maximum discrepancy < 2.5 × 10⁻⁷ s). Results: Flight time equivalence (t^H = t^G) was formally established. The free-fall model overestimated flight time by up to 18.82% (Δt = 0.096 s; L_OW = 0.50 m, V_H,0 = 2.50 m/s) and maximum height by up to 28.43% (Δh = 0.087 m; L_OW = 0.50 m, t_flight = 0.50 s), with both errors growing nonlinearly with initial velocity. Overestimation in height was proportionally larger at shorter pendulum arm lengths (18.18% at t_flight = 0.30 s for L_OW = 0.50 m vs. 10.91% for L_OW = 1.00 m). Conclusions: The pendulum model provides a physically consistent, analytically tractable framework for geometry-adjusted upper-body power assessment from four field-obtainable anthropometric inputs. These results reflect computational self-consistency; prospective experimental validation against force-plate kinematics is required before applied deployment. Prospective empirical validation against dual force-plate and motion-capture reference data is required to establish the model’s accuracy boundaries under real push-up kinematics. Full article

Critical minerals are central to industrial strategies in the Global South, but evidence on how such policies reshape domestic production is limited. This paper maps Indonesia’s nickel ecosystem before and after the 2014 export ban using input–output multipliers and labor intensity from the 2010, 2016, and 2020 input–output tables. We provide a descriptive account of nickel’s evolving economic trajectory during the downstreaming push. Three patterns stand out. Forward linkages declined from 16 to 8 and backward linkages moved from 75 to 73, suggesting a narrower structure with greater specialization in higher value, more capital-intensive activities. Output multipliers rose most in sectors that support the electric vehicle supply chain, including professional and technical services, machinery, fabricated metals, transport equipment, energy, and finance. In contrast, the labor multiplier fell from about 6514 to 3366 jobs per IDR 1 trillion of final demand, implying a higher value added alongside lower employment intensity. Overall, downstreaming appears to work through structural concentration and growth in complementary sectors rather than broad-based diversification. Complementary policies in skills, regional development, and energy infrastructure are therefore critical for inclusive industrial transformation. Full article

Morocco’s recent legalization of industrial and medicinal cannabis has created a rapidly expanding seed-oil sector whose sustainability has yet to be fully assessed. This study applies an environmental life cycle assessment (LCA) in accordance with ISO 14040:2006 and ISO 14044:2006, complemented by a qualitative social responsibility assessment based on ISO 26000:2010, aiming to evaluate the life cycle sustainability of Moroccan cannabis seed oil. Three representative processing chains, traditional artisanal presses, producer cooperatives and regulated industrial plants are compared using a functional unit of 1 kg of cold-pressed oil packaged for local distribution. Inventory data were drawn from field measurements and interviews and were modeled in OpenLCA with background datasets from Ecoinvent 3.8 and Agribalyse v3.1. Impact assessment used the ReCiPe 2016 (H) method at the midpoint level across nine categories (climate change, fossil resource scarcity, water use, freshwater eutrophication, terrestrial acidification, land occupation, carcinogenic, non-carcinogenic human toxicity, and fine particulate matter formation). Sensitivity analyses varied seed yield, electricity mix and transport distances by ±20% to gauge uncertainty. Results show that the cooperative scenario achieves the lowest impacts across nearly all categories because of higher extraction yields (3 kg seed per kg oil), lower energy use (0.54 kWh kg⁻¹ oil) and more effective co-product recovery. In contrast, artisanal extraction requires approximately 1 kg of additional seed input per functional unit compared to optimized scenarios, significantly increasing upstream environmental burdens and causing upstream agricultural burdens to multiply. Industrial facilities perform comparably to cooperatives if powered by renewable electricity. Integrating a semi-quantitative social responsibility assessment reveals that legalization has markedly improved organizational governance, labor conditions, consumer protection and community involvement. Cooperatives display the most balanced social performance, whereas industrial plants excel in governance and quality control. A set of recommendations, including drip irrigation, cultivar improvement, co-product valorisation, renewable energy adoption, eco-designed packaging and cooperative governance, is proposed to enhance the environmental and socio-economic sustainability of Morocco’s emerging cannabis seed-oil industry. Full article

Hybrid electric mining trucks operating in open-pit environments encounter highly variable gradients and payload conditions that standard energy management strategies fail to address adequately. Existing approaches are predominantly calibrated for full-load scenarios and neglect the accelerated battery degradation resulting from sustained high-power cycling, undermining long-term operational viability. This study presents a multi-objective energy management framework that couples real-time driving condition recognition with dynamic programming (DP) optimization for a 130-tonne hybrid mining truck. Field data collected from an open-pit mine in Heilongjiang Province were used to construct six physically representative driving conditions via principal component analysis and K-means clustering. A Bidirectional Gated Recurrent Unit (Bi-GRU) network (2 layers, 128 hidden units per direction) was trained on a route-based temporal split, attaining 95.8% classification accuracy across all six conditions. Condition-specific powertrain modes were subsequently defined, and a DP formulation with a weighted-sum cost function was solved to jointly minimize diesel consumption and battery capacity fade—quantified through a semi-empirical effective electric quantity metric. A marginal rate of substitution (MRS) analysis was conducted to identify the optimal trade-off between fuel economy and battery life preservation. In the DP cost function, the weight coefficient μ (ranging from 0 to 1) governs the relative emphasis placed on battery degradation minimization versus fuel consumption minimization: μ = 0 corresponds to pure fuel minimization, whereas μ = 1 corresponds to pure battery degradation minimization. The MRS analysis identified μ = 0.1 as the knee point of the Pareto trade-off: relative to pure fuel minimization (μ = 0), this setting reduces effective electric quantity by 6.1% while increasing fuel consumption by only 1.4% (MRS = 4.36). Against a rule-based baseline, the proposed strategy improves fuel economy by 12.3% and extends battery service life by 15.7%. Co-simulation results were validated against onboard fuel-flow measurements; absolute simulated and measured fuel consumption values are reported route-by-route, with deviations within 4.5%. A three-layer BP neural network (3 inputs, two hidden layers of 20 and 10 neurons, 1 output) trained on the DP solution reproduces near-optimal performance—with fuel consumption and effective electric quantity increases below 1.0% and 1.1%, respectively—while reducing computation time by over 96% (from approximately 52,860 s to 1836 s for the 1800 s driving cycle), demonstrating practical feasibility for real-time deployment. Full article

Cloud cover significantly reduces the electrical power output of photovoltaic systems, making accurate short-term cloud movement predictions essential for reliable solar energy production planning. This article presents a deep learning framework that directly estimates cloud movement from ground-based all-sky camera images, rather than predicting future production from past power data. The system is based on a three-step process: First, a lightweight Convolutional Neural Network segments cloud regions and produces probabilistic masks that represent the spatial distribution of clouds in a compact and computationally efficient manner. This allows subsequent models to focus on the geometry of clouds rather than irrelevant visual features such as illumination changes. Second, a Vector Quantized Variational Autoencoder compresses these masks into discrete latent token sequences, reducing dimensionality while preserving fundamental cloud structure patterns. Third, a GPT-style autoregressive transformer learns temporal dependencies in this token space and predicts future sequences based on past observations, enabling iterative multi-step predictions, where each prediction serves as the input for subsequent time steps. Our evaluations show an average intersection-over-union ratio of 0.92 and a pixel accuracy of 0.96 for single-step (5 s ahead) predictions, while performance smoothly decreases to an intersection-over-union ratio of 0.65 and an accuracy of 0.80 in 10 min autoregressive propagation. The framework also provides prediction uncertainty estimates through token-level entropy measurement, which shows positive correlation with prediction error and serves as a confidence indicator for downstream decision-making in solar energy forecasting applications. Full article

Improving energy efficiency and reducing the carbon footprint of crop production are critical for sustainable agriculture, particularly in semi-arid regions where resource use efficiency is essential. This study evaluated the effects of different fertilization strategies on energy use efficiency and carbon footprint in maize production. A field experiment was conducted during the 2023 growing season in Konya Province, Türkiye, using a randomized block design with three treatments and three replications. The treatments included an unfertilized control (U1), inorganic fertilizer application (U2), and liquid animal manure application (U3). The results showed that the highest grain yield was obtained in the liquid manure treatment, which was 2.08 times higher than the unfertilized treatment and 1.18 times higher than the inorganic fertilizer treatment. The highest total energy input was recorded in the inorganic fertilizer treatment (26,235.12 MJ ha⁻¹), while the highest total energy output was observed in the liquid manure treatment (203,154 MJ ha⁻¹). The liquid manure treatment also showed higher net energy efficiency, output–input ratio, carbon efficiency, and carbon sustainability index, while producing the lowest carbon footprint per unit of product. These findings indicate that liquid animal manure can improve maize productivity while enhancing energy efficiency and reducing carbon emissions in semi-arid agroecosystems. Full article

Rills on the snow surface are a common phenomenon frequently reported by field observers. The interpretation of these field observations and an understanding of the underlying physical processes are important for forecasting routines and models used in avalanche warning as well as in hydrological and meteorological forecasting. Rills on the snow surface are typically associated with rain-on-snow (ROS) events and are often interpreted as an indicator of the approximate snowfall level. However, recent field observations of rills on the snow surface without significant liquid precipitation in the Austrian Alps challenge the assumption that ROS events are the sole cause of rill formation. In this study, we quantitatively compare liquid water input into the snowpack from melt processes to the amount of rain during a documented rill formation event. Using a combination of field observations, energy balance calculations, and model simulations, our results strongly suggest that, in this case study, meltwater was the predominant source of liquid water input and snowmelt the main driver of rill formation. Our results indicate that more than 97% of the total liquid water input originated from melt, while rain contributed only roughly 2%. These findings highlight the need for a revised interpretation of rill formation, suggesting that meltwater-driven rills may be more significant than previously assumed. Full article

Supergiants are luminous post-main-sequence massive stars whose effective temperatures ($T_{\mathrm{eff}}$) are key inputs for stellar evolution and feedback studies. We present a photometry-based procedure to derive $T_{\mathrm{eff}}$ for a sample of galactic supergiants of spectral types B and A by fitting the spectral energy distributions (SEDs) in the UV-to-mid-IR range to ATLAS9 model spectra converted into synthetic photometry using the corresponding passband transmission profiles while simultaneously solving for the line-of-sight extinction. The SEDs were constructed from published data taken in different photometric systems (Johnson or Kron–Cousins $UBVRI$, Strömgren $uvby$, $JHK$ magnitudes from various sources, and AllWISE) and supplemented with UV TD-1 fluxes for brighter stars. The interstellar extinction law is based on Cardelli, Clayton & Mathis approximation assuming a total-to-selective ratio $R_V=A_V/E(B-V)=3.1$. The best-fitting parameters are obtained by minimizing a covariance-weighted ${\chi }^2$ statistic in logarithmic flux space over a grid of $A_V$ values and a discrete model grid. We test the method on 20 targets and find generally good agreement with published literature temperature estimates. The main limitations are non-simultaneous photometry for possibly variable objects and the residual coupling between temperature and reddening in broadband SED fitting. This study is intended as a methodological demonstration on a pilot sample rather than a definitive parameter catalog. Full article

Electroweak, QCD, and QED radiative corrections to the nucleon low-energy coupling constants $g_V$ and $g_A$ are enhanced by large perturbative logarithms between the electroweak and hadronic scale, as well as between the hadronic scale and the low-energy MeV scale. Additionally, higher-order pion-mass splitting corrections to the nucleon axial-vector charge might be large. By consistently incorporating these effects, we provide an updated relation between the lattice-QCD and physical $g_A$, finding a total radiative correction of $3.5\left(2.1\right)\%$ ($5.6\left(0.7\right)\%$). This leads to an expected lattice-QCD result of $g_A^{\mathrm{QCD}}=1.265\left(26\right)$ ($g_A^{\mathrm{QCD}}=1.240\left(9\right)$) when based on a combination of lattice-QCD and data-driven (or only data-driven) inputs, respectively. Future phenomenological, chiral perturbation theory, and lattice-QCD studies can improve both the central value and the uncertainty of this estimate. Full article

This article presents the potential for maliciously influencing a control system by interfering with the program code of an industrial controller, using a temperature control system for a heating process based on resistive electric heating as an example. The presented attack scenarios are crucial for the energy efficiency of electric heating systems, which is related to the issue of cybersecurity in the area of energy security. The aim of this research was to demonstrate that a cyberattack involving the malicious manipulation of the setpoint can be carried out in a manner invisible to the heating process operator and be difficult to detect using classical time-domain control quality indicators (time-response specifications). The first involves incorporating proportional elements with mutually inverted gains into the input and output of a closed-loop system. The second method is based on adding an additional transfer function G_m(s) in parallel to the control system. The difference between the correct and manipulated setpoints is introduced into the input, and the output signal is added to the actual (hidden) value of the controlled variable. In the first method, at the moment of starting the control system, there is a difference between the apparent (falsified) value and the ambient temperature. In the second method, the inclusion of an additional G_m(s) ensures that the apparent (falsified) value of the controlled variable matches the temperature at the moment of starting the system. PID control enables achieving satisfactory control quality in heating processes, which are characterized by high inertia and time delays. Compared to classical PID regulation, advanced control methods can, under certain conditions, provide better performance in terms of quality indicators. However, due to their high computational complexity and sensitivity to model uncertainty—particularly in methods relying on accurate system identification—PID controllers continue to be widely used in industrial practice. For this reason, the present study focuses on a control system based on a PID controller as a practical solution. Based on the results, it was found that the most effective manipulation occurred within the range from 0.9 to 1.1 of the actual setpoint value for both the first and second method, using a model with $T_m$ between 5 s and 30 s. In these cases, the quality indicators referenced to the nominal values, determined for the falsified control system responses to a step change in the setpoint, were as follows: overshoot—0.97 and 1.30 (method 1), and 0.90 and 1.10 (method 2 for 5 s), 0.75 and 1.30 (method 2 for 30 s); settling time—1.06 (method 1), and 0.98 and 1.17 (method 2 for 5 s), 0.85 and 1.14 (method 2 for 30 s). The settling times determined for the system’s response to a disturbance were: 1.00 and 1.15 (method 1), and 1.13 and 1.16 (method 2 for 5 s), 1.12 and 1.02 (method 2 for 30 s). Based on the conducted analysis, it was demonstrated that the relatively simple setpoint manipulation methods presented can effectively mask the impact of malicious interference on the temperature value in the control system of a heating process. Full article

In order to reach net-zero by 2050, we need to have strong decarbonization policies, especially in hard-to-abate clean-ups like steel (8% of the global emissions), cement (7%), and power generation (30%), and negative emissions through direct air capture (DAC) and bioenergy with carbon capture and storage (BECCS). This review paper summarizes the progress in CO₂ capture, compression, transportation, and storage technologies between 2020 and 2025, including energy penalty (20–40%) and cost (15–30%) reductions, with innovations such as metal–organic frameworks (MOFs), bio-inspired catalysts, ionic liquids, and artificial intelligence (AI)-based optimization. This paper, as a new input into the carbon capture and storage (CCS) field, uses the Weighted Sum Model (WSM) as a multi-criteria decision-making tool to rank the best technologies in the capture, storage, monitoring, and transportation sectors. The weights of the criteria are calculated based on Shannon entropy, and the assessment is performed in three conditions, namely, optimistic, pessimistic, and expected. The weights are computed with sensitivity analysis to make the assessment robust. The viability of key projects, such as Northern Lights (Norway, 1.5 MtCO₂/year), Porthos (The Netherlands, 2.5 MtCO₂/year), Quest (Canada, 1 MtCO₂/year), and Petra Nova (USA, 1.6 MtCO₂/year), is evident, and it is projected that, globally, CCS will reach 49 MtCO₂/year across 43 plants in 2025. The review incorporates socio-economic and environmental justice, including barriers such as high costs ($30–600/MtCO₂), energy penalties (1–10 GJ/tCO₂), and opposition between people (20–40% in EU/US). In comparison with previous reviews, this article has a more comprehensive focus, provides quantitative synthesis through WSM, and discusses the implications for researchers, policymakers, and stakeholders towards achieving faster CCS implementation on the path to net-zero. Full article

The large-scale integration and grid connection of renewable energy sources and charging stations introduce a multitude of nonlinear and impact loads, resulting in more severe distortion and higher complexity of disturbance signals in power systems. As a consequence, power quality disturbances (PQDs) in active distribution networks, including overvoltage and harmonics, display greater randomness and diversity, which increases the challenge of PQD identification. To tackle this problem, this study presents a dual-channel early-fusion approach for PQD recognition based on Spatial Post-MultiScale Fusion Entropy (SMFE). SMFE is used as an entropy-based feature-construction pipeline in which a time–frequency representation is formed prior to spatial post-multiscale aggregation to produce a compact complexity map complementary to waveform morphology. Subsequently, a dual-channel model is constructed by integrating waveform-morphology input with SMFE-derived complexity features for joint learning. By leveraging the ConvNeXt architecture and a Squeeze-and-Excitation (SE) mechanism, a multimodal channel-recalibration model is implemented to emphasize informative feature responses during PQD recognition. Experimental verification with simulated signals shows that the proposed approach achieves an identification accuracy of 97.83% under an SNR of 30 dB, indicating robust performance under the tested noise settings. Full article

Reliable property prediction and process selection in laser powder bed fusion are hindered by small, set-level datasets in which key morphology descriptors are intermittently missing, limiting both generalization and actionable co-design. A hybrid multimodal surrogate strategy is introduced that couples engineered process physics features with morphology proxies through a deployable two-stage embedding module and gradient-boosted tree regressors. Set-resolved inputs are assembled from L-PBF parameters, linear energy density and related energy-density variants, pore and prior-β grain summary statistics, and stress–strain-derived descriptors, followed by missingness-aware feature filtering, median imputation, and 5-fold GroupKFold evaluation grouped by set_id, with morphology embeddings learned on training folds and predicted when absent. Across six targets, the final deployable models achieve an RMSE/R² of 11.07 MPa/0.895 (yield), 13.88 MPa/0.873 (UTS), 0.677%/0.861 (elongation), and 2.38 GPa/0.663 (modulus), while roughness and hardness remain challenging (RMSE 2.31 μm and 16.54 HV; R² about 0.12 and 0.11). These surrogates enable constraint-aware candidate generation that identifies a concise set of manufacturing recipes balancing strength and surface objectives under uncertainty-aware screening. The resulting framework provides a practical blueprint for multimodal, small-data additive manufacturing studies and can be extended to richer microstructure measurements and prospective validation to accelerate functional and biomedical alloy development. Full article