Search Results (178)

The energy transition, driven by the global shift toward renewable and electrification, necessitates accurate and efficient prediction of electrical load profiles to quantify energy consumption. Therefore, the systematic literature review (SLR), followed by PRISMA guidelines, synthesizes hybrid architectures for sequential electrical load profiles, aiming to span statistical techniques, machine learning (ML), and deep learning (DL) strategies for optimizing performance and practical viability. The findings reveal a dominant trend towards complex hybrid models leveraging the combined strengths of DL architectures such as long short-term memory (LSTM) and optimization algorithms such as genetic algorithm and Particle Swarm Optimization (PSO) to capture non-linear relationships. Thus, hybrid models achieve superior performance by synergistically integrating components such as Convolutional Neural Network (CNN) for feature extraction and LSTMs for temporal modeling with feature selection algorithms, which collectively capture local trends, cross-correlations, and long-term dependencies in the data. A crucial challenge identified is the lack of an established framework to manage adaptable output lengths in dynamic neural network forecasting. Addressing this, we propose the first explicit idea of decoupling output length predictions from the core signal prediction task. A key finding is that while models, particularly optimization-tuned hybrid architectures, have demonstrated quantitative superiority over conventional shallow methods, their performance assessment relies heavily on statistical measures like Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). However, for comprehensive performance assessment, there is a crucial need for developing tailored, application-based metrics that integrate system economics and major planning aspects to ensure reliable domain-specific validation. Full article

Plastic pollution raises concerns for health and the environment. Plastics are not biodegradable but gradually erode to microplastic and nanoplastic particles spreading almost everywhere. Nanoplastics exhibit colloidal behavior. Thereby, their analysis can be accomplished by refractometry, preferably by an on-chip tool. We present a study of such colloids using a microfabricated Fabry–Pérot cavity with curved mirrors, which holds a capillary micro-tube used both for fluid handling and light collimation, resulting in an optically stable microresonator. Despite the numerous scatterers within the sample, the sub-millimeter scale cavity provides the advantages of reduced interaction length while maintaining light confinement. This significantly reduces optical loss and hence keeps resonance modes with quality factors (resonant frequency/bandwidth) above 1100. Therefore, small quantities of colloids can be measured by the interference spectral response through the shift in resonant wavelengths. The particles’ Brownian motion potentially causing perturbations in the spectra can be overcome either by post-measurement cross-correlation analysis or by avoiding it entirely by taking the measurements at once by a wideband source and a spectrum analyzer. The effective refractive index of solutions with solid contents down to 0.34% could be determined with good agreement with theoretical predictions. Even lower detection capabilities might be attained by slightly altering the technique to cause particle aggregation achieved solely by light. Full article

This research examined the development and testing of hot-melt adhesives incorporating metallic (Al and Fe powders averaging 800 nm) and ferrite additives, designed for reversible bonding technology of polyolefins through electromagnetic energy. The experimental models with Al displayed smooth particles that were fairly evenly distributed within the polymer matrix. Experimental models with Fe suggested that Fe nanopowders are more difficult to disperse within the polymer matrix, frequently resulting in agglomeration. For ferrite powder, there were fewer agglomerations noticed, and the dispersion was more uniform compared to similar composites containing Fe particles. Regarding water absorption, the extent of swelling was greater in the composites that included Al. Because of toluene’s affinity for the matrices, the swelling measurements stayed elevated even with reduced exposure times, and the composites with ferrite showed the lowest swelling compared to those with metallic particles. A remarkable evolution of the dielectric loss factor peak shifting towards higher frequencies with rising temperatures was observed, which is particularly important when the materials are exposed to thermal activation through electromagnetic energy. The reversible bonding experiments were performed on polyolefin samples which were connected longitudinally by overlapping at the ends; specialized hot-melts were employed, using electromagnetic energy at 2.45 GHz, with power levels between 140 and 850 × 10³ W/kg and an exposure duration of up to 2 min. The feasibility of bonding polyolefins using homologous hot-melts that include metallic/ferrite elements was verified. Composites with both matrices showed that the hot-melts with Al displayed the highest mechanical tensile strength values, but also had a relatively greater elongation. All created hot-melts were suitable for reversible adhesion of similar polyolefins, with the one based on HDPE and Fe considered the most efficient for bonding HDPE, and the one based on PP and Al for PP bonding. When bonding dissimilar polyolefins, it seems that the technique is only effective with hot-melts that include Al. According to the reversible bonding diagrams for specific substrates and hot-melt combinations, and considering the optimization of energy consumption in relation to productivity, the most cost-effective way is to utilize 850 × 10³ W/kg power with a maximum exposure time of 1 min. Full article

Photoacoustic (PA) velocimetry offers a promising solution to the limitations of conventional techniques for measuring blood flow velocity. Given its moderate penetration depth and high spatial resolution, PA imaging is considered suitable for measuring low-velocity blood flow in capillaries located at moderate depths. High-resolution measurements based on PA signals from individual blood cells can be achieved using a high-frequency transducer. However, high-frequency signals attenuate rapidly within biological tissue, restricting the measurable depth. Consequently, low-frequency transducers are required for deeper measurements. To date, PA flow velocimetry employing low-frequency transducers remains insufficiently explored. In this study, we investigated the effect of the concentration of particles that mimic blood cells within vessels under low-concentration conditions. The performance of flow velocity measurement was evaluated using an ultrasonic transducer (UST) with a center frequency of 10 MHz. The volume fraction of particles in the solution was systematically varied, and the spatially averaged flow velocity was assessed using two different distinct analysis methods. One method employed a time-shift approach based on cross-correlation analysis. Flow velocity was estimated from PA signal redpairs generated by particles dispersed in the fluid, using consecutive pulsed laser irradiations at fixed time intervals. The other method employed a pulsed Doppler method in the frequency domain, widely applied in ultrasound Doppler measurements. In this method, flow velocity redwas estimated from the Doppler-shifted frequency between the transmitted and received signals of the UST. For the initial analysis, numerical simulations were performed, followed by experiments based on displacement measurements equivalent to velocity measurements. The target was a capillary tube filled with an aqueous solution containing particles at different concentration levels. The time–domain method tended to underestimate flow velocity as particle concentration increased, whereas the pulsed Doppler method yielded estimates consistent with theoretical values, demonstrating its potential for measurements at high concentrations. Full article

Textile dye effluents, particularly cationic dyes, pose a major environmental challenge, demanding efficient and sustainable adsorbent materials to remove harmful synthetic dyes. In this study, a reference thiourea–formaldehyde (TU/FA) composite and a series of thiourea–poly(acrylic acid)–formaldehyde (TU/PAA/FA) composites were synthesized and systematically characterized. The composites were prepared by varying the volume of poly(acrylic acid) PAA (from 1 to 7.5 mL) to assess how PAA incorporation influences morphology, crystallinity, surface chemistry, charge, and thermal stability. Analytical techniques including SEM, XRD, FT-IR, particle size distribution, zeta potential, and TGA/DTG revealed that increasing PAA content induced more porous and amorphous microstructures, intensified carbonyl absorption, reduced particle size (optimal at 2.5–5 mL PAA), and shifted the zeta potential from near-neutral to highly negative values (−37 to −41 mV). From TU/PAA/FA composite analysis, it was depicted that the TU/PAA-5/FA material has the better characteristics as a potential cationic dye absorbent. Thus, the adsorption performance of this composite toward crystal violet dye was subsequently investigated and compared to the reference material thiourea–formaldehyde (TU/FA). The TU/PAA-5/FA material exhibited the highest capacity (145 mg/g), nearly twice that of TU/FA (74 mg/g), due to the higher density of carboxylic groups facilitating electrostatic attraction. Adsorption was pH-dependent, maximized at pH 6, and decreased with temperature, confirming an exothermic process. Kinetic data followed a pseudo-second-order model (R² = 0.99), implying chemisorption as the rate-limiting step, while Langmuir isotherms (R² > 0.97) indicated monolayer adsorption. Thermodynamic analysis (ΔH° < 0, ΔS° < 0, ΔG° > 0) further supported an exothermic, non-spontaneous mechanism. Overall, the TU/PAA-5/FA composite combines enhanced structural stability with high adsorption efficiency, highlighting its potential as a promising, low-cost material for the removal of cationic dyes from textile effluents. Full article

This study investigated how Japanese speakers interpret structurally ambiguous wh-questions, testing whether filler–gap resolution is guided by syntactic resolution based on hierarchical structure or linear locality based on surface word order. We combined behavioral key-press responses with fine-grained eye-tracking data and applied cluster-based permutation analysis to capture the moment-by-moment time course of syntactic interpretation as sentences were processed in real time. Key-press responses revealed a preference for resolving the dependency at the main clause (MC) gap position. Eye-tracking data showed early predictive fixations to the MC picture, followed by shifts to the embedded clause (EC) picture as the embedded event was described. These shifts occurred prior to the appearance of syntactic cues that signal the presence of an EC structure, such as the complementizer -to, and were therefore most likely guided by referential alignment with the linguistic input rather than by syntactic reanalysis. A subsequent return of the gaze to the MC picture occurred when the clause-final question particle -ka became available, confirming the interrogative use of the wh-phrase. Both key-press and eye-tracking data showed that participants did not commit to the first grammatically available EC interpretation but instead waited until clause-final particle information confirmed the interrogative use of the wh-phrase, ultimately favoring the MC interpretation. This pattern supports the view that filler–gap resolution is guided by structural locality rather than linear locality. By using high-resolution temporal data and statistically robust analytic techniques, this study demonstrates that Japanese comprehenders engage in predictive yet structurally cautious parsing. These findings challenge earlier claims that filler–gap resolution in Japanese is primarily driven by linear locality and instead showed a preference for resolving dependencies at the structurally higher MC position, consistent with parsing biases previously observed in English, despite typological differences in word order between the two languages. This preference also reflects sensitivity to language-specific morpho-syntactic cues in Japanese, such as clause-final particles. Full article

Woody plants, comprising forest and fruit tree species, provide essential ecological and economic benefits to society. Their genetic improvement is challenging due to long generation intervals and high heterozygosity. Genetic transformation, which combines targeted DNA delivery with plant regeneration from transformed cells, offers a powerful alternative to accelerating their domestication and improvement. Agrobacterium tumefaciens, Rhizobium rhizogenes, and particle bombardment have been widely used for DNA delivery into a wide variety of explants, including leaves, stems, hypocotyls, roots, and embryos, with regeneration occurring via direct organogenesis, callus-mediated organogenesis, somatic embryogenesis, or hairy root formation. Despite successes, conventional approaches are hampered by low efficiency, genotype dependency, and a reliance on challenging tissue culture. This review provides a critical analysis of the current landscape in woody plant transformation, moving beyond a simple summary of techniques to evaluate the co-evolution of established platforms with disruptive technologies. Key advances among these include the use of developmental regulators to engineer regeneration, the rise in in planta systems to bypass tissue culture, and the imperative for DNA-free genome editing to meet regulatory and public expectations. By examining species-specific breakthroughs in key genera, including Populus, Malus, Citrus, and Pinus, this review highlights a paradigm shift from empirical optimization towards rational, predictable engineering of woody plants for a sustainable future. Full article

This paper explores the structural, mechanical, thermal, and electrochemical properties of copper matrix composites (CMCs) enhanced by Crassostrea madrasensis seashell powder, which were produced via powder metallurgy and resistance sintering. FESEM images showed a uniform distribution of bio-ceramic particles in the copper matrix composites (CMCs), leading to an improved microstructure and enhanced mechanical behavior. Mechanical tests showed that after incorporating 12 wt.% seashell powder, the average hardness increased to 56 HV, and compressive strength improved to 686 MPa. Density analysis showed a decrease in porosity, which was attributed to better particle diffusion during sintering. The corrosion resistance was evaluated using electrochemical techniques, including OCPT, Tafel polarization, EIS, LSV, and chronocoulometry, which were employed in 3.5 wt.% NaCl media with varying concentrations of the extract of Allium sativum (garlic) as a green inhibitor. Garlic-derived phytochemicals facilitated surface passivation, which was proven by shifts in potential, reduced corrosion rates, and minor charge transfer. The findings confirm that Crassostrea madrasensis bio-ceramic reinforcements and garlic extract-based corrosion inhibition provide a sustainable method for improving the performance and durability of copper matrix composites. Full article

Maintaining an effective facial seal is critical for the performance of tight-fitting industrial respirators used in high-risk sectors such as mining, manufacturing, and construction. Traditional fit verification methods—Qualitative Fit Testing (QLFT) and Quantitative Fit Testing (QNFT)—are limited to periodic assessments and cannot detect fit degradation during active use. This study presents a real-time fit detection system based on embedded breath sensors and machine learning algorithms. A compact sensor module inside the respirator continuously measures pressure, temperature, and humidity, transmitting data via Bluetooth Low Energy (BLE) to a smartphone for on-device inference. This system functions as a multimodal biosensor: intra-mask pressure tracks flow-driven mechanical dynamics, while temperature and humidity capture the thermal–hygrometric signature of exhaled breath. Their cycle-synchronous patterns provide an indirect yet reliable readout of respirator–face sealing in real time. Data were collected from 20 healthy volunteers under fit and misfit conditions using OSHA-standardized procedures, generating over 10,000 labeled breathing cycles. Statistical features extracted from segmented signals were used to train Random Forest, Support Vector Machine (SVM), and XGBoost classifiers. Model development and validation were conducted using variable-size sliding windows depending on the person’s breathing cycles, k-fold cross-validation, and leave-one-subject-out (LOSO) evaluation. The best-performing models achieved F1 scores approaching or exceeding 95%. This approach enables continuous, non-invasive fit monitoring and real-time alerts during work shifts. Unlike conventional techniques, the system relies on internal physiological signals rather than external particle measurements, providing a scalable, cost-effective, and field-deployable solution to enhance occupational safety and regulatory compliance. Full article

In recent years, there has been a growing shift toward the use of screw extruders in the pitch modification process. To further improve the mixing efficiency of twin-screw extruders in pitch processing, this study focuses on redesigning the mixing elements of a co-rotating twin-screw extruder. By integrating the conventional kneading block assembly with PTA technology, three innovative screw mixing elements were developed. In this study, numerical simulations were performed using the finite element method (FEM) in the ANSYS Polyflow 2022 R1 software. The dynamic mesh technique was employed to model the screw rotation. The mixing performance of these novel screw elements was then evaluated in terms of distribution, mixing, and shear effects by utilizing the Particle Tracking Analy sis (PTA) technique within the Polyflow statistical module. The results demonstrate that the configuration and structural design of the mixing screw elements significantly influence the mixing effectiveness of spinnable pitch. Among the tested configurations, the slotted thread mixing element with six slots and a 30° slot angle (Model 2) was identified as the optimal design, exhibiting markedly superior mixing performance compared to the traditional kneading block (Model 4). Full article

Mesenchymal stem cells are widely cultivated in stirred tank bioreactors. Due to their adhesion properties, they are attached to small spherical spheres called microcarriers. To understand the hydromechanical stresses encountered by the cells, it is essential to characterize the flow using the PIV technique. However, the usual solid–liquid system used in cell cultures has poor optical properties. Thus, shifting to one with better optical properties, while respecting the physical characteristics, is mandatory to achieve a relevant representation. PMMA microparticles suspended with 61 wt% ammonium thiocyanate solution NH₄SCN were found to be a robust candidate. The refractive index (RI) of both sides is of the order of 1.491 with a density ratio of ${\rho }_f/{\rho }_p\approx$ 0.96, and particle size averaged around 168 μm. Using the RIM-PIV (refractive index matched particle image velocimetry) technique for a 0.7 L volume stirred tank equipped with an HTPG down-pumping axial impeller and operating at full homogeneous speed $N=150$ rpm, mean and turbulence quantities of the liquid phase were measured as a function of PMMA particle volume fractions ${\alpha }_p$, which ranged from 0.5 to 3 v%. This corresponds to a particle number density of $n=12$ particles/mm³, which is considered original and challenging for the PIV technique. At 3 v%, the addition of particles dampened the turbulent kinetic energy (TKE) of the liquid phase locally by 20% near the impeller. This impact became trivial (<10%) at the local-average level. The structure and direction of the recirculation loop also shifted. Full article

The Cherenkov effect, whereby a charged particle emits light when traveling faster than the phase velocity of light in a dielectric medium, is widely employed in particle identification techniques. However, Cherenkov light yield is relatively low, typically amounting to only 100–200 visible photons per centimeter of path length in materials like water, plastic, or glass. In this study, we investigate the optical response of FB118, a wavelength-shifting (WLS) plastic developed by Glass to Power, under exposure to ionizing particles. Our measurements confirm the absence of residual scintillation in FB118, allowing for a clean separation of Cherenkov signals. Moreover, the intrinsic WLS properties of the material enable a significant enhancement of light detection in the visible range. These features make FB118 a promising candidate for use in compact Cherenkov detectors, particularly in astroparticle physics experiments where space and power constraints demand efficient, compact solutions. Full article

In this paper, we provide a complete analysis of the most general spherical solution of the Lyra scalar-tensor (LyST) gravitational theory based on the proper definition of a Lyra manifold. Lyra’s geometry features the metric tensor and a scale function as fundamental fields, resulting in generalizations of geometrical quantities such as the affine connection, curvature, torsion, and non-metricity. A proper action is defined considering the correct invariant volume element and the scalar curvature, obeying the symmetry of Lyra’s reference frame transformations and resulting in a generalization of the Einstein–Hilbert action. The LyST gravity assumes zero torsion in a four-dimensional metric-compatible spacetime. In this work, geometrical quantities are presented and solved via Cartan’s technique for a spherically symmetric line element. Birkhoff’s theorem is demonstrated so that the solution is proven to be static, resulting in the Lyra–Schwarzschild metric, which depends on both the geometrical mass (through a modified version of the Schwarzschild radius $r_S$) and an integration constant dubbed the Lyra radius $r_L$. We study particle and light motion in Lyra–Schwarzschild spacetime using the Hamilton–Jacobi method. The motion of massive particles includes the determination of the $r_{\mathrm{ISCO}}$ and the periastron shift. The study of massless particle motion shows the last photon’s unstable orbit. Gravitational redshift in Lyra–Schwarzschild spacetime is also reviewed. We find a coordinate transformation that casts Lyra–Schwarzschild spacetime in the form of the standard Schwarzschild metric; the physical consequences of this fact are discussed. Full article

Subway train operators face the risk of cumulative cognitive stress due to factors such as visual fatigue from prolonged high-speed tunnel driving, irregular shift patterns, and the monotony of automated operations. This can lead to cognitive decline and human error accidents. Current monitoring of cognitive stress risk predominantly relies on single-modal methods, which are susceptible to environmental interference and offer limited accuracy. This study proposes an intelligent multimodal framework for cognitive stress monitoring by leveraging the symmetry principles in physiological and behavioral manifestations. The symmetry of photoplethysmography (PPG) waveforms and the bilateral symmetry of head movements serve as critical indicators reflecting autonomic nervous system homeostasis and cognitive load. By integrating these symmetry-based features, this study constructs a spatiotemporal dynamic feature set through fusing physiological signals such as PPG and galvanic skin response (GSR) with head and facial behavioral features. Furthermore, leveraging deep learning techniques, a hybrid PSO-CNN-GRU-Attention model is developed. Within this model, the Particle Swarm Optimization (PSO) algorithm dynamically adjusts hyperparameters, and an attention mechanism is introduced to weight multimodal features, enabling precise assessment of cognitive stress states. Experiments were conducted using a full-scale subway driving simulator, collecting data from 50 operators to validate the model’s feasibility. Results demonstrate that the complementary nature of multimodal physiological signals and behavioral features effectively overcomes the limitations of single-modal data, yielding significantly superior model performance. The PSO-CNN-GRU-Attention model achieved a predictive coefficient of determination (R²) of 0.89029 and a mean squared error (MSE) of 0.00461, outperforming the traditional BiLSTM model by approximately 22%. This research provides a high-accuracy, non-invasive solution for detecting cognitive stress in subway operators, offering a scientific basis for occupational health management and the formulation of safe driving intervention strategies. Full article

Phosphorus (P) release from reservoir sediments critically influences water quality and ecosystem stability. This study analyzed surface sediments from four representative zones to investigate phosphorus fraction distribution, key influencing factors, and implications for water quality. Results showed that total phosphorus (TP) content in sediments from main and tributary inflow zones was significantly higher than in open-water and transition zones. Inorganic phosphorus (IP) was the dominant form, with iron-bound phosphorus (Fe-P) accounting for 33.2–42.0% of IP. A strong correlation existed between P release and the Fe/P molar ratio; notably, when the ratio approached 10, phosphorus desorption increased significantly, indicating a shift from sink to source. Sediments with grain sizes <0.01 mm had the highest P release rates, suggesting particle size, Fe content, and hydrodynamics jointly regulate P mobilization. Using the Diffusive Gradients in Thin Films (DGT) technique, phosphorus release in inflow zones exceeded 1 g/m² in all hydrological periods, contributing substantially to internal loading. Sediment-derived P primarily influenced bottom water, while surface water was more affected by external inputs. These findings highlight the spatial heterogeneity of P release and underscore the need for zone-specific management strategies in reservoir systems. Full article