Search Results (679)

Maize silage can play a key role in policies aimed at stabilising local energy systems, as it constitutes a critical renewable feedstock for European biogas plants. By providing a dense and predictable source of chemical energy, it supports balance and reliability in the agricultural energy sector. To convert this potential into stable energy production, operators require kinetic models that translate routine silage quality indicators into concrete guidance for digester operation and control. Therefore, the aim of this article was to evaluate the batch kinetics of anaerobic digestion (AD) of maize silage and to select an adequate model for describing biochemical methane potential (BMP) profiles and associated energy recovery in the context of start-up, organic loading rate (OLR), hydraulic retention time (HRT) and feedstock preparation. Ten batches of silage (A–J) were examined, covering a realistic range of pH, electrical conductivity (EC), dry and volatile solids, ash, protein–fat–fibre fractions, fibre composition (NDF, ADF and ADL), derived fractions (hemicellulose, cellulose, and residual organic matter (OM)), C/N ratio and macro-/micronutrient profiles, including trace elements relevant to methanogenesis (Ni, Co, Mo, and Se). BMP tests were carried out in batch mode, and the resulting curves were fitted using the modified Gompertz and a first-order kinetic model. Methane yields of approx. 100–120 m³ CH₄/Mg fresh matter (FM) and 336–402 m³ CH₄/Mg volatile solids (VS), with CH₄ contents of 52–57% v/v, were typical for energy-grade maize silage. Kinetic and energetic behaviours were governed mainly by residual OM and hemicellulose (shortening the lag phase and increasing the maximum methane production rate), the ADL/cellulose ratio (controlling the slower hydrolytic tail), EC and Na/Cl/S (extending the lag phase), and C/N together with Ni/Co/Mo/Se (stabilising methanogenesis). The modified Gompertz model reproduced BMP curves with a pronounced lag phase and asymmetry more accurately (lower error and better information criterion values), and its parameters directly support start-up design, OLR ramp-up and energetic performance optimisation in bioenergy reactors. The novelty of this work lies in combining batch BMP tests, comparative kinetic modelling and detailed silage characterisation to establish quantitative links between kinetic parameters and routine maize silage quality indicators that are directly relevant for biogas plant operation and renewable energy production. Full article

Bovine chymosin is key for cheese production, yet its traditional sourcing is unsustainable. While microbial and plant-based alternatives exist, they often cause non-specific proteolysis, leading to bitter flavors in cheese. This study aims to develop a high-yield, methanol-independent platform for recombinant bovine chymosin production by engineering the expression system of Komagataella phaffii through multi-factorial optimization. Initially, the native bovine prochymosin gene (pcw) was codon-optimized (pcm14) and cloned, along with an mCherry-tag construct (clpcm14), into inducible vector pPIC9 for expression in Komagataella phaffii GS115. Screening identified the fusion-tagged strain clp2-91 as the highest producer. Subsequently, the inducible AOX1 promoter in the previously selected clp2-91 strain was replaced with a constitutive GAP promoter, yielding engineered strain GH1. Cultivated in a 3L fermenter, GH1 exhibited a volumetric productivity of 105.03 SU/(mL·h), twice that of inducible strain clp2-91 (53.59 SU/(mL·h)). The further optimization of fermentation conditions (pH 4.0, glucose as carbon source, fed-batch mode) boosted the enzyme activity of GH1 to 12,000 SU/mL. The recombinant chymosin exhibited enzymatic properties similar to those of the native enzyme and, importantly, demonstrated a broader pH stability (pH 2.0–6.0). This study demonstrates an efficient strategy for chymosin expression in K. phaffii, offering insights that may support the future development and optimization of heterologous protein production in this yeast. Full article

A 2D model of an InGaAs/InAlAs single-photon avalanche photodiode has been developed. The influence of the active area structure in the multiplication region on the diode’s operating parameters has been studied. It was found that changing the diameter of the structure’s active region leads to a change in the dark current in the linear part of the current–voltage curve and a change in the breakdown voltage. Reducing the diameter of the active region from 25 $\mathsf{\mu }$m to 10 $\mathsf{\mu }$m allowed decreasing the dark current in the linear mode by about 10 dB. It has been shown that the quality of the SPAD device can be assessed by knowing the avalanche breakdown voltage and the overall current–voltage curve plot if we consider structures with the same multiplication region thickness and different remaining layers. The higher the breakdown voltage, the better the structure’s quality due to smaller local increases in the field strength. Following this statement, we conclude that for further use in single-photon detectors, it is reasonable to pick specific SPADs from a batch on the sole basis of their current–voltage curves. Full article

The increasing demand for recombinant proteins has driven innovation in bioprocessing strategies using Komagataella phaffii as a host organism. Conventional fed-batch cultivation with pure methanol induction remains widely used but presents challenges including high methanol consumption, extended downtime, and elevated operational costs. This study evaluates alternative strategies combining mixed induction (methanol/sorbitol) with continuous cultivation to enhance productivity, sustainability, and improved economic outcome. Using KEX2 protease as a model industrial recombinant protein, we compared four cultivation modes: fed-batch with methanol (benchmark), fed-batch with mixed induction, continuous with methanol, and continuous with mixed induction. Cell growth, volumetric yield, and specific productivity were evaluated at 5L scale and then modelled to simulate industrial scales (40 L and 400 L). Results demonstrate that continuous cultivation with mixed induction significantly improves yield up to 9-fold compared to conventional fed-batch and reduces methanol usage and oxygen demand. Techno-economic simulations reveal that a 40 L continuous process can match or exceed the output of two 400 L fed-batch runs, while lowering capital and operating costs and minimizing environmental footprint. This integrated strategy offers a scalable, low-cost, and safer method for recombinant protein production, supporting compact and sustainable manufacturing solutions. Full article

Meat-processing wastewater (MPWW) is rich in nutrients and organic matter. This study assessed its potential as feedstock for microalgal biomass production while enabling wastewater treatment. In batch assays, the microalgae-based consortium grew in raw MPWW, and its synergy with the native wastewater microbial community enhanced the chemical oxygen demand (COD) removal rate. If suspended solids were pre-removed from wastewater, COD removing rates improved from 828.5 ± 60.5 to 1097.5 ± 22.2 mg L⁻¹ d⁻¹. In a raceway system operated in fed-batch mode with sieved and sedimented MPWW, COD removal was consistently achieved across feeding cycles, despite the variability in wastewater composition, reaching rates of up to 806.3 ± 0.0 mg L⁻¹ d⁻¹. Total nitrogen also decreased in most cycles. Microalgal biomass, estimated from total photosynthetic pigment’s concentration, increased from 0.4 to 17.9 µg mL⁻¹. The microalgae-based consortium became more diverse over time, harboring at the end, additional eukaryotic taxa such as protozoan grazers and fungi (e.g., Heterolobosea class and Trichosporonaceae and Dipodascaceae families), although their roles in removal processes remain unknown. This study highlights the potential use of real MPWW as feedstock for microalgal-based biomass production with concomitant carbon/nutrient load reduction, aligning its implementation with circular economy percepts. Full article

Background: Whole-slide imaging and algorithmic advances have moved computational pathology from research to routine consideration. Despite notable successes, real-world deployment remains limited by generalization, validation gaps, and human-factor risks, which can be amplified in resource-constrained settings. Content/Scope: This narrative review and implementation perspective summarizes clinically proximate AI capabilities in cancer pathology, including lesion detection, metastasis triage, mitosis counting, immunomarker quantification, and prediction of selected molecular alterations from routine histology. We also summarize recurring failure modes, dataset leakage, stain/batch/site shifts, misleading explanation overlays, calibration errors, and automation bias, and distinguish applications supported by external retrospective validation, prospective reader-assistance or real-world studies, and regulatory-cleared use. We translate these evidence patterns into a practical checklist covering dataset design, external and temporal validation, robustness testing, calibration and uncertainty handling, explainability sanity checks, and workflow-safety design. Equity Focus: We propose a stepwise adoption pathway for low- and middle-income countries: prioritize narrow, high-impact use cases; match compute and storage requirements to local infrastructure; standardize pre-analytics; pool validation cohorts; and embed quality management, privacy protections, and audit trails. Conclusions: AI can already serve as a reliable second reader for selected tasks, reducing variance and freeing expert time. Safe, equitable deployment requires disciplined validation, calibrated uncertainty, and guardrails against human-factor failure. With pragmatic scoping and shared infrastructure, pathology programs can realize benefits while preserving trust and accountability. Full article

Characterization of propeller blade vibrations is essential to ensure aerodynamic performance, minimize noise emissions, and maintain structural integrity in aerospace and unmanned aerial vehicle applications. Conventional high-fidelity finite-element and fluid–structure simulations yield precise modal predictions but incur prohibitive computational costs, limiting rapid design exploration. This paper introduces a data-driven surrogate modeling framework based on a feedforward neural network to predict natural vibration frequencies of propeller blades with high accuracy and a dramatically reduced runtime. A dataset of 1364 airfoil geometries was parameterized, meshed, and analyzed in ANSYS 2024 R2 across a range of rotational speeds and boundary conditions to generate modal responses. A TensorFlow/Keras model was trained and optimized via randomized search cross-validation over network depth, neuron counts, learning rate, batch size, and optimizer selection. The resulting surrogate achieves $R^2>0.90$ and $NRMSE<0.08$ for the second and higher-order modes, while reducing prediction time by several orders of magnitude compared to full finite-element workflows. The proposed approach seamlessly integrates with CAD/CAE pipelines and supports rapid, iterative optimization and real-time decision support in propeller design. Full article

Black liquor, the side stream from Kraft pulping, is a promising feedstock for the production of renewable fuels via hydrothermal liquefaction (HTL). However, further upgrading of the black liquor HTL oil is required to reduce the oxygen content for fuel use. In this work, the hydrodeoxygenation (HDO) of black liquor HTL oil model compounds was investigated to enhance the understanding of catalyst activity and selectivity under hydrothermal conditions. The study focused on isoeugenol and 4-methylcatechol as model compounds, representing different functionalities in black liquor-derived HTL-oil. Sulfided NiMo catalysts supported on titania, zirconia, activated carbon, and α-alumina were evaluated in batch mode at subcritical and supercritical upgrading using hydrogen gas. The results show that isoeugenol was fully converted in all experiments, while 4-methylcatechol conversion varied depending on the catalyst and reaction conditions. Phenols were obtained as the main products and the maximum degree of deoxygenation achieved was around 40%. This research provides insights into the potential of hydrothermal HDO for upgrading BL-derived biocrudes, emphasising the importance of catalyst selection and reaction conditions in hydrothermal conditions. Full article

Synthetic Aperture Radar (SAR) and Interferometric SAR (InSAR) techniques have received rapid advance in recent years, and the Multi-temporal InSAR (MT-InSAR) has been widely applied in various earth observations. Distributed scatterer (DS) InSAR is one of the most advanced MT-InSAR methods, and has overcome the limitation of the lack of enough measurement points in the low coherent regions for traditional methods. While the Joint-Scatterer InSAR (JS-InSAR) is the extension of DS InSAR method, which exploited the overall information of Joint Scatterers to carry out DS identification and phase optimization. And it can avoid the inaccuracy caused by the offset errors between scatterers in complex terrain areas. However, the intensive computation and low efficiency have severely restricted the application of JS-InSAR, especially when dealing with massive and long historical SAR images. As the sequential estimator has proven to successfully improve the efficiency of MT-InAR and obtain near-time deformation time series, in this work, we proposed the sequential-based JS-InSAR (S-JSInSAR) method with flexible batches. This method has adaptively divided large single look complex (SLC) stack into different batches with flexible number and certain overlaps. Then, the JS-InSAR processing is performed on each batch, respectively, and these estimated results are integrated into the final deformation time series based on the connection mode. Thus, S-JSInSAR can efficiently process large InSAR dataset, and mitigate the decorrelation effect caused by long temporal baselines. To demonstrate the effectiveness of the S-JSInSAR, a multi-year of 145 Sentinel-1 ascending SAR images in Tangshan, China, were collected to estimate the long deformation time series. And the results compared with other methods have shown the processing time has substantially decreased without the loss of deformation accuracy, and obtain deformation spatial distribution with more details in local regions, which have well validated the efficiency and reliability of the proposed method. Full article

With the development of manufacturing industry, traditional fixed process processing methods cannot adapt to the changes in workshop operations and the demand for small batches and multiple orders. Therefore, it is necessary to introduce multiple robots to provide a more flexible production mode. Currently, some Job Shop Scheduling Problems with Transportation (JSP-T) only consider job scheduling and vehicle task allocation, and does not focus on the problem of collision free paths between vehicles. This article proposes a novel solution framework that integrates workshop scheduling, material handling robot task allocation, and conflict free path planning between robots. With the goal of minimizing the maximum completion time (Makespan) that includes handling, this paper first establishes an extended JSP-T problem model that integrates handling time and robot paths, and provides the corresponding workshop layout map. Secondly, in the scheduling layer, an improved Deep Q-Network (DQN) method is used for dynamic scheduling to generate a feasible and optimal machining scheduling scheme. Subsequently, considering the robot’s position information, the task sequence is assigned to the robot path execution layer. Finally, at the path execution layer, the Priority Based Search (PBS) algorithm is applied to solve conflict free paths for the handling robot. The optimized solution for obtaining the maximum completion time of all jobs under the condition of conflict free path handling. The experimental results show that compared with algorithms such as PPO, the scheduling algorithm proposed in this paper has improved performance by 9.7% in Makespan, and the PBS algorithm can obtain optimized paths for multiple handling robots under conflict free conditions. The framework can handle scheduling, task allocation, and conflict-free path planning in a unified optimization process, which can adapt well to job changes and then flexible manufacturing. Full article

Cheese whey, the major by-product of the dairy industry, poses an environmental challenge due to its high organic load but simultaneously represents a nutrient-dense matrix suitable for biotechnological valorization. This review synthesizes recent advances positioning whey as (i) a fermentation substrate for lactic acid bacteria, yeasts/fungi, and microalgae, enabling the production of functional biomass, organic acids, bioethanol, exopolysaccharides, enzymes, and wastewater bioremediation; (ii) a platform for postbiotic generation, supporting cell-free preparations with functional activities; and (iii) a food-grade encapsulating material, particularly through whey proteins (β-lactoglobulin, α-lactalbumin), which can form emulsions, gels, and films that protect biotics and bioactive compounds during processing, storage, and gastrointestinal transit. We analyze key operational variables (whey type and pretreatment, supplementation strategies, batch and continuous cultivation modes), encapsulation routes (spray drying, freeze-drying, and hybrid protein–polysaccharide systems), and performance trade-offs relevant to industrial scale-up. Finally, we outline future directions, including precision fermentation, mixed-culture processes with in situ lactase activity, microfluidics-enabled encapsulation, and life-cycle assessment, to integrate product yields with environmental performance. Collectively, these strategies reframe whey from a high-impact waste into a circular bioeconomy resource for the food, nutraceutical, and environmental sectors. Full article

Phycobiliproteins are recognized as potential bioactive compounds and described as highly valued natural products for industrial and biotechnological applications. Moreover, they have been observed to possess antioxidant, anticancer/antineoplastic, and anti-inflammatory activities. Therefore, the search for new methods of their extraction and isolation is still ongoing. Foam fractionation, a bubble separation technique that allows amphiphilic molecules to be separated from their aqueous solutions, is a promising but understudied method. The process may be carried out both under mild conditions that are suitable for proteins and also for diluted solutions. This paper presents the results of applying the foam fractionation process to concentrate and separate phycobiliproteins. Allo- and C-phycocyanin from a thermophilic Synechococcus PCC 6715 strain were used in extract form after biomass cultivation and disintegration. Two ways of running the process were investigated: batch mode and continuous mode, the latter of which has not been reported in the literature previously. The results indicate that the method can be applied on a larger scale, as the outcomes of the continuous mode processes were comparable to those of the batch mode. Moreover, the results indicate that the process provides, to a certain extent, the opportunity of separating phycobiliproteins from each other. Full article

Control of elbow exoskeletons using muscular signals, although promising for the rehabilitation of millions of patients, has not yet been widely commercialized due to challenges in real-time intention estimation and management of dynamic uncertainties. From a practical perspective, millions of patients with stroke, spinal cord injury, or neuromuscular disorders annually require active rehabilitation, and elbow exoskeletons with precise and safe motion intention tracking capabilities can restore functional independence, reduce muscle atrophy, and lower treatment costs. In this research, an intelligent control framework was developed for an elbow joint exoskeleton, designed with the aim of precise and safe real-time tracking of the user’s motion intention. The proposed framework consists of two main stages: (a) real-time estimation of desired joint angle (as a proxy for movement intention) from High-Density Surface Electromyography (HD-sEMG) signals using an LSTM network and (b) implementation and comparison of three PID, impedance, and sliding mode controllers. A public EMG dataset including signals from 12 healthy individuals in four isometric tasks (flexion, extension, pronation, supination) and three effort levels (10, 30, 50 percent MVC) is utilized. After comprehensive preprocessing (Butterworth filter, 50 Hz notch, removal of faulty channels) and extraction of 13 time-domain features with 99 percent overlapping windows, the LSTM network with optimal architecture (128 units, Dropout, batch normalization) is trained. The model attained an RMSE of 0.630 Nm, R² of 0.965, and a Pearson correlation of 0.985 for the full dataset, indicating a 47% improvement in R² relative to traditional statistical approaches, where EMG is converted to desired angle via joint stiffness. An assessment of 12 motion–effort combinations reveals that the sliding mode controller consistently surpassed the alternatives, achieving the minimal tracking errors (average RMSE = 0.21 Nm, R² ≈ 0.96) and showing superior resilience across all tasks and effort levels. The impedance controller demonstrates superior performance in flexion/extension (average RMSE ≈ 0.22 Nm, R² > 0.94) but experiences moderate deterioration in pronation/supination under increased loads, while the classical PID controller shows significant errors (RMSE reaching 17.24 Nm, negative R² in multiple scenarios) and so it is inappropriate for direct myoelectric control. The proposed LSTM–sliding mode hybrid architecture shows exceptional accuracy, robustness, and transparency in real-time intention monitoring, demonstrating promising performance in offline simulation, with potential for real-time clinical applications pending hardware validation for advanced upper-limb exoskeletons in neurorehabilitation and assistive applications. Full article

Structural problems are a common topic among several optimization works; with the use of finite element analysis (FEA), the aim of these works is to improve the mechanical behavior of the distinct elements or bodies involved in these optimization problems. However, the impact of the meshing discretization on the outcome of the optimization process has not been studied in previous works. The present work investigates the effect of mesh element size on the mechanical optimization of two cases of study; the first one is about a modal optimization on a cantilever beam, and the second one is about a cellular beam, where the aim is to reduce the weight of the beam under static load. In these two optimization problems, variables commonly used in the literature were employed, while additionally including the mesh size as an extra variable. The computational framework is implemented on MATLAB R2022a, and the modal and weight optimizations are carried out through APDL (ANSYS Parametric Design Language) executed in batch mode. The results demonstrate that the consideration of the mesh size element can improve the computational time that is required to perform this mechanical optimization, achieving a 96% percentage of time reduction instead of making the analysis with the finest element size (in case 1) and a 90 percent time reduction for the second case of study. Full article

Cactus leaves from the Cactaceae family, particularly the Opuntia genus, have attracted increasing attention as natural coagulants for water treatment applications. In this work, Cactus-based extracts were investigated for drinking water treatment through the coagulation–flocculation process. Several extraction routes were examined, including Ca-J, Ca-H₂O, Ca-NaOH (0.05 M), Ca-NaCl (0.5 M), and Ca-HCl (0.05 M), and their performance was evaluated using jar test experiments. The removal efficiencies of total coliforms (TC), anaerobic sulfite-reducing bacteria (ASRB), total suspended solids (TSS), and turbidity were assessed, and the most effective extract was subsequently tested in a semi-industrial pilot-scale coagulation–flocculation–settling system. The physicochemical properties of the Cactus material were characterized using FTIR, SEM, XRD, and MALDI-TOF analyses. Results revealed bioactive components, including carbohydrates, proteins, tannins, flavonoids, and glucose, with functional groups (carboxyl, hydroxyl, carbonyl) responsible for coagulation. XRD and SEM analyses showed a semi-crystalline structure and a heterogeneous surface with fiber networks, while MALDI-TOF confirmed the presence of flavonoid and tannin compounds. These features collectively contribute to the effective removal of turbidity, suspended solids, and microbial contaminants. Among the tested extracts, Ca-NaOH (0.05 M) exhibited the highest removal efficiencies, achieving 100% removal of TC and ASRB, 94.15% removal of TSS, and 70.38% turbidity reduction under laboratory conditions. Pilot-scale application of this extract resulted in a turbidity reduction of 66.65%. Additional water quality parameters, including total alkalinity (TA), total dissolved solids (TDS), pH, and electrical conductivity (EC), were monitored to evaluate process performance. Overall, the results highlight the strong potential of Cactus leaves as an effective, cost-efficient, and environmentally friendly alternative to conventional chemical coagulants. However, further research is required to enhance their scalability and commercialization. Full article