Seal defect detection in food packaging integrity inspection presents substantial challenges, primarily owing to minimal textural disparities between defective and intact regions under industrial illumination. These challenges intensify in production environments where moderate class imbalance intersects with pronounced inter-class similarity. This study introduces an enhanced MobileNetV4 architecture incorporating adaptive feature learning mechanisms to overcome these limitations. A novel LocalAttention module, employing dynamically weighted rectangular sliding windows, augments the model's texture discrimination capabilities by effectively isolating elongated structural anomalies characteristic of seal defects. To address suboptimal illumination conditions, we integrate Pinwheel-shaped Convolution (PConv), which leverages Gaussian-inspired distribution patterns to amplify weak signal detection in poorly lit environments. Additionally, an Adaptive Similarity-Modulated Loss function concurrently mitigates class imbalance while refining feature space organization for morphologically close categories. Evaluated on a grain packaging dataset from Zhangzhou, Fujian, the proposed framework achieves 99.54% accuracy and 99.75% F1-score with only 3.7M parameters and 25.27 FPS on Raspberry Pi 4B. To validate industrial reliability, robustness experiments under simulated LED degradation and sensor noise confirm consistent performance advantages over baseline architectures, and an 8-hour continuous stress test demonstrates stable edge deployment without thermal throttling. Cross-domain validation on NEU-CLS further achieves 99.72% accuracy with superior parameter efficiency, suggesting transferability to related industrial defect classification tasks.
Color difference detection remains a critical challenge in textile manufacturing, where traditional visual inspection and offline measurement methods suffer from subjectivity, low efficiency, and delayed feedback. This study emphasizes engineering integration for online industrial fabric inspection rather than proposing a single new color-difference algorithm. The proposed system integrates a custom-designed optical acquisition platform with a lightweight color analysis pipeline, including bilateral filtering for noise suppression, K-means clustering for representative color extraction, RGB-to-CIELab color space conversion, and perceptually weighted [Formula: see text] computation. The system was deployed on an actual textile production line and evaluated using ten fabric rolls with different colors and materials. Experimental results show roll-level agreement with manual inspection in the tested samples and indicate the feasibility of continuous monitoring of chromatic variations along the fabric length. The proposed system provides a practical engineering solution for automated textile color quality control and may support production-line decision making while reducing dependence on subjective visual inspection in industrial environments.
Segmental feather analysis provides a chronological and physiological resolution missing in whole-feather measurements. Here, the concentrations of 24 trace elements (Ag, Al, As, Ba, Be, Bi, Cd, Co, Cr, Cs, Cu, Fe, Ga, In, Mn, Ni, Pb, Rb, Se, Sr, Tl, U, V, and Zn) and stable isotope ratios ( C, δ15N, and S) were determined in three distinct feather sections of juvenile Magellanic penguins (Spheniscus magellanicus) from Magdalena Island, southern Chile. Most toxic metal(loid)s peaked significantly at the feather tip (Section C), following the descending order (μg g-1 dw): Cr (4.8) > Pb (3) > As (1.2) > Cd (0.2). Uncommonly high concentrations of Al (>1,200 μg g-1) and Fe (>1,300 μg g-1) were also detected, driven by restricted coastal foraging near localized industrial activities and the juvenile individuals' developing homeostatic systems. Isotopic analyses revealed a tight coupling between feeding ecology and contamination; S ratios significantly correlated with 14 out of 24 elements, proving that coastal habitat selection modulates exposure. Conversely, Cu exhibited an exceptional inverse trend, highlighting strict metabolic regulation in the calamus. Elevated less-known trace element levels (e.g., Ba, Be, Co, Ni, or Tl) may pose significant health risks to juvenile penguins, underscoring the urgent need for further research into the long-term toxicological impacts on this seabird population. These findings establish a critical ecotoxicological baseline for juvenile sub-Antarctic sentinels. Ultimately, this high-resolution approach demonstrates that juvenile plumage serves as a sensitive regional archive, providing essential data to mitigate the expanding industrial footprint in vulnerable Patagonian marine ecosystems.
Efficient and stable enzyme systems are crucial for lignocellulosic biomass conversion, yet they remain a significant limitation in second-generation biofuel production. Here, we applied a weighted sum matrix (WSM)-based multi-criteria screening strategy to identify superior cellulolytic fungus from a diverse collection of natural isolates. This approach revealed Talaromyces marneffei as a previously unreported hyper-cellulolytic organism. The T. marneffei secretome exhibited nearly two-fold higher biomass saccharification efficiency than a commercial enzyme preparation under industrially relevant conditions. Label-free quantitative nano-LC-MS/MS analysis identified 549 secreted proteins, including 201 carbohydrate-active enzymes (CAZymes) spanning multiple glycoside hydrolase and auxiliary activity families. Quantitative profiling revealed a high abundance of key cellulolytic enzymes, particularly GH7 cellobiohydrolase I (CBH1), along with diverse accessory enzymes enabling synergistic biomass deconstruction. The enzyme concoction displayed broad operational robustness, with optimal activity at acidic pH (4-4.5), elevated temperature optima (60-70 °C), and exceptional storage stability, retaining over 96% activity after 60 days at ambient temperature. Molecular modeling and dynamics simulations of CBH1 revealed an open, flexible active-site architecture with enhanced ligand interactions, providing mechanistic insight into its thermostability. Collectively, this study highlights T. marneffei as a promising source of industrially relevant lignocellulolytic enzymes and demonstrates the value of proteomics-driven secretome analysis for enzyme discovery.
The increasing use of nuclear technology in medicine, industry, and energy requires effective durable radiation shielding. This study aimed to develop and characterize nano-modified cementitious composites for enhanced gamma-ray shielding with quantitative structure-property relationships. Two-year-aged cementitious composites were prepared by partially replacing ordinary Portland cement with 5 wt% nano-silica or nano-alumina, labeled Si-C and Al-C, respectively, alongside unmodified cement (B-C) as a reference. Comprehensive multi-scale characterization included density measurement, surface area and porosity analysis (BET), particle size and colloidal stability assessment (DLS and zeta-potential), phase analysis (XRD), morphological observation (SEM-EDX), and chemical bonding analysis (FTIR). Density values were 2.78, 2.81, and 2.92 ±0.01 g·cm[Formula: see text] for Si-C, B-C, and Al-C, respectively. BET analysis showed that Si-C and Al-C have 3.1-4.4-fold higher surface areas (290.11-416.07 m[Formula: see text]/g) and smaller pores (6-9 nm) than B-C (94.64 m[Formula: see text]/g, 28.82 nm). DLS/zeta-potential measurements showed particle sizes of 48.4 ±2.1 nm (Si-C) and 78.8 ±3.5 nm (Al-C) with -15.6 to -17.8 mV zeta-potentials versus B-C (1718 ±35.2 nm, -2.0 mV), confirming enhanced electrostatic stabilization and nano-modifiers dispersion. Quantitative XRD phase analysis revealed that Si-C exhibited significantly higher tobermorite content (53.6%) compared to B-C (37.7%) and Al-C (35.5%), indicating enhanced pozzolanic reactivity and C-S-H gel formation. Gamma-ray shielding parameters-including linear and mass attenuation coefficients (LAC, MAC), half- and tenth-value layers (HVL, TVL), effective atomic number (Z[Formula: see text]), and exposure and energy absorption buildup factors (EBF, EABF) were evaluated over an energy range of 1 keV to 100 GeV. Calculations were performed using the Py-MLBUF (Python Machine Learning Buildup Factor) code, and the Py-AMA.Seidy model, validated against NIST XCOM data (differences <0.4%). Al-C showed the highest LAC (53.37 cm[Formula: see text] at 0.015 MeV) and the lowest HVL and TVL, consistent with its highest density and increased Al/Fe content. The Z[Formula: see text] values ranged from ∼ 11.8 to ∼ 17.3, with Al-C exhibiting the highest values. Buildup factors (EBF/EABF) at 1 mean free path (mfp) were lowest for Al-C, indicating reduced secondary photon contribution. Double-layer shielding analysis revealed that placing B-C as the first layer, followed by Si-C or Al-C reduced double-layer buildup factors by 15-25% compared to the reverse order. Microstructural characterization confirmed that nano-silica promoted a dense, homogeneous C-S-H-rich matrix with high tobermorite content, while nano-alumina increased density and promoted C-A-S-H formation. The established structure-density-shielding relationships demonstrate that Al-C is a promising candidate for advanced radiation shielding in nuclear, medical, and industrial facilities.
The optimization of hydroxypropylated cassava starch (HPS) films reinforced with açaí residue (AR) for sustainable packaging applications using low glycerol content (7.5 wt%) is reported. A 23 full factorial design of experiments (DoE) combined with a random forest (RF) algorithm was applied to optimize the hydroxypropylation reaction by evaluating the effects of propylene oxide/hydroxyl groups molar ratio (PO/OH), AR percentage, and reaction temperature. Hydroxypropylation significantly improved film flexibility even at 7.5 wt% glycerol amount, from around 6% to 13%, while AR incorporation maintained tensile strength and Young's modulus similar to the control sample, of around 6 MPa and 250 MPa, respectively. The optimized films also exhibited thermal stability comparable to that of native starch films with maximum decomposition rate at around 250 °C and rapid disintegration under soil burial conditions, occurring within 1 day. Statistical analysis and machine learning consistently indicated that higher molar ratio and fiber content favor enhanced mechanical performance. Overall, the results demonstrate that optimized hydroxypropylation enables the production of starch-based films with improved properties, reduced synthetic plasticizer content, and potential for agro-industrial waste upcycling following green chemistry principles.
The release of hazardous pollutants into aquatic systems has increased due to rapid industrialization, making the development of effective and sustainable remediation techniques necessary. Although biochar made from biomass has drawn interest as an inexpensive adsorbent, its limited adsorption capacity and surface reactivity limit its practical use. Systematic research on underutilized biomass feedstocks, especially teak wood, and their structure-property connections is still lacking, even though nanobiochar offers better performance. Here, we describe the production of nanobiochar obtained from teak wood as a novel nanohybrid adsorbent, which is made via mechanical size reduction after regulated high-temperature pyrolysis. A highly porous architecture, richer functional groups, and improved surface reactivity are revealed by thorough analysis (BET, FTIR, XRD, SEM-EDX, and zeta potential). Through cooperative processes including surface complexation, electrostatic interactions, and pore filling, these characteristics facilitate the effective adsorption of heavy metal ions and organic contaminants. The proposed material has the potential to be an affordable, sustainable, and scalable solution for biomass valorization and water remediation due to its high removal efficiency and operational adaptability.
Micro-Electro-Mechanical Systems (MEMS) are extensively utilized in many different applications because of its compact design, low power consumption and greater sensitivity. Compared to piezoresistive alternatives, a MEMS- based Touch Mode Capacitive Pressure Sensor (TMCPS) is designed and simulated to achieve better sensitivity, stability and linearity. This work presents a Double Touch Mode MEMS-based Capacitive Pressure Sensor (DTMCPS) with a flexible circular diaphragm integrated into an M-shaped silicon substrate. By increasing the diaphragm-substrate contact area, the proposed M-shaped structural arrangement improves sensor linearity and sensitivity. The diaphragms deflection characteristics are modeled using small deflection theory to minimize nonlinearity. A comprehensive analysis of the sensors capacitive behavior is carried out. The analytical formulations for capacitance, capacitive sensitivity and mechanical sensitivity are derived and generated using MATLAB based simulations. The diaphragm deformation is further assessed through structural analysis using COMSOL Multiphysics. The goal is to improve the performance of the conventional DTMCPS by integrating a silicon (Si) circular diaphragm with an M-shaped silicon substrate. The position of the touch point is crucial in defining the overall sensitivity, hence the notch size have a considerable impact on the sensors operational properties. Small deflection theory is used to model the diaphragm behavior in order to minimize nonlinear effects. These results show the potential use in cutting edge medical, automotive, aerospace and industrial sensing applications by demonstrating the higher sensitivity, accuracy and durability of the preferred TMCPS design.
This paper examines sustainable development and tribological performance of fly ash reinforced AA8011 aluminium matrix composites by stir casting. Composites with 0, 4, 8 and 12 wt.% fly ash content were made and tested under dry sliding conditions using pin on disc apparatus. The Taguchi L16 orthogonal array with the aid of the multi-response optimization through the use of the Grey Relational Analysis (GRA) was used to analyze wear rate, frictional force, and coefficient of friction (COF). The experimental outcomes indicate that the wear rate was 0.00447-0.00813 mm3/m, 4.84-9.89 N frictional force, 0.228-0.711 Coefficient of Friction (COF). The best parameters were found to be 8 wt.% fly ash, 30 N load, 3 m/s sliding velocity and 3200 m sliding distance which gave a maximum Grey Relational Grade (GRG) of 0.831. The results of ANOVA showed that applied load had the greatest contribution (48.95%), and then followed by the sliding distance (23.57%), but fly ash content had a smaller but significant contribution (3.12%). The confirmatory test showed that there was a small error of 2.6% in the value of predicted and experimental GRG. SEM analysis revealed the shift towards the severe abrasive wear in the base alloy to the mild oxidative wear with the stable mechanically mixed layer in higher reinforcement level. The research confirms that the wear resistance with the use of fly ash is enhanced, and sustainable development of materials through the effective use of industrial waste is improved.
Potassium dichromate (Cr) is a ubiquitous inorganic chemical reagent, most frequently employed as an oxidizing agent in variety of laboratory and industrial settings and can result in nephrotoxicity. The current study was done to explore the nephrotoxic effects of Cr with a specific focus on alterations in renal aquaporins (AQPs) expression, renal water channel protein, providing insights into how exposure to Cr may compromise renal water homeostasis in a dose-dependent pattern. Thirty-six Wistar albino male rats were assigned into 3 groups evenly; control rats received only distilled water daily, while Cr-Low and Cr-High groups received, 2.5 mg/kg bw, and 7.5 mg/kg bw of Cr i.p for 14 days, respectively. Blood and kidney samples were obtained. Cr induced nephrotoxicity in the current study in a dose-dependent manner, as exhibited by a remarkable deterioration of the renal parameters, oxidative status, histopathological and ultrastructural changes. Renal function registered a significant rise in urea and creatinine, as opposed to total protein and albumin, which decreased substantially. A significant dose dependent rise in malondialdehyde (MDA) level as well as dwindle in the antioxidant enzymes level. Furthermore, hematology divulged a notable reduction in hemoglobin concentration (Hb%) while significant upsurge in white blood cells (WBCs) in exposed groups. In addition, our study demonstrated that Cr, alters the expression level of AQP1 and AQP2 in renal tissue which are salutary for detecting the renal damage early. On the other hand, Cr boosted the upregulation of kidney injury molecule-1 (KIM-1) in kidney tissue confirming its disruption. Interestingly, gene expression of KIM-1, AQP1 and AQP2's in renal membrane correlates well with creatine level, reinforcing their role as sensitive markers of tubular damage and reflect impaired tubular water handling. High exposure produced a severe nephrotoxic profile, distinctly different from controls and far more pronounced than the Cr-Low group.
The recycling of thermal power plant ash and other industrial wastes in ceramic wall products is a promising strategy for reducing natural clay consumption; however, the interaction between ash dosage, multi-waste composition, pressing time, firing temperature, and ceramic performance remains insufficiently clarified. This study evaluates ceramic wall products based on Ekibastuz thermal power plant ash, red brick waste, metallurgical slag, and glass waste, with emphasis on composition-processing-property relationships and microstructural mechanisms. Ceramic mixtures containing 0-30% thermal power plant ash and selected multi-waste combinations were pressure-molded at 15 MPa, pressed for 60-90 s, dried, and fired at 900-1100 °C. Compressive strength, water absorption, density, and microstructure were assessed to identify the optimal balance between waste incorporation and material performance. The highest performance was achieved by the mixture containing 20% thermal power plant ash, which reached 43.5 MPa compressive strength, 6.1% water absorption, and 2.30 g/cm³ density after firing at 1100 °C and pressing for 90 s. Increasing ash content to 30% reduced strength because of increased residual porosity and microstructural heterogeneity. Multi-waste mixtures containing red brick waste, metallurgical slag, and glass waste produced technically acceptable ceramics but did not exceed the optimized ash-only composition, showing that maximum waste replacement does not automatically provide maximum performance. The main innovation of the study is the identification of a controlled composition-processing-property window in which moderate ash incorporation improves densification and pore refinement, whereas excessive ash or multi-waste loading promotes heterogeneity. The findings demonstrate the engineering significance of optimized waste-derived ceramic systems for sustainable wall-product manufacturing.
Nutrition shapes development, health and risk of disease over the life course and across generations. The predominant approaches to understanding these relationships have either been to consider the effects of single nutrients, one at a time, or to consider associations with food types and dietary patterns. Although, to date, the single-nutrient approach has defined much of the scientific enquiry and public debate on the macronutrients - carbohydrate, fat and protein - there is an emerging appreciation that their proportions and quality matter more than their individual effects. Growing evidence demonstrates that macronutrient interactions operate at multiple biological levels, and research on dietary protein has proven a particularly productive entry point for characterizing these mixture effects. In this narrative Review, we begin by analysing key issues and introducing a framework for navigating the complexity of macronutrient mixtures (nutritional geometry), then consider the role of macronutrient proportions on food intake, systemic physiology, health and the risk of disease across the life course. Finally, we discuss how human nutritional biology has been subverted within the modern, industrialized food environment, contributing to the global burden of obesity and related diseases of unhealthy ageing.
Copper is an essential element involved in metabolic processes in both plants and animals. However, in its nanoparticle form, copper is widely used in industrial and biomedical applications, raising concerns about its potential health risks. This study aimed to evaluate the impact of CuO-NPs on cognitive and memory functions in mice, with a particular focus on the cholinergic system and oxidative stress pathways. Adult male mice were exposed to CuO-NPs at different doses (0, 5, and 10mg/kg bw) via intraperitoneal administration over a period of 21 days. Behavioral assessments, including the open field test (OF), novel object recognition test (NORT), and Morris water maze (MWM), revealed significant impairments in spatial learning, short-term memory, and recognition ability in the group treated with 10mg/kg CuO-NPs. In contrast, no alterations in memory or anxiety-like behaviors were observed at the low dose (5mg/kg) compared to controls. Biochemical analyses of brain tissues showed increased lipid peroxidation and altered antioxidant and cholinesterase activities, while qRT-PCR analysis revealed significant downregulation of cholinergic-related genes in mice exposed to the high dose (10mg/kg) compared with controls. Additionally, histopathological examination confirmed pronounced neuronal damage, particularly in the hippocampus and cortex, indicating severe neuropathological lesions at the highest dose. Importantly, no significant toxic effects were observed at the 5mg/kg dose. These findings suggest that CuO-NPs are relatively safe at lower doses; however, higher exposure may impair cognitive and memory functions by disrupting cholinergic neurotransmission and inducing oxidative stress, emphasizing the need for caution in their widespread application.
To investigate the leaching behavior of HMs from recycled industrial solid waste-based materials under cyclic-hydrostatic pressure and wet-dry cycling (C/C) caused by fluctuating groundwater tables in underground applications, a novel C/C environment simulation apparatus was designed. The temporal variations in HM leaching concentrations of red mud-flue gas desulfurization gypsum-based backfilling grout were compared under TCLP, C/C leaching, and constant-hydrostatic pressure leaching at 40, 80, 120, and 160 kPa. The effects of different leaching environments on the microstructure, mineral phase, and chemical characteristics of the grout were examined using SEM, MIP, FTIR, and XRD. Compared with the TCLP, C/C leaching under 160 kPa elevated the leachable concentrations of heavy metals. Specifically, the concentration of Pb rose from 1.6 ppb to 3.5 ppb, Cu from 18.9 ppb to 58.9 ppb, Cr from 25.6 ppb to 59.6 ppb, Cd from 0.43 ppb to 1.44 ppb, Mn from 0.25 ppm to 3.6 ppm, and As from 0.9 ppb to 48.2 ppb. The leaching concentrations of HMs showed strong correlations with those of structural elements (Fe, Na, S, and Si), especially with the Fe-S matrix. Combined with the chemical fractionation results, it indicates that C/C environment remobilizes part of the oxidizable fraction and a small amount of the reducible fraction of HMs. FTIR detected the penetration of leaching agent into the harden grout at a depth of 2 mm under C/C leaching at 160 kPa. MIP results revealed that C/C leaching significantly increased the total porosity and the proportion of macropores, demonstrating severe degradation of the hardened grout microstructure and enhanced leaching agent penetration. XRD results indicated obvious damage to the C(N)-(A)-S-H and AFt phases, while SEM images confirmed a substantial loss of surface compactness and integrity.
This study investigated the conventional fixed-bed catalytic pyrolysis using metal oxides (CaO, Fe₂O₃, and TiO₂) to enhance bio-oil yield from three lignocellulosic biomasses: safflower press cake (SPC), coconut press cake (CPC), and coconut shell (CS). The primary characterization, using ultimate, proximate, and compositional analyses, as well as TGA-DTG-DSC, was carried out in the experiment. SPC biomass had a high hemicellulose content (50.8 ± 0.11%), which is suited for bio-oil applications. An initial non-catalytic pyrolysis experiment was conducted at 550 °C with a heating rate of 25 °C/min for 30 min; the bio-oil yields were 34% (SPC), 29% (CPC), and 26% (CS), respectively. During the catalytic pyrolysis, TiO₂ showed its strongest catalytic activity, primarily combined with SPC biomass. Under optimized conditions 550 °C, 25 °C/min heating rate, and a 30-minute reaction time SPC with TiO₂ yielded a maximum bio-oil output of 71.75 ± 0.16%. In contrast, under the same conditions without a catalyst, SPC produced 34 ± 0.16% bio-oil, indicating a significant 37.5% improvement with the addition of TiO₂. Among the three biomasses, SPC showed the greatest responsiveness to catalytic enhancement, while CPC offered a better balance between oil quality and char usability. The GC-MS analysis of TiO₂-catalyzed SPC bio-oil confirmed TiO₂'s selective catalytic influence on bio-oil compound distribution by revealing a predominance of phenolic compounds, such as Guaiacol (19.8%), 2,4-Dimethoxyphenol (31.12%), and hydroxy ketones such as -Hydroxy-2-butanone (29.49%) and 1-Hydroxypropan-2-one (26.36%), confirming the superior Lewis acid catalytic mechanism of TiO₂ for the production of phenolic compounds and ketone - rich bio-oil from lignocellulosic agro-industrial wastes.
Injection molding of complex three-dimensional curved components is highly sensitive to processing conditions, particularly when dimensional accuracy and packing quality must be balanced simultaneously. This study proposes an integrated optimization and decision-making framework to improve the molding quality of a commercial curved shin guard plate (SGP) by combining Response Surface Methodology (RSM), adaptive non-dominated sorting genetic algorithm II (NSGA-II), and entropy-weighted Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). A Box-Behnken design was employed to investigate the effects of packing pressure (PP), melt temperature (MT), and cooling time (CT) on absolute dimensional deviation (ΔD) and molded part weight (PW). The results showed that the developed quadratic response surface models exhibited good predictive capability, and analysis of variance identified PP as the most influential parameter. The surrogate models were integrated with standard and adaptive NSGA-II algorithms to perform multi-objective optimization and generate well-distributed Pareto-optimal solutions, with the adaptive NSGA-II showing improved Pareto-front exploration capability based on crowding-distance and hypervolume analyses. Entropy-weighted TOPSIS was subsequently applied to identify the most suitable compromise solution. Experimental validation was conducted at the TOPSIS-selected optimum together with two additional Pareto-optimal conditions. The validation results showed satisfactory agreement between predicted and experimental responses, with deviations remaining below 7.41% for ΔD and below 1.70% for PW. These results demonstrate the practical applicability of the proposed optimization framework for complex injection-molded components under industrial manufacturing conditions.
Stone flounder (Kareius bicoloratus) is an emerging commercially important flatfish species in China, Japan, Korea, and Taiwan, because of its significant industrial value. However, no stone flounder genome sequence was reported to date, which greatly hinders the research on genetics and evolutionary genomics. In this study, we successfully assembled the first gap-free telomere-to-telomere (T2T) genome of stone flounder combining Illumina short-reads sequencing, PacBio HiFi sequencing, Hi-C sequencing, and ONT ultra-long sequencing. The genome spans 587.80 Mb with a contig N50 of 25.64 Mb. 98.58% of the genome sequences were assembled onto 24 chromosomes without gaps. Telomeres were identified at both ends of 8 chromosomes and at one end of 15 chromosomes. We identified 128.10 Mb repetitive sequences and 22,312 protein-coding genes, among which 98.03% of the coding genes were functionally annotated. BUSCO assessment, mapping ratio, and quality value (QV) evaluation collectively indicated the high quality and completeness of the genome assembly. This work advances the progress of evolutionary genomics, genetics and molecular genetic breeding research on stone flounder.
Rising global meat demand and nutritional awareness have fuelled interest in sustainable, ethical protein sources. Animal agriculture generates greenhouse gas emissions, land degradation, and water scarcity, creating a need for plant-based meat alternatives. While plant sources face drawbacks such as incomplete amino acid profiles, anti-nutritional factors, and land requirements. Algae emerge as a superior option, delivering exceptionally high protein content (up to 70% dry weight), complete essential amino acids, omega-3 fatty acids, vitamins, polysaccharides, and potent antioxidants, surpassing plant sources in nutrient density, bioavailability, and environmental footprint. This review evaluates the nutritional, environmental, and technological potential of key algal species (microalgae and macroalgae) for meat substitute applications. Algal formulations excel over plant-based counterparts with superior protein quality (PDCAAS >0.9 vs. often <0.8 for plants), rapid biomass growth (10-50× faster than plants), and no arable land requirements, enabling scalable, low-water production. The review addresses challenges such as off-flavors, digestibility, and cost through solutions such as strain selection, biorefinery optimization, and hybrid cultivation systems. An overview of key market players highlights the growing role of algae in alternative meats. By integrating nutritional and industrial perspectives, this work reveals trends positioning algae at the forefront for health-conscious consumers, advocating a "best-of-everything" approach with diverse species to revolutionize sustainable food systems.
Heavy metal pollution has silently insinuated itself into the fabric of modern life - from the vegetables on our dinner plates and the tap water we drink to the cosmetics we use daily. This reality underscores global environmental and public health crises intensified by industrialization and urban expansion. Conventional physical and chemical remediation methods for heavy metals, while effective to a degree, often involve prohibitive costs and risk disrupting the delicate balance of the original ecosystem. Consequently, the search for green, sustainable, and economically viable remediation alternatives has become imperative. This special issue brings together nine cutting-edge research papers that explore recent advances in heavy metal pollution control and resource recovery from diverse angles - including microbial remediation, plant-microbe combined approaches, bioleaching for resource utilization, soil amendment applications, and the ecological toxicity of nanoparticles. Collectively, these studies offer theoretical insights and novel practical strategies to support the development of efficient and sustainable technologies for managing heavy metal contamination. These research results can pave the way for deeper investigation into the efficacy of the proposed remediation with the eventual aim of taking the science from the lab to the field.
The growing demand for sustainable construction materials has accelerated the need for alternatives to natural aggregates in concrete. Although steel slag has been extensively studied, the use of steel sludge as a fine aggregate substitute in concrete paver blocks has not been sufficiently examined, especially regarding durability and microstructural characteristics. This study investigates the performance of steel sludge as a micro-filler and partial replacement of fine aggregate at 10%, 20%, and 30% by mass, maintaining a constant water-cement ratio of 0.38. Mechanical and durability properties were evaluated using compressive strength, water absorption, sorptivity, rapid chloride penetration, and weight loss tests, complemented by SEM, EDX, and XRD analyses. The results indicate a consistent improvement in performance with increasing steel sludge content. The 28-day compressive strength increased from 42.77 MPa for the control mix to 54.13 MPa at 30% replacement, representing an approximate 26.6% increase. Water absorption decreased from 6.12% to 4.20%, the initial absorption rate decreased from 0.0332 to 0.0250 mm/min⁰·⁵. and RCPT values declined from 640 to 552 Coulombs, reflecting reduced permeability. Microstructural analysis demonstrated pore refinement, a lower Ca/Si ratio, and enhanced formation of polymerized C-(A)-S-H gel. XRD analysis confirmed the absence of new crystalline phases. Within the investigated range, steel sludge enhances strength and durability through matrix densification and microstructural refinement. These findings demonstrate its potential as a sustainable industrial waste management to natural sand in paver block applications.