Structural Matching Research Articles

Tandem repeats provide evidence for convergent evolution to similar protein structures.

Homology is a key concept underpinning the comparison of sequences across organisms. Sequence-level homology is based on a statistical framework optimized over decades of work. Recently, computational protein structure prediction has enabled large-scale homology inference beyond the limits of accurate sequence alignment. In this regime it is possible to observe nearly identical protein structures lacking detectable sequence similarity. In the absence of a robust statistical framework for structure comparison, it is largely assumed similar structures are homologous. However, it is conceivable that matching structures could arise through convergent evolution, resulting in analogous proteins without shared ancestry. Large databases of predicted structures offer a means of determining whether analogs are present among structure matches. Here, I find that a small subset (∼2.6%) of Foldseek clusters lack sequence-level support for homology, including ∼1% of strong structure matches with TM-score ≥ 0.5. This result by itself does not imply these structure pairs are non-homologous, since their sequences could have diverged beyond the limits of recognition. Yet, strong matches without sequence-level support for homology are enriched in structures with predicted repeats that could induce spurious matches. Some of these structural repeats are underpinned by sequence-level tandem repeats in both matching structures. I show that many of these tandem repeat units have genealogies inconsistent with their corresponding structures sharing a common ancestor, implying these highly similar structure pairs are analogous rather than homologous. This result suggests caution is warranted when inferring homology from structural resemblance alone in the absence of sequence-level support for homology.

Design of Urban Waterlogging Emergency Rescue Boat from the Perspective of Human-Computer Interaction

With the acceleration of the urbanization process, urban waterlogging occurs frequently and the demand for emergency rescue is increasing. However, the existing urban waterlogging emergency rescue boats have deficiencies in human-computer interaction design, such as poor operation convenience and low information visualization, which affect the rescue efficiency. This study aims to improve the rescue efficiency and ease of use. The Delphi method, Analytic Hierarchy Process (AHP), and Decision Making Trial and Evaluation Laboratory (DEMATEL) are used to construct the human-computer interaction design factor system and quantify the factor weights and relationships. The design factors are determined through discussions among experts in multiple fields. The relative weights are calculated by AHP, and the causal relationships are clarified by DEMATEL, and key factors such as safety protection and space layout are obtained. A mother-child rescue boat and auxiliary equipment are designed, including clear functional positioning, optimization of the shape structure and color matching, etc., which improves the comfort and rescue efficiency. This study provides a basis for the human-computer interaction design of rescue boats, improves the deficiencies in related research, provides ideas for similar designs, and can integrate advanced technologies and continue to be optimized in the future to deal with complex rescue scenarios.

Automated karyogram analysis for early detection of genetic and neurodegenerative disorders: a hybrid machine learning approach

Anomalous chromosomes are the cause of genetic diseases such as cancer, Alzheimer's, Parkinson's, epilepsy, and autism. Karyotype analysis is the standard procedure for diagnosing genetic disorders. Identifying anomalies is often costly, time-consuming, heavily reliant on expert interpretation, and requires considerable manual effort. Efforts are being made to automate karyogram analysis. However, the unavailability of large datasets, particularly those including samples with chromosomal abnormalities, presents a significant challenge. The development of automated models requires extensive labeled and incredibly abnormal data to accurately identify and analyze abnormalities, which are difficult to obtain in sufficient quantities. Although the deep learning-based architecture has yielded state-of-the-art performance in medical image anomaly detection, it cannot be generalized well because of the lack of anomalous datasets. This study introduces a novel hybrid approach that combines unsupervised and supervised learning techniques to overcome the challenges of limited labeled data and scalability in chromosomal analysis. An Autoencoder-based system is initially trained with unlabeled data to identify chromosome patterns. It is fine-tuned on labeled data, followed by a classification step using a Convolutional Neural Network (CNN). A unique dataset of 234,259 chromosome images, including the training, validation, and test sets, was used. Marking a significant achievement in the scale of chromosomal analysis. The proposed hybrid system accurately detects structural anomalies in individual chromosome images, achieving 99.3% accuracy in classifying normal and abnormal chromosomes. We also used a structural similarity index measure and template matching to identify the part of the abnormal chromosome that differed from the normal one. This automated model has the potential to significantly contribute to the early detection and diagnosis of chromosome-related disorders that affect both genetic health and neurological behavior.

Using Semantic Similarity to Proxy for Knowledge Structures in Accounting Research

ABSTRACT Accountants and auditors frequently make judgments that require complex evaluations of facts and rules. Researchers examine these judgments by conducting experiments in which participants make accounting decisions and then describe the information they used and how they used it in making their decisions. These elicited descriptions provide researchers insight into participants’ knowledge structures. This study uses a design science approach to develop a semantic similarity (a natural language processing analytic method) measure of how closely participants’ knowledge structures match an exemplar knowledge structure derived from authoritative accounting sources. The study then tests the semantic similarity metric in an experimental setting with two accounting tasks: assessing inventory obsolescence and production process business risk. Results show that the semantic similarity metric evaluates the knowledge structure match comparably to other methods requiring human judges. Importantly, the semantic similarity metric yields comparable results more efficiently.

Deep reinforcement learning for inverse inorganic materials design

A major obstacle to the realization of novel inorganic materials with desirable properties is efficient materials discovery over both the materials property and synthesis spaces. In this work, we propose and compare two novel reinforcement learning (RL) approaches to inverse inorganic oxide materials design to target promising compounds using specified property and synthesis objectives. Our models successfully learn chemical guidelines such as negative formation energy, charge neutrality, and electronegativity balance while maintaining high chemical diversity and uniqueness. We demonstrate multi-objective RL algorithms that can generate novel compounds with both desirable materials properties (band gap, formation energy, bulk modulus, shear modulus) and synthesis objectives (low sintering and calcination temperatures). We apply template-based crystal structure prediction to suggest feasible crystal structure matches for target inorganic compositions identified by our machine learning (ML) algorithms to highlight the plausibility of the identified target compositions. We analyze the benefits and drawbacks of the ML approaches tested in this work in the context of accelerated inorganic materials design. This work isolates and evaluates the effects of different RL methodologies to suggest promising, valid compounds of interest by exploring the chemical design space for materials discovery.

Numerical simulation on the three-dimension mesoscopic model of aerated ceramsite concrete based on CT scan

As a kind of composite construction material integrated with relatively high strength and excellent acoustic and thermal insulation, aerated ceramsite concrete (ACC) characterizes with multilevel pore structures with different features in aerated cement slurry base, ceramsite aggregate and the interfacial transition zone (ITZ) respectively. It was found that pores distribution in ceramsite aggregates was relatively regular with pore walls being smooth and compact. In contrast, pore sizes and distribution in aerated cement slurry base were obviously discrete and pore walls were loose. Prominently, the ITZ was denser than both of aerated cement slurry base and ceramsite with less pores inside. To reproduce reasonable 3D mesoscopic model of ACC for effective simulation, the ACC specimen was first remodeled using MIMICS by CT scanning. The proposed simulation method well represented the real ceramsite aggregates and distribution in ACC and the result coincided with the test based on the related defining of crushable foam material model of the aerated mortar cement slurry and ceramsite aggregates. The uniaxial compressive test of ACC and simulation indicate that, the porous structure and strength matching between the aerated cement slurry base and ceramsite aggregates were the key factors to affect the mechanism and failure of ACC. The internal cracks mostly originated from the aerated cement slurry base or in the ceramsite aggregates being more porous with lower strength.

A164: Research on the Matching of Talent Supply and Demand in China's Sports Industry

Background/Purpose: Developing a systematic approach is essential for the sports industry development. Talent optimization plays a crucial part in this process. The present study gathered talent-related data pertaining to China's sports industry between 2015 and 2021. Method: This study used the structural deviation coefficient model to analyze the supply and demand structural matching of data related to China's sports industry and talents from 2015–2021. The gray prediction model was used to predict and analyze the demand for talents in China's sports industry in the middle and late 14th Five-Year Plan. The participation of relevant subjects in the systematization of the sports industry and the path of collaborative training of talents in the sports industry was explored. Results: Several notable findings have been observed: (1) the number of talented individuals has steadily increased, (2) the scale of the sports service industry, as well as the proportion of market players and talented individuals, continue to rise, (3) there is a serious oversupply of talent in the sports industry, and (4) the sports industry lacks systematic and composite talent. A grey forecasting model was utilized to predict the demand for China's sport market by 2025. This study identified the need for collaborative professional training and examined potential approaches. Conclusion/Discussion: Firstly, the administrative departments responsible for sports may prioritize development needs and coordinate talent construction. Secondly, sports enterprises may actively engage in talent training system construction by initiating relevant talent standards. Lastly, social training institutions, universities, and colleges may facilitate curriculum alignment with industry development to accelerate talent cultivation.

Mitigating impulsive noise in airborne PLC: Introducing the S-SAMP-PV algorithm for MIMO OFDM systems

Power Line Communication (PLC) offers an efficient solution for data transmission over electrical power lines, presenting a promising avenue for in-flight communication in More Electrical Aircraft (MEA). A significant challenge in airborne PLC is Impulsive Noise (IN), which hampers transmission reliability. Existing noise mitigation methods, while valuable, face limitations in airborne settings due to computational intensiveness and sub-optimal sparse recovery performance. This paper introduces the Structured Sparsity Adaptive Matching Pursuit with Preliminary partial support estimation and Variable step-size (S-SAMP-PV) algorithm, devised for Multiple-Input-Multiple-Output (MIMO) systems. It uniquely pre-estimates partial support of sparse IN signals, enabling adaptive convergence without prior sparsity knowledge. This methodology substantially reduces computational demands, satisfying stringent real-time requirements of airborne applications. In simulation, the S-SAMP-PV algorithm exhibits marked advantages over traditional algorithms such as Orthogonal Matching Pursuit (OMP). Specifically, it realizes an approximate 81.3% reduction in Normalized Mean Square Error (NMSE) and demonstrates around 37% improvement in computational efficiency relative to OMP. Moreover, its Bit Error Rate (BER) performance at high Signal to Noise Ratio (SNR) approaches the ideal scenario where IN is assumed to be perfectly eliminated. These results emphasize the promise of S-SAMP-PV in elevating the performance of airborne PLC systems by efficient IN mitigation.

STNGS: a deep scaffold learning-driven generation and screening framework for discovering potential novel psychoactive substances.

The supervision of novel psychoactive substances (NPSs) is a global problem, and the regulation of NPSs was heavily relied on identifying structural matches in established NPSs databases. However, violators could circumvent legal oversight by altering the side chain structure of recognized NPSs and the existing methods cannot overcome the inaccuracy and lag of supervision. In this study, we propose a scaffold and transformer-based NPS generation and Screening (STNGS) framework to systematically identify and evaluate potential NPSs. A scaffold-based generative model and a rank function with four parts are contained by our framework. Our generative model shows excellent performance in the design and optimization of general molecules and NPS-like molecules by chemical space analysis and property distribution analysis. The rank function includes synthetic accessibility score and frequency score, as well as confidence score and affinity score evaluated by a neural network, which enables the precise positioning of potential NPSs. Applied STNGS framework with molecular docking and a G protein-coupled receptor (GPCR) activation-based sensor (GRAB), we successfully identify three novel synthetic cannabinoids with activity. STNGS constrains the chemical space to generate NPS-like molecules database with diversity and novelty, which assists in the ex-ante regulation of NPSs.

The role of surface and structural similarities in the retrieval of realistic perceptual events.

This study investigated whether structural similarities (i.e. abstract frames, e.g. once bitten twice shy) can prevail over surface similarities (i.e. contexts, e.g. restaurant) in driving the retrieval of realistic events involving dynamic, multimodal and perceptually crowded data. After watching an initial set of video clips, participants had to indicate whether a new video clip, that shared surface similarities with an initial event and structural similarities with another one, elicited a retrieval. The results of Experiment 1A showed that retrieval was more likely to be elicited by structural rather than by surface similarities. Experiment 1B confirmed that the surface similarities manipulated in this study were strong enough to elicit substantial retrievals when the competing structural match was neutralized. The pattern of results obtained in Experiment 1A remained unchanged when the number of unrelated video clips within the initial set was increased. The findings suggest that structurally based retrievals still prevail when familiar structures underlie realistic perceptual events. They open new perspectives regarding the settings that promote structurally based retrievals in educational contexts where unfamiliar principles are introduced.

Unpaired Multi-View Graph Clustering With Cross-View Structure Matching.

Multi-view clustering (MVC), which effectively fuses information from multiple views for better performance, has received increasing attention. Most existing MVC methods assume that multi-view data are fully paired, which means that the mappings of all corresponding samples between views are predefined or given in advance. However, the data correspondence is often incomplete in real-world applications due to data corruption or sensor differences, referred to as the data-unpaired problem (DUP) in multi-view literature. Although several attempts have been made to address the DUP issue, they suffer from the following drawbacks: 1) most methods focus on the feature representation while ignoring the structural information of multi-view data, which is essential for clustering tasks; 2) existing methods for partially unpaired problems rely on pregiven cross-view alignment information, resulting in their inability to handle fully unpaired problems; and 3) their inevitable parameters degrade the efficiency and applicability of the models. To tackle these issues, we propose a novel parameter-free graph clustering framework termed unpaired multi-view graph clustering framework with cross-view structure matching (UPMGC-SM). Specifically, unlike the existing methods, UPMGC-SM effectively utilizes the structural information from each view to refine cross-view correspondences. Besides, our UPMGC-SM is a unified framework for both the fully and partially unpaired multi-view graph clustering. Moreover, existing graph clustering methods can adopt our UPMGC-SM to enhance their ability for unpaired scenarios. Extensive experiments demonstrate the effectiveness and generalization of our proposed framework for both paired and unpaired datasets.

Application of distal radius coronal bone structure matching in the treatment of distal radius fractures

To investigate the clinical effect of coronal bone structure matching(CBSM)in the treatment of distal radius fracture. A total of 39 cases of distal radius fracture between Jannary 2018 and Jannary 2022 were included in this study. Among them there were 22 males and 17 females with an average age of (48.9±16.3) years old, ranged from 22 to 65 years old. All patients were treated with open reduction and internal fixation with plates. Based on the measurement of CBSM value on the X-ray film the next day after surgery. All patients were divided into matched group and mismatched group according to the coronal bone structure matching in the normal range or not. There were 27 patients in the matched group, including 15 males and 12 females, the age ranged from 22 to 64 years old with an average of (48.0±16.2) years old. AO classification of fracture was C1 in 6 cases and C2 in 21 cases;the operation time ranged from 1 to 6 days after injury;9 cases were complicated with ulnar styloid process fracture. There were 12 patients in the mismatched group, including 7 males and 5 females;the age ranged from 22 to 65 years old with an average of (48.8±15.8) years old. AO classification of fracture was C1 in 4 cases and C2 in 8 cases;the time from injury to operation ranged from 1 to 5 days;4 cases were complicated with ulnar styloid process fracture. The X-ray films were used to evaluate fracture healing, humeral height, ulnar angle and palm tilt angle at 3 months after operation. The range of wrist motion(pronation, supination, palmar inclination and dorsiflexion), function outcomes(Gartland-Werley score) and pain levels (visual analogue scale, VAS) were compared between the two groups at the last follow-up. The average follow-up time of 39 patients were(9.5±4.3)months, ranged from 6 to 14 months. All patients healed in one stage without postoperative infection, fracture nonunion and fracture displacement occurred. Compared with match group at the last follow-up, the VAS in the mismatch group was increased[(2.5±1.3)points vs (1.6±1.0)points], the wrist pronation were decreased[(70.5±12.6)° vs (80.5±9.4)°], with statistically significant difference(P<0.05). There was no significant difference in the range of motion(supination, palmar inclination, dorsiflexion)and excellent good rate between the two groups at last follow-up after operation (P>0.05). Wrist dysfunction, limited pronation, and wrist pain may occur when the postoperative matching degree of the distal radius fracture is not within the normal range.

Stretchable wireless optoelectronic synergistic patches for effective wound healing

Physiotherapies play a crucial role in noninvasive tissue engineering for wound healing. However, challenges such as the implementation of complex interventions and unsatisfactory treatment outcomes impede widespread application. Here, we proposed a stretchable and wirelessly-powered optoelectronic synergistic patch with a dual-layer serpentine wireless receiver circuit to drive the optoelectronic modulation component. Optimized structure and impedance matching enable the patch to seamlessly attach to irregular skin surfaces and operate robustly over a 30% tensile strain range. Based on Sprague-Dawley rat wound model. The wound closure rate of the optoelectronic synergistic group significantly outperformed both monointervention and blank control groups. Mechanistically, optoelectronic synergistic intervention enhances the secretion of vascular endothelial marker proteins and growth factors, and stabilizes mitochondrial function during oxidative stress. Overall, the scalable amalgamation of flexible electronics, wireless transmission, and synergistic interventions promise to improve wound care.

Does Structural Matching between Finance and Law Promote Economic Development?

AbstractFinancial systems generally operate under two different models: one where capital markets play a primary role in financial transactions and resource allocation (market‐oriented model), and another where banks are the central institutions for these activities (bank‐oriented model). The degree of investor protection afforded by the legal system varies across these systems. What is the most efficient alignment between financial structure and investor protection? This paper attempts to quantify the structural matching relationship between the financial structure and investor protection in various countries, and examines empirically the impact of this relationship on economic development. Using a sample of cross‐country panel data, it finds that the degree of structural matching was positively related to economic development. Both the combination of a securities market and strong investor protection and the combination of a banking system and weak investor protection can contribute to promoting economic development. It extends the law and finance theory framework, enhances the analysis of scenarios with weak alignment between financial structure and investor protection, explains the growth path of developing countries, and highlights the diversity and the dynamic nature of the optimal financial structure.

Chameleon Sequences-Structural Effects in Proteins Characterized by Hydrophobicity Disorder.

Repeated protein folding processes both in vivo and in vitro leading to the same structure for a specific amino acid sequence prove that the amino acid sequence determines protein structuring. This is also evidenced by the variability of structuring, dependent on the introduced mutations. An important phenomenon in this regard is the presence of a differentiated secondary structure for chain fragments of identical sequence representing distinct forms of the secondary-order structure. Proteins termed chameleon proteins contain polypeptide chain fragments of identical sequence (length 6-12 aa) showing structural differentiation: helix versus β-structure. In the present paper, it was shown that these fragments represent components matching the structural status dictated by the physicochemical properties of the entire structural unit. This structural matching is related to achieving the goal of the biological function of the structural unit. The corresponding secondary structure represents a means to achieving this goal, not an end in itself. A selected set of proteins from the ChSeq database have been analyzed using a fuzzy oil drop model (FOD-M) identifying the uniqueness of the hydrophobicity distribution taken as a medium for recording the specificity of a given protein and a given chameleon section in particular. It was shown that in the vast majority, the status of chameleon sections turns out to be comparable regardless of the represented secondary structure.

A comparison of antibody-antigen complex sequence-to-structure prediction methods and their systematic biases.

The ability to accurately predict antibody-antigen complex structures from their sequences could greatly advance our understanding of the immune system and would aid in the development of novel antibody therapeutics. There have been considerable recent advancements in predicting protein-protein interactions (PPIs) fueled by progress in machine learning (ML). To understand the current state of the field, we compare six representative methods for predicting antibody-antigen complexes from sequence, including two deep learning approaches trained to predict PPIs in general (AlphaFold-Multimer and RoseTTAFold), two composite methods that initially predict antibody and antigen structures separately and dock them (using antibody-mode ClusPro), local refinement in Rosetta (SnugDock) of globally docked poses from ClusPro, and a pipeline combining homology modeling with rigid-body docking informed by ML-based epitope and paratope prediction (AbAdapt). We find that AlphaFold-Multimer outperformed other methods, although the absolute performance leaves considerable room for improvement. AlphaFold-Multimer models of lower quality display significant structural biases at the level of tertiary motifs (TERMs) toward having fewer structural matches in non-antibody-containing structures from the Protein Data Bank (PDB). Specifically, better models exhibit more common PDB-like TERMs at the antibody-antigen interface than worse ones. Importantly, the clear relationship between performance and the commonness of interfacial TERMs suggests that the scarcity of interfacial geometry data in the structural database may currently limit the application of ML to the prediction of antibody-antigen interactions.

Ice Nucleation Mechanisms on Platinum Surfaces in PEM Fuel Cells: Effects of Surface Morphology and Wettability.

Understanding the ice nucleation mechanism in the catalyst layers (CLs) of proton exchange membrane (PEM) fuel cells and inhibiting icing by designing the CLs can optimize the cold start strategies, which can enhance the performance of PEM fuel cells. Herein, mitigating the structural matching and templating effects by adjusting the surface morphology and wettability can inhibit icing on the platinum (Pt) catalyst surface effectively. The Pt(211) surface can inhibit icing because the atomic spacing of (211) crystalline surface is much larger than the characteristic distance of ice crystal, thereby mitigating the structural matching effects. A water overlayer on the Pt surface induced by the strong attraction of Pt can act as a template for ice layers and plays an important role in the icing process. Buckling of water overlayer due to the larger atomic spacing of (211) crystalline surface mitigates the templating effect and inhibits icing. Moreover, the water overlayer on the hydrophobic Pt(211) surface with fewer water molecules also mitigates the templating effect, which makes ice nucleation more difficult than homogeneous nucleation. These findings reveal the ice nucleation mechanisms on the Pt catalyst surface from the molecular level and are valuable for catalyst designs to inhibit icing in CL.

Research on stiffness matching of support structure of high-precision equipment on satellite

Abstract This paper takes the support structure of high-precision equipment on satellites as the research object, and combining it with the characteristics of an actual satellite platform, analyses the stiffness matching between the platform and the load mounting point represented by the axial connecting relay. On this basis, the support structure of two different types of installation points is quantitatively evaluated, the optimal configuration is selected through the analysis result, and the design improvement measures are proposed. It provides a new design idea for the support structure of the same type of high-precision equipment on satellite.

Surface matches prevail over distant analogs during retrieval.

Laboratory studies using a reception paradigm have found that memory items sharing similar entities and relations with a working memory cue (surface matches) are easier to retrieve than items sharing only a system of abstract relations (structural matches). However, the naturalistic approach has contended that the observed supremacy of superficial similarity could have originated in a shallow processing of somewhat inconsequential stories, as well as in the inadvertent inclusion of structural similarity during the construction of surface matches. We addressed the question of which kind of similarity dominates retrieval through a hybrid paradigm that combines the ecological validity of the naturalistic production paradigm with the experimental control of the reception paradigm. In Experiment 1 we presented participants with a target story that maintained either superficial or structural similarities with two popular movies that had received a careful processing prior to the experimental session. Experiment 2 replicated the same procedure with highly viralized public events. In line with traditional laboratory results, surface matches were significantly better retrieved than structural matches, confirming the supremacy of superficial similarities during retrieval.

Dysprosium doped SrCeO3 nanophosphors for advanced photochemical applications: Synthesis, characterization, photo-assisted dye degradation and latent fingerprint visualization

The present work investigates the novel potential of Sr(1-x)CeO3: xDy3+ (x = 1–9 mol%) nanoparticles, fabricated through Solution Combustion method using citric acid as fuel. The as-fabricated samples were thoroughly characterised using a variety of techniques to obtain structural, morphological, and optical data. Powder X-ray diffraction (PXRD) examination revealed the presence of an orthorhombic crystal structure (Pnma) matching PDF card #01-083-1156. Tauc's plot evaluating band gap energy utilizing absorption spectra reveals a range of 3.4 eV–4.2 eV with refractive index ranging from 0.64 to 1.22. Using Field Emission Scanning Electron Microscopy (FESEM), it was possible to see a coral-like aggregation for the host and initial concentration of Dy3+ ions, and a porous structure for higher concentrations. With an average particle size of 38.20 nm, a comprehensive Transmission Electron Microscopy (TEM) study demonstrates the fused aggregation structure. Moreover, quantifiable evidence of the precursors presence is provided by X-ray Photoelectron Spectroscopy (XPS). For the as-fabricated samples, photoluminescence investigations were carried out. Two strong peaks with a dominating 4F9/2 → 6H13/2 diploe-dipole transition was seen in the observed emission spectra under 366 nm (6H15/2 → 6P7/2) excitation. These peaks are positioned at 573 nm (4F9/2 → 6H13/2) and 478 nm (4F9/2 → 6H15/2). Under UV light, two cationic dyes, Rhodamine-B (Rh–B) and Methylene Blue (MB), were photo-catalytically degraded using the prepared sample (SCD5). According to 0th and 1st order kinetics, the observed data show evidence of effective degradation up to 89.64 % (Rh–B) and 78.86 % (MB) at 80th minute. In addition, the impact of several analytical parameters on the percentage degradation of Rh–B and MB dyes was investigated. These parameters included catalyst changes, dye dosage, and scavenger effect. The produced nanoparticles were used to create latent fingerprints on two non-porous surfaces: glass and aluminium. The results clearly showed that on both surfaces a type-III level of detection was attained. To summarise, the outcomes indicate that Sr(1-x)CeO3: xDy3+ nanoparticles that have been synthesized can be used for advanced forensic applications as well as for the degradation of dye pollutants.