Interesting Patterns Research Articles

Exploring soliton solutions and interesting wave-form patterns of the (1 + 1)-dimensional longitudinal wave equation in a magnetic-electro-elastic circular rod

Exploring soliton solutions and interesting wave-form patterns of the (1 + 1)-dimensional longitudinal wave equation in a magnetic-electro-elastic circular rod

Alternative currencies, community support and social cohesion: why the Okinawan moai (模合) is closer to an informal social policy mechanism than to a standard ROSCA

To challenge the notion of the Okinawan moai as a Rotating Saving and Credit Association, this article provides a short overview of the practice before reflecting on an interesting pattern. While moai has been phased out in most of Japan, it persists in some areas where, incidentally, life expectancy is higher than the rest of the country (and of much of the rest of the world). Expanding upon the idea that moai’s main function is social rather than economic makes it possible to suggest the existence of alternative currencies that, although less tangible than money, have a major role in the people’s lives, create mutual dependency and ultimately enhance people’s wealth, intended here to refer not to economic wealth but also in a more inclusive or socially mediated manner including social relations, happiness, a sense of protection and so on.

Examining the impact of different LED road stud layouts on driving performance and gaze behaviour at night-time

The risks associated with night-time driving on dimly lit roads are substantial and are attributable to the limitations of human visual abilities. As a result, drivers often struggle to distinguish road geometry from ordinary road markings, thus increasing the likelihood of mistakes. These circumstances contribute to a 60 % higher likelihood of road crashes compared to daytime conditions. To mitigate these risks, active LED road studs, which previous studies have shown to have a positive influence on driving performance, can be used. However, there remains a gap in research regarding the optimal arrangement of these studs along road markings for an improvement in driver behaviour and traffic safety. In this study, we assessed the influence of five different LED road stud layouts (unlit, edge, centre, edge-centre, and lane) during night-time driving on two-lane rural highways with curves of different radii (120, 210, 300, 440 m) and directions (left, right). Following a within subject design, thirty-five participants drove in a simulator along a road track with 8 spiralled curves (4 radii × 2 directions) linked to straights. We monitored the longitudinal (i.e., speed), transversal (i.e., lateral position and standard deviation of lateral position) and gaze behaviours.Our findings indicate that the presence of LED road studs promotes safer driving, by helping drivers to adjust their speed when negotiating curves. Transversal behaviour analysis revealed layout-dependent effects on lateral position. The presence of road studs both at the lane centreline and edge allows drivers to maintain centred trajectories and improve steering control. Gaze behaviour analysis uncovered interesting patterns, demonstrating a strong correlation between road stud layout and the driver's focus on specific road targets. Illuminated markings prompt drivers to concentrate their gaze on distinct points, subsequently altering their transversal behaviour.

Субрегиональные различия в отношении к бытовой коррупции на Дальнем Востоке России

This study examines differences in experiences with petty corruption and attitudes toward it among residents of four major urban centers in Russia’s Far East, the country’s largest but understudied macro-region. Analyzing our own survey data, we find that one subregion stands out from the other three considered in the study. Residents of the Sakha Republic (Yakutia) demonstrate more negative attitudes toward corruption and report less frequent occurrences of petty corruption. Our qualitative findings confirm the existence of a distinct subregional pattern in perceived petty corruption. We hypothesize that the detected difference in attitudes may be rooted in the national composition of the Sakha Republic (Yakutia), which, in contrast to the other regions under study, is dominated by native people. As an extension of our main analysis, we explore the conditional correlations between individual characteristics and perceptions of corruption (while accounting for region of residence) and observe several interesting patterns.

A Pilot Immunohistochemical Study Identifies Hedgehog Pathway Expression in Sinonasal Adenocarcinoma

Tumors of the head and neck, more specifically the squamous cell carcinoma, often show upregulation of the Hedgehog signaling pathway. However, almost nothing is known about its role in the sinonasal adenocarcinoma, either in intestinal or non-intestinal subtypes. In this work, we have analyzed immunohistochemical staining of six Hedgehog pathway proteins, sonic Hedgehog (SHH), Indian Hedgehog (IHH), Patched1 (PTCH1), Gli family zinc finger 1 (GLI1), Gli family zinc finger 2 (GLI2), and Gli family zinc finger 3 (GLI3), on 21 samples of sinonasal adenocarcinoma and compared them with six colon adenocarcinoma and three salivary gland tumors, as well as with matching healthy tissue, where available. We have detected GLI2 and PTCH1 in the majority of samples and also GLI1 in a subset of samples, while GLI3 and the ligands SHH and IHH were generally not detected. PTCH1 pattern of staining shows an interesting pattern, where healthy samples are mostly positive in the stromal compartment, while the signal shifts to the tumor compartment in tumors. This, taken together with a stronger signal of GLI2 in tumors compared to non-tumor tissues, suggests that the Hedgehog pathway is indeed activated in sinonasal adenocarcinoma. As Hedgehog pathway inhibitors are being tested in combination with other therapies for head and neck squamous cell carcinoma, this could provide a therapeutic option for patients with sinonasal adenocarcinoma as well.

Isolation and Characterization of Heavy Metal-Tolerant Bacterial Isolates from Industrial Effluents in Uttar Pradesh, India

Industrial activities over the past century have significantly increased human exposure to pollutants such as heavy metals. Industrial emissions of heavy metals, which are carcinogenic, mutagenic and toxic, contaminate natural water supplies and the agricultural environment. Due to the high concentration of heavy metals in industrial effluents, the bacteria present there naturally develop resistance to heavy metals. The aim of this study is to isolate and characterize bacteria resistant to heavy metal, lead (Pb), copper (Cu), chromium (Cr), cadmium (Cd), and nickel (Ni) from sites contaminated by industrial effluents in Uttar Pradesh, India. A total of 58 bacterial isolates were isolated from 9 samples according to their various morphological parameters and 32 isolates were found positive for all heavy metals, which revealed that samples contained metal-resistant bacterial diversity. Two isolates were identified up to species level based on their physiological, biochemical and molecular characterization as Comamonas testosteroni (S4C1) and Bacillus cereus (S5C3). Both isolates are highly resistant to lead (Pb), copper (Cu), chromium (Cr), cadmium (Cd), and nickel (Ni) and they show different MICs against the above heavy metals at different levels.  A growth experiment showed that the presence of heavy metals concentration had no discernible impact on the growth rate among the isolates. Gel analysis showed interesting patterns of protein expression were observed in the presence of various heavy metals. MALDI-TOF analysis found that specific proteins (S layer protein, F0F1ATP synthase subunit b, Flagellin, 50S ribosomal protein L4, Molecular chaperone) were overexpressed in the presence of heavy metals. As a result, identifying the heavy metal resistance bacteria and their proteins study could be useful as a preliminary investigation for the development of prospective bioremediation agents of potentially hazardous waste treatment technology.

Sensemaking Process of Parents of Children with Autism Spectrum Disorder: Identification of Specific Clusters of “Sense of Grip”

AbstractAutism spectrum disorder (ASD) represents a pervasive neurodevelopmental disorder characterized by persistent atypicalities in social communication and social interactions across different domains of life, often with restricted, repetitive, stereotyped patterns of behaviors, interests, and activities. A large corpus of previous research shows that parental involvement in caregiving is perceived as stressful and challenging, in many cases resulting in poorer parental quality of life. The authors present a quali-quantitative mixed method research on sensemaking process of 34 parents (16 mothers and 18 fathers) of children with ASD. The interviews were collected and analyzed according to the “Sense of Grip” (SoG) methodology. SoG indeed refers to the sensemaking processes that a person realizes to manage the relationship with diseases or disorders by organizing several degrees of flexibility, differentiation, and adaptation to the variability of experiences and contexts of life. This construct of health psychology—semiotically and clinically oriented—is based on specific narrative functions (i.e., temporal, integrative, emotional, sharing, and agentive functions). The cluster analysis procedure allowed to identify three different SoG profiles: (a) enterprising (parents who are able to construct flexible and dynamic familiar routines), (b) opposing (parents who experience the ASD disorder as a deep unacceptable crisis, hindering the rest of familiar and professional activities), and (c) condescending (parents who organize family life completely on adherence to medical prescriptions and care protocols). These profiles show interesting implications for clinical practice and support as showing specific aspects of treating, managing, and sharing children conditions within family and relational contexts of life.

Money Talks: A Holistic and Longitudinal View of the Budget Basket in the Face of Climate Change and Sustainable Finance Matters

This study presents a holistic and longitudinal view of the household budget basket concerning climate change and sustainable finance matters. It aims to understand their impact on the budget basket by examining the relationship between money, climate change and sustainable finance in a global economy for transition countries. Comprehensive CPI data were collected in Kosovo from 2002 to 2022, and data analysis was performed using statistical methods such as t-tests and proximity matrixes in SPSS. The results show significant differences between the average and desired values within the budget basket, indicating changes in consumer behavior, particularly in food expenditures, budget allocations, and climate change impacts. Interesting patterns emerge, such as correlations between bread, cereals, and meat, and the absence of fish in some purchases. Spending on clothing and other goods also deviates from desired values. These findings highlight the complex relationship between money, climate change, sustainable finance, and consumer spending patterns, and underscore the need to address the gap between expected and desired spending values for the global economy in transition economies. Future research should focus on analyzing household spending and its interaction with other factors to improve personal financial management and promote sustainable financial behavior in a larger number of global economies.

Regionalization-based Collaborative Filtering: Harnessing Geographical Information in Recommenders

Regionalization, also known as spatially constrained clustering, is an unsupervised machine learning technique used to identify and define spatially contiguous regions. In this work, we introduce a methodology to regionalize recommendation systems (RSs) based on a collaborative filtering approach. Two main challenges arise when performing regionalization based on users’ preferences in RSs: (1) unstructured data, as interactions are often scarce and observed on a smaller scale; and (2) the difficulty of evaluation of the quality of the clustering results. To address these challenges, our methodology relies on inductive matrix completion (IMC), a fundamental approach to recover unknown entries of a rating matrix while utilizing region information to extract a region-based feature matrix. With this feature matrix, our method becomes adaptive and seamlessly integrates with various regionalization algorithms to create regionalization candidates. This enables us to derive more accurate recommendations that consider regionalized effects and discover interesting patterns in localized user behavior. We experimentally evaluate our model on synthetic datasets to demonstrate its efficacy in settings where our underlying assumptions are correct. Furthermore, we present a real-world case study illustrating the interpretable information the model can derive in terms of regionalized recommendation relevance.

Utilizing Constructed Neural Networks for Autism Screening

Autism Spectrum Disorder is known to cause difficulties in social interaction and communication, as well as repetitive patterns of behavior, interests, or hobbies. These challenges can significantly affect the individual’s daily life. Therefore, it is crucial to identify and assess children with Autism Spectrum Disorder early to significantly benefit the long-term health of children. Unfortunately, many children are not diagnosed or are misdiagnosed, which means they miss out on the necessary interventions. Clinicians and other experts face various challenges during the diagnostic process. Digital tools can facilitate early diagnosis effectively. This study aimed to explore the use of machine learning techniques on a dataset collected from a serious game designed for children with autism to investigate how these techniques can assist in classification and make the clinical process more efficient. The responses were gathered from children who participated in interactive games deployed on mobile devices, and the data were analyzed using various types of neural networks, such as multilayer perceptrons and constructed neural networks. The performance metrics of these models, including error rate, precision, and recall, were reported, and the comparative experiments revealed that the constructed neural network using the integer rule-based neural networks approach was superior. Based on the evaluation metrics, this method showed the lowest error rate of 11.77%, a high accuracy of 0.75, and a good recall of 0.66. Thus, it can be an effective way to classify both typically developed children and children with Autism Spectrum Disorder. Additionally, it can be used for automatic screening procedures in an intelligent system. The results indicate that clinicians could use these techniques to enhance conventional screening methods and contribute to providing better care for individuals with autism.

Autism Severity and its Relationship to Disability.

Autism severity is currently defined and measured based exclusively on the severity levels of the two core symptom domains: social-communication and restricted or repetitive patterns of behaviors and interests. Autistic individuals, however, are often diagnosed with other medical, developmental, and psychological co-occurring conditions. These additional challenges such as intellectual disability, limited expressive and/or receptive language, and anxiety disorders, can have a tremendous impact on the day-to-day lives of autistic individuals, for both their adaptive functioning as well as their sense of wellbeing. Furthermore, the initial presentation of core symptoms and their likelihood of changing over time are influenced by the presence of such co-occurring conditions. In order to truly understand how a person's autism impacts their life, both core symptoms as well as other challenges should be considered. This approach was recently taken by The Lancet Commission on the future of care and clinical research in autism, which proposed the term "profound autism" for a subgroup of individuals presenting with high core symptom severity, co-occurring intellectual disability, and little or no language, who require extensive long-term care. Considering other individual factors such as daily living skills, specific support needs and environmental resources would also enhance the evaluation of disability in autistic individuals. As currently employed in the assessment of intellectual disability, a multidimensional approach to autism could provide a more comprehensive system for classification of impairment. At present, however, there is no formal way to designate the combined effect of these different aspects of autism on a person's life. A comprehensive outlook that acknowledges impairments, capabilities, co-occurring conditions, and environmental factors would be useful for identifying subgroups of individuals as well as for determining individual needs and strengths in clinical assessments. Lay Summary: The severity of a person's autism is currently defined based on the severity of their core autism symptoms: impaired social-communication and the presence of restricted or repetitive patterns of behaviors and interests. But autistic people often face additional challenges such as intellectual disability, epilepsy, and anxiety disorder, that considerably impact their everyday life, wellbeing, and the need for support. A more complete view of autism severity, one that includes core symptoms as well as additional challenges, could help identify meaningful sub-groups of autistic individuals and could be useful in clinical care. Appeared originally in Autism Res 2023; 16:685-696.

Systematic Review: Autism Spectrum Disorder and the Gut Microbiota.

Autism spectrum disorders (ASD) are a varying group of disorders characterized by deficiency in social interaction and restrictive patterns of behavior and interests. While there are several studies focusing on the neuro-psychiatric pathogenesis of ASD, its etiology remains unclear. The role of gut-brain-axis in ASD has been studied increasingly and a correlation between symptoms and the composition of gut microbiota has been documented in various works. Despite this, the significance of individual microbes and their function is still widely unknown. This work aims to elucidate the current knowledge of the interrelations between ASD and the gut microbiota in children based on scientific evidence. This is a systematic review done by a literature search focusing on the main findings concerning the gut microbiota composition, interventions targeting the gut microbiota, and possible mechanisms explaining the results in children aged between 2 and 18 years of age. Most studies in this review found significant differences between microbial communities, while there was notable variation in results regarding diversity indices or taxonomic level abundance. The most consistent results regarding taxa differences in ASD children's gut microbiota were higher levels of Proteobacteria, Actinobacteria and Sutterella compared to controls. These results show that the gut microbiota of children with ASD is altered compared to one of neurotypically developed children. More research is needed to discover whether some of these features could be used as potential biomarkers for ASD and how the gut microbiota could be targeted in therapeutical interventions.Appeared originally in Acta Psychiatr Scand 2023;148:242-254.

Molecular docking studies, structural analysis, biological studies, and synthesis of certain novel Schiff base from benzohydrazide derivate

The research described involves the synthesis and characterization of a new benzohydrazide based Schiff base compound namely 4-methyl-N′-(2,4,5-trimethoxybenzylidene) benzohydrazide (HL). The compound was synthesized by condensing a primary amine and an aldehyde functional group. The characterization of the compound was carried out using various spectroscopic techniques, including elemental analysis, Ultraviolet spectroscopy, nuclear magnetic resonance spectroscopy, and infrared spectroscopy. These techniques helped confirm the excellent quality of the synthesized molecules. In addition to the characterization, the Schiff base compound was subjected to docking studies and biological studies. The antibacterial activity of the compound was tested against three strains of bacteria, namely E. faecalis, B. subtills, and E. coli. The results of these tests provided information on the compound effectiveness against these bacterial strains. Furthermore, docking studies was performed to assess the interaction between the synthesized compound and three target enzymes like GlcN-6-p synthase, DNA polymerase, and EGFR tyrosine kinase. Docking studies are computational simulations that provide insights into the binding interactions between Schiff base ligand and target proteins. In this case, the docking studies helped understand the potential mechanisms of action of the synthesized compound by calculating binding constants and the number of binding modes. The biological activity studies revealed interesting fragmentation patterns, which could be further investigated to understand the compound modes of action. Moreover, the docking studies evaluated the compound’s potential as a drug candidate by assessing its binding interactions with the target enzymes. The present study suggests that the synthesized benzohydrazide based Schiff base compound exhibits potential antibacterial activity and shows promising binding interactions with the target protein/enzymes. This information is valuable for designing and developing more potent compounds in the future.

Multivariate Functional Clustering with Variable Selection and Application to Sensor Data from Engineering Systems

Multisensor data that track system operating behaviors are widely available nowadays from various engineering systems. Measurements from each sensor over time form a curve and can be viewed as functional data. Clustering of these multivariate functional curves is important for studying the operating patterns of systems. One complication in such applications is the possible presence of sensors whose data do not contain relevant information. Hence, it is desirable for the clustering method to equip with an automatic sensor selection procedure. Motivated by a real engineering application, we propose a functional data clustering method that simultaneously removes noninformative sensors and groups functional curves into clusters using informative sensors. Functional principal component analysis is used to transform multivariate functional data into a coefficient matrix for data reduction. We then model the transformed data by a Gaussian mixture distribution to perform model-based clustering with variable selection. Three types of penalties, the individual, variable, and group penalties, are considered to achieve automatic variable selection. Extensive simulations are conducted to assess the clustering and variable selection performance of the proposed methods. The application of the proposed methods to an engineering system with multiple sensors shows the promise of the methods and reveals interesting patterns in the sensor data. History: Kwok Tsui served as the senior editor for this article. Funding: The research by J. Min and Y. Hong was partially supported by the National Science Foundation [Grant CMMI-1904165] to Virginia Tech. The work by Y. Hong was partially supported by the Virginia Tech College of Science Research Equipment Fund. Data Ethics & Reproducibility Note: The original dataset is proprietary and cannot be shared. The full code to replicate the results in this paper, based on summary statistics of the original data, is available at https://github.com/jiem3/MultiFuncClustering . The code applied to a simplified version is available at https://codeocean.com/capsule/4041000/tree/v1 , which covers the data analysis and part of the simulation scenarios with a single dataset under each scenario using a fixed set of hyperparameters, for reducing computation time, and at https://doi.org/10.1287/ijds.2022.0034 .

REVISITING TITLE DESIGN IN VERNACULAR CINEMA: A CINE SEMIOTIC ANALYSIS

Indian cinema is a term that refers to films made in India since time immemorial and has greatly influenced world cinema. Indian Cinema includes various regional languages and among them is Tamil cinema that produces films in the Tamil language with a very wide audience. It showcases varied and interesting patterns of visual culture. Film posters play a vital role in exhibiting these visual cultures and serve as the essence of a film’s soul. Film posters have a deep-rooted relationship with Indian cinema and one of the important parts of these posters is the title design. Title designs are synonymous with the story of the film with a view to create a visual force. The title designs have a modern facelift in the digital era. The use of new graphic techniques, expressive typography, composition, and color creates a lasting impact particularly in new age film title designs. The current research aims to examine the title designs in Kollywood film posters using a semiotics approach. Analysis is done based on the following aspects such as structural parameters, relationship with the theme of the film, context of use and production techniques.

Are realistic details important for learning with visualizations or can depth cues provide sufficient guidance?

The optimal choice of the level of realism in instructional visualizations is a difficult task. Previous studies suggest that realism can overwhelm learners, but a growing body of research demonstrates that realistic details can enhance learning. In the first experiment (n = 107), it was assessed whether learning using realistic visualizations can be distracting and therefore particularly benefits from pre-training. Participants learned the anatomy of the parotid gland using labeled visualizations. While pre-training did not have an effect, a more realistic visualization enhanced learning compared to a schematic visualization. In the second experiment (n = 132), a schematic diagram was compared to a more realistic style featuring basic depth cues, and a highly realistic visualization containing a detailed surface. Regarding retention performance, no significant differences were found. However, an interesting pattern regarding subjective cognitive load ratings emerged: the schematic version received the highest cognitive load ratings, while the version featuring simplified shading was rated as least demanding. The version containing simplified depth cues also elicited lower cognitive load ratings than the detailed visualization. The two experiments demonstrate that fears concerning a detrimental effect of realistic details should not be over-generalized. While schematic visualizations may be easier to visually process in some cases, extracting depth information from contour drawings adds cognitive demands to a learning task. Thus, it is advisable that computer-generated visualizations contain at least simplified forms of shading, while the addition of details does not appear to have a strong positive effect.

Research Methods for IDs and TBLT: A Substantive and Methodological Review

Abstract As part of ongoing efforts to characterize the extent to which tasks and interaction-driven language learning are influenced by individual differences (IDs), task-based researchers have thus far examined variables like learners’ levels of L2 anxiety, motivation, cognitive creativity, working memory capacity, and aptitude. Building on a tradition of prior syntheses in task-based language teaching (TBLT, e.g., Plonsky & Kim, 2016), we carried out a methodological review of the practices used by researchers who have examined learners’ IDs in task-based language learning. We searched journal articles published between 2000 and 2023 and identified 135 unique samples for analysis. Each empirical study was coded for relevant contextual and demographic variables as well as for methodological features related to the investigation of individual differences. We observed that of 30 individual differences investigated in TBLT research over the last two decades, the top five most common were motivation, working memory, L2 proficiency, anxiety, and aptitude. Interesting patterns related to operationalizations, instruments, coding, analyses, and reporting practices. In this paper, we report these results and summarize the most and least common methodological practices, also pointing out gaps and possibilities for future directions. We conclude with recommendations for researchers interested in embarking on empirical investigations of individual differences and TBLT based on best practices.

Direct Numerical Simulation of Involute Channel Turbulence

Abstract A direct numerical simulation (DNS) study was performed on turbulent flow in the high flux isotope reactor involute channel geometry to develop a numerical database and determine the differences compared with a flat parallel channel. The varying channel curvature along the walls was studied for differences in mean profiles. Parameters of interest include streamwise velocity, turbulent kinetic energy (TKE), and turbulence dissipation rate, as well as Reynolds stresses and turbulence transport terms. Profile sampling was carried out at 10 locations along the span of the involute. Additional DNS studies were performed on smaller domains of comparable curvature to the involute domain: a high curvature channel (high circular), a low curvature channel (low circular), and a flat channel (flat). Each of these four cases was compared against each other and to other DNS studies performed on parallel flows. The results indicate that the bulk involute channel flow does not differ significantly from a flat parallel channel flow and that the curvature of the walls does not significantly alter the mean flow parameters. However, the regions of the involute channel near the side walls exhibit relatively low magnitude twin recirculation structures driven toward the side walls from the centerline of the channel, which warrants further study.

Mining frequent temporal duration-based patterns on time interval sequential database

Sequential databases have wide applications, such as market basket analysis, medical prediction, and sign language recognition. Most prior research is based on pointed-based sequential databases, which assume each item/event occurs instantaneously. However, in many real-world scenarios, events persist over intervals of varying durations, such as varying time intervals of a symptom or a gesture of sign language. Assigning the same weight to different times of events and neglecting the duration of events can hinder the recognition of interesting patterns, such as concurrent symptoms preceding a disease. To address these issues, this paper integrates duration with temporal patterns in interval-based sequential databases, introduces the concept of temporal duration-based patterns (TDPs), and designs two algorithms called FTDPMiner-EP (Frequent TDPMiner based on endpoint representation) and FTDPMiner-TM (Frequent TDPMiner based on triangular matrix representation) by using different extension methods to mine frequent TDPs. Due to the complex relationships between events, temporal pattern mining is more challenging than sequential pattern mining. Strategies are used in this paper to accelerate the algorithms' search process. Experiments are conducted on both real and synthetic databases, which show good performance of the two algorithms.

Temperature sensing performance of M2La8(SiO4)6O2:Ce3+(M=Ca, Sr, Ba) based on fluorescence intensity ratio of dual emission centers in different coordination environments

Rare earth doped luminescent sensing materials as non-contact thermometers have attracted extensive research interests due to their wide applications in areas like military, medical care, aerospace, etc. How to expand the working temperature range and improve the sensitivity has become the focus. In this work, we synthesized a series of apatite-structured luminescent temperature sensors M2La7.9Ce0.1(SiO4)6O2 (M = Ca, Sr, Ba) via isomorphic substitution. By monitoring the differential changes of emission intensities from Ce3+ at the 7-coordinate and 9-coordinate site respectively when temperature varies, the real-time temperature can be determined. These materials were proven to possess the maximum absolute sensitivity up to 0.825 × 10−3 K−1, 2.63 × 10−3 K−1 and 4.33 × 10−3 K−1, respectively. Meanwhile interesting regular patterns were observed for their temperature sensitivities with the substitution of Ca, Sr and Ba. Furthermore, through studying the temperature sensitivity of Ba2La8(SiO4)6O2:xCe3+ samples with different concentrations, it was found that when the Ce3+ content was at the optimal doping concentration of 0.7, the absolute sensitivity of the sample reached 14.64 × 10−3 K−1 at 573 K. This finding has opened up a new avenue for designing fluorescent temperature sensors.