Discrete Modulation Research Articles

Multifunctional Smart Fabrics with Integration of Self-Cleaning, Energy Harvesting, and Thermal Management Properties.

Due to their good wearability, smart fabrics have gradually developed into one of the important components of multifunctional flexible electronics. Nevertheless, function integration is typically accomplished through the intricate stacking of diverse modules, which inevitably compromises comfort and elevates processing complexities. The integration of these discrete functional modules into a unified design for smart fabrics represents a superior solution. Here, we put forward a rational approach to functional integration for the typical challenges of thermal management, energy supply, and surface contamination in smart fabrics. This sandwich-structured multilayer fabric (MLF) is obtained by continuous electrospinning of two layer P(VDF-HFP) fabric and one layer P(VDF-HFP) fabric functionalized with core-shell SiO2/ZnO/ZIF-8 (SZZ) nanoparticles. Specifically, MLFs achieve effective and stable energy harvesting in triboelectric nanogenerators (TENGs) with hydrophobicity and antibacterial properties. Meanwhile, MLFs also have high mid-infrared emissivity and sunlight reflectivity, successfully realizing radiative cooling under different climates, and have been applied in wearing clothing, roof shading, and car covers. This work may contribute to the design and manufacturing of next-generation thermal comfort smart fabrics and wearable electronics, particularly in terms of the rational design of multifunctional devices.

Discrete modulation continuous variable quantum key distribution under fast fading channel

After decades of development, continuous variable quantum key distribution technology still cannot meet people’s needs for secure communication over long distances. Free-space quantum communication provides a new way for secure communication over long distances. However, because the communication connected in free space is inevitably affected by atmospheric turbulence, the transmission rate of the channel changes rapidly according to the probability distribution, making it difficult for the communication personnel to determine the transmission rate of the channel. At the same time, discrete modulated continuously variable quantum key Distribution protocol (DM-CVQKD) has certain advantages over Gauss modulated continuously variable quantum key Distribution protocol (GM-CVQKD) in the communication distance. Therefore, this paper analyzes the key rate of DM-CVQKD in this case (that is, in the fast attenuation channel), proves that it can resist the collective attack, and observes the relationship between the parameters and the key rate in the fast attenuation channel by numerical simulation and control variables. Finally, through the analysis of numerical simulation results, it is found that DM-CVQKD can still maintain a high key rate under certain conditions, which proves that long-distance free space quantum communication is feasible under certain conditions. At the same time, it was found that the performance of DM-CVQKD was affected by the extreme case where Eve completely controlled the instantaneous transmittance.

Performance comparison of various end-to-end learning technologies with a bandwidth-limited OWC system

Recently, end-to-end (E2E) learning methodologies have garnered significant attention as a compelling approach to attain global optimal communication within the domain of 6 G native intelligent systems. Nevertheless, a precise evaluation of the diverse E2E techniques is still lacking, leading to uncertainties regarding their applicable scenarios and effectiveness. In this paper, we present a comprehensive comparison applying three advanced E2E methods including the autoencoder-based geometric shaping (AEGS) model, comprehensive autoencoder (CAE) model, and wave-wise auto-equalization (WWAE) model in a real bandwidth-limited optical wireless communication (OWC) system. A novel attention-based comprehensive noise joint channel estimator (ACNJCE) is proposed to serve as a universal channel model adaptable to the existing E2E methods. Based on traditional carrier-less amplitude and phase modulation (CAP) modulation, AEGS, WWAE, and CAE are compared under the conditions of 2 GBaud and 3 GBaud respectively. The final results demonstrate that the CAE exhibits the capability to autonomously allocate bandwidth and achieves the highest dynamic adjustment range, which is increased by 69% compared with CAP based on neural network (NN) equalization. In contrast, AEGS has obvious advantages in terms of received optical power (ROP) gain. Based on bit-power loading discrete multi-tone modulation (DMT) modulation, WWAE can effectively compensate the signal spectrum after modulation order optimization and finally achieves the highest data rate under the condition that the −3 dB bandwidth of the channel is only close to 1 GHz. The BER of WWAE with DMT at this rate is 25.2% of that using the NN equalization. Furthermore, experimental results under turbulent conditions reveal that AEGS exhibits superior and more stable performance amidst the perturbations caused by turbulence due to its ability to achieve end-to-end autonomous optimization while integrating traditional modulation and bringing additional shaping gain. According to our knowledge, this marks the first comprehensive evaluation and comparison of existing major E2E algorithms and traditional communication algorithms in a real OWC system.

Concurrent performance and security enhancement of ultrahigh-order DSM signals via null subcarriers.

In this paper, we propose leveraging null subcarriers in discrete multi-tone modulation (DMT) to process the DMT signal in both time and frequency domains. Additionally, we employ discrete memory enhanced chaos (DMEC) to scramble the signal in the frequency domain, thereby achieving physical layer signal encryption while ensuring a more uniform power distribution in the time-domain waveform. In our experimental demonstration, we achieved high-security transmission of a DSM-based 65536-QAM signal at a data rate of 16.01 Gb/s over a 25 km single-mode fiber (SMF) in an intensity-modulation direct-detection (IMDD) system. Additionally, in the transmission experiments for 13684-QAM and 65536-QAM signals, the proposed method demonstrated a receiver sensitivity gain of over 0.5 dB compared to the traditional DSM-based ultrahigh-order transmission.

2224 What are we teaching UK medical students about ageing? Results of the third BGS national curriculum survey

Abstract Introduction The ageing population means all doctors, regardless of specialty, will need knowledge, skills, and attitudes to care for older people with complex health conditions. An essential component of preparing the medical workforce to best care for older people is by including teaching on ageing and geriatric medicine in undergraduate medical curricula. Here we present results of the British Geriatrics Society (BGS) national curriculum survey 2021–22, highlighting progress made in undergraduate teaching in geriatric medicine. Methods All 35 UK GMC-registered medical schools at the time of data collection were invited to participate in an online survey on content, methodology, timing, and duration of teaching in ageing and geriatric medicine. The survey was structured around the 2013 BGS recommended undergraduate curriculum, for consistency with previous surveys. Results 30/35 of UK medical schools responded (83% response rate). Most teaching occurred in the fourth year of study (21/30, 70%). The majority (15/30, 50%) reported a discrete module for geriatric medicine lasting 4–8 weeks, an increase on previous surveys. However, several programmes have reduced the amount of in-person teaching since the COVID-19 pandemic. Notably, three schools reported geriatric medicine exposure lasting >12 weeks. Of these, two were integrated clerkships and one a dedicated geriatric medicine module. There is increasing focus on multidisciplinary education, with emphasis on combining virtual or simulated teaching with other healthcare professions (n = 7). Every school (n = 30) taught at least one topic as small-group or case-based learning. Conclusion There is a trend towards increasing exposure to geriatric medicine compared to previous surveys in 2008 and 2013. However, several of the programmes reporting greater exposure incorporate geriatric medicine in an integrated clerkship rather than as a dedicated module. Programmes demonstrated a move from didactic teaching towards small-group and case-based learning, employing a wider variety of assessment methods than previous.

The role of functional emotion circuits in distinct dimensions of psychopathology in youth

Several mental disorders emerge during childhood or adolescence and are often characterized by socioemotional difficulties, including alterations in emotion perception. Emotional facial expressions are processed in discrete functional brain modules whose connectivity patterns encode emotion categories, but the involvement of these neural circuits in psychopathology in youth is poorly understood. This study examined the associations between activation and functional connectivity patterns in emotion circuits and psychopathology during development. We used task-based fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC, N = 1221, 8–23 years) and conducted generalized psycho-physiological interaction (gPPI) analyses. Measures of psychopathology were derived from an independent component analysis of questionnaire data. The results showed positive associations between identifying fearful, sad, and angry faces and depressive symptoms, and a negative relationship between sadness recognition and positive psychosis symptoms. We found a positive main effect of depressive symptoms on BOLD activation in regions overlapping with the default mode network, while individuals reporting higher levels of norm-violating behavior exhibited emotion-specific lower functional connectivity within regions of the salience network and between modules that overlapped with the salience and default mode network. Our findings illustrate the relevance of functional connectivity patterns underlying emotion processing for behavioral problems in children and adolescents.

A novel evaluation method of the aging performance of MEMS flow sensor

The development of Micro Electro Mechanical Systems (MEMS) flow sensor towards high level market applications generates various challenges, in particular also on the reliable functionality. With the advancement of reliability engineering technology, aging phenomenon of MEMS devices has been widely concerned. As a result, an aging evaluation method is essential. The accelerated aging testing (AAT) is the most widely used in traditional aging methods. However, its performance is limited by highly economic and time cost. In this paper, a novel aging effect model is proposed, in which a comprehensive approach that integrates AAT, lifetime distribution modeling, and either Finite Element Modeling Simulation (FEMS) or fatigue simulation (FS) as fundamental is explored. However, the difference from the conventional approach was that the AAT results is imported in FS, to confirm fatigue analysis, while FS predictions are instrumental in analyzing degradation or fatigue phenomena and estimation lifetime. In this way, aging performance is illustrated detailed with the lower aging cost and high efficiency. Meanwhile, the results of proposed FS are verified by a thermal cycle (TC) AAT. Specifically, the resistor degradation mechanism, the characteristic parameter degradation is calculated. Moreover, the lifetime evaluation was acquired by the Arrhenius model. Finally, the proposed aging performance evaluation method can be applied to both discrete devices and modules. Compared with the traditional aging method the high aging cost can be eliminated, and the proposed aging evaluation strategy can be used in various temperature conditions.

Security of discrete-modulated continuous-variable quantum key distribution

Continuous variable quantum key distribution with discrete modulation has the potential to provide information-theoretic security using widely available optical elements and existing telecom infrastructure. While their implementation is significantly simpler than that for protocols based on Gaussian modulation, proving their finite-size security against coherent attacks poses a challenge. In this work we prove finite-size security against coherent attacks for a discrete-modulated quantum key distribution protocol involving four coherent states and heterodyne detection. To do so, and contrary to most of the existing schemes, we first discretize all the continuous variables generated during the protocol. This allows us to use the entropy accumulation theorem, a tool that has previously been used in the setting of discrete variables, to construct the finite-size security proof. We then compute the corresponding finite-key rates through semi-definite programming and under a photon-number cutoff. Our analysis provides asymptotic rates in the range of 0.1−10−4 bits per round for distances up to hundred kilometres, while in the finite case and for realistic parameters, we get of the order of 10 Gbits of secret key after n∼1011 rounds and distances of few tens of kilometres.

Effect of inter-module connection properties on structural sway of high-rise modular buildings

The modular steel building is a rapidly evolving prefabricated structural system, consisting of discrete volumetric modules assembled with inter-module connections. Despite numerous studies on the design optimization of inter-module connections, research on the development of realistic connection models and the correlation between connection properties and the overall structural behavior is relatively scarce. This paper introduces a multiple-spring inter-module connection model and supplies detailed property calculations of different load transfer components. The model can effectively simulate different stiffness and strength levels at inter-module connections and predict relative dislocations between assembled modules. The model is then applied to analyze the structural sway of modular buildings under seismic loads. Influences of connection modelling methods and the real connecting abilities on structural sway responses are investigated through comparative analysis of connection and module drift deformations when using conventional pin or rigid constraint connection models and the proposed realistic multiple spring model. Results indicate that simplified pin or rigid constraints cannot accurately represent the actual load transfer mechanisms at inter-module connections. The horizontal link determines the composition extent of horizontally assembled columns and modules. The horizontal shear resistance is closely related to column loads and follows the downwardly increased mode, which induces different structural sway and module drift levels in modular buildings.

Topological endomorphism rings of tilting complexes

AbstractIn a compactly generated triangulated category, we introduce a class of tilting objects satisfying a certain purity condition. We call these the decent tilting objects and show that the tilting heart induced by any such object is equivalent to a category of contramodules over the endomorphism ring of the tilting object endowed with a natural linear topology. This extends the recent result for n‐tilting modules by Positselski and Št'ovíček. In the setting of the derived category of modules over a ring, we show that the decent tilting complexes are precisely the silting complexes such that their character dual is cotilting. The hearts of cotilting complexes of cofinite type turn out to be equivalent to the category of discrete modules with respect to the same topological ring. Finally, we provide a kind of Morita theory in this setting: Decent tilting complexes correspond to pairs consisting of a tilting and a cotilting‐derived equivalence as described above tied together by a tensor compatibility condition.

The phonological store of working memory: A critique and an alternative, perceptual-motor, approach to verbal short-term memory.

A key quality of a good theory is its fruitfulness, one measure of which might be the degree to which it compels researchers to test it, refine it, or offer alternative explanations of the same empirical data. Perhaps the most fruitful element of Baddeley and Hitch's (1974) Working Memory framework has been the concept of a short-term phonological store, a discrete cognitive module dedicated to the passive storage of verbal material that is architecturally fractionated from perceptual, language, and articulatory systems. This review discusses how the phonological store construct has served as the main theoretical springboard for an alternative perceptual-motor approach in which serial-recall performance reflects the opportunistic co-opting of the articulatory-planning system and, when auditory material is involved, the products of obligatory auditory perceptual organisation. It is argued that this approach, which rejects the need to posit a distinct short-term store, provides a better account of the two putative empirical hallmarks of the phonological store-the phonological similarity effect and the irrelevant speech effect-and that it shows promise too in being able to account for nonword repetition and word-form learning, the supposed evolved function of the phonological store. The neuropsychological literature cited as strong additional support for the phonological store concept is also scrutinised through the lens of the perceptual-motor approach for the first time and a tentative articulatory-planning deficit hypothesis for the "short-term memory" patient profile is advanced. Finally, the relation of the perceptual-motor approach to other "emergent-property" accounts of short-term memory is briefly considered.

DISCRETE METHODS FOR MEASURING PARAMETERS OF AMPLITUDE CHARACTERISTICS OF QUADIPOLES

The paper discusses the measurement of amplitude characteristics parameters of quadripole networks that is one of important and relatively new areas of measurement technology. In this paper, this aspect is considered in relation to discrete methods for measuring parameters of amplitude characteristics using digital procedures for generating and converting test signals. It is shown that, in contrast to the constant one, with a discrete modulation law it is possible to weaken the influence of transient processes and dynamic distortions of the test signal in the studied quadripole network, which helps to significantly increase the sensitivity of analyzers of the amplitude characteristics of quadrupole networks.

Pathway analysis for functional characterization of microRNAs

DIANA-miRPath webserver enables the exploration of combined miRNA effects using predicted or experimentally supported miRNA interactions. Its latest version (DIANA-miRPath v4.0, http://www.microrna.gr/miRPathv4 ), introduces the capacity to tailor its target-based miRNA functional analysis engine towards specific biological/experimental contexts. Via a modular interface with rich interaction, annotation and parameterization options, users can perform enrichment analysis on Gene Ontology (GO) terms, KEGG and REACTOME pathways, gene sets from Molecular Signatures Database (MSigDB) and PFAM. Included miRNA interaction sets are derived from state-of-the-art resources of experimentally supported (DIANA-TarBase v8.0, miRTarBase and microCLIP cell-type-specific interactions) or from in silico miRNA-target interactions (DIANA-microT-2023 and TargetScan predictions). Bulk and single-cell expression datasets from The Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression project (GTEx), as well as single-cell expression atlases can be used to assess expression change of targeted genes within terms, across a wide range of states. A discrete module to perform miRNA-tailored CRISPR knock-out screen analyses deems possible the investigation of selected miRNAs within conditions under study. Hellenic Foundation for Research and Innovation (HFRI) under the 1 st Call for HFRI Research Projects to Support Faculty Members & Researchers and Procure High-Value Research Equipment grant (Project Number 2563). This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Ключевые особенности протоколов квантового распределения ключей на непрерывных переменных

The key features of continuous variables quantum key distribution protocols are considered in the paper. The motivation for studying and developing methods of quantum cryptography is substantiated. The main aspects inherent to continuous-variable protocols are highlighted, and they are classified based on various characteristics, peculiarities, and implementation variants. A detailed examination of a protocol example with discrete modulation and signal registration through balanced homodyne detection is provided. A classification of attacks on the quantum protocol is presented. A general overview of post-processing methods is given. The role of noise is indicated, and approaches to assessing the level of security applicable to continuous-variable protocols are discussed. In conclusion, the main conclusions regarding the current status of this problem are presented, and aspects requiring further study are identified.

Transferable multi-objective factory layout planning using simulation-based deep reinforcement learning

Factory layout planning aims at finding an optimized layout configuration under consideration of varying influences such as the material flow characteristics. Manual layout planning can be characterized as a complex decision-making process due to a large number of possible placement options. Automated planning approaches aim at reducing the manual planning effort by generating optimized layout variants in the early stages of layout planning. Recent developments have introduced deep Reinforcement Learning (RL) based planning approaches that allow to optimize a layout under consideration of a single optimization criterion. However, within layout planning, multiple partially conflicting planning objectives have to be considered. Such multiple objectives are not considered by existing RL-based approaches. This paper addresses this research gap by presenting a novel deep RL-based layout planning approach that allows consideration of multiple objectives for optimization. Furthermore, existing RL-based planning approaches only consider analytically formulated objectives such as the transportation distance. Consequently, dynamic influences in the material flow are neglected which can result in higher operational costs of the future factory. To address this issue, a discrete event simulation module is developed that allows simulating manufacturing and material flow processes simultaneously for any layout configuration generated by the RL approach. Consequently, the presented approach considers material flow simulation results for multi-objective optimization. To investigate the capabilities of RL-based factory layout planning, different RL architectures are compared based on a simplified application scenario. Throughput time, media supply, and material flow clarity are considered as optimization objectives. The best performing architecture is then applied to an exemplary application scenario and compared with the results obtained by a combined version of the genetic algorithm and tabu search, the non-dominated sorting genetic algorithm, and the optimal solution. Finally, two industrial planning scenarios, one focusing on brownfield and one on greenfield planning, are considered. The results show that the performance of RL compared to meta-heuristics depends on the considered computation time. With time the results generated by the RL approach exceed the quality of the best conventional solution by up to 11%. Finally, the potential of applying transfer learning is investigated for three different application scenarios. It is observed that RL can learn generalized patterns for factory layout planning, which allows to significantly reduce the required training time and can lead to improved solution quality. Thus, the use of pre-trained RL models shows a substantial performance potential for automated factory layout planning which cannot be achieved with conventional automated planning approaches.

Improvement and security analysis of multi-ring discrete modulation continuous variable quantum secret sharing scheme

Abstract In order to avoid the complexity of Gaussian modulation and the problem that the traditional point-to-point communication DM-CVQKD protocol cannot meet the demand for multi-user key sharing at the same time, we propose a multi-ring discrete modulation continuous variable quantum key sharing scheme (MR-DM-CVQSS). In this paper, we primarily compare single-ring and multi-ring M-symbol amplitude and phase-shift keying modulations. We analyze their asymptotic key rates against collective attacks and consider the security key rates under finite-size effects. Leveraging the characteristics of discrete modulation, we improve the quantum secret sharing scheme. Non-dealer participants only require simple phase shifters to complete quantum secret sharing. We also provide the general design of the MR-DM-CVQSS protocol. We conduct a comprehensive analysis of the improved protocol’s performance, confirming that the enhancement through multi-ring M-PSK allows for longer-distance quantum key distribution. Additionally, it reduces the deployment complexity of the system, thereby increasing the practical value.

A Meta-analysis of Transcriptome Data to Investigate the Effect of Soy Isoflavones on Breast Cancer Cell.

Breast cancer ranks as the second highest cause of cancer-linked deaths in women, with varying rates between Western and Asian countries. The consumption of phytoestrogens can influence breast cancer occurrence. To comprehend how soy isoflavones impact breast cancer cells, we conducted a meta-analysis, combining gene expression data from multiple studies. This approach aimed to identify crucial transcriptional characteristics driving breast cancer cell response to soy phytoestrogens. The gene expression profiles obtained from the Gene Expression Omnibus and Array Express and were grouped into control and isoflavones exposure conditions. We performed a meta-analysis based on the effect size combination method to identify the differentially expressed genes (DEGs). In addition, we performed Gene Ontology (GO) enrichment analysis, pathway analysis, weighted gene co-expression network analysis (WGCNA) and recursive support vector machine (R-SVM) algorithm. Based on this meta-analysis, we identified 3,890 DEGs, of which 2,173 were up-regulated and 1,717 were down-regulated. For example, SGCG, PLK2, and TBC1D9 were the most highly down-regulated genes and EGR3, WISP2, and FKBP4 were the most highly expressed genes in the isoflavones exposure condition. The functional enrichment and pathway analysis were revealed "cell division" and "cell cycle" among the most enriched terms. Among the identified DEGs, 269 transcription factor (TF) genes belonged to 42 TF families, where the C2H2 ZF, bZIP, and bHLH were the most prominent families. We also employed the R-SVM for detecting the most important genes to classify samples into isoflavones exposure and control conditions. It identified a subset of 100 DEGs related to regulation of cell growth, response to estradiol, and intermediate ribonucleoside monophosphate in the purine (IMP) metabolic process. Moreover, the WGCNA separated the DEGs into five discrete modules strongly enriched for genes involved in cell division, DNA replication, embryonic digit morphogenesis, and cell-cell adhesion. Our analysis provides evidence suggesting that isoflavone affects various mechanisms in cells, including pathways associated with NF-κB, Akt, MAPK, Wnt, Notch, p53, and AR pathways, which can lead to the induction of apoptosis, the alteration of the cell cycle, the inhibition of angiogenesis, and interference in the redox state of cells. These findings can shed light on the molecular mechanisms that underlie the response of breast cancer cells to isoflavones.

CREAD: A Classification-Restoration Framework with Error Adaptive Discretization for Watch Time Prediction in Video Recommender Systems

The watch time is a significant indicator of user satisfaction in video recommender systems. However, the prediction of watch time as a target variable is often hindered by its highly imbalanced distribution with a scarcity of observations for larger target values and over-populated samples for small values. State-of-the-art watch time prediction models discretize the continuous watch time into a set of buckets in order to consider the distribution of watch time. However, it is highly uninvestigated how these discrete buckets should be created from the continuous watch time distribution, and existing discretization approaches suffer from either a large learning error or a large restoration error. To address this challenge, we propose a Classification-Restoration framework with Error-Adaptive-Discretization (CREAD) to accurately predict the watch time. The proposed framework contains a discretization module, a classification module, and a restoration module. It predicts the watch time through multiple classification problems. The discretization process is a key contribution of the CREAD framework. We theoretically analyze the impacts of the discretization on the learning error and the restoration error, and then propose the error-adaptive discretization (EAD) technique to better balance the two errors, which achieves better performance over traditional discretization approaches. We conduct detailed offline evaluations on a public dataset and an industrial dataset, both showing performance gains through the proposed approach. Moreover, We have fully launched our framework to an online video platform, which resulted in a significant increase in users' video watch time by 0.29% through A/B testing. These results highlight the effectiveness of the CREAD framework in watch time prediction in video recommender systems.

All-dielectric high-NA achromatic metalenses in the mid-infrared band based on subregions.

For conventional refractive lenses, chromatic aberration inevitably occurs due to the refractive index variation of the lens material with the incident wavelength, leading to axial aberrations and lower imaging system quality. Achromatic metalenses have demonstrated a great capability to solve this problem and been extensively investigated. However, the metalens achromatic method involves construction of a unit structure satisfying a phase distribution greater than 0-2π or phase compensation. Although this design method can obtain a good achromatic effect, finding a unit that satisfies a linear distribution during design is difficult. In this paper, we use subregion discrete wavelength modulation to achieve broadband achromatism. The total number of structural units in each region is optimized for different incident wavelengths, and the internal and external ring unit structures are also optimized. This achromatic metalens exhibits a large aperture and a high numerical aperture in the 4.2-4.7µm mid-infrared band (NA=0.83). Our research has strong potential and application prospects in ultracompact imaging and laser beam shaping.

Development of Discrete Mathematics Module Based on Discovery Learning for Mathematical Understanding in Higher Education

This research aims to develop a discrete mathematics module based on discovery learning to improve students’ mathematical understanding in higher education. This research used the Research and Development (R&D) method with a mixed approach and ADDIE model in the product development stage. The data collection techniques used questionnaires, tests and interview guides. Meanwhile, the data analysis technique used in this research combines qualitative and quantitative descriptive. The results include (1) Reviewing the study of discrete mathematics module development; it obtained valid results after going through several assessments and revisions, and the practicality of developing this discrete mathematics module was categorized as practical; it is not surprising that the module can increase students’ mathematical understanding with a range of 37.97; (2) Judging from effectiveness; it is obtained from inferential statistics that significance is 0.00, which means there is an influence on the use of discovery learning-based discrete mathematics modules on students’ mathematical understanding. These results have implications that the module has proved effective in improving students’ mathematical understanding in higher education.