Real-time Properties Research Articles

Composite metastructure with tunable ultra-wide low-frequency three-dimensional band gaps for vibration and noise control

Low-frequency vibration and noise control present enduring engineering challenges that garner extensive research attention. Despite numerous active and passive control solutions, achieving multiple ultra-wide attenuation regions remains elusive. Addressing vibration and noise control across a multidirectional broad low-frequency spectrum, three-dimensional metastructures have emerged as innovative solutions. This study introduces a novel three-dimensional composite metastructure featuring multiple ultra-wide three-dimensional complete band gaps. The research emphasizes the design strategy of elastic ligaments to achieve multiple ultra-wide attenuation regions spanning from 0.7 to 40 kHz. The band structures are elucidated through modal analysis and further substantiated by an analytical model based on a spring-mass chain with an additional resonator. The underlying physical mechanism for the formation of multiple ultra-wide band gaps is revealed through novel vibration modes from finite element analyses. Furthermore, we demonstrate that the distribution and the relative width of the ultra-wide band gaps can be tuned by modifying the geometric parameters of the metastructure. Utilizing additive manufacturing, prototypes are fabricated, and low-amplitude vibration tests are conducted to evaluate real-time vibration attenuation properties. Consistency is observed among theoretical, numerical, and experimental results. The proposed structure shows significant potential for high-performance meta-devices aimed at controlling noise and vibration across an extremely wide low-frequency spectrum.

Real-time synthesis of a nonuniformly correlated, partially coherent beam using an optical coordinate transformation

We design, build, and validate an optical system for generating light beams with complex spatial coherence properties in real time. Beams of this type self-focus and are resistant to turbulence degradation, making them potentially useful in applications such as optical communications. We begin with a general theoretical analysis of our proposed design. Our approach starts by generating a Schell-model (uniformly correlated or shift-invariant) source by spatially filtering incoherent light. We then pass this light through an optical coordinate transformer, which converts the Schell-model source into a nonuniformly correlated field. After the general analysis, we discuss system engineering, including trade-offs among system parameters and expected performance. Finally, we test and validate the system by comparing experimental results to theoretical predictions. We conclude with a brief summary and a discussion of future work.

Differential Game-Based Cooperative Interception Guidance Law with Collision Avoidance

To deal with the offense-defense confrontation problem of multi-missile cooperative intercepting a high-speed and large-maneuvering target, a differential game-based cooperative interception guidance law with collision avoidance is proposed, in which the offense-defense parties are the incoming target and the interceptors, respectively. Given that both offense-defense parties have uniformly decreasing speeds and first-order biproper dynamics, the relative motion models among the offense-defense parties are established, and the performance indices of the target and the interceptors are proposed. After that, the cooperative interception guidance law with collision avoidance is derived based on a differential game. The guidance law considers the effects of speed variations and rudder layouts on the motions of both offense-defense parties, ensuring excellent algorithmic real-time property and interception accuracy while introducing inter-missile collision avoidance constraints. In addition, the parameters of the target performance index are set according to the target acceleration information estimated by the interceptors. The simulation results verify the effectiveness of the guidance law designed in this paper, under various three-to-one scenarios, the interceptors could achieve collision-free interceptions with the interception accuracy of less than 5 m and the interception time difference of less than 0.1 s.

Significantly enhancing high-temperature piezoelectric response and Tdr of BF-BT-based ceramics through multi-component optimization strategy

Phase boundary-domain engineering modulation with diverse group elements and coexistence of multiphase and composite domain structures is a good pathway that can be realized to simultaneously improve the piezoelectric properties of BF-BT ceramics and their thermal stability. Here, we propose a multicomponent optimization strategy. We innovatively introduce the 0.8(Bi0.5Na0.5)TiO3-0.2(Bi0.5K0.5)TiO3 (BNT-20BKT) component to 0.69BF-0.31BT ceramic, to build a morphotropic phase boundary (MPB) for enhancing d33. Meanwhile, Bi(Zn0.5Ti0.5)O3 with high Curie temperature and high tetragonal degree was introduced with the expectation of obtaining higher thermal stability along with high piezoelectric properties. The 0.69BiFeO3-0.31BaTiO3-0.005[0.8(Bi0.5Na0.5)TiO3-0.2(Bi0.5K0.5)TiO3]-xBi(Zn0.5Ti0.5)O3 (referred to as BF31BT-0.005(BNT-20BKT)-xBZT, 0.00 ≤ x ≤ 0.025) ceramics were successfully prepared. For the first time, an ultra-high piezoelectric performance of 749.6 pC/N was achieved at a real-time depolarization temperature (Tdr) of 395.6 °C, reflecting the real-time piezoelectric properties and ultra-high temperature stability of the ceramic at high temperatures. A two-phase coexisting structure of R and PC phases was produced in BF31BT-0.005(BNT-20BKT)-0.025BZT ceramic. TEM results showed the formation of nanodomains and a large number of polar nanoregions (PNRs). By adjusting the appropriate R/PC phase ratio, oxygen octahedral distortion, and optimizing the local domain engineering, an effective strategy is provided for simultaneously achieving ultra-high temperature stability and ultra-high piezoelectric response.

Enhancing refinery heavy oil fractions analytical performance through real-time predicative modeling.

This study introduces a suite of robust models aimed to advance the determination of physiochemical properties in heavy oil refinery fractions. By integrating real-time analytical technique inside the refinery analysis, we have developed a single analyzer capable of employing six partial least square regression equations. These designed models enable to provide real-time prediction of critical petroleum properties, such as sulfur content, micro carbon residues (MCR), asphaltene content, heating value, and the concentrations of nickel and vanadium metals. Specifically tailored for heavy oil in refinery feeds with an American petroleum institute (API) gravity range of 3° to 32° and sulfur content of 2.8 to 5.5 wt%, the models streamline the analysis process within refinery operations, bridging the gap between catalytic and non-catalytic processes across refinery units. The accuracy of our physiochemical prediction models has been validated against American Society for Testing and Materials (ASTM) standards, demonstrating their capability to deliver precise real-time property values. This approach not only enhances the efficiency of refinery analysis but also sets a new standard for the monitoring and optimization of heavy oil processing in real-time approach.

Radar Based Security System Alert

Abstract: Radar-based security systems are now a reliable option for both intruder detection and perimeter defence. This research presents a unique approach to integrate state-of-the-art signal processing methods to improve the performance of such systems. The suggested system tracks and detects moving objects inside an established region using radar technology. The system utilizes advanced processing of signals methods, such as fuzzy logic, neural networks, and wavelet analysis, to differentiate between everyday activities and possible security risks. The network of radar sensors that are placed strategically around the perimeter of the secured region makes up the radar-based security system. Every radar sensor keeps an eye on the environment and detects any moving objects that come within range. Wavelet analysis is used to analyze the collected signals in real-time and extract important properties, such as object velocity, direction, and size.

The thermal 'Buddha Board'-application of microstructured polyolefin films for variable thermal infrared transparency materials.

In this study, we explore the innovative application of biological principles of scattering foams and structural colouration of white materials to manipulate the transmission properties of thermal infrared (IR) radiation, particularly within the 8-14 μm wavelength range in polyolefin materials. Inspired by the complex skin of organisms such as chameleons, which can dynamically change colour through structural alterations, as well as more mundane technologies such as Buddha Boards and magic water colouring books, we are developing methods to control thermal IR transmission using common thermoplastic materials that are semi-transparent to thermal IR radiation. Polyethylene and polypropylene, known for their versatility and cost-effectiveness, can be engineered into microstructured sheets with feature sizes spanning from 5 to 100 μm. By integrating these precisely moulded microstructures with index-matching fluids, specifically IR transparent oils, we achieve a reversible modification of the thermal transmission properties. This novel approach not only mimics the adaptive functionality of natural systems but also offers a practical and scalable solution for dynamic thermal management. Our results indicate a promising pathway for the development of new materials that can adapt their IR properties in real time, paving the way for smarter thermal management solutions via radiative emission/absorption.

Research on fire detection method of complex space based on multi-sensor data fusion

Efficient and reliable fire detection methods are essential for addressing complex spatial characteristics of older residential communities, which often face poor fire safety conditions, various fire types, and challenges in rescue operations. In response to the problem of lacking comprehensive analysis of multidimensional data in traditional fire detection, which often leads to false alarms and missed alarms, this paper proposes a fire detection method based on multi-sensor data fusion. Various sensors are employed, including smoke sensors, temperature sensors, CO sensors, and flame sensors. These sensors collect multidimensional data, which are fused using improved adaptive filters and fuzzy Bayesian logical reasoning algorithms to establish a fire detection model based on multi-sensor data fusion. To additionally, false alarms and missed detections caused by sensor damage are further solved by exploring hidden spaces. Experimental results demonstrate that the proposed fire detection system based on multi-sensor data fusion effectively analyses fire occurrence and progression in older residential communities, and performs stability, accuracy and real-time property.

Model checking timed recursive CTL

We introduce Timed Recursive CTL, a merger of two extensions of the well-known branching-time logic CTL: Timed CTL is interpreted over real-time systems like timed automata; Recursive CTL introduces a powerful recursion operator which takes the expressiveness of this logic CTL well beyond that of regular properties. The result is an expressive logic for real-time properties. We show that its model checking problem is decidable over timed automata, namely 2-EXPTIME-complete.

Abstract 2748: Single cell biomechanical profiling of the metastatic potential of MDA-MB-231 cells

Abstract Cancer metastasis is a multistep complex process accompanied by changes in the mechanical properties of cancer cells, such as adhesion, viscoelasticity, deformability, and motility. Connexin 43 (Cx43), a gap junction protein with a putative tumor suppressor function, plays a pivotal role in Epithelial to Mesenchymal transition (EMT). It affects cell motility indicators and promotes cytoskeleton reorganization and, ultimately, metastasis when downregulated, corroborating resistance to chemotherapy. The lack of universal markers that can predict the metastatic ability of cancer cells necessitates the evaluation of parameters, such as cell adhesion, viscoelasticity, and stiffness, to serve as unique biophysical metrics that predict cancer cell metastatic potential and elucidate how metastasis and cell mechanics are coupled. Therefore, we utilized fluidic-based single-cell force spectroscopy (SCFS) and Quartz Crystal Microbalance with Dissipation Monitoring (QCM-D) to measure the mechanical properties of single cancer cells with different metastatic potential. To explore the relationship between cell mechanics and metastatic cell behavior, we quantitatively demonstrate the progression of cancer cell biomechanics across different metastatic states using MDA-MB-231 breast cancer cells with varying connexin 43 (Cx43) expression as a model of cells with varying metastatic potential. We measured, at a single-cell level and in real-time, the adhesion and viscoelastic properties of MDA-MB-231 cells and their response to chemotherapy. Our data show that cells with higher metastatic potential (down-regulated Cx43 expression) exhibited a softer, less adherent, and more viscoelastic phenotype, consistent with their metastatic state when in clusters compared to cells at the lower spectrum of metastatic potential (up-regulated Cx43 expression). These mechanical biomarkers are significantly absent in single cells outside of a cluster. Of particular interest, we also observed significant cellular stiffening upon treatment with docetaxel (DTX), a chemotherapeutic agent that targets microtubules, with a rapid and profound response of highly aggressive cells when compared to cells with attenuated metastatic potential. Such treatment schemes could render cancer cells less deformable, potentially impeding their ability to invade and metastasize to other tissues. Our study demonstrates a unique quantitative metric that unfolds biomechanical markers for discerning the metastatic state of cancer cells. Our data represents a stepping stone to understanding how mechanical changes in cancer cells may determine how cancer progresses and responds to chemotherapeutic agents and may provide novel modalities to inhibit metastasis. Citation Format: Zeina Habli, Ahmad Zantout, Nadine Al Hajj, Raya Saab, Marwan El-Sabban, Massoud L. Khraiche. Single cell biomechanical profiling of the metastatic potential of MDA-MB-231 cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2748.

A novel derivative synchronous fluorescence method for the rapid, non-destructive and intuitive differentiation of denitrifying bacteria

It is challenging to differentiate bacteria residing in the same habitat by direct observation. This difficulty impedes the harvest, application and manipulation of functional bacteria in environmental engineering. In this study, we developed a novel method for rapid differentiation of living denitrifying bacteria based on derivative synchronous fluorescence spectroscopy, as exemplified by three heterotrophic nitrification-aerobic denitrification bacteria having the maximum nitrogen removal efficiencies greater than 90%. The intact bacteria and their living surroundings can be analyzed as an integrated target, which eliminates the need for the complex pre-processing of samples. Under the optimal synchronous scanning parameter (Δλ = 40 nm), each bacterium possesses a unique fluorescence spectral structure and the derivative synchronous fluorescence technique can significantly improve the spectral resolution compared to other conventional fluorescence methods, which enables the rapid differentiation of different bacteria through derivative synchronous fluorescence spectra as fast as 2 min per spectrum. Additionally, the derivative synchronous fluorescence technique can extract the spectral signals contributed by bacterial extracellular substances produced in the biological nitrogen removal process. Moreover, the results obtained from our method can reflect the real-time denitrification properties of bacteria in the biological nitrogen removal process of wastewater. All these merits highlight derivative synchronous fluorescence spectroscopy as a promising analytic method in the environmental field.

Characteristics of bio-tar catalytic cracking by char’s inherent Fe2O3 and K2O for biomass gasification process

In this research, a newly-designed micro fluidized bed reaction analyzer (MFBRA) was used to evaluate the catalytic activity of char’s inherent Fe2O3 and K2O on bio-tar in Ar at temperatures of 1023 K, 1073 K, 1123 K, and 1173 K. The adopted samples of char and tar were obtained by biomass pyrolysis in Ar at 1273 K. Fe2O3- and K2O-loaded char were prepared by doping metal oxides on the demineralized char samples to simulate the inherent metal oxides in char. The real-time cracking properties of tar were examined and compared on the basis of the analyzer’s high heating rate, quick sample loading at the predetermined temperature, and online analysis of gas products. The results show that both Fe2O3 and K2O displayed obviously catalytic activity by improving tar conversion and reaction rate, and lowering activation energy (Ea). Compared to K2O at 1173 K, Fe2O3-loaded char promoted the generation of H2, CO, CO2, CH4, C3H6, and the total gas products with the conversion ratio (XFe2O3/K2O) of 2.04, 1.02, 1.24, 1.32, 1.56, and 1.12, respectively. However, it suppressed the formation of C2H6 with the XFe2O3/K2O of 0.43. Meanwhile, the maximum generation rates for CO, CO2, CH4, and the total gas products improved to different extent. During tar catalytic cracking, Fe2O3 suppressed carbon deposition on the char surface because of the larger specific area after experiments. Finally, the generation Ea of CH4, H2, CO, CO2, C3H6 and the total gas products by Fe2O3-loaded char further decreased drastically and that of C2H6 increased obviously. All of these indicated the catalytic activity of the inherent Fe2O3 in char was better than K2O.

Convolutional Neural Network-Driven Impedance Flow Cytometry for Accurate Bacterial Differentiation.

Impedance flow cytometry (IFC) has been demonstrated to be an efficient tool for label-free bacterial investigation to obtain the electrical properties in real time. However, the accurate differentiation of different species of bacteria by IFC technology remains a challenge owing to the insignificant differences in data. Here, we developed a convolutional neural networks (ConvNet) deep learning approach to enhance the accuracy and efficiency of the IFC toward distinguishing various species of bacteria. First, more than 1 million sets of impedance data (comprising 42 characteristic features for each set) of various groups of bacteria were trained by the ConvNet model. To improve the efficiency for data analysis, the Spearman correlation coefficient and the mean decrease accuracy of the random forest algorithm were introduced to eliminate feature interaction and extract the opacity of impedance related to the bacterial wall and membrane structure as the predominant features in bacterial differentiation. Moreover, the 25 optimized features were selected with differentiation accuracies of >96% for three groups of bacteria (bacilli, cocci, and vibrio) and >95% for two species of bacilli (Escherichia coli and Salmonella enteritidis), compared to machine learning algorithms (complex tree, linear discriminant, and K-nearest neighbor algorithms) with a maximum accuracy of 76.4%. Furthermore, bacterial differentiation was achieved on spiked samples of different species with different mixing ratios. The proposed ConvNet deep learning-assisted data analysis method of IFC exhibits advantages in analyzing a huge number of data sets with capacity for extracting predominant features within multicomponent information and will bring about progress and advances in the fields of both biosensing and data analysis.

Probing Activation and Conformational Dynamics of the Vesicle-Reconstituted β2 Adrenergic Receptor at the Single-Molecule Level.

G-protein-coupled receptors (GPCRs) are structurally flexible membrane proteins that mediate a host of physiological responses to extracellular ligands like hormones and neurotransmitters. Fine features of their dynamic structural behavior are hypothesized to encode the functional plasticity seen in GPCR activity, where ligands with different efficacies can direct the same receptor toward different signaling phenotypes. Although the number of GPCR crystal structures is increasing, the receptors are characterized by complex and poorly understood conformational landscapes. Therefore, we employed a fluorescence microscopy assay to monitor conformational dynamics of single β2 adrenergic receptors (β2ARs). To increase the biological relevance of our findings, we decided not to reconstitute the receptor in detergent micelles but rather lipid membranes as proteoliposomes. The conformational dynamics were monitored by changes in the intensity of an environmentally sensitive boron-dipyrromethene (BODIPY 493/503) fluorophore conjugated to an endogenous cysteine (located at the cytoplasmic end of the sixth transmembrane helix of the receptor). Using total internal reflection fluorescence microscopy (TIRFM) and a single small unilamellar liposome assay that we previously developed, we followed the real-time dynamic properties of hundreds of single β2ARs reconstituted in a native-like environment─lipid membranes. Our results showed that β2AR-BODIPY fluctuates between several states of different intensity on a time scale of seconds, compared to BODIPY-lipid conjugates that show almost entirely stable fluorescence emission in the absence and presence of the full agonist BI-167107. Agonist stimulation changes the β2AR dynamics, increasing the population of states with higher intensities and prolonging their durations, consistent with bulk experiments. The transition density plot demonstrates that β2AR-BODIPY, in the absence of the full agonist, interconverts between states of low and moderate intensity, while the full agonist renders transitions between moderate and high-intensity states more probable. This redistribution is consistent with a mechanism of conformational selection and is a promising first step toward characterizing the conformational dynamics of GPCRs embedded in a lipid bilayer.

Quantifying the Light-Absorption Properties and Molecular Composition of Brown Carbon Aerosol from Sub-Saharan African Biomass Combustion.

Sub-Saharan Africa is a hotspot for biomass burning (BB)-derived carbonaceous aerosols, including light-absorbing organic (brown) carbon (BrC). However, the chemically complex nature of BrC in BB aerosols from this region is not fully understood. We generated smoke in a chamber through smoldering combustion of common sub-Saharan African biomass fuels (hardwoods, cow dung, savanna grass, and leaves). We quantified aethalometer-based, real-time light-absorption properties of BrC-containing organic-rich BB aerosols, accounting for variations in wavelength, fuel type, relative humidity, and photochemical aging conditions. In filter samples collected from the chamber and Botswana in the winter, we identified 182 BrC species, classified into lignin pyrolysis products, nitroaromatics, coumarins, stilbenes, and flavonoids. Using an extensive set of standards, we determined species-specific mass and emission factors. Our analysis revealed a linear relationship between the combined BrC species contribution to chamber-measured BB aerosol mass (0.4-14%) and the mass-absorption cross-section at 370 nm (0.2-2.2 m2 g-1). Hierarchical clustering resolved key molecular-level components from the BrC matrix, with photochemically aged emissions from leaf and cow-dung burning showing BrC fingerprints similar to those found in Botswana aerosols. These quantitative findings could potentially help refine climate model predictions, aid in source apportionment, and inform effective air quality management policies for human health and the global climate.

Physics-Embedded Deep Learning to Predict Real-Time Flow Parameters in Complex Thermodynamic Machinery

Flow through complex thermodynamic machinery is intricate, incorporating turbulence, compressibility effects, combustion, and solid–fluid interactions, posing a challenge to classical physics. For example, it is not currently possible to simulate a three-dimensional full-field gas flow through the propulsion of an aircraft. In this study, a new approach is presented for predicting the real-time fluid properties of complex flow. This perspective is obtained from deep learning, but it is significant in that the physical context is embedded within the deep learning architecture. Cases of excessive working states are analyzed to validate the effectiveness of the given architecture, and the results align with the experimental data. This study introduces a new and appealing method for predicting real-time fluid properties using complex thermomechanical systems.

LEOCN: Real-time and complete network simulation framework for LEO constellation networks1

With the development of the space industry, LEO simulation platforms are increasingly important. Prior work mainly focused on the dynamics of constellation topology, routing algorithms against the topological motion, and the behavior of LEO constellation networks, but neglected real-time and completeness properties. In our work, we design a real-time and complete network simulation framework, named LEOCN, that is more meaningful and detailed for researchers than previous work on LEO satellite constellation simulations. LEOCN is supported by Linux System, which makes the framework more realistic and relieves the computational burden. Meanwhile, LEOCN deeply considers topology and routing algorithms, visualization, satellite mobility, communication protocols, and network traffic problems. On LEOCN, we can deploy routing algorithm experiments via SDN. We evaluate the performance of LEOCN in large-scale constellation scenarios, and the results demonstrate the superiority of the real-time and complete network simulation framework.

Hierarchical Resource Orchestration Framework for Real-time Containers

Container-based virtualization is a promising deployment model in fog and edge computing applications, because it allows a seamless co-existence of virtualized applications in a heterogeneous environment without introducing significant overhead. Certain application domains (e.g., industrial automation, automotive, or aerospace) mandate that applications exhibit a certain degree of temporal predictability. Container-based virtualization cannot be easily used for such applications, since the technology is not designed to support real-time properties and handle temporal disturbances. This article proposes a framework consisting of a static offline and a dynamic online phase for resource allocation and adaptive re-dimensioning of real-time containers. In the offline phase, the optimal initial deployment and dimensioning of containers are decided based on ideal system models. Additionally, to adapt to dynamic variations caused by changing workloads or interferences, the online phase adapts the CPU usage and limits of real-time containers at runtime to improve the real-time behavior of the real-time containerized applications while optimizing resource usage. We implement the framework in a real Linux-based system and show through a series of experiments that the proposed framework is able to adjust and re-distribute computing resources between containers to improve the real-time behavior of containerized applications in the presence of temporal disturbances while optimizing resource usage.

Experimental study on phase compensation method based on autocorrelation in laser heterodyne detection

Spatial distortion phase seriously deteriorates the performance of heterodyne detection system. Effective phase compensation is necessary to reduce the impact of the distortion phase. Compared with the compensation method based on the combination of an optimization algorithm and sequence shifting, that by use of autocorrelation does not require repetitive iterations and avoids false alarms, resulting in better stability and real-time properties. In this paper, the distortion phase is generated by an atmospheric turbulence simulator to examine the performance of the new compensation method. The experiment results show that compared with the single point detector method, the new method can even increase the SNR by 50.3 dB, which confirms the effectiveness of the compensation method. Due to easy implementation and excellent performance, it is possible that the new method will become a universal means to compensate for the spatial distortion phase in the heterodyne detection.

Herbal Melanin Inhibits Real-Time Cell Proliferation, Downregulates Anti-Apoptotic Proteins and Upregulates Pro-Apoptotic p53 Expression in MDA-MB-231 and HCT-116 Cancer Cell Lines.

Background and Objectives: Cancer is the second-most-important deadly disease in the world, leading to severe socioeconomic consequences and posing a public threat. Consequently, breast and colorectal cancers are significant cancer types that affect women and men more commonly, respectively. Treatment failure or recurrent diseases frequently occur due to resistance, in addition to the side effects of the currently available anticancer agents. Therefore, in this study, herbal melanin anticancer activity was investigated against human breast adenocarcinoma (MDA-MB-231) and human colorectal (HCT 116) cell proliferation and the expression of downregulated anti-apoptotic proteins and upregulated pro-apoptotic p53. Materials and Methods: MDA-MB-231 and HCT 116 cells were monitored for their real-time proliferation properties using Xcelligence. Herbal melanin of various concentrations significantly inhibited MDA-MB-231 and HCT 116 cell proliferation. Then, the expression of proapoptotic and anti-apoptotic proteins such as p53, Bcl-2 and Bcl-xl was studied using Western blotting. Results: The Bcl-2 and Bcl-xl expressions were downregulated, while the p53 expression was upregulated after treatment with herbal melanin. Similarly, the expression of apoptotic proteins such as Bcl-2, Bcl-xl, XIAP, Survivin, Bid, Bax, p53, Cytochrome C, PARP genes and mRNA was studied after herbal melanin treatment using real-time PCR, which revealed the downregulation of Bcl-2, Bcl-xl, XIAP and Survivin and the upregulation of Bid, Bax, p53, Cytochrome C and PARP apoptotic protein expression. Also, caspase 3 and 9 expressions were monitored after the treatment with herbal melanin, which revealed the upregulation of both the MDA-MB-231 and HCT 116 cell types. Conclusions: Overall, herbal melanin can be used as an alternative anticancer agent against the MDA-MB-231 and HCT 116 cell types.