Conventional Constraints Research Articles

Generalized Coherent Wave Control at Dynamic Interfaces

AbstractCoherent wave control is of key importance across a broad range of fields such as electromagnetics, photonics, and acoustics. It enables us to amplify or suppress the outgoing waves via engineering amplitudes and phases of multiple incidences. However, within a purely spatially (temporally) engineered medium, coherent wave control requires the frequency of the associated incidences to be identical (opposite). In this work, this conventional constraint is broken by generalizing coherent wave control into a spatiotemporally engineered medium is broken, i.e., the system featuring a dynamic interface. Owing to the broken translational symmetry in space and time, both the subluminal and superluminal interfaces allow interference between scattered waves regardless of their different frequencies and wavevectors. Hence, one can flexibly eliminate the backward‐ or forward‐propagating waves scattered from the dynamic interfaces by controlling the incident amplitudes and phases. The work not only presents a generalized way for reshaping arbitrary waveforms but also provides a promising paradigm to generate ultrafast pulses using low‐frequency signals. It has also implemented suppression of forward‐propagating waves in microstrip transmission lines with fast photodiode switches.

Disturbance observer-based fuzzy adaptive switched tuning control of uncertain nonlinear systems with full state constraints

This article examines the issue of disturbance observer-based fuzzy adaptive switched tuning output feedback control for a family of uncertain nonlinear systems subjected to full state constraints. By introducing monotonically bounded positive time-varying gain functions (TVGFs), a set of improved intermediate-variable-based disturbance observers (IVBDOs) is devised. Applying the convex combination technique and the LMI toolbox, the solution of the observation gain matrix is realized. Combining the switched tuning control function and the composite integral barrier Lyapunov method, a new switching nature controller is constructed. Particularly, the developed framework overcomes the conservatism of conventional constraints. Through theoretical analysis, it is exhibited that for any given initial value of the system, all states of the system are enclosed within predetermined compact sets while all signals are bounded. Lastly, simulation and experimental results under unknown disturbances attest the validity and superiority of the suggested strategy.

Synchronization of nonlinear neural networks with hybrid couplings and uncertain time-varying perturbations: A novel distributed-delay impulsive comparison principle

This paper investigates the synchronization of nonlinear drive-response neural networks subject to uncertain time-varying perturbations, non-delayed coupling, and distributed delay coupling. To address the influence of distributed and discrete delays on the system, we establish a novel impulsive comparison principle, extending the Halanay inequality. By leveraging Lyapunov stability theory, we derive sufficient conditions for the exponential synchronization of the neural networks using a delayed impulsive controller with historical status information. This approach relaxes the conventional constraint that impulsive delays must be smaller than impulsive intervals, thereby generalizing existing synchronization results for distributed delay networks. Numerical simulations for chaotic neural networks validate the theoretical results and demonstrate the sensitivity of the control gain matrix.

Unit commitment of power systems considering system inertia constraints and uncertainties

AbstractLarge‐scale integration of renewable energy into the power grid results in a lack of system inertia, posing challenges to the optimal operation and scheduling of systems considering frequency stability. This article proposes a unit commitment model that considers both inertia constraints and the uncertainty of load and renewable energy. First, the time‐domain expression of the system frequency response is calculated based on the aggregated System Frequency Response (SFR) model, considering the system's maximum frequency deviation and the maximum Rate of Change of Frequency (RoCoF) limit. This calculation determines the minimum inertia requirement for the system. Furthermore, inertia constraints suitable for mixed‐integer programming model are derived to address the nonlinearity of conventional frequency constraints. Second, considering the uncertainties of load and wind energy from renewable sources, a unit commitment model with inertia constraints is constructed, and a robust method is used to solve the uncertainties. Finally, the accuracy of the proposed inertia constraints and unit commitment model is validated using case study of IEEE standard test cases and a provincial power grid in China.

A Three-Dimensional Reconstruction Method Based on Telecentric Epipolar Constraints

When calibrating a microscopic fringe projection profile system with a telecentric camera, the orthogonality of the camera causes an ambiguity in the positive and negative signs of its external parameters. A common solution is to introduce additional constraints, which often increase the level of complexity and the calibration cost. Another solution is to abandon the internal/external parameter models derived from the physical imaging process and obtain a numerically optimal projection matrix through the least squares solution. This paper proposes a novel calibration method, which derives a telecentric epipolar constraint model from the conventional epipolar constraint relationship and uses this constraint relationship to complete the stereo calibration of the system. On the one hand, since only the camera’s intrinsic parameters are needed, there is no need to introduce additional constraints. On the other hand, the solution is optimized based on the full consideration of the imaging model to make the parameters confirm to the physical model. Our experiments proved the feasibility and accuracy of the method.

The Impact of Digital Transformation on the Innovation Capacity of Chinese-Listed Firms: The Role of Government Subsidies

As digital transformation progresses, listed Chinese firms are undergoing significant changes in their practices. These changes are crucial for establishing and maintaining a competitive advantage. This research investigates the process by which digital transformation affects firms' capacity to innovate, specifically focusing on the influence of government subsidies. The data was obtained from 1,063 publicly traded in China from 2018 to 2022 with a total of 4,027 data points. A fixed-effects model, stepwise regression analysis, and bootstrapping techniques were employed to construct the models. To address the conventional quantitative constraints and provide a nuanced comprehension of digital transformation's influence, this study uses textual analysis. Research has shown that digital transformation has a substantial positive impact on the ability of companies to innovate. Additionally, government subsidies are proven to have a role in facilitating this process. This paper offers a fresh viewpoint on comprehending the mechanism of government subsidies for digital transformation and corporate innovation capability. It also provides evidence supporting the idea that government subsidies may enhance innovation incentives more effectively

External and internal factors affecting collaborative consumption practices: insights from new-middle classes in an emerging market

Purpose This study aims to unravel the perceived barriers, and external and internal factors affecting the new middle-class Turkish consumers’ collaborative consumption practices (e.g. accommodation rental and ride-sharing services). Design/methodology/approach This study conducts semi-structured, in-depth interviews with 21 new middle-class consumers from Turkey. The qualitative data were analyzed with reflexive thematic analysis. Findings Findings show that environmental stimuli (economic crisis, transparent legal requirements, word-of-mouth, urban mobility, unique experiences, cosmopolitan thinking) affect organism factors (financial gains and psychological barriers such as difficulty in relying on service providers, guarded vulnerability, unfavorable brand image of local companies, conventional constraints). Organism factors affect the response of intention to engage in collaborative consumption. Practical implications Collaborative consumption services can integrate socially responsible projects into their lower-priced services giving signals of “unique,” “feeling at home” and “rely on”. This will help to overcome perceived barriers of conventional constraints, difficulty in relying on service providers and contribute to financial gains and unique experiences. Originality/value The extant literature focuses mainly on intrinsic and extrinsic motives without thoroughly investigating barriers and their connections to external and internal factors influencing collaborative consumption. This study answers the calls for further research on barriers, external and internal factors affecting collaborative consumption practices. Moreover, this study addresses the need for research on the collaborative consumption practices of middle-class consumers.

Critical thinking, assessment, and educational policy in Palestinian universities

This study examines the relationship between critical thinking and grades at the tertiary level, focusing on their social, political, and ethical implications. Employing a mixed-methods approach, this study combines survey data collection with in-depth interviews to generate comprehensive insights into the complex relationship between critical thinking and grades. The survey targets 173 faculty members, while the interviews focus on seven selected academic staff members from Palestinian universities, enabling a comprehensive understanding of the research objectives. Results showed that grades often hinder critical thinking skills and creativity, leading to rote memorization and limited creativity. The study also highlights the political implications of grades, as standardized testing influences education policies and curriculum decisions. Faculty members expressed criticism of the prioritization of grades, citing conventional evaluation methods, temporal limitations, and resource constraints. As critical thinking is crucial for comprehensive student development, contributing to problem-solving, decision-making, creativity, innovation, effective communication, and active citizenship, the study proposes diverse approaches to strike a balance between valuing grades and nurturing critical thinking abilities. By fostering critical thinking abilities, Palestinian students can enhance their preparedness for academic pursuits, personal growth, and societal contributions.

Harnessding the Power of Machine Learning to Superchange CAPM Forecasts

This paper investigates the improvement of the Capital Asset Pricing Model (CAPM) by incorporating machine learning techniques. The objective is to address the model's conventional constraints in accurately predicting stock returns. The conventional Capital Asset Pricing Model (CAPM), which heavily depends on the beta coefficient to explain returns, frequently proves inadequate in emerging markets and neglects to consider market irregularities such as size and value effects. Our methodology enhances the estimation of beta by refining the Capital Asset Pricing Model (CAPM) to exclude the intercept term and incorporating a dynamic rolling regression method. This approach captures the fluctuations of beta more effectively across different periods, resulting in a more precise estimation. This study utilises ten years of weekly closing price data from companies listed on the NSE Nifty 50 index. It employs rolling regression and two-stage regression analysis to improve the prediction of excess returns above the risk-free rate. The revised CAPM model, which omits the intercept, exhibits a superior level of accuracy, as evidenced by higher adjusted R-squared values and improved F-statistics. In addition, the paper analyses the Fama-French models within the Chinese stock market and explores the potential of advanced machine learning models, such as Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN), to improve forecasting methods in unpredictable markets. Integrating LSTM-RNN models presents a promising approach to capturing intricate patterns in financial time series data, demonstrating substantial enhancement in forecasting accuracy compared to conventional methods such as OLS.

Staging Desire Androgynously: An Alternative Vision of Identity

This article examines the intersection of sexualities and identities, with a particular focus on the ways these elements can be transgressed in Western theater. The study postulates that theater represents a distinctive forum for challenging established societal structures, thereby enabling actors and, by proxy, spectators to perform multiple identities that challenge conventional constraints and break traditional expectations. The research demonstrates the dynamic nature of identity formation on the modern stage, illustrating that identity can both reflect societal norms and serve as an alternative to them. This allows for the expression of diverse sexual identities that deviate from the heteronormative order. Consequently, the theater becomes a site for subverting conventional codes and for negotiating with mainstream society the potential for exploring a plethora of non-standardized identities. This process culminates in the crystallization of norm-divergent gender identities.

Evaluation of seedless wavelet-based optical flow velocimetry for schlieren images

In harsh flow environments, traditional particle-based velocimetry methods face challenges. This study explores the use of seedless schlieren images for velocimetry through a novel algorithm, namely, wavelet-based optical flow velocimetry (wOFV). Various data term constraints for wOFV were examined. It is found that the data term derived from the integrated continuity equation (ICE) outperformed the conventional displaced frame difference constraint and the schlieren-tailored constraints (SE and SSE). Evaluation based on the root mean square error (RMSE) and turbulence energy spectrum (TES) reveals that the choice of wavelet becomes insignificant for the optimal estimated velocity field when the wavelet support length is sufficiently long. In addition, the implementation of a proper truncation in wOFV shows little dependence of the RMSE on the weighting coefficient, therefore alleviating the uncertainty associated with selecting an appropriate weighting coefficient. It is found that the retrieved flow field from schlieren images approximates a down-sampled result based on available structural scales in images. Considering the prevalence of under-resolved velocity field in practical applications, schlieren-based wOFV offers a reasonable alternative to particle-based velocimetry, particularly in harsh flow environments.

Impact of Digital Transformation on the Innovation Capacity of Chinese-Listed Firms: Role of Government Subsidies

As digital transformation progresses, listed Chinese firms are undergoing significant changes in their practices. These changes are crucial for establishing and maintaining a competitive advantage. This research investigates the process by which digital transformation affects firms' capacity to innovate, specifically focusing on the influence of government subsidies. The data was obtained from 1,063 publicly traded in China from 2018 to 2022 with a total of 4,027 data points. A fixed-effects model, stepwise regression analysis, and bootstrapping techniques were employed to construct the models. To address the conventional quantitative constraints and provide a nuanced comprehension of digital transformation's influence, this study uses textual analysis. Research has shown that digital transformation has a substantial positive impact on the ability of companies to innovate. Additionally, government subsidies are proven to have a role in facilitating this process. This paper offers a fresh viewpoint on comprehending the mechanism of government subsidies for digital transformation and corporate innovation capability. It also provides evidence supporting the idea that government subsidies may enhance innovation incentives more effectively.

Systematic investigation and controlled synthesis of Ag/Ti co-doped hydroxyapatite for bone tissue engineering

Hydroxyapatite (HA) is a cornerstone bio ceramic in bone tissue engineering applications, prized for its inherent biocompatibility and structural resemblance to natural bone tissue. However, both porous and dense, conventional HA formulations face notable stability and mechanical robustness constraints, impeding their broader utility. We introduce an innovative biomaterial synthesized via a straightforward and efficient sol-gel method to surmount these challenges and imbue novel scaffolds with multifunctional capabilities. Leveraging a double doping strategy, our approach seeks to enhance mechanical performance and incorporate antibacterial features, maintaining the biocompatibility of HA-based scaffolds for advanced tissue engineering applications. In pursuit of this objective, we synthesized Ag-doped, Ag, Ti-doped, and Ti-doped HA samples to explore the impact of varying dopant types and concentrations on various structural, thermal, crystallographic, chemical, morphological, mechanical, and biocompatibility characteristics. X-ray Diffraction (XRD) analysis confirmed the presence of apatitic phase compositions across all samples, while Fourier-transform Infrared Spectroscopy (FTIR) data corroborated phosphate formation and functional group identification. Scanning Electron Microscopy (SEM) studies revealed a correlation between particle size and dopant concentration, consistent with XRD findings. Nanoindentation results indicated optimal mechanical performance was achieved with balanced incorporation of Ag and Ti ions despite increased Vickers microhardness with higher Ti concentrations. All doped samples exhibited effective antibacterial activity against Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus), except for the Ti-doped HA sample. Moreover, the HA sample co-doped with equal amounts of Ag and Ti demonstrated noncytotoxicity for human lung cancer (A549) cells. In contrast, all doped samples exhibited cytotoxicity towards human prostate cancer cell (PC3) cells. Statistical analysis confirmed a synergistic enhancement of biocompatibility and mechanical performance in HA samples doped with Ag and Ti ions in equal proportions. In conclusion, our study presents a simple and effective approach for enhancing HA's mechanical and antibacterial properties through co-doping with Ag and Ti. This innovative biomaterial holds significant promise for advancing bone tissue engineering applications.

Advanced impedance mismatch technique for detecting faults in photovoltaic systems

AbstractThis paper presents a comprehensive exploration of the Advanced Impedance Mismatch Technique (AIMT), a novel approach designed for the accurate detection of simultaneous and varied faults within photovoltaic (PV) systems. This investigation integrates a spectrum of fault detection strategies, pinpointing reflectometry as a notably effective tool. Despite its utility, conventional reflectometry applications face critical constraints, notably the limitation to identify only the primary fault location within a PV array and the inability to distinguish between different fault types. This work introduces an innovative mathematical model that estimates the impedance of PV modules, enhancing the reflectometry method to enable the precise identification and localization of multiple defective modules within a string. The proposed technique exhibits a remarkable sensitivity to detect slight impedance differences between a functional PV string and one with defective modules. The validity of the AIMT's mathematical model is corroborated through simulation experiments on a string of seven PV modules afflicted with multiple simultaneous faults. These experiments rigorously evaluate the technique's accuracy in pinpointing the locations of defective modules within a PV string. The outcomes of our proposed ‐times faster AIMT reveal a strong concordance between the simulated reflective signals and the impedance values forecasted by the model, highlighting the proposed method's proficiency in the detailed detection and diagnosis of progressive faults within PV systems.

Integrating machine learning and electrochemistry: A hybrid SA-DE-RF approach for optimizing electrode composition in water treatment

The imperative to address the burgeoning challenge of industrial water pollution has catalyzed the pursuit of sophisticated treatment methodologies capable of neutralizing non-biodegradable contaminants. This investigation focuses on the optimization of Ti/SnO2-Sb-Ni anode composition, leveraging a synergistic hybrid machine learning strategy that integrates Simulated Annealing (SA), Differential Evolution (DE), and Random Forest (RF) algorithms. This triad of algorithms is meticulously applied to ascertain the optimal doping concentrations of antimony (Sb) and nickel (Ni), pivotal in maximizing the efficacy of electrochemical oxidation processes. Our findings elucidate that the meticulously optimized Ti/SnO2-Sb-Ni anode, with a specific doping ratio of 100:3.74:5.36, engenders a degradation efficiency of benzoic acid (BA) that approximates completeness within a constrained temporal framework of 60 minutes. The RF regression model, exemplifying a robust R² value of 0.9565 and an RMSE of 0.0576, surpasses its counterparts in prognostication accuracy, underscoring its preeminence in electrode composition optimization for the amelioration of electrochemical water treatment matrices. The convergence of SA, DE, and RF methodologies not only refines the predictive fidelity of the degradation kinetics but also extricates the optimization process from the conventional constraints, thereby enhancing the scalability and precision of the model. This research advances the frontiers of electrochemical oxidation and etches a pioneering niche for the amalgamation of machine learning within environmental engineering paradigms. The hybrid algorithmic scaffolding proffered herein is suggested as a robust and versatile framework, propelling forward the discourse on the remediation of polluted water bodies and offering a springboard for future investigative endeavors.

Analysis of the fecal metagenome in pelvic radiation survivors with or without chronic gastrointestinal toxicity.

e24041 Background: Whole pelvis radiation therapy (WPRT) is often used as a definitive or adjuvant treatment in patients with genitourinary, gynecologic, and gastrointestinal (GI) malignancies. Irradiation of nearby bowel can cause acute and chronic GI symptoms, which can affect patients’ quality of life. Radiation therapy has been shown to alter the microbial flora and may select for pathogenic species, which may enhance toxicity. Characteristics of the fecal metagenome in patients with or without chronic GI toxicity is unknown. Severity of GI symptoms can vary between patients who receive a similar radiation dose to normal bowel; therefore, there is a critical need to evaluate the interaction of RT and microbial species in the development of toxicity. Methods: An institutional database was used to identify survivors that completed WPRT for prostate, gynecologic, and anal cancer within the last 5 years. Patients were excluded if they had evidence of recurrent disease requiring additional therapy. Eligible patients were asked to participate via telephone. Consenting patients provided a stool sample and completed a customized PRO-CTCAE form (scale 1-5) assessing chronic GI symptoms. Comprehensive shotgun metagenomics were performed on stool. Stool was sequenced using an Illumina NextSeq500. The fecal metagenome in patients with or without chronic GI symptoms were compared. Patients were dichotomized by PRO-CTCAE diarrhea score (0-1 [mild] vs ≥2 [moderate-severe]). Raw reads were processed with Kneaddata for trimming, Kracken2 for taxonomic profiling, and Bracken for abundance estimation for bacterial, fungal and viral genomes using sequences from the National Center for Bioinformatics to determine quantitative genus and species differences. For microbial diversity assessments, estimated inverse Simpson and Shannon indices were performed. Results: Fecal metagenome and PRO-CTCAE results were available for 13 survivors. Thirty percent (n = 4) of patients reported moderate to severe diarrhea. The mean age was 66 years, 38% were male, 46% received concurrent or adjuvant chemotherapy, and the mean time from completion of WPRT to stool analysis was 34 months (range 14-62 months). Conventional radiation bowel constraints were met for each patient. Several species were enriched in patients with mild GI toxicity. Notably, Akkermansia muciniphilia had the largest differential enrichment in patients with without chronic GI toxicity. Conclusions: In this cohort of patients, A. muciniphilia, a mucin-degrading bacteria that modulates the gut barrier integrity and mucin layer thickness, was enriched in patients with no to mild diarrhea compared to patients with moderate to severe diarrhea. A. muciniphilia supplementation has been shown to decrease systemic inflammation in Western diet-fed mice. Evaluation of A. muciniphilia as a radioprotective gut bacterium warrants further evaluation.

Monolithic three-dimensional tier-by-tier integration via van der Waals lamination.

Two-dimensional (2D) semiconductors have shown great potential for monolithic three-dimensional (M3D) integration due to their dangling-bonds-free surface and the ability to integrate to various substrates without the conventional constraint of lattice matching1-10. However, with atomically thin body thickness, 2D semiconductors are not compatible with various high-energy processes in microelectronics11-13, where the M3D integration of multiple 2D circuit tiers is challenging. Here we report an alternative low-temperature M3D integration approach by van der Waals (vdW) lamination of entire prefabricated circuit tiers, where the processing temperature is controlled to 120 °C. By further repeating the vdW lamination process tier by tier, an M3D integrated system is achieved with 10 circuit tiers in the vertical direction, overcoming previous thermal budget limitations. Detailed electrical characterization demonstrates the bottom 2D transistor is not impacted after repetitively laminating vdW circuit tiers on top. Furthermore, by vertically connecting devices within different tiers through vdW inter-tier vias, various logic and heterogeneous structures are realized with desired system functions. Our demonstration provides a low-temperature route towards fabricating M3D circuits with increased numbers of tiers.

Challenges and Opportunities for Sustainable Supply Chain Management

Green supply chain management (GSCM) is an advanced management approach that comprehensively considers the environmental impact, resource utilization efficiency, and enterprise income throughout the entire supply chain process. Compared to traditional supply chain management, GSCM exhibits higher complexity in terms of management and network characteristics. It emphasizes sustainable development for both society and enterprises, incorporating ecological and environmental protection design principles from raw material sourcing to waste material recycling, thereby achieving optimized environmental outcomes through enhanced cooperation within and between organizations. In essence, green supply chain management practices encompass green design, green procurement, green manufacturing, green packaging, green materials usage, and green recycling initiatives. The critical distinction between a green supply chain and a traditional one lies in the former's consideration not only of optimizing and coordinating the supply chain under conventional economic cost constraints but also recognizing the negative impacts of economic activities on the environment as significant factors warranting investigation. By embedding environmental awareness into supply chain management processes, it seeks to achieve a balanced approach that harmonizes the economy with environmental concerns. The proposition of adopting a green supply chain aligns with the developmental trajectory of economic globalization and free trade while meeting demands for environmentally friendly products and promoting greener trade.

Engineered Cell Membrane-Coated Nanoparticles: New Strategies in Glioma Targeted Therapy and Immune Modulation.

Gliomas, the most prevalent primary brain tumors, pose considerable challenges due to their heterogeneity, intricate tumor microenvironment (TME), and blood-brain barrier (BBB), which restrict the effectiveness of traditional treatments like surgery and chemotherapy. This review provides an overview of engineered cell membrane technologies in glioma therapy, with a specific emphasis on targeted drug delivery and modulation of the immune microenvironment. This study investigates the progress in engineered cell membranes, encompassing physical, chemical, and genetic alterations, to improve drug delivery across the BBB and effectively target gliomas. The examination focuses on the interaction of engineered cell membrane-coated nanoparticles (ECM-NPs) with the TME in gliomas, emphasizing their potential to modulate glioma cell behavior and TME to enhance therapeutic efficacy. The review further explores the involvement of ECM-NPs in immunomodulation techniques, highlighting their impact on immune reactions. While facing obstacles related to membrane stability and manufacturing scalability, the review outlines forthcoming research directions focused on enhancing membrane performance. This review underscores the promise of ECM-NPs in surpassing conventional therapeutic constraints, proposing novel approaches for efficacious glioma treatment.

Novel pH-responsive hybrid hydrogels for controlled delivery of curcumin: Overcoming conventional constraints and enhancing cytotoxicity in MCF-7 cells

Traditional drug delivery techniques often have drawbacks such inadequate bioavailability, quick clearance, and non-specific targeting, which may result in unfavorable side effects. Hybrid nanocarriers composed of nanoclay have the potential to be a viable solution to these problems. The goal of this work was to design a biocompatible hybrid nanocarrier that would release curcumin under regulated conditions to specific locations, especially cancer cells. FTIR, XRD, and FESEM were used to effectively construct and evaluate our polyvinyl pyrrolidone/polyethylene glycol/montmorillonite (PVP/PEG/MMT) hybrid hydrogels, verifying their synthesis and intended features. Zeta potential and DLS studies showed a suitable size distribution (∼300 nm) and high stability. The curcumin encapsulation efficiency and release profile were assessed using the use of UV spectrophotometry and the dialysis bag technique. The findings showed a gradual and continuous release that was highly impacted by the acidic environment (which mimicked the characteristics of cancer cells), suggesting pH-responsive activity. The MTT test was used in vitro cytotoxicity experiments to show that curcumin-loaded nanocarriers were more effective against MCF-7 cancer cells than curcumin in solution. Increased rates of necrosis and apoptosis in cancer cells treated with the nanocarrier formulation were further validated by flowcytometry. According to these results, PVP/PEG/MMT hybrid hydrogels have a lot of potential as a cutting-edge drug delivery strategy for curcumin. They may be able to overcome some of the drawbacks of traditional techniques and improve the effectiveness of treatment against cancer cells.