Minimal Requirements Research Articles

Requirements for upgrading trusted nodes to a repeater chain over 900 km of optical fiber

Abstract We perform a numerical study of the distribution of entanglement on a real-world fiber grid connecting the German cities of Bonn and Berlin. The connection is realized using a chain of processing-node quantum repeaters spanning roughly 900 kilometers. Their placement is constrained by the fiber grid we consider, resulting in asymmetric links. We investigate how minimal hardware requirements depend on the target application, as well as on the number of repeaters in the chain. We find that requirements for blind quantum computing are markedly different than those for quantum key distribution, with the required coherence time being around two and a half times larger for the former. Further, we observe a trade-off regarding how target secret-key rates are achieved when using different numbers of repeaters: comparatively low-quality entangled states generated at a high rate are preferred for higher numbers of repeaters, whereas comparatively high-quality states generated at a lower rate are favored for lower numbers of repeaters. To obtain our results we employ an extensive simulation framework implemented using NetSquid, a discrete-event simulator for quantum networks. These are combined with an optimization methodology based on genetic algorithms to determine minimal hardware requirements.

A Case Series of Deep Subgluteal Block: A New Block Targeting the Missed Portion of the Hip for Analgesia After Total Hip Replacement

The study aimed to evaluate the effectiveness of deep subgluteal block (DSGB) for pain relief after posterolateral-approached total hip replacement. The cadaver study and observational case series assessed the spread and outcomes of ultrasound-guided DSGB. Results showed low postoperative pain scores, minimal opioid requirements, and no complications related to DSGB. Anatomical dissection revealed effective spread of the injected substance. These findings suggest that DSGB could be a promising regional analgesic technique for postoperative pain management after posterolateral-approached total hip replacement.

Minimal Requirements for Cancer Initiation: A Comparative Consideration of Three Prototypes of Human Leukemia.

Even if its completed form is complex, cancer originates from one or two events that happened to a single cell. A simplified model can play a role in understanding how cancer initiates at the beginning. The pathophysiology of leukemia has been studied in the most detailed manner among all human cancers. In this review, based on milestone papers and the latest research developments in hematology, acute promyelocytic leukemia (APL), chronic myeloid leukemia (CML), and acute myeloid leukemia (AML) with RUNX1-RUNX1T1 are selected to consider minimal requirements for cancer initiation. A one-hit model can be applied to the initiation of APL and CML whereas a two-hit model is more suitable to the initiation of AML with RUNX1-RUNX1T1 and other AMLs. Even in cancer cells with multiple genetic abnormalities, there must be a few mutant genes critical for the mutant clone to survive and proliferate. Such genes should be identified and characterized in each case in order to develop individualized target therapy.

Transcriptome Regulation Mechanisms Difference between Female and Male Buchloe dactyloides in Response to Drought Stress and Rehydration.

Drought, a pervasive global challenge, significantly hampers plant growth and crop yields, with drought stress being a primary inhibitor. Among resilient species, Buchloe dactyloides, a warm-season and dioecious turfgrass, stands out for its strong drought resistance and minimal maintenance requirements, making it a favored choice in ecological management and landscaping. However, there is limited research on the physiological and molecular differences in drought resistance between male and female B. dactyloides. To decipher the transcriptional regulation dynamics of these sexes in response to drought, RNA-sequencing analysis was conducted using the 'Texoka' cultivar as a model. A 14-day natural drought treatment, followed by a 7-day rewatering period, was applied. Notably, distinct physiological responses emerged between genders during and post-drought, accompanied by a more pronounced differential expression of genes (DEGs) in females compared to males. Further, KEGG and GO enrichment analysis revealed different DEGs enrichment pathways of B. dactyloides in response to drought stress. Analysis of the biosynthesis and signaling transduction pathways showed that drought stress significantly enhanced the biosynthesis and signaling pathway of ABA in both female and male B. dactyloides plants, contrasting with the suppression of IAA and JA pathways. Also, we discovered BdMPK8-like as a potential enhancer of drought tolerance in yeast, highlighting novel mechanisms. This study demonstrated the physiological and molecular mechanisms differences between male and female B. dactyloides in response to drought stress, providing a theoretical basis for the corresponding application of female and male B. dactyloides. Additionally, it enriches our understanding of drought resistance mechanisms in dioecious plants, opening avenues for future research and genetic improvement.

Learning About Lunar Phases: Exploring Taiwanese University Students’ Conceptions of and Approaches to Learning by Holographic Projection

Extant literature has preliminarily confirmed the potential benefits of holographic projections for educational purposes. Yet, students’ experiences of learning by holographic projection have rarely been addressed. The main purpose of this study was therefore to explore students’ conceptions of and approaches to learning by holographic projection. A holographic projection system was devised to assist students in learning the concept of lunar phases. This study also investigated the relationships between students’ conceptions and approaches, and determined if there were any variations based on students’ learning gains. Phenomenographic analysis was adopted to analyze 30 university students from Taiwan, and a multiple-choice conceptual test was conducted to assess their learning achievements. The main findings indicated that students with conceptions of learning by holographic projection as increasing the presence of a scene or mapping 2D images to 3D structures tended to adopt surface approaches, such as only observing the changes presented in the holographic projection, repetitively operating the system functions without intended purposes, or simply following the instruction guidelines to meet the minimal requirements. However, this study unexpectedly found that students who utilized surface approaches had significantly higher learning gains on the conceptual test than their counterparts did.

The Dutch Dystrophinopathy Database: A National Registry with Standardized Patient and Clinician Reported Real-World Data.

Duchenne and Becker muscular dystrophy lack curative treatments. Registers can facilitate therapy development, serving as a platform to study epidemiology, assess clinical trial feasibility, identify eligible candidates, collect real-world data, perform post-market surveillance, and collaborate in (inter)national data-driven initiatives. In addressing these facets, it's crucial to gather high-quality, interchangeable, and reusable data from a representative population. We introduce the Dutch Dystrophinopathy Database (DDD), a national registry for patients with DMD or BMD, and females with pathogenic DMD variants, outlining its design, governance, and use. The design of DDD is based on a system-independent information model that ensures interoperable and reusable data adhering to international standards. To maximize enrollment, patients can provide consent online and participation is allowed on different levels with contact details and clinical diagnosis as minimal requirement. Participants can opt-in for yearly online questionnaires on disease milestones and medication and to have clinical data stored from visits to one of the national reference centers. Governance involves a general board, advisory board and database management. On November 1, 2023, 742 participants were enrolled. Self-reported data were provided by 291 Duchenne, 122 Becker and 38 female participants. 96% of the participants visiting reference centers consented to store clinical data. Eligible patients were informed about clinical studies through DDD, and multiple data requests have been approved to use coded clinical data for quality control, epidemiology and natural history studies. The Dutch Dystrophinopathy Database captures long-term patient and high-quality standardized clinician reported healthcare data, supporting trial readiness, post-marketing surveillance, and effective data use using a multicenter design that is scalable to other neuromuscular disorders.

Loop-mediated isothermal amplification (LAMP) for detection of atypical enterovirus D68 strain VR-1197

A method that has rapidly evolved for detection of viral pathogens are loop-mediated isothermal amplification (LAMP) assays. The available LAMP assays usually target the most common viral strains, including enteroviruses, but for the atypical enterovirus D68 strain VR-1197 this method has not yet been developed. Enterovirus D68 are known for severe respiratory distress in children, and atypical strains are less likely to be detected by traditional methods. This study targets the atypical EVD68 strain VR-1197 and have developed a rapid detection method saving time when differentiating enterovirus strains. This study present method development and review the sensitivity and specificity compared to traditional RT-qPCR, and wet lab cross reactivity with other airway pathogens. The EVD68 VR-1197 assay can be a rapid POC (Point of care) test for atypical EVD68 VR-1197 and have the potential as reliable detection method with minimal technological requirements.

Evaluating the Efficacy of Large Language Models in CPT Coding for Craniofacial Surgery: A Comparative Analysis.

The advent of Large Language Models (LLMs) like ChatGPT has introduced significant advancements in various surgical disciplines. These developments have led to an increased interest in the utilization of LLMs for Current Procedural Terminology (CPT) coding in surgery. With CPT coding being a complex and time-consuming process, often exacerbated by the scarcity of professional coders, there is a pressing need for innovative solutions to enhance coding efficiency and accuracy. This observational study evaluated the effectiveness of 5 publicly available large language models-Perplexity.AI, Bard, BingAI, ChatGPT 3.5, and ChatGPT 4.0-in accurately identifying CPT codes for craniofacial procedures. A consistent query format was employed to test each model, ensuring the inclusion of detailed procedure components where necessary. The responses were classified as correct, partially correct, or incorrect based on their alignment with established CPT coding for the specified procedures. The results indicate that while there is no overall significant association between the type of AI model and the correctness of CPT code identification, there are notable differences in performance for simple and complex CPT codes among the models. Specifically, ChatGPT 4.0 showed higher accuracy for complex codes, whereas Perplexity.AI and Bard were more consistent with simple codes. The use of AI chatbots for CPT coding in craniofacial surgery presents a promising avenue for reducing the administrative burden and associated costs of manual coding. Despite the lower accuracy rates compared with specialized, trained algorithms, the accessibility and minimal training requirements of the AI chatbots make them attractive alternatives. The study also suggests that priming AI models with operative notes may enhance their accuracy, offering a resource-efficient strategy for improving CPT coding in clinical practice. This study highlights the feasibility and potential benefits of integrating LLMs into the CPT coding process for craniofacial surgery. The findings advocate for further refinement and training of AI models to improve their accuracy and practicality, suggesting a future where AI-assisted coding could become a standard component of surgical workflows, aligning with the ongoing digital transformation in health care.

A tunable transition metal dichalcogenide entangled photon-pair source

Entangled photon-pair sources are at the core of quantum applications like quantum key distribution, sensing, and imaging. Operation in space-limited and adverse environments such as in satellite-based and mobile communication requires robust entanglement sources with minimal size and weight requirements. Here, we meet this challenge by realizing a cubic micrometer scale entangled photon-pair source in a 3R-stacked transition metal dichalcogenide crystal. Its crystal symmetry enables the generation of polarization-entangled Bell states without additional components and provides tunability by simple control of the pump polarization. Remarkably, generation rate and state tuning are decoupled, leading to equal generation efficiency and no loss of entanglement. Combining transition metal dichalcogenides with monolithic cavities and integrated photonic circuitry or using quasi-phasematching opens the gate towards ultrasmall and scalable quantum devices.

Road traffic crash data in Southwestern Nigeria: a qualitative analysis of sources, contents and management methods

Objective This study qualitatively examined road traffic crash (RTC) data collection and management in southwest Nigeria, with the goal of addressing the tenets of the UN Decade of Action Plan on Road Safety’s call for an effective data system. Methods Data on RTCs data collection in the study area was obtained through key informant interviews with five hospital accident emergency unit directors, three Federal Road Safety Commission (FRSC) Sector commanders, and three Nigeria Police Force (NPF) commissioners. Datasets were transcribed, categorized, and interpreted using content analysis, descriptive statistics, and WHO recommended minimal crash data element requirements. Results The study found disparate systems, a lack of synergy, and discrepancy in the various data sources in the country when compared with WHO recommendations on road traffic data collection systems, with 55% of agencies using non-standardized paper forms. The study also reveals that the minimum crash data elements recommended by WHO are not consistently captured in the NPF, FRSC, and hospital traffic crash data sources. Conclusions The study suggested the use of an upgraded National Road Traffic Crash Data Management System (NRTCDMS) Data Template to unify data gathering and linkage issues, but recommends a digital version of the template.

Over 100-fold improvement in the accuracy of relaxed eddy accumulation flux estimates through error diffusion

Measurements of atmosphere-surface exchange are largely limited by the availability of fast-response gas analyzers; this limitation hampers our understanding of the role of terrestrial ecosystems in atmospheric chemistry and global change. Current micrometeorological methods, compatible with slow-response gas analyzers, are difficult to implement, or rely on empirical parameters that introduce large systematic errors.Here, we develop a new micrometeorological method, optimized for slow-response gas analyzers, that directly measures exchange rates of different atmospheric constituents, with minimal requirements. The new method requires only the sampling of air at a constant rate and directing it into one of two reservoirs, depending on the direction of the vertical wind velocity. An integral component of the new technique is an error diffusion algorithm that minimizes the biases in the measured fluxes and achieves direct flux estimates.We demonstrate that the new method provides an unbiased estimate of the flux, with accuracy within 0.1% of the reference eddy covariance flux, and importantly, allows for significant enhancements in the signal-to-noise ratio of measured scalars without compromising accuracy. Our new method provides a simple and reliable way to address complex environmental questions and offers a promising avenue for advancing our understanding of ecological systems and atmospheric chemistry.

Efficacy of Rezūm water vapor therapy for the treatment of catheter-dependent urinary retention: A single-center, Canadian experience.

Urinary retention secondary to benign prostatic hyperplasia (BPH) requiring catheterization is a prevalent and morbid condition. The objective of this study was to evaluate the real-world efficacy and safety of Rezūm as the primary treatment of catheter-dependent urinary retention. A single-center, retrospective study analyzed patients with catheter-dependent urinary retention secondary to BPH who were treated with Rezūm between April 2022 and April 2024. Standardized postoperative followup was required for inclusion. Patient demographics, medication use, volume drained at time of urinary retention, catheter-free status, complications, and postoperative International Prostate Symptom Score (IPSS) was collected. A total of 53 patients were included. Mean age was 73.4 years (standard deviation 9.4), and the mean Charlson comorbidity index score was 3.7. The baseline mean prostate volume was 81.7 (range 33-179) mL. Patients were catheter-dependent for an average of 225 (range 30- 1821) days prior to surgical intervention. Average followup time was 10.2 months. Of the 53 patients treated, 42 (79%) patients were able to become catheter-free after treatment. Twenty-six (49%) patients failed their initial trial of void at 14 days postoperatively; 11% (n=6) of patients experienced hematuria with one admitted to hospital due to hematuria/clot retention. There were no Clavien Dindo ≥3 complications. Only retention volume ≥1 L was a significant independent predictor of treatment failure on univariate and multivariate logistic regression analysis. Rezūm effectively treated catheter-dependent urinary retention. Given the simplicity of treatment, accessibility, and minimal anesthetic requirements, providers should consider Rezūm to minimize indwelling catheter-related morbidity for catheter-dependant patients.

High-resolution population maps derived from Sentinel-1 and Sentinel-2

Detailed population maps play an important role in diverse fields ranging from humanitarian action to urban planning. Generating such maps in a timely and scalable manner presents a challenge, especially in data-scarce regions. To address it we have developed Popcorn, a population mapping method whose only inputs are free, globally available satellite images from Sentinel-1 and Sentinel-2; and a small number of aggregate population counts over coarse census districts for calibration. Despite the minimal data requirements our approach surpasses the mapping accuracy of existing schemes, including several that rely on building footprints derived from high-resolution imagery. E.g., we were able to produce population maps for Rwanda with 100m GSD based on less than 400 regional census counts. In Kigali, those maps reach an R2 score of 66% w.r.t. a ground truth reference map, with an average error of only ±10 inhabitants/ha. Conveniently, Popcorn retrieves explicit maps of built-up areas and local building occupancy rates, making the mapping process interpretable and offering additional insights, for instance about the distribution of built-up, but unpopulated areas, e.g., industrial warehouses. With our work we aim to democratize access to up-to-date and high-resolution population maps, recognizing that some regions faced with particularly strong population dynamics may lack the resources for costly micro-census campaigns. Project page: https://popcorn-population.github.io/.

Optimizing and Exploring Untapped Micro-Hydro Hybrid Systems: a Multi-Objective Approach for Crystal Lake as a Large-Scale Energy Storage Solution

Increasing electricity demand and concerns about climate change and fossil fuel consumption have highlighted the importance of renewable energy resources and storage systems. This paper proposes a method for exploring untapped pumped hydro storage potentials to accommodate intermittent renewable energy generation profiles. Hourly measured data from 2022 in Benzie County, Michigan, United States, were gathered for system sizing and a thorough, realistic analysis. By employing the multi-objective grey wolf optimization algorithm, we formulated optimal sizing and energy-management strategies for three different scenarios. Unlike similar studies, the 3rd with triple objective functions (OFs) scenario aims to maximize both reliability and ecological OFs while minimizing the cost OF. It has shown promising results with multiple solutions, considering economic, environmental, and reliability factors. A case study conducted in Crystal Lake, Michigan, revealed that although Crystal Lake would function only as a micro-hydro power facility, it is a promising and huge storage unit with a substantial storage capacity of around 14.9734GWh. The system investigated is significant in the USA due to its rapid deployment capabilities, minimal construction requirements, and ease of integration with the distribution grid. The fuzzy logic method was employed to identify the best non-dominant solution among the other solutions. These outcomes include a notably low levelized cost of energy at 0.046147$/kWh, a robust index of reliability of 99.705%, and a significant reduction in CO2 emissions amounting to 7.9142×103 tons/year, when considering the triple OFs. The paper’s methodology provides valuable insights for regions aiming to utilize renewable energy from untapped storage sources.

A simple subtropical high‐pressure system index over the South Atlantic

AbstractThis research introduces a novel index for the South Atlantic High Pressure (SAHP) system to enhance understanding of regional climate variability and change. Subtropical highs significantly influence regional climates, yet comprehensive indices to measure their behaviours are lacking. Utilizing ERA5 reanalysis data from 1940 to 2023, the proposed index estimates a weighted centroid of the area surrounding the maximum sea level pressure within a 3 hPa range. This method ensures robustness and flexibility in contiguous area estimation specific to subtropical high events. Results showed the index effectively reflects the position and intensity of the SAHP. The study reveals that latitudinal variability of the SAHP has a strong unimodal structure, whereas longitudinal variability exhibits a bimodal structure. Seasonal patterns of the index show noticeable changes, with winter (JJA) and spring (SON) months having relatively high index values compared to summer (DJF) and autumn (MAM) months, underscoring the intra‐annual variability of the SAHP index. During ENSO events, the mean centroid position of the SAHP shifts significantly, moving westwards and polewards during El Niño and showing greater stability during La Niña. The index, with minimal computation requirements and flexibility, can be applied across diverse datasets, aiding in the assessment of future subtropical high changes.

Feasibility of implementing RPA coupled with CRISPR-Cas12a (RPA-Cas12a) for Hepatozoon canis detection in dogs

Hepatozoonosis, caused by the protozoan Hepatozoon canis, is a prevalent blood disease affecting owned and stray dogs and cats. The prevalence of these parasites among companion animals in Thailand remains poorly understood. Diagnosing the old-world form of the disease is challenging due to the wide range of nonspecific clinical signs and the reliance on finding low levels of Hepatozoon gamonts in blood smears for conventional diagnosis. PCR demonstrates high specificity and sensitivity but it requires sophisticated instrumentation. Therefore, we established recombinase polymerase amplification (RPA) coupled with Cas12a for H. canis detection based on 18S rRNA. Our findings showed that RPA-Cas12a using gRNA_H was highly specific to H. canis, without yielding positives for other pathogen species including Babesia species. Even in cases of co-infection, RPA-Cas12a only detected positives in samples containing H. canis. This approach detected minimal amounts of H. canis18S rRNA-harboring plasmid at 10 copies per reaction, whereas plasmid-spiked canine blood enabled detection at a minimal amount of 100 copies per reaction. The performance of RPA-Cas12a was validated by comparing it with quantitative PCR-high resolution melting analysis (qPCR-HRM) and sequencing based on 35 canine blood samples. RPA-Cas12a demonstrated precision and accuracy values of 94 % and 90 %, respectively comparable to qPCR-HRM. Overall, these results indicate that RPA-Cas12a serves as a promising tool for H. canis detection as indicated by comparable performance to qPCR-HRM and is suitable for implementation in small animal hospitals or clinics due to its minimal resource requirements, thereby contributing to effective diagnosis and treatment for infected dogs.

Interfacial stability and electronic properties of YBCO/ABO3 heterostructures: A comparative DFT study

In this study, we utilized density functional theory (DFT) to analyze the interfacial properties of YBa2Cu3O7 (YBCO) with perovskite metal oxides LaAlO3 (LAO), KTaO3 (KTO), and SrTiO3 (STO). We focused on surface energies, lattice mismatches, strain energies, and adhesion energies to gauge the stability and compatibility of these interfaces. Our findings indicate that KTO exhibits the highest surface preference due to its lowest surface energy (0.054 eV/Å 2), suggesting superior surface stability. Moreover, LAO and STO show minor lattice mismatches with YBCO, implying effective interface integration, while YBCO/KTO interfaces experience significant strain due to extensive lattice mismatches. STO is distinguished by the lowest strain energy (0.07 eV), indicating minimal energy requirement for lattice mismatch accommodation, unlike KTO, which demonstrates high strain energy (0.42 eV) and potential structural distortions. The strongest interfacial bond, as indicated by an adhesion energy of −2.16 eV, was observed at YBCO/LAO, while the weakest was found at the YBCO/KTO interface, with an adhesion energy of −0.56 eV. Additionally, charge density difference (CDD) analysis highlighted electron density redistribution at the interfaces, predominantly around interfacial oxygen atoms, indicating a mix of ionic and covalent bonding. This study provides comparative insights into the interfacial characteristics of YBCO/ABO3 heterostructures, suggesting pathways for optimizing their design and performance.

Hardware requirements for trapped-ion-based verifiable blind quantum computing with a measurement-only client

Abstract In blind quantum computing (BQC), a user with a simple client device can perform a quantum computation on a remote quantum server such that the server cannot gain knowledge about the computation. Here, we numerically investigate hardware requirements for verifiable BQC using an ion trap as server and a distant measurement-only client. While the client has no direct access to quantum-computing resources, it can remotely execute quantum programs on the server by measuring photons emitted by the trapped ion. We introduce a numerical model for trapped-ion quantum devices in NetSquid, a discrete-event simulator for quantum networks. Using this, we determine the minimal hardware requirements on a per-parameter basis to perform the verifiable BQC protocol. We benchmark these for a five-qubit linear graph state, with which any single-qubit rotation can be performed, where client and server are separated by 50 km. Current state-of-the-art ion traps satisfy the minimal requirements on a per-parameter basis, but all current imperfections combined make it impossible to perform the blind computation securely over 50 km using existing technology. Using a genetic algorithm, we determine the set of hardware parameters that minimises the total improvements required, finding directions along which to improve hardware to reach our threshold error probability that would enable experimental demonstration. In this way, we lay a path for the near-term experimental progress required to realise the implementation of verifiable BQC over a 50 km distance.

Preparing the Electrical Signal Data of the Heart by Performing Segmentation Based on the Neural Network U-Net

Research on the automated extraction of essential data from an electrocardiography (ECG) recording has been a significant topic for a long time. The main focus of digital processing processes is to measure fiducial points that determine the beginning and end of the P, QRS, and T waves based on their waveform properties. The presence of unavoidable noise during ECG data collection and inherent physiological differences among individuals make it challenging to accurately identify these reference points, resulting in suboptimal performance. This is done through several primary stages that rely on the idea of preliminary processing of the ECG electrical signal through a set of steps (preparing raw data and converting them into files that are read and then processed by removing empty data and unifying the width of the signal at a length of 250 in order to remove noise accurately, and then performing the process of identifying the QRS in the first place and P-T implicitly, and then the task stage is determining the required peak and making a cut based on it. The U-Net pre-trained model is used for deep learning. It takes an ECG signal with a customisable sampling rate as input and generates a list of the beginning and ending points of P and T waves, as well as QRS complexes, as output. The distinguishing features of our segmentation method are its high speed, minimal parameter requirements, and strong generalization capabilities, which are used to create data that can be used in diagnosing diseases or biometric systems.

Advancements in Synthetic Biology for Enhancing Cyanobacterial Capabilities in Sustainable Plastic Production: A Green Horizon Perspective

This comprehensive review investigates the potential of cyanobacteria, particularly nitrogen-fixing strains, in addressing global challenges pertaining to plastic pollution and carbon emissions. By analyzing the distinctive characteristics of cyanobacteria, including their minimal growth requirements, high photosynthetic efficiency, and rapid growth rates, this study elucidates their crucial role in transforming carbon sequestration, biofuel generation, and biodegradable plastic production. The investigation emphasizes cyanobacteria’s efficiency in photosynthesis, positioning them as optimal candidates for cost-effective bioplastic production with minimized land usage. Furthermore, the study explores their unconventional yet promising utilization in biodiesel production, mitigating environmental concerns such as sulfur emissions and the presence of aromatic hydrocarbons. The resulting biodiesel exhibits significant combustion potential, establishing cyanobacteria as a viable option for sustainable biofuel production. Through a comprehensive assessment of both achievements and challenges encountered during the commercialization process, this review offers valuable insights into the diverse contributions of cyanobacteria. Its objective is to provide guidance to researchers, policymakers, and industries interested in harnessing bio-inspired approaches for structural and sustainable applications, thereby advancing global efforts towards environmentally conscious plastic and biofuel production.