Ultrahigh-purity ammonia recovery from synthetic coke wastewater via membrane contactor: Overcoming phenolic interference and assessing cost efficiency.

Ammonia recovery from industrial wastewater using membrane contactor processes is emerging as a promising method owing to the diverse applications of ammonia. This study uniquely addressed ammonia recovery from coke plant wastewater, which is challenging due to the presence of numerous toxic and volatile phenolic compounds. Experiments were conducted using a synthetic coke plant effluent to assess the effects of various pH levels and temperatures on ammonia recovery. Specifically, the aim was to achieve high-purity ammonia recovery while minimizing the permeation of phenolic compounds. The results demonstrate that ammonia recovery in the membrane contactor processes is highly efficient, even in the presence of phenolic compounds. During temperature variations at 25 °C and 40 °C, the recovery of ammonia increased from 42.36% to 52.97% at pH 11. Additionally, increasing the pH of a feed solution from 7 to 12 significantly increased the ammonia content to 58.3%. At this pH, the recovered ammonia was of exceptional purity (>99%), with phenol, p-Cresol, and 2,4-xylenol present at negligible concentrations (0.001%, 0.002%, and 0.004%, respectively). This was attributed to the ionization of phenolic compounds at higher pH levels, which prevents their permeation through the hydrophobic membrane. The estimated cost analysis revealed that the membrane contactor process at pH 12 was approximately 1.41 times more cost-effective than conventional air-stripping processes over eight years of operating period (pH-12 membrane contactor: $19.79; pH-12 air stripping: $23.75). This study provides a detailed analysis of the optimal conditions for selective ammonia recovery from complex wastewater, highlighting both effective treatment and sustainable resource recovery and offering a superior alternative to traditional methods.

Prenatal diagnosis of autosomal recessive renal tubular dysgenesis caused by variants in the ACE gene: Two fetuses with anhydramnios.

INTRODUCTION: Autosomal recessive renal tubular dysgenesis (ARRTD) is a rare genetic disorder with a very high mortality rate. The typical symptoms of the disease during pregnancy are oligohydramnios, anhydramnios, and nearly all affected fetuses die after birth or have a stillbirth in late gestation, which can adversely increase maternal risks. METHODS: Oligohydramnios/anhydramnios can make both amniocentesis for diagnostic testing and morphological evaluation via ultrasound more difficult. In cases of oligohydramnios/anhydramnios suspicious for urinary tract anomalies, amnioinfusion is a meaningful technique that facilitates sampling of amniotic fluid for genetic diagnosis. RESULTS: We report two cases of fetuses with anhydramnios and invisible urinary bladder. Clinical exome sequencing from amniotic fluid revealed a biparentally inherited homozygous pathogenic nonsense ACE variant c.2503G 〉 T [p.Glu853Ter] in proband 1 and a biparentally inherited homozygous pathogenic nonsense ACE variant c.2992C 〉 T [p.Gln998Ter] in proband 2. The prognosis was poor and the patients elected to terminate the pregnancies. Additional post-mortem histopathological examination from the renal tissue of the second fetus showed renal tubular hypoplasia. CONCLUSION: To our knowledge for the first time, we describe the prenatal diagnosis of ARRTD in Vietnam, and highlight the benefit of detecting ACE variants associated with ARRTD in fetuses with oligohydramnios/anhydramnios through amnioinfusion and amniocentesis, which improves genotype-phenotype correlations and provides valuable information for reproductive counseling.

Advancement in nanomaterials for environmental pollutants remediation: a systematic review on bibliometrics analysis, material types, synthesis pathways, and related mechanisms.

Environmental pollution is a major issue that requires effective solutions. Nanomaterials (NMs) have emerged as promising candidates for pollution remediation due to their unique properties. This review paper provides a systematic analysis of the potential of NMs for environmental pollution remediation compared to conventional techniques. It elaborates on several aspects, including conventional and advanced techniques for removing pollutants, classification of NMs (organic, inorganic, and composite base). The efficiency of NMs in remediation of pollutants depends on their dispersion and retention, with each type of NM having different advantages and disadvantages. Various synthesis pathways for NMs, including traditional synthesis (chemical and physical) and biological synthesis pathways, mechanisms of reaction for pollutants removal using NMs, such as adsorption, filtration, disinfection, photocatalysis, and oxidation, also are evaluated. Additionally, this review presents suggestions for future investigation strategies to improve the efficacy of NMs in environmental remediation. The research so far provides strong evidence that NMs could effectively remove contaminants and may be valuable assets for various industrial purposes. However, further research and development are necessary to fully realize this potential, such as exploring new synthesis pathways and improving the dispersion and retention of NMs in the environment. Furthermore, there is a need to compare the efficacy of different types of NMs for remediating specific pollutants. Overall, this review highlights the immense potential of NMs for mitigating environmental pollutants and calls for more research in this direction.

Mechanism of chorismate dehydratase MqnA, the first enzyme of the futalosine pathway, proceeds via substrate-assisted catalysis.

MqnA, the only chorismate dehydratase known so far, catalyzes the initial step in the biosynthesis of menaquinone via the futalosine pathway. Details of the MqnA reaction mechanism remain unclear. Here, we present crystal structures of Streptomyces coelicolor MqnA and its active site mutants in complex with chorismate and the product 3-enolpyruvyl-benzoate, produced during heterologous expression in Escherichia coli. Together with activity studies, our data are in line with dehydration proceeding via substrate assisted catalysis, with the enol pyruvyl group of chorismate acting as catalytic base. Surprisingly, structures of the mutant Asn17Asp with copurified ligand suggest that the enzyme converts to a hydrolase by serendipitous positioning of the carboxyl group. All complex structures presented here exhibit a closed Venus flytrap fold, with the enzyme exploiting the characteristic ligand binding properties of the fold for specific substrate binding and catalysis. The conformational rearrangements that facilitate complete burial of substrate/product, with accompanying topological changes to the enzyme surface, could foster substrate channeling within the biosynthetic pathway.

A survey on adverse drug reaction studies: data, tasks and machine learning methods.

MOTIVATION: Adverse drug reaction (ADR) or drug side effect studies play a crucial role in drug discovery. Recently, with the rapid increase of both clinical and non-clinical data, machine learning methods have emerged as prominent tools to support analyzing and predicting ADRs. Nonetheless, there are still remaining challenges in ADR studies. RESULTS: In this paper, we summarized ADR data sources and review ADR studies in three tasks: drug-ADR benchmark data creation, drug-ADR prediction and ADR mechanism analysis. We focused on machine learning methods used in each task and then compare performances of the methods on the drug-ADR prediction task. Finally, we discussed open problems for further ADR studies. AVAILABILITY: Data and code are available at https://github.com/anhnda/ADRPModels.

SPARSE: a sparse hypergraph neural network for learning multiple types of latent combinations to accurately predict drug-drug interactions.

MOTIVATION: Predicting side effects of drug-drug interactions (DDIs) is an important task in pharmacology. The state-of-the-art methods for DDI prediction use hypergraph neural networks to learn latent representations of drugs and side effects to express high-order relationships among two interacting drugs and a side effect. The idea of these methods is that each side effect is caused by a unique combination of latent features of the corresponding interacting drugs. However, in reality, a side effect might have multiple, different mechanisms that cannot be represented by a single combination of latent features of drugs. Moreover, DDI data are sparse, suggesting that using a sparsity regularization would help to learn better latent representations to improve prediction performances. RESULTS: We propose SPARSE, which encodes the DDI hypergraph and drug features to latent spaces to learn multiple types of combinations of latent features of drugs and side effects, controlling the model sparsity by a sparse prior. Our extensive experiments using both synthetic and three real-world DDI datasets showed the clear predictive performance advantage of SPARSE over cutting-edge competing methods. Also, latent feature analysis over unknown top predictions by SPARSE demonstrated the interpretability advantage contributed by the model sparsity. AVAILABILITY AND IMPLEMENTATION: Code and data can be accessed at https://github.com/anhnda/SPARSE. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Progress in Sensors for Monitoring Reinforcement Corrosion in Reinforced Concrete Structures-A Review.

Non-destructive monitoring methods and continuous monitoring systems based on them are crucial elements of modern systems for the management and maintenance of assets which include reinforced concrete structures. The purpose of our study was to summarise the data on the most common sensors and systems for the non-destructive monitoring of reinforced concrete structures developed over the past 20 years. We considered systems based on electrochemical (potentiometry, methods related to polarisation) and physical (electromagnetic and ultrasonic waves, piezoelectric effect, thermography) examination methods. Special focus is devoted to the existing sensors and the results obtained using these sensors, as well as the advantages and disadvantages of their setups or other equipment used. The review considers earlier approaches and available commercial products, as well as relatively new sensors which are currently being tested.

Application of deep learning models to detect coastlines and shorelines.

Identifying and monitoring coastlines and shorelines play an important role in coastal erosion assessment around the world. The application of deep learning models was used in this study to detect coastlines and shorelines in Vietnam using high-resolution satellite images and different object segmentation methods. The aims are to (1) propose indicators to identify coastlines and shorelines; (2) build deep learning (DL) models to automatically interpret coastlines and shorelines from high-resolution remote sensing images; and (3) apply DL-trained models to monitor coastal erosion in Vietnam. Eight DL models were trained based on four artificial-intelligent-network structures, including U-Net, U2-Net, U-Net3+, and DexiNed. The high-resolution images collected from Google Earth Pro software were used as input data for training all models. As a result, the U-Net using an input-image size of 512 × 512 provides the highest performance of 98% with a loss function of 0.16. The interpretation results of this model were used effectively for the coastline and shoreline identification in assessing coastal erosion in Vietnam due to sea-level rise in storm events over 20 years. The outcomes proved that while the shoreline is ideal for observing seasonal tidal changes or the immediate motions of current waves, the coastline is suitable to assess coastal erosion caused by the influence of sea-level rise during storms. This paper has provided a broad scope of how the U-Net model can be used to predict the coastal changes over vietnam and the world.

Successful endoscopic endonasal surgery for very huge trigeminal schwannomas in nasopharynx.

We present a 60-year-old female diagnosed with a giant trigeminal tumor (5.2*6.4*8.2 cm) situated in the middle cranial fossa and nasopharyngeal area. The patient was operated on by endoscopic endonasal transmaxillary, transpterygoid and infratemporal approaches. Postoperatively she was stable, with no neurologic complication and no cerebrospinal fluid leakage. We review the literature on extremely large trigeminal schwannomas.

A pragmatic adaptive enrichment design for selecting the right target population for cancer immunotherapies.

One of the challenges in the design of confirmatory trials is to deal with uncertainties regarding the optimal target population for a novel drug. Adaptive enrichment designs (AED) which allow for a data-driven selection of one or more prespecified biomarker subpopulations at an interim analysis have been proposed in this setting but practical case studies of AEDs are still relatively rare. We present the design of an AED with a binary endpoint in the highly dynamic setting of cancer immunotherapy. The trial was initiated as a conventional trial in early triple-negative breast cancer but amended to an AED based on emerging data external to the trial suggesting that PD-L1 status could be a predictive biomarker. Operating characteristics are discussed including the concept of a minimal detectable difference, that is, the smallest observed treatment effect that would lead to a statistically significant result in at least one of the target populations at the interim or the final analysis, respectively, in the setting of AED.

Modulation of Junction Modes in SnSe2/MoTe2 Broken-Gap van der Waals Heterostructure for Multifunctional Devices.

We study the electronic and optoelectronic properties of a broken-gap heterojunction composed of SnSe2 and MoTe2 with gate-controlled junction modes. Owing to the interband tunneling current, our device can act as an Esaki diode and a backward diode with a peak-to-valley current ratio approaching 5.7 at room temperature. Furthermore, under an 811 nm laser irradiation the heterostructure exhibits a photodetectivity of up to 7.5 × 1012 Jones. In addition, to harness the electrostatic gate bias, Voc can be tuned from negative to positive by switching from the accumulation mode to the depletion mode of the heterojunction. Additionally, a photovoltaic effect with a fill factor exceeding 41% was observed, which highlights the significant potential for optoelectronic applications. This study not only demonstrates high-performance multifunctional optoelectronics based on the SnSe2/MoTe2 heterostructure but also provides a comprehensive understanding of broken-band alignment and its applications.

Magnetic Ni-Doped TiO2 Photocatalysts for Disinfection of Escherichia coli Bacteria.

Ni-doped TiO2 nanoparticles have been synthesized by a modified sol-gel method. The crystal phase composition, particle size, and magnetic and optical properties of the samples were comprehensively examined using x-ray diffraction analysis, transmission electron microscopy, Brunauer-Emmett-Teller surface area analysis, Raman spectroscopy, magnetization measurements, and ultraviolet-visible (UV-Vis) absorption techniques. The results showed that the prepared Ni-doped TiO2 samples sintered at 400°C crystallized completely in anatase phase with average particle size in the range from 8 nm to 10 nm and presented broad visible absorption. The bactericidal efficiency of TiO2 was effectively enhanced by Ni doping, with an optimum Ni doping concentration of 6% (x = 0.06), at which 95% of Escherichia coli were killed after just 90 min of irradiation. Density functional theory (DFT) calculations revealed good agreement with the experimental data. Moreover, the Ni dopant induced magnetic properties in TiO2, facilitating its retrieval using a magnetic field after use, which is an important feature for photocatalytic applications.

Multi-Sensor Data Fusion in A Real-Time Support System for On-Duty Firefighters.

While working on fire ground, firefighters risk their well-being in a state where any incident might cause not only injuries, but also fatality. They may be incapacitated by unpredicted falls due to floor cracks, holes, structure failure, gas explosion, exposure to toxic gases, or being stuck in narrow path, etc. Having acknowledged this need, in this study, we focus on developing an efficient portable system to detect firefighter's falls, loss of physical performance, and alert high CO level by using a microcontroller carried by a firefighter with data fusion from a 3-DOF (degrees of freedom) accelerometer, 3-DOF gyroscope, 3-DOF magnetometer, barometer, and a MQ7 sensor using our proposed fall detection, loss of physical performance detection, and CO monitoring algorithms. By the combination of five sensors and highly efficient data fusion algorithms to observe the fall event, loss of physical performance, and detect high CO level, we can distinguish among falling, loss of physical performance, and the other on-duty activities (ODAs) such as standing, walking, running, jogging, crawling, climbing up/down stairs, and moving up/down in elevators. Signals from these sensors are sent to the microcontroller to detect fall, loss of physical performance, and alert high CO level. The proposed algorithms can achieve 100% of accuracy, specificity, and sensitivity in our experimental datasets and 97.96%, 100%, and 95.89% in public datasets in distinguishing between falls and ODAs activities, respectively. Furthermore, the proposed algorithm perfectly distinguishes between loss of physical performance and up/down movement in the elevator based on barometric data fusion. If a firefighter is unconscious following the fall or loss of physical performance, an alert message will be sent to their incident commander (IC) via the nRF224L01 module.

Highly Accurate Step Counting at Various Walking States Using Low-Cost Inertial Measurement Unit Support Indoor Positioning System.

Accurate step counting is essential for indoor positioning, health monitoring systems, and other indoor positioning services. There are several publications and commercial applications in step counting. Nevertheless, over-counting, under-counting, and false walking problems are still encountered in these methods. In this paper, we propose to develop a highly accurate step counting method to solve these limitations by proposing four features: Minimal peak distance, minimal peak prominence, dynamic thresholding, and vibration elimination, and these features are adaptive with the user's states. Our proposed features are combined with periodicity and similarity features to solve false walking problem. The proposed method shows a significant improvement of 99.42% and 96.47% of the average of accuracy in free walking and false walking problems, respectively, on our datasets. Furthermore, our proposed method also achieves the average accuracy of 97.04% on public datasets and better accuracy in comparison with three commercial step counting applications: Pedometer and Weight Loss Coach installed on Lenovo P780, Health apps in iPhone 5s (iOS 10.3.3), and S-health in Samsung Galaxy S5 (Android 6.01).

A sustainable approach for efficient ammonium recovery, and simultaneous TOC removal from aqueous solutions by vesicle-like iron phosphate-based carbon nanomaterials.

NH4+ is an ion with versatile potential; however, the release of wastewater containing this component, regardless of its high or low concentration, causes severe eutrophication in aquatic systems and contaminates numerous manufacturing processes. Thus, this study developed a sustainable method that can simultaneously remove, recover NH4+, polish water, oxidize organic matter, and yet release a material that can still be used as fertilizer. Regarding NH4+ removal, FeP400 rapidly exhibited an exceptional NH4+ uptake capacity (343.5 mg g-1) within 8 min, even in dairy processing wastewater with high NH4+ concentrations and diverse co-existing components. FeP400 could oxidize organic compounds spontaneously to remove TOC, indirectly enhancing its NH4+ uptake up to 33.5 % through charge balance mechanisms. The adsorption process involved both chemical (i.e., double-salt precipitation) and physical mechanisms (i.e., H-bonding and electrostatic interaction), as confirmed by thermodynamics, FT-IR, and XPS analyses. Regarding recovery, FeP400 can be reused for over 10 cycles with high removal (81 %) and NH4+ recovery (88 %), a significant improvement over conventional options. FeP400 also performed efficiently under flowing conditions using low-range NH4+ and TOC samples over 10 cycles, polishing not only 34.1 L of water with undetected NH4+, neutral pH, and extremely low TOC but also effectively recovering the NH4+ uptake at an economical cost. Lastly, its environmentally friendly nature, which contains essential nutrients for plant growth, further enhances its recyclability after release. Thus, FeP400 is believed to offer a transformative, sustainable, and highly efficacious solution to the NH4+contamination and critical ultrapure water issues that industries urgently address.

Improvement in energy performance from the construction of inlet guide vane and diffuser vane geometries in an axial-flow pump.

Advanced inlet guide vane (IGV) and diffuser vane (DV) geometries were constructed in an effort to increase the energy performance of an axial-flow pump at the best efficiency point (BEP). DV setting angles were also investigated to increase energy performance at the off-design points. By integrating the advantages of an adjustable IGV, combinations of adjustable IGV and DV geometries were constructed and thoroughly analyzed by way of energy loss. The asymmetrical geometry of the IGV, upgraded through the use of a hydrofoil profile, resulted in higher hydraulic performance compared to that of the reference model. The efficiency and total head at the BEP increased significantly with the implementation of the new DV, by 1.456% and 5.756% over those of the reference model, respectively. Using the new DV reduced the unsteady turbulent flow behind the trailing edge of the DV under all flow rate conditions, resulting in a reduction in vibration and noise. The positive setting angles of the DV increased the energy performance in the high-flow-rate region, whereas the negative DV setting angles produced a good performance in the low-flow-rate region. Combining an adjustable IGV with an adjustable DV model resulted in a significant increase in the total head, with more optimal energy performance provided by the positive IGV setting angles. At the BEP and under high-flow-rate conditions, the low-velocity zone is closely related to high entropy generation. Furthermore, these high-entropy generation regions follow the trajectory of the low-velocity zones. However, the low-velocity zone is not strongly associated with the high-entropy generation region when operating under low-flow-rate conditions.

Ultrahigh-porosity Ranunculus-like MgO adsorbent coupled with predictive deep belief networks: A transformative method for phosphorus treatment.

Phosphorus is a nonrenewable material with a finite supply on Earth; however, due to the rapid growth of the manufacturing industry, phosphorus contamination has become a global concern. Therefore, this study highlights the remarkable potential of ranunculus-like MgO (MO4-MO6) as superior adsorbents for phosphate removal and recovery. Furthermore, MO6 stands out with an impressive adsorption capacity of 596.88 mg/g and a high efficacy across a wide pH range (2-10) under varying coexisting ion concentrations. MO6 outperforms the top current adsorbents for phosphate removal. The process follows Pseudo-second-order and Langmuir models, indicating chemical interactions between the phosphate species and homogeneous MO6 monolayer. MO6 maintains 80 % removal and 96 % recovery after five cycles and adheres to the WHO and EUWFD regulations for residual elements in water. FT-IR and XPS analyses further reveal the underlying mechanisms, including ion exchange, electrostatic, and acid-base interactions. Ten machine learning (ML) models were applied to simultaneously predict multi-criteria (sorption capacity, removal efficiency, final pH, and Mg leakage) affected by 15 diverse environmental conditions. Traditional ML models and deep neural networks have poor accuracy, particularly for removal efficiency. However, a breakthrough was achieved by the developed deep belief network (DBN) with unparalleled performance (MAE = 1.3289, RMSE = 5.2552, R2 = 0.9926) across all output features, surpassing all current studies using thousands of data points for only one output factor. These captivating MO6 and DBN models also have immense potential for effectively applying in the real water test with error < 5 %, opening immense horizons for transformative methods, particularly in phosphate removal and recovery.

Sustainable urea treatment by environmental-friendly and highly hydrophilic vesicle-like iron phosphate-based carbon.

Despite the versatile potential applications of urea, its unfavorable characteristics for conventional treatment methods hinder its utilization. Therefore, this study developed vesicle-like iron phosphate-based carbon (IP@C400) as a breakthrough urea removal and recovery material for a wide range of urea-containing sources. IP@C400 rapidly exhibited an exceptional capacity (2242 mg/g in 1 h) across a wide range of pH, even in synthetic hemodialysis wastewater with high urea concentrations and diverse co-existing components, compared with the 60 prominent adsorbents. The adsorption process followed dual Pseudo-kinetic, Langmuir-isotherm models with the involvement of primary robust physical (i.e., H-bonding and electrostatic interaction) and chemical mechanisms (i.e., hydrolysis). Remarkably, IP@C400 can maintain high urea removal (90 %) or recovery efficiency (95 %) even after 10 cycles with minimal leakages of Fe and P (far below WHO and EUWFD standards)-a significant improvement over disposable options. IP@C400 could also perform efficiently on batch and a new approach integrating with a naturally accessible material based on the fixed-bed column using low-range urea realistic samples, achieving 65.2 L water over 10 cycles with undetected urea, neutral pH, and well-aligned water safety standards with a minimal adsorbent dose (0.1 g.L-1) and economical cost ($0.05 L-1). Lastly, its environmentally friendly nature, which contains essential nutrients for plant growth, further enhances its recyclability after release. Thus, IP@C400 offers a solution to environmental sustainability and the urgent ultrapure water issue that industries are facing.

Graph Network-Based Simulation of Multicellular Dynamics Driven by Concentrated Polymer Brush-Modified Cellulose Nanofibers.

Manipulating the three-dimensional (3D) structures of cells is important for facilitating to repair or regenerate tissues. A self-assembly system of cells with cellulose nanofibers (CNFs) and concentrated polymer brushes (CPBs) has been developed to fabricate various cell 3D structures. To further generate tissues at an implantable level, it is necessary to carry out a large number of experiments using different cell culture conditions and material properties; however this is practically intractable. To address this issue, we present a graph-neural network-based simulator (GNS) that can be trained by using assembly process images to predict the assembly status of future time steps. A total of 24 (25 steps) time-series images were recorded (four repeats for each of six different conditions), and each image was transformed into a graph by regarding the cells as nodes and the connecting neighboring cells as edges. Using the obtained data, the performances of the GNS were examined under three scenarios (i.e., changing a pair of the training and testing data) to verify the possibility of using the GNS as a predictor for further time steps. It was confirmed that the GNS could reasonably reproduce the assembly process, even under the toughest scenario, in which the experimental conditions differed between the training and testing data. Practically, this means that the GNS trained by the first 24 h images could predict the cell types obtained 3 weeks later. This result could reduce the number of experiments required to find the optimal conditions for generating cells with desired 3D structures. Ultimately, our approach could accelerate progress in regenerative medicine.

2D van der Waals Heterostructure with Tellurene Floating-Gate for Wide Range and Multi-Bit Optoelectronic Memory.

Intensive research on optoelectronic memory (OEM) devices based on two-dimensional (2D) van der Waals heterostructures (vdWhs) is being conducted due to their distinctive advantages for electrical-optical writing and multilevel storage. These features make OEM a promising candidate for the logic of reconfigurable operations. However, the realization of nonvolatile OEM with broadband absorption (from visible to infrared) and a high switching ratio remains challenging. Herein, we report a nonvolatile OEM based on a heterostructure consisting of rhenium disulfide (ReS2), hexagonal boron nitride (hBN) and tellurene (2D Te). The 2D Te-based floating-gate (FG) device exhibits excellent performance metrics, including a high switching on/off ratio (∼106), significant endurance (>1000 cycles) and impressive retention (>104 s). In addition, the narrow band gap of 2D Te endows the device with broadband optical programmability from the visible to near-infrared regions at room temperature. Moreover, by applying different gate voltages, light wavelengths, and laser powers, multiple bits can be successfully generated. Additionally, the device is specifically designed to enable reconfigurable inverter logic circuits (including AND and OR gates) through controlled electrical and optical inputs. These significant findings demonstrate that the 2D vdWhs with a 2D Te FG are a valuable approach in the development of high-performance OEM devices.