Reconfigurable Resistive Switching Memory for Telegraph Code Sensing and Recognizing Reservoir Computing Systems.

Reservoir computing (RC) system is based upon the reservoir layer, which non-linearly transforms input signals into high-dimensional states, facilitating simple training in the readout layer-a linear neural network. These layers require different types of devices-the former demonstrated as diffusive memristors and the latter prepared as drift memristors. The integration of these components can increase the structural complexity of RC system. Here, a reconfigurable resistive switching memory (RSM) capable of implementing both diffusive and drift dynamics is demonstrated. This reconfigurability is achieved by preparing a medium with a 3D ion transport channel (ITC), enabling precise control of the metal filament that determines memristor operation. The 3D ITC-RSM operates in a volatile threshold switching (TS) mode under a weak electric field and exhibits short-term dynamics that are confirmed to be applicable as reservoir elements in RC systems. Meanwhile, the 3D ITC-RSM operates in a non-volatile bipolar switching (BS) mode under a strong electric field, and the conductance modulation metrics forming the basis of synaptic weight update are validated, which can be utilized as readout elements in the readout layer. Finally, an RC system is designed for the application of reconfigurable 3D ITC-RSM, and performs real-time recognition on Morse code datasets.

Sense and Shoot: Unveiling the Electro-/Photocatalytic Potential of 2D White Graphene-Supported Perovskite Strontium Cobaltite from Detection to Remediation of Oxidative Stress Herbicide (Mesotrione).

In this research, we employed a strategy akin to "Feeding Two Birds with One Stone" aiming for the dual objectives of highly selective electrochemical detection and photocatalytic degradation of the environmentally hazardous herbicide mesotrione (MTN). We achieved this by utilizing hexagonal boron nitride (BN)-supported strontium cobaltite perovskite nanocomposites (SrCoO3/BN). The fabrication of the innovative bifunctional SrCoO3/BN nanocomposites involved a straightforward process of precipitation, followed by an annealing treatment and ultrasonication. The successful formation of these nanocomposites was corroborated through the application of diverse spectroscopic tools. Notably, as-prepared SrCoO3/BN nanocomposites exhibited a remarkable sensing platform for MTN, characterized by a notably low detection limit (11 nm), considerable sensitivity (3.782 µA µM-1 cm-2), and outstanding selectivity, alongside remarkable stability. Concurrently, these SrCoO3/BN nanocomposites demonstrated exceptional visible-light-driven photocatalytic efficacy for MTN degradation (99%) and complete mineralization. Our investigation systematically delved into the influence of operational parameters, including catalyst loading and the involvement of reactive oxidative species, in both the electrocatalytic and photocatalytic reactions. Drawing from these comprehensive studies, we have proposed plausible mechanisms for detecting and degrading MTN. Our findings pave the way for catalyst development, offering a unified solution for detecting and eliminating toxic organic compounds from the environment.

Robust Human Activity Recognition by Integrating Image and Accelerometer Sensor Data Using Deep Fusion Network.

Studies on deep-learning-based behavioral pattern recognition have recently received considerable attention. However, if there are insufficient data and the activity to be identified is changed, a robust deep learning model cannot be created. This work contributes a generalized deep learning model that is robust to noise not dependent on input signals by extracting features through a deep learning model for each heterogeneous input signal that can maintain performance while minimizing preprocessing of the input signal. We propose a hybrid deep learning model that takes heterogeneous sensor data, an acceleration sensor, and an image as inputs. For accelerometer data, we use a convolutional neural network (CNN) and convolutional block attention module models (CBAM), and apply bidirectional long short-term memory and a residual neural network. The overall accuracy was 94.8% with a skeleton image and accelerometer data, and 93.1% with a skeleton image, coordinates, and accelerometer data after evaluating nine behaviors using the Berkeley Multimodal Human Action Database (MHAD). Furthermore, the accuracy of the investigation was revealed to be 93.4% with inverted images and 93.2% with white noise added to the accelerometer data. Testing with data that included inversion and noise data indicated that the suggested model was robust, with a performance deterioration of approximately 1%.

Effect of Post-Sintering Conditions on the Mechanical Properties of a New Co-Cr Alloy Produced by New Subtractive Manufacturing.

In this study, we investigated the effect of sintering temperature (1300, 1350, or 1400 °C) and holding time (1 or 2 h) on the mechanical properties of a cobalt-chromium (Co-Cr) alloy (Soft Metal) produced by milling/post-sintering, using a tensile test (n = 6). Prior to the test, the different nanostructures arising from the sintering conditions were also analyzed. The phase ratio of γ (face-centered cubic) phase to ɛ (hexagonal close-packed) phase increased mainly with increasing temperature. The formation of Cr23C6 carbide was greatest in the 1350 °C groups when compared to the other temperature groups. The 1400 °C groups had a substantially greater grain size than the 1300 °C and 1350 °C groups, together with a significant number of annealing twins inside the matrix phases. Overall, the 1350 °C groups showed the most superior properties. The 1400 °C groups showed a mean 0.2% yield strength under 500 MPa. The holding times did not significantly affect the mechanical properties (p > 0.05).

Bioassay-guided isolation of active anti-adipogenic compound from royal jelly and the study of possible mechanisms.

BACKGROUND: Royal jelly (RJ) has been used traditionally for dietary, cosmetic and health purposes for a long time in different parts of the world. Scientific studies have also shown its numerous health-promoting properties including hypoglycemic and anti-hypercholesterolemic action. In this study, we investigated the anti-adipogenic activity of RJ in 3 T3-L1 cells and isolated the major responsible root component for the activity. METHODS: An active anti-adipogenic compound was isolated through bioassay-guided isolation process by successive treatment of RJ and its active fractions on 3 T3-L1 cell line. (E)-10-Hydroxy-2-decenoic Acid (10-HDA) was identified using NMR spectroscopy and ultra-performance liquid chromatography (UPLC). As 10-HDA showed significant anti-adipogenic activity with Oil Red O staining and TG content assay on 3 T3-L1 adipocytes, further study was carried out in molecular level for the expression of adipogenic transcription factors such as PPARγ, FABP4, C/EBPα, SREBP-1c, and Leptin. The effect of 10-HDA on preliminary molecules such as pAkt, pERK, C/EBPß, and pCREB were studied in the early stage of adipogenesis. The effect of 10-HDA on reactive oxygen species (ROS) production in fully differentiating adipocytes was measured by nitro blue tetrazolium (NBT) assay. RESULT: Results showed that triacylglycerol accumulation and ROS production was markedly suppressed by 10-HDA. Preliminary molecules such as pAkt, pERK, pCERB, and C/EBPß were found to be down-regulated by 10-HDA, which led to down-regulation of key adipogenic transcription factors such as PPARγ, FABP4, CEBPα, SREBP-1c, and Leptin on 3 T3-L1 adipocytes. CONCLUSION: Our results suggest that anti-adipogenesis of 10-HDA on 3 T3-L1 adipocyte takes place via two mechanisms: inhibition of cAMP/PKA pathway and inhibition of p-Akt and MAPK dependent insulin signaling pathway. So it is considered that 10-HDA, a major component of RJ, can be a potential therapeutic medicine for obesity.

Involuntary Attention Restoration During Exposure to Mobile-Based 360° Virtual Nature in Healthy Adults With Different Levels of Restorative Experience: Event-Related Potential Study.

BACKGROUND: With the global trend of urbanization, there are increasing reports of a possible association between decreased exposure to nature and increased occurrence of mental disorders. New 360° virtual reality (VR) technology using smartphones and portable VR glasses can overcome spatial and temporal limitations to help people deal with mental fatigue in everyday life. OBJECTIVE: On the basis of attention restoration theory (ART), this study aimed to investigate whether the amplitude of the mismatch negativity (MMN)/P3a complex could act as an event-related potential (ERP) biomarker of involuntary attention restoration during exposure to 360° virtual nature in healthy young adults with different levels of restorative VR experience. METHODS: A total of 40 healthy adults completed prequestionnaires on demographics and simulator sickness and postquestionnaires on simulator sickness and perceived restorativeness before and after exposure to virtual nature, respectively. During the VR exposure, brain activity was measured by electroencephalography as participants were asked to conduct a 2-tone passive auditory oddball task. RESULTS: The amplitude and latency of the MMN/P3a complex were compared between individuals reporting a highly restorative experience and those reporting a less restorative experience. Although viewing a virtual nature environment, the high restorative group (N=19) exhibited significantly reduced P3a amplitudes compared with the low restorative group (N=20; t38=2.57; P=.02; d=0.59). Particularly, a moderate but significant negative correlation was found between the self-reported restorativeness scores and the P3a amplitudes at the fronto-central region (r=-.38; P=.02). However, the latency of the MMN/P3a complex did not significantly differ between the 2 groups (auditory mismatch negativity: t38=-1.47; P=.15 and P3a: t38=-0.31; P=.76). CONCLUSIONS: Considering individuals' restorative experience, the amplitude of the fronto-central MMN/P3a complex can potentially be employed as a distinct ERP component of interest in involuntary attention restoration during virtual nature experience in healthy young adults. The findings for the 360° virtual nature experience seem to be consistent with those of previous ERP studies on the effects of meditation practice. This study extends the findings of previous ART and ERP studies of real-world meditation, restoration, and mental fatigue management into the virtual world created by mobile phone-based VR glasses and 360° video content.

Prognostic tissue biomarker exploration for patients with metastatic renal cell carcinoma receiving vascular endothelial growth factor receptor tyrosine kinase inhibitors.

In metastatic renal cell carcinoma (mRCC), the prognostic role of several tumor tissue biomarkers has been evaluated, but the results were controversial. This study aims to verify the prognostic importance of selected tumor tissue biomarkers in patients with mRCC. The clinicopathological features, immunohistochemical staining and scoring for select tissue biomarkers, treatment, and outcome of patients with mRCC treated with vascular endothelial growth factor receptor (VEGFR) tyrosine kinase inhibitors (TKIs) between July 2006 and March 2011 at Asan Medical Center in Seoul, South Korea, were reviewed. In total, 123 patients met the inclusion criteria. Most patients had clear-cell carcinoma (107 patients, 87.0 %). First-line VEGFR TKIs were sunitinib (97 patients, 78.9 %), sorafenib (23 patients, 18.7 %), and pazopanib (3 patients, 2.4 %). With a median follow-up period of 60.0 months (95 % confidence interval (CI), 56.3-63.6), median overall survival (OS) and progression-free survival (PFS) were 25.6 months (95 % CI, 19.2-32.0) and 12.2 months (95 % CI, 8.1-16.3), respectively. In the multivariable analysis for OS, carbonic anhydrase IX (CAIX; 47.5 % or less vs. more than 47.5 %, p = 0.014), sarcomatoid change (40 % or less vs. more than 40 %, p < 0.001), tumor necrosis (20 % or less vs. more than 20 %, p = 0.006), and Heng's risk group (good vs. intermediate vs. poor, p = 0.011) were identified as independent prognostic factors. In the multivariable analysis for PFS, CAIX (p < 0.001), phosphatase and tensin homolog (PTEN; 45 % or less vs. more than 45 %, p = 0.004), sarcomatoid change (p = 0.002), and tumor necrosis (p = 0.001) were identified as independent factors affecting PFS. CAIX and PTEN had prognostic importance for mRCC patients receiving first-line VEGFR TKI. Future validation and mechanistic studies are required.

In situ monitoring of laser-assisted hydrothermal growth of ZnO nanowires: thermally deactivating growth kinetics.

The laser-assisted hydrothermal growth kinetics of a cluster of ZnO nanowires are studied based on optical in situ growth monitoring. The growth yields are orders of magnitude higher than those of conventional hydrothermal methods that use bulk heating. This remarkable improvement is attributed to suppression of precursor depletion occurring by homogeneous growth reactions, as well as to enhanced mass transport. The obtained in situ data show gradually decaying growth kinetics even with negligible precursor consumption. It is revealed that the growth deceleration is caused by thermal deactivation resulting from heat dissipation through the growing nanowires. Finally, it is demonstrated that the tailored temporal modulation of the input power enables sustained growth to extended dimensions. These results provide a key to highly efficient use of growth precursors that has been pursued for industrial use of this functional metal oxide semiconductor.

Hadamard slice encoding for reduced-FOV diffusion-weighted imaging.

PURPOSE: To improve the clinical utility of diffusion-weighted imaging (DWI) by extending the slice coverage of a high-resolution reduced field-of-view technique. THEORY: Challenges in achieving high spatial resolution restrict the use of DWI in assessment of small structures such as the spinal cord. A reduced field-of-view method with 2D echo-planar radiofrequency (RF) excitation was recently proposed for high-resolution DWI. Here, a Hadamard slice-encoding scheme is proposed to double the slice coverage by exploiting the periodicity of the 2D echo-planar RF excitation profile. METHODS: A 2D echo-planar RF pulse and matching multiband refocusing RF pulses were designed using the Shinnar-Le Roux algorithm to reduce band interference, and variable-rate selective excitation to shorten the pulse durations. Hadamard-encoded images were resolved through a phase-preserving image reconstruction. The performance of the method was evaluated via simulations, phantom experiments, and in vivo high-resolution axial DWI of spinal cord. RESULTS: The proposed scheme successfully extends the slice coverage, while preserving the sharp excitation profile and the reliable fat suppression of the original method. For in vivo axial DWI of the spinal cord, an in-plane resolution of 0.7 × 0.7 mm(2) was achieved with 16 slices. CONCLUSION: The proposed Hadamard slice-encoding scheme doubles the slice coverage of the 2D echo-planar RF reduced field-of-view method without any scan-time penalty.

Performance analysis and modelling of circular jets aeration in an open channel using soft computing techniques.

Dissolved oxygen (DO) is an important parameter in assessing water quality. The reduction in DO concentration is the result of eutrophication, which degrades the quality of water. Aeration is the best way to enhance the DO concentration. In the current study, the aeration efficiency (E20) of various numbers of circular jets in an open channel was experimentally investigated for different channel angle of inclination (θ), discharge (Q), number of jets (Jn), Froude number (Fr), and hydraulic radius of each jet (HRJn). The statistical results show that jets from 8 to 64 significantly provide aeration in the open channel. The aeration efficiency and input parameters are modelled into a linear relationship. Additionally, utilizing WEKA software, three soft computing models for predicting aeration efficiency were created with Artificial Neural Network (ANN), M5P, and Random Forest (RF). Performance evaluation results and box plot have shown that ANN is the outperforming model with correlation coefficient (CC) = 0.9823, mean absolute error (MAE) = 0.0098, and root mean square error (RMSE) = 0.0123 during the testing stage. In order to assess the influence of different input factors on the E20 of jets, a sensitivity analysis was conducted using the most effective model, i.e., ANN. The sensitivity analysis results indicate that the angle of inclination is the most influential input variable in predicting E20, followed by discharge and the number of jets.

Silt erosion and cavitation impact on hydraulic turbines performance: An in-depth analysis and preventative strategies.

The primary issues in the Himalayan Rivers are sediment and cavitation degradation of the hydroelectric power turbine components. During the monsoon season, heavy material is transported by streams in hilly areas like the Himalayas through regular rainfalls, glacial and sub-glacial hydrological activity, and other factors. The severe erosion of hydraulic turbines caused by silt abrasion in these areas requires hydropower facilities to be regularly shut down for maintenance, affecting the plant's overall efficiency. This article provides an in-depth examination of the challenges that can lead to cavitation, silt erosion, and a decrease in the efficiency of various hydroelectric turbines, and it demands attention on the design, manufacture, operation, and maintenance of the turbines. This study's main objective is to critically evaluate earlier theoretical, experimental, and numerical evaluation-based studies (on cavitation and silt erosion) that are provided and addressed throughout the study. As a part of this study, various strategies for mitigating the effects of these problems and elongating the time that turbine may be utilized before they must be replaced have been provided.

Progress and Perspective in harnessing MXene-carbon-based composites (0-3D): Synthesis, performance, and applications.

MXene is recognized as a promising catalyst for versatile applications due to its abundant metal sites, physicochemical properties, and structural formation. This comprehensive review offers an in-depth analysis of the incorporation of carbon into MXene, resulting in the formation of MXene-carbon-based composites (MCCs). Pristine MXene exhibits numerous outstanding characteristics, such as its atomically thin 2D structure, hydrophilic surface nature, metallic electrical conductivity, and substantial specific surface area. The introduction of carbon guides the assembly of MCCs through electrostatic self-assembly, pairing positively charged carbon with negatively charged MXene. These interactions result in increased interlayer spacing, reduced ion/electron transport distances, and enhanced surface hydrophilicity. Subsequent sections delve into the synthesis methods for MCCs, focusing on MXene integrated with various carbon structures, including 0D, 1D, 2D, and 3D carbon. Comprehensive discussions explore the distinctive properties of MCCs and the unique advantages they offer in each application domain, emphasizing the contributions and advancements they bring to specific fields. Furthermore, this comprehensive review addresses the challenges encountered by MCCs across different applications. Through these analyses, the review promotes a deeper understanding of exceptional characteristics and potential applications of MCCs. Insights derived from this review can serve as guidance for future research and development efforts, promoting the widespread utilization of MCCs across a broad spectrum of disciplines and spurring future innovations.

An overview of techniques for monitoring and compensating tool wear in micro-electrical discharge machining.

Micro-electrical discharge machining (µEDM) is severely affected by tool wear and its process variants, which can lead to compromised precision and dimensional disruptions in micro-part production. Several attempts have been made to address this problem by suggesting offline and online (real-time) tool wear compensation strategies. Research efforts in this area have intensified over the last 20 years. However, most methods proposed were applicable only for a few work-tool combinations and in limited input process parametric settings. Instead of tool wear compensation, several research articles have focused on strategies to reduce the negative impact of tool wear on the quality of fabricated parts. The present study systematically reviews various investigations conducted in this area and aims to add cutting-edge compensation for tool wear to future inquiries. The articles reviewed here are explored in detail. Critical findings/innovations are classified into four categories: tool wear in µEDM, tool wear compensation techniques, and offline and online compensation methods.

355 nm Nanosecond Ultraviolet Pulsed Laser Annealing Effects on Amorphous In-Ga-ZnO Thin Film Transistors.

Bottom-gate thin-film transistors (TFTs) with n-type amorphous indium-gallium-zinc oxide (a-IGZO) active channels and indium-tin oxide (ITO) source/drain electrodes were fabricated. Then, an ultraviolet (UV) nanosecond pulsed laser with a wavelength of 355 nm was scanned to locally anneal the active channel at various laser powers. After laser annealing, negative shifts in the threshold voltages and enhanced on-currents were observed at laser powers ranging from 54 to 120 mW. The energy band gap and work function of a-IGZO extracted from the transmittance and ultraviolet photoelectron spectroscopy (UPS) measurement data confirm that different energy band structures for the ITO electrode/a-IGZO channel were established depending on the laser annealing conditions. Based on these observations, the electron injection mechanism from ITO electrodes to a-IGZO channels was analyzed. The results show that the selective laser annealing process can improve the electrical performance of the a-IGZO TFTs without any thermal damage to the substrate.

Alpha-lipoic acid treatment reverses 2-deoxy-D-ribose-induced oxidative damage and suppression of insulin expression in pancreatic beta-cells.

We investigated whether α-lipoic acid (LA) could prevent 2-deoxy-D-ribose (dRib)-induced oxidative damage and suppression of insulin expression in pancreatic ß-cells. Stimulation with 50 mM dRib elevated cytotoxicity, apoptosis and intracellular reactive oxygen species (ROS) levels in HIT-T15 cells, but pretreatment with LA significantly reversed the dRib-induced changes. LA directly scavenged hydroxyl radicals generated by a Fenton reaction. Intracellular reduced glutathione (GSH) and oxidized glutathione (GSSG) were depleted by stimulation with dRib but levels were restored by addition of LA to HIT-T15 cells. However, the GSH/GSSG ratio was unchanged by LA treatment. In rat islets, stimulation with 10 mM dRib for 6 h suppressed expression of insulin and pancreatic and duodenal homeobox 1 mRNA and decreased insulin content, but these were dose-dependently reversed when LA was added. Treatment with l-buthionine-sulfoximine, an inhibitor of intracellular glutathione biosynthesis, completely abolished the protective effects of LA on dRib-mediated glutathione depletion and cytotoxicity in HIT-T15 cells. In summary, LA reversed the dRib-induced oxidative damage and suppression of insulin expression in ß-cells. Enhanced intracellular total glutathione production, rather than the scavenging of ROS, is possibly the mechanism for the protective effect of LA.

Design and Fabrication of Ultrathin Nanoporous Donor-Acceptor Copolymer-Based Organic Field-Effect Transistors for Enhanced VOC Sensing Performance.

The development of organic field-effect transistor (OFET) chemical sensors with high sensing performance and good air stability has remained a persistent challenge, thereby hindering their practical application. Herein, an OFET sensor based on a donor-acceptor copolymer is shown to provide high responsivity, sensitivity, and selectivity toward polar volatile organic compounds, as well as good air stability. In detail, a polymer blend of N-alkyl-diketopyrrolo-pyrrole-dithienylthieno[3,2-b]thiophene (DPP-DTT) and polystyrene is coated onto an FET substrate via shearing-assisted phase separation (SAPS) combined with selective solvent etching to fabricate the DPP-DTT-based OFET device having an ultrathin nanoporous structure suitable for gas sensing applications. This is achieved via optimization of the film morphology by varying the shear rate to adjust the dynamic balance between the shear and capillary forces to obtain an ultrathin thickness (∼8 nm) and nanopore size (80 nm) that are favorable for the efficient diffusion and interaction of analytes with the active layer. In particular, the sensor presents high responsivities toward methanol (∼70%), acetone (∼51.3%), ethanol (∼39%), and isopropyl alcohol (IPA) (∼29.8%), along with fast response and recovery times of ∼80 and 234 s, respectively. Moreover, the average sensitivity was determined to be 5.75%/ppm from the linear plot of the responsivity against the methanol concentration in the range of 1-100 ppm. Importantly, the device also exhibits excellent long-term (30-day) air and thermal storage stability, thereby demonstrating its high potential for practical applications.

Advancement in COVID-19 detection using nanomaterial-based biosensors.

Coronavirus disease 2019 (COVID-19) pandemic has exemplified how viral growth and transmission are a significant threat to global biosecurity. The early detection and treatment of viral infections is the top priority to prevent fresh waves and control the pandemic. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been identified through several conventional molecular methodologies that are time-consuming and require high-skill labor, apparatus, and biochemical reagents but have a low detection accuracy. These bottlenecks hamper conventional methods from resolving the COVID-19 emergency. However, interdisciplinary advances in nanomaterials and biotechnology, such as nanomaterials-based biosensors, have opened new avenues for rapid and ultrasensitive detection of pathogens in the field of healthcare. Many updated nanomaterials-based biosensors, namely electrochemical, field-effect transistor, plasmonic, and colorimetric biosensors, employ nucleic acid and antigen-antibody interactions for SARS-CoV-2 detection in a highly efficient, reliable, sensitive, and rapid manner. This systematic review summarizes the mechanisms and characteristics of nanomaterials-based biosensors for SARS-CoV-2 detection. Moreover, continuing challenges and emerging trends in biosensor development are also discussed.

A comprehensive review of techniques for increasing the efficiency of evacuated tube solar collectors.

With the growing energy needs, a conscious effort has been made to use non-conventional energy sources to generate clean energy efficiently. Solar energy has always been popular among humanity. Using solar energy in heating applications is one of the most prominent ways to extract solar power. Various developments have been made to augment solar water heating system (SWHS) performance. Each modified design of SWHS has its advantages and disadvantages. Broadly, solar water heaters are categorized into two types: Flat plate solar collectors (FPSCs) and evacuated tube solar collectors (ETSCs). Earlier studies show that evacuated tube SWHS are more efficient than flat plate SWHS. Extensive research is going on the design modification of evacuated tube SWHS to enhance their efficiency. Firstly, the present review article briefly introduced the numerous types of solar collectors and their range of operation. The current work reviews various techniques used for heat transfer enhancement in SWHS, their advantages, and the region of applications. The article concentrates on research involving the usage of energy storage medium, nanomaterials, reflecting surfaces, and hybrid techniques for performance augmentation of ETSCs.

Analytical Investigation of the Impact of Jet Geometry on Aeration Effectiveness Using Soft Computing Techniques.

Jet aeration is a commonly used technique for introducing air into water during wastewater treatment. In this investigation, the efficacy of different soft computing models, namely, Random Forest, Reduced Error Pruning Tree, Artificial Neural Network (ANN), Gaussian Process, and Support Vector Machine, was examined in predicting the aeration efficiency (E20) of circular and square jet configurations in an open channel flow. A total of 126 experimental data points were utilized to develop and validate these models. To assess the models' performance, three goodness-of-fit parameters were employed: correlation coefficient (CC), root-mean-square error (RMSE), and mean absolute error (MAE). The analysis revealed that all of the developed models exhibited predictive capabilities, with CC values surpassing 0.8. Nonetheless, when it comes to predicting E20, the ANN model outperformed other soft computing models, achieving a CC of 0.9748, MAE of 0.0164, and RMSE of 0.0211. A sensitivity analysis emphasized that the angle of inclination exerted the most significant influence on the aeration in an open channel. Furthermore, the results demonstrated that square jets delivered superior aeration compared to that of circular jets under identical operating conditions.

Laser-Based Selective Material Processing for Next-Generation Additive Manufacturing.

The connection between laser-based material processing and additive manufacturing is quite deeply rooted. In fact, the spark that started the field of additive manufacturing is the idea that two intersecting laser beams can selectively solidify a vat of resin. Ever since, laser has been accompanying the field of additive manufacturing, with its repertoire expanded from processing only photopolymer resin to virtually any material, allowing liberating customizability. As a result, additive manufacturing is expected to take an even more prominent role in the global supply chain in years to come. Herein, an overview of laser-based selective material processing is presented from various aspects: the physics of laser-material interactions, the materials currently used in additive manufacturing processes, the system configurations that enable laser-based additive manufacturing, and various functional applications of next-generation additive manufacturing. Additionally, current challenges and prospects of laser-based additive manufacturing are discussed.