Processing Steps Research Articles

Prediction of nitrate, phosphate and ammonia removal in wastewater by phytoremediation-vortex system using artificial neural network

Abstract Clean water is an essential component for human survival. However, the growing global population and the relentless pace of industrial development have resulted in an alarming increase in water contamination. This renders many bodies of water as unfit for consumption or usage. In order to safeguard our natural resources, it is imperative that wastewater should be treated to follow a standard set by environmental specialists. In the Philippines, the enactment of the DAO 2021-19 has updated the requirements for release to more stringent standards. Because of this, a tertiary treatment, such as a phytoremediation bed, can be employed as an additional step in a wastewater treatment process. This study involves a comprehensive approach that consists of a phytoremediation setup, which includes the use of three plant species and specialized soil matrices, and a vortex system. The three plant species, Phragmites australis, Vetiveria zizanioides, and Canna indica are known for their capability for removal of the three pollutants, nitrates, phosphates and ammonia. The phytoremediation-vortex system was able to remove the pollutants and effectively reduce the pollutant concentration that the treated wastewater passes the standards for release. The predictive model, artificial neural network (ANN), was employed to assess the results. By using this technique, the study aimed to not only understand the intricate workings of the vortex system but also to optimise its performance for the effective reduction of pollutants, such as, nitrates, phosphates and ammonia, in wastewater. This research represents a critical step towards developing sustainable and efficient solutions for addressing the pressing challenges posed by water pollution, thereby fostering the availability of clean and potable water for human consumption and various industrial purposes.

3D volume construction methodology for cold spray additive manufacturing

Cold spraying has proved as an attractive and rapidly developing solid-state material deposition process that allows for fast formation of high quality, large 3D volume objects. Low risks of undesirable heat effects lead to increased interest in cold spraying based rapid additive manufacturing. However, by continuous powder spraying and high-pressure gas operation, cold spray additive manufacturing in terms of shape building is sensitive to operating parameters and imposes high requirements on the control of process conditions and locally needed kinematics. Every step of the manufacturing process therefore needs to be meticulously conceived and planned, especially the toolpath planning and implementation. This is not only essential to meet basic performance requirements, but also needed for the desired geometric accuracy. To address these needs, this study presents a new implementation method for cold spray additive manufacturing to improve manufacturing accuracy and flexibility. The workflow and principles of the proposed method are explained and strategies are presented for building up various component types, including rotational geometries as well as freestanding parts. The developed algorithms for 3D applications can be well integrated into the cold spray additive manufacturing process for toolpath planning and path parameter determination. Reliable and accurate robot programs were developed to make the process easy to repeat and to follow. Toolpath planning and robot programming are supported and performed by applying virtual cells to simulate the automation process for the real scenario. Path parameter adjustment and kinematic optimization can then be conducted according to the feedback from the simulation result. Applied benchmarking tests prove acceptable shape accuracy and demonstrate that the current method can enhance the capabilities of cold spray additive manufacturing for near-net shape construction. This implies that careful planning and manufacturing strategies should enable overcoming the challenges associated with cold spray additive manufacturing.

Ferroptosis-related alternative splicing signatures as potential biomarkers for predicting prognosis and therapy response in gastric cancer

Ferroptosis is linked to various tumor biological traits, and alternative splicing (AS), a crucial step in mRNA processing, plays a role in the post-transcriptional regulation of ferroptosis-related genes (FRGs). A least absolute shrinkage and selection operator (LASSO) penalized Cox regression analysis was utilized to build a prognostic signature based on 12 AS events (p < 0.05), which was validated in gastric cancer (GC) patients. The high-risk group (n = 203) showed enrichment in cancer and metastasis pathways (p < 0.05). Significant differences existed between the high- and low-risk groups in terms of tumor microenvironment (TME) cell infiltration and immune activities (p < 0.05). The low-risk group (n = 203) was characterized by immune activation and improved prognosis (p < 0.001). Additionally, targeted treatment and immunotherapy were more likely to benefit the low-risk group (p < 0.05). Correlation analysis was performed to detect related splicing factors (SF) (Cor>0.4, FDR<0.05). Furthermore, our functional assay results suggested that high SF3A2 expression might increase ferroptosis resistance and promote cell proliferation. In conclusion, the FRAs model we built has an advantage in predicting GC prognosis. The model's demonstration of variations in the immune microenvironment and drug response could potentially inform decisions regarding treatment strategies.

“Do all bisexuals have this power?”: An exploratory study of “crippling nicotine addiction,” identity, and other emergent themes in vaping messages on QueerTok

Nicotine and tobacco use disproportionally affects sexual and gender minority (SGM) populations in the United States. Social media narratives may contribute to these disparities. This qualitative study delineated perceptions and experiences depicted in SGM-related videos about nicotine vaping on TikTok. Young adult researchers engaged in every step of the research process, adding an insider perspective. Using four TikTok accounts, we used vaping and SGM-related search terms to sample videos in March–April 2022. Three TikTok accounts collected SGM-specific videos; a fourth provided non-SGM specific videos for comparison. We iteratively sorted 303 unique videos into 32 a priori and emergent codes and identified themes in SGM videos and comparison videos. In their videos, creators displayed awareness of and ambivalence toward vaping and nicotine dependence. SGM videos reflected vaping as a salient feature of identity and a consideration in romantic partnership. Studying video-based social media platforms, like TikTok, using an insider-engaged qualitative lens promotes rich interpretation of content to identify prevalent and emerging messages, which can inform appropriate interventions for SGM young people.

Electrocatalytic Reduction of Nitrate to Ammonia at Oxidized Vanadium Surfaces with V(3+) and V(4+) Oxidation States

The electrochemical reduction of nitrate to ammonia is of interest as an energy/environmentally friendly source of ammonia for agriculture and energy applications and as a route toward groundwater purification. We report in situ photoemission data, electrochemical results, and density functional theory calculations that demonstrate vanadium oxide—prepared by ambient exposure of V metal, with a distribution of surface V3+ and V4+ oxidation states—specifically adsorbs and reduces nitrate to ammonia at pH 3.2 at cathodic potentials. Negligible cathodic activity in the absence of NO3 − indicates high selectivity with respect to non-nitrate reduction processes. In situ photoemission data indicate that nitrate adsorption and reduction to adsorbed NO2 is a key step in the reduction process. NO3RR activity is also observed at pH 7, albeit at a much slower rate. The results indicate that intermediate (non-d0) oxidation states are important for both molecular nitrogen and nitrate reduction to ammonia.

Modeling of thermodynamic processes in internal combustion engine cylinder during cranking in compression measurement tests

The thermodynamics of in-cylinder processes of the internal combustion engine in cranking mode when measuring compression were examined. Analysis of various theoretical models and comparison of known data showed that the problems of constructing mathematical models of the engine operating cycle remain unresolved. The vast majority of practical data and recommendations for measuring cylinder compression are based on empirical knowledge, numerous experiments and tests. In accordance with this, there is a need for computational models of the compression measurement process and their theoretical justification. To solve the identified problems, a mathematical model of the thermodynamic processes in the cylinder was developed. Unlike existing ones, the model describes the processes in the cylinder step by step, taking into account the real nature of the intake-exhaust processes, air leakage through the interfaces of parts and heat exchange with the walls. Using modeling, the main patterns of changes in compression depending on the modes, the nature of damage to the associated parts of the valve mechanism and cylinder-piston group, the amount of leakage and engine temperature were found. Based on the results of the study, the properties of the model which make its use effective in diagnosing and monitoring the technical condition of engines in operation were concluded.

Biomechanical validation of novel polyurethane-resin synthetic osteoporotic femoral bones in axial compression, four-point bending and torsion

In addition to human donor bones, bone models made of synthetic materials are the gold standard substitutes for biomechanical testing of osteosyntheses. However, commercially available artificial bone models are not able to adequately reproduce the mechanical properties of human bone, especially not human osteoporotic bone.To overcome this issue, new types of polyurethane-based synthetic osteoporotic bone models have been developed. Its base materials for the cancellous bone portion and for the cortical portion have already been morphologically and mechanically validated against human bone. Thus, the aim of this study was to combine the two validated base materials for the two bone components to produce femur models with real human geometry, one with a hollow intramedullary canal and one with an intramedullary canal filled with synthetic cancellous bone, and mechanically validate them in comparison to fresh frozen human bone.These custom-made synthetic bone models were fabricated from a computer-tomography data set in a 2-step casting process to achieve not only the real geometry but also realistic cortical thicknesses of the femur. The synthetic bones were tested for axial compression, four-point bending in two planes, and torsion and validated against human osteoporotic bone.The results showed that the mechanical properties of the polyurethane-based synthetic bone models with hollow intramedullary canals are in the range of those of the human osteoporotic femur. Both, the femur models with the hollow and spongy-bone-filled intramedullary canal, showed no substantial differences in bending stiffness and axial compression stiffness compared to human osteoporotic bone. Torsional stiffnesses were slightly higher but within the range of human osteoporotic femurs.Concluding, this study shows that the innovative polyurethane-based femur models are comparable to human bones in terms of bending, axial compression, and torsional stiffness.

A single microfluidic device approach to direct isolation, purification, and amplification of cfDNA from undiluted plasma

Circulating cell free DNA (cfDNA) is a valuable source of biomarkers for a range of medical applications including detection and monitoring of diseases. Currently, cfDNA sequence analysis must take place in a laboratory setting, due to the multiple steps required for processing including collection, purification, amplification, and analysis. Developing a point-of-care test system that combines these steps would simplify DNA processing thereby increasing diagnostic screening accessibility and enabling real-time monitoring for individual patients. Here, we have developed a system that combines multiple cfDNA processing steps into a single microfluidic-based device. This includes cfDNA collection directly from undiluted human plasma followed by purification and on chip amplification. A microelectrode array embedded within the microfluidic chip collected cfDNA through the creation of dielectrophoretic (DEP) forces followed by a wash to achieve purification. DEP utilizes differences in dielectric properties between cfDNA and plasma to preferentially induce a force on cfDNA. We then achieved on-chip amplification of collected DNA by designing a thermal cycling system to enable polymerase chain reaction (PCR) directly on the chip. This successfully consolidated the most labor-intensive steps of collection, purification, and amplification into a single device. Compared to elution of cfDNA for off-chip amplification, our on-chip PCR method improved the lower limit of detection by 3-fold and improved the total DNA yield by 5-fold. Furthermore, we demonstrate its clinical diagnostic potential by detecting KRAS mutations from a pancreatic ductal adenocarcinoma patient using only 60 µL of plasma. This paves the way for future development of a fully self-contained system facilitating the rapid detection of mutations in cfDNA.

Advances in Blood Biomarkers for Alzheimer's Disease: Ultra-Sensitive Detection Technologies and Impact on Clinical Diagnosis.

Alzheimer's disease has escalated into a critical public health concern, marked by its neurodegenerative nature that progressively diminishes cognitive abilities. Recognized as a continuously advancing and presently incurable condition, AD underscores the necessity for early-stage diagnosis and interventions aimed at delaying the decline in mental function. Despite the proven efficacy of cerebrospinal fluid and positron emission tomography in diagnosing AD, their broader utility is constrained by significant costs and the invasive nature of these procedures. Consequently, the innovation of blood biomarkers such as Amyloid-beta, phosphorylated-tau, total-tau et al, distinguished by their high sensitivity, minimal invasiveness, accessibility, and cost-efficiency, emerges as a promising avenue for AD diagnosis. The advent of ultra-sensitive detection methodologies, including single-molecule enzyme-linked immunosorbent assay and immunoprecipitation-mass spectrometry, has revolutionized the detection of AD plasma biomarkers, supplanting previous low-sensitivity techniques. This rapid advancement in detection technology facilitates the more accurate quantification of pathological brain proteins and AD-associated biomarkers in the bloodstream. This manuscript meticulously reviews the landscape of current research on immunological markers for AD, anchored in the National Institute on Aging-Alzheimer's Association AT(N) research framework. It highlights a selection of forefront ultra-sensitive detection technologies now integral to assessing AD blood immunological markers. Additionally, this review examines the crucial pre-analytical processing steps for AD blood samples that significantly impact research outcomes and addresses the practical challenges faced during clinical testing. These discussions are crucial for enhancing our comprehension and refining the diagnostic precision of AD using blood-based biomarkers. The review aims to shed light on potential avenues for innovation and improvement in the techniques employed for detecting and investigating AD, thereby contributing to the broader field of neurodegenerative disease research.

Highly elastic fibers in a shear flow can form double helices

The long-time behavior of highly elastic fibers in a shear flow is investigated experimentally and numerically. Characteristic attractors of the dynamics are found. It is shown that for a small ratio of bending to hydrodynamic forces, most fibers form a spinning elongated double helix, performing an effective Jeffery orbit very close to the vorticity direction. Recognition of these oriented shapes, and how they form in time, may prove useful in the future for understanding the time history of complex microstructures in fluid flows and considering processing steps for their synthesis.

Effects of aging on the microstructure and mechanical properties of metastable β-type Ti-23.6Nb-5.1Mo-6.7Zr alloy with low elastic modulus

In this study, the influence of thermomechanical treatments on the microstructure and mechanical properties of a new metastable β-titanium Ti-23.6Nb-5.1Mo-6.7Zr alloy was assessed. The objective was to determine if the alloy is a potential candidate for a future fabrication of orthopedic implants, in particular prosthetic hip stems with a functional gradient of mechanical properties. Two thermomechanical processing routes were investigated: (a) 90% cold-rolled, (b) 90% cold-rolled, followed by annealing at 950 °C for 1 h. After these initial processing steps, samples were aged between 300 °C and 500 °C from 0.5 h to 4 h. Microstructural characterization was conducted by optical microscopy, transmission electron microscopy and x-ray diffraction. Young's modulus and microhardness were measured. Young's modulus of the sample annealed after cold rolling is lower than that after the cold rolled one. Aging was effective at increasing hardness but also increasing Young's modulus. The hardening during aging resulted from fine ωiso and α precipitation. The transmission electron microscopy investigations indicated that an aging treatment at 500 °C leads to a fine (α + β) microstructure, avoiding brittle ωiso precipitation. For these reasons, this alloy is a potential candidate for the manufacture of a hybrid hip prosthetic stem by employing localized aging treatment at 500 °C in the neck region, creating a functional strength gradient and maintaining a low Young's modulus in the distal part, which is needed to mitigate the stress shielding of the bone.

The End-to-End Fetal Head Circumference Detection and Estimation in Ultrasound Images.

In prenatal examinations, the fetal head circumference (HC) measurement is essential for assessing fetal weight and health conditions. The sonographers obtain the fetal HC manually by fitting peripheral skull ellipse in clinical practice, which is highly subjective, time-consuming, and experience-dependent. Recently, many fetal HC automatic measurement algorithms have been proposed to improve workflow efficiency in prenatal examination. But most automatic measurement algorithms focus on using fetal head segmentation as an intermediate processing step, and HC estimation relies heavily on segmentation results, which causes the accumulation of errors in the above two stages. Independent of the segmentation method, we design a regression network to generate the oriented bounding box to detect the head contour, and directly obtain the fetal head parameters with a pixel-based ellipse regression (PER) loss. Moreover, an effective 3D attention mechanism is integrated into the network to estimate HC more precisely without adding parameters in complex ultrasound images. The extensive experimental results on the public HC18 and our clinical dataset show that the proposed network provides a feasible scheme for end-to-end estimating fetal HC, and avoids the mistake brought by the intermediary processes.

Ethanol distillation performance test of porang flour processing residue in SME scale distillator

Abstract One of the porang processing steps involves the production of porang flour with glucomannan concentrations that meet market requirements. To increase glucomannan levels, impurities are separated. The common solvent used is ethanol. In the extraction procedure, ethanol is not only used once but reused in another extraction procedure by redistilled to increase the concentration of ethanol. This study’s objective is to determine and analyze the effect of input volume variations (5,7.5 and 10 liters) at heating temperatures (85°C, 90°C, and 95°C) on ethanol product volume and concentration. In the distillation process 5; 7.5 and 10 L, ethanol levels respectively in the range of 58.41-71.87%, 61.81-65.87, and 63.76-65.93%. Based on the analysis, the constant value (k) in the change of ethanol distillation level during the distillation process predicted at 5L volume were 0.0149, 0.0160, and 0.0182, for 7.5L volume were 0.0098, 0.0134, and 0.0153, 0.0071, 0.0083, and 0.0102 minutes−1 at 10 L sample volume for each distillation temperature. The optimal values for a distillation device evaluated on a Small Medium Enterprises (SME) scale by varying the volume and temperature of the substance in the distillation vessel were determined to be 5 liters of volume and 95°C, respectively.

A robust CETSA data analysis automation workflow for routine screening

The Cellular Thermal Shift Assay (CETSA) enables the study of protein-ligand interactions in a cellular context. It provides valuable information on the binding affinity and specificity of both small and large molecule ligands in a relevant physiological context, hence forming a unique tool in drug discovery. Though high-throughput lab protocols exist for scaling up CETSA, subsequent data analysis and quality control remain laborious and limit experimental throughput. Here, we introduce a scalable and robust data analysis workflow which allows integration of CETSA into routine high throughput screening (HT-CETSA). This new workflow automates data analysis and incorporates quality control (QC), including outlier detection, sample and plate QC, and result triage. We describe the workflow and show its robustness against typical experimental artifacts, show scaling effects, and discuss the impact of data analysis automation by eliminating manual data processing steps.

Impact of processing steps (filtration, creaming and pasteurization) on the botanical classification of honey using LC-QTOF-MS

The present study investigated the impact of filtration, creaming and pasteurization on the authentication of the botanical origin of honey using the dilute-and-shoot method in liquid chromatography coupled to mass spectrometry (LC-MS). The analytical method performances were satisfactory (analyte recoveries ranging from 95 % to 103 % and inter-day precision below 12 %). Three types of raw honeys including blueberry, canola and clover were processed under controlled conditions. Filtration, creaming and pasteurization had no impact on honey botanical classification based on the LC-MS fingerprint, and the key molecular fingerprints were retained after processing. However, results revealed that testing the impact of processing is essential when selecting honey authenticity markers because some candidates (e.g. adenosine) are not stable or can be removed during honey processing. The results of the present study also highlighted the suitability of the dilute-and-shoot approach to both develop authentication tools for honey and study the impact of processing methods on specific chemicals in honeys.

Geographical and linguistic perspectives on developing geoparsers with generic resources

Geoparsers aim to find place names in unstructured texts and locate them geographically. This process produces georeferenced data usable for spatial analyses or visualisations. Much geoparsing research and development has thus far focused on the English language, yet languages are not alike. Geoparsing them may necessitate language-specific processing steps or data for training geoparsing systems. In this article, we applied generic language and GIS resources to geoparsing Finnish texts. We argue that using generic resources can ease the development of geoparsers, and free up resources to other tasks, such as annotating evaluation corpora. A quantitative evaluation on new human-annotated news and tweet corpora indicates robust overall performance. A systematic analysis of the geoparser output reveals errors and their causes at each processing step. Some of the causes are specific to Finnish, and offer insights to geoparsing other morphologically complex languages as well. Our results highlight how the language of the input text affects geoparsing. Additionally, we argue that toponym resolution metrics based on error distance have limitations, and proposed metrics based on spatial intersection with ground-truth polygons.

Assessment of bacteriological quality of fruits and vegetables sold in Gombe metropolitan market

Fruits and vegetables are important components of human diet that could be contaminated by different kinds of microorganisms. Microbial contamination may occur during any steps of transportation or processing from farm-to-table due to many environmental, animal, and human sources as well as technological applications. This study aimed at assessing the bacteriological contaminants of fruits and vegetables sold in Gombe metropolitan. A total of 30 fruits and vegetables samples were purchased from different vendors and transported to the Microbiology Laboratory for processing. Ten-fold serial dilution was performed on each sample and aliquots of 10-1-10-3 dilutions were inoculated on Nutrient Agar and MacConkey Agar plates using pour-plate technique. The plates were incubated for 24 hours at 37oC and then observed for aerobic mesophilic and faecal coliform counts. The bacterial loads of the samples ranged from 3.7x104 to 2.7x105 and 2.9x104 to 8.7x104 for total mesophilic and faecal coliform counts, respectively. Subsequently, a total of eight bacteria were isolated and identified as Escherichia coli, Salmonella spp., Klebsiella spp., Enterobacter spp., Pseudomonas spp., Staphylococcus aureus, Bacillus cereus, and Shigella spp. In conclusion, the fruits and vegetables were found to be contaminated with different bacterial species of public health concern.

Synthesis of Bromo Eugenol Derivatives with Molecular Bromine

The bromination of eugenol using molecular bromine (Br2) has been widely reported. However, the outcomes have been inconsistent, and as a result, the specific steps of the bromination process have not been definitively established. This research aims to synthesize various derivatives of bromo eugenol, incorporating bromine atoms either in the alkene group, the aromatic ring, or both. The synthetic approaches employed include: (1) direct bromination of eugenol using 1.2, 2.4, and 3.6 equivalents (equiv) of Br2 in chloroform, (2) bromination of eugenyl benzoate with 2.4 equiv of Br2 in chloroform, and (3) debromination of the 1,2-dibromide functionality in selected bromination products using an excess of zinc in ethanol. The bromination steps of eugenol were then proposed based on the composition of the products obtained. Alkene bromination of eugenol predominated with 1.2 equiv of Br2, followed by aromatic bromination with excess Br2 (2.4 and 3.6 equiv). Aromatic substitution primarily occurred at position 6 (ortho to the hydroxyl group) and subsequently at position 5 (para to the methoxy group). Based on these results, we propose that the bromination of eugenol with Br2 proceeds initially through electrophilic addition to the alkene group, followed by electrophilic substitution on the aromatic ring. Protection of the phenol as a benzoyl ester shifted the regioselectivity of the first aromatic bromination from position 6 to 5. Furthermore, the 1,2-dibromide group has been successfully removed by zinc, resulting in derivatives containing bromine atoms only at the aromatic ring. This is by far the first comprehensive report on the bromination of eugenol with Br2 and the first one reporting the bromination of alkene as the main route of bromination with a nearly equimolar amount of Br2.

IMPROVE THE SAFETY OF AIR TRANSPORT, ESPECIALLY IN MILITARIZED TERRAIN, BY USE OF SIDE LOOKING AIRBORNE RADAR AND SPACE TIME ADAPTIVE PROCESSING

The paper explores the potential to enhance aviation safety, particularly in militarized regions, by outfitting aircraft with Side Looking Airborne Radar (SLAR) and employing space-time adaptive processing (STAP) algorithms. The research objective revolves around implementing a model of side-looking airborne radar and the corresponding STAP algorithms. This technology enables the detection of slow-moving targets amidst strong interference, encompassing both passive (clutter) and active (jammer) elements. Slow-moving targets relative to the aircraft's speed include tanks, combat vehicles, command vehicles, artillery, and logistical assets of enemy forces. The theoretical framework of space-time adaptive processing is presented, elucidating the sequential steps of the classical Sample Matrix Inversion Space-Time Adaptive Processing (SMI STAP) algorithm. The paper underscores the significance of characteristic parameters delineating a linear STAP processor. The proposed solution facilitates the detection of enemy combat measures and enhances aviation safety. It outlines a radar model installed beneath the aircraft's fuselage and elucidates algorithms for space-time adaptive processing of radar signals. The simulations conducted within the article were executed using the MATLAB environment. The simulation results indeed suggest that the proposed solution holds promise for deployment in equipping aircraft of one's own military and those engaged in operations within conflict zones. This paper stands as one of the few contributions in the literature addressing the augmentation of aircraft safety through radar and space-time adaptive processing.

ROLE OF WOMEN EMPLOYEES TOWARDS INVESTOR ANALYSIS IN THE POST-PANDEMIC ERA

The post-pandemic era has prompted significant changes in the global workforce, with a notable impact on women employees. This paper examined the role of women employees in shaping investor analysis, focusing on how gender diversity influences corporate performance, investment decisions, and long-term value creation. Women employees, particularly in leadership roles, contribute to organizational resilience, innovation, and sustainability. By assessing the role of women in the workforce and their influence on investor sentiment, this study highlights the interconnectedness between corporate diversity and financial growth in the post-pandemic context. The main objectives are to understand the role of gender diversity in corporate performance, governance, and long-term value creation. The selection of a research strategy is a crucial step in the research design process, as it dictates how relevant data for the study will be collected. The study utilized both primary and secondary data sources. Primary data was obtained through a questionnaire distributed via Google Forms, completed by a group of respondents. In addition, to build the theoretical and analytical framework, secondary data was sourced from various books, published materials, newspapers, journals, and websites. A sample size of 100 women was selected from an unlimited population. The researcher employed a random sampling method to choose the participants.