Independent Parameters Research Articles

Modeling and optimization of laying hen manure drying process to reduce protein and ammonium-N losses by adding sodium bentonite and wheat straw

Laying hen manure (LHM) is a major source of pollution due to its high nitrogen (N) and moisture content (MC). Therefore, reducing the MC of LHM is necessary to retain its recyclable value and reduce environmental pollution. One effective way is by incorporating sodium bentonite (SB) and wheat straw (WS) as amendments in the LHM. This work aimed to optimize the drying conditions of LHM and investigate the effect of SB and WS utilization on the dehydration rate, reduction of crude protein (CP), and reduction of ammonium-N (N H4+-N). The response surface methodology (RSM) was used to optimize these processes. For this purpose, two sets of experiments (drying of LHM with and without SB and Ws) were designed. The independent parameters were air temperature (70, 80, and 90 °C), air velocity (1, 1.5, and 2 m s−1), layer thickness (5, 10, and 15 mm), SB (2%, 4%, and 6%), and WS (3%, 7.5%, and 12%). The results indicated that temperature and WS had the most significant influence on all responses. To maximize the dehydration rate and minimize the reduction of CP and N H4+-N, the optimal conditions were a temperature of 78 °C, air velocity of 1 m s−1, and layer thickness of 5 mm in the first set of experiments, and a temperature of 80 °C, air velocity of 1.5 m s−1, layer thickness of 11 mm, 6% SB, and 12% WS in the second set of experiments. Under the optimum conditions, LHM treated with 6% SB and 12% WS retained 10% more CP and 58% more N H4+-N than untreated LHM. Therefore, according to the obtained results, SB and WS are recommended as additives to reduce the CP and N H4+-N losses of LHM during the drying process.

Solid-liquid phase transition temperature prediction of alloys based on machine learning key feature screening

A machine learning strategy is proposed based on the demand for prediction of solid-liquid phase transition temperature properties of multi-component precious metal alloys. Firstly, the candidate feature set are constructed by mathematical operation of the physical and chemical parameters of the material according to the chemical ratio of the alloy chemical formula. Then, a novel feature selection framework of correlation screening → genetic algorithm screening → feature weight ranking → exhaustive screening is proposed to effectively identify key feature combinations affecting solid-liquid phase transition temperature. Finally, the support vector regression algorithm is used to establish the "key feature combination-solid phase transition temperature" model with an error less than 9.83% and the "key feature combination-liquid phase transition temperature" model with an error less than 9.35%. The proposed new feature framework overcomes the inability of conventional feature selection techniques to simultaneously meet the requirements of interpretability, low computational complexity, and strong feature generality. Moreover, the comprehensive equilibrium complexity and accuracy (R2) constructed "solid temperature-key feature combination" with an error of less than 6.50% and "liquid phase transition temperature-key feature combination" with an error of less than 6.71 % by symbolic regression algorithm, which strengthened the understanding of the influence of key independent variable feature parameters on solid-liquid phase transition temperature prediction. This established machine learning strategy has excellent reliability in the prediction of solid-liquid phase transition temperature of multi-component alloys, and the proposed feature selection framework is expected to develop into a new feature selection technique.

Edukasi gizi menggunakan media cakram dalam meningkatkan pengetahuan ibu balita tentang kunjungan ke Posyandu

Background: One of the factors causing mothers' low number of visits to Integrated Healthcare (in Indonesia called Posyandu) is the lack of knowledge about Integrated Healthcare (IHC) visits, which impacts not detecting the nutritional status of children under five. Knowledge of mothers of toddlers is very important to improve the health of toddlers so that they can direct mothers to behave positively when visiting IHC.Objectives: The research aims to increase the knowledge of mothers of toddlers aged 24-59 months about visiting IHC using disc media at the Sawah Lebar Health Center, Bengkulu. Methods: Quantitative research using the Quasy experimental design was carried out at the Sawah Lebar Bengkulu Health Center from March to April 2023. The subjects involved were 40 mothers who were divided into two groups. The intervention used disc media and leaflet media with a Visit to IHC theme. Data was collected by interview using a ten-question questionnaire during the pre-test and post-test. The data obtained was tested using the Dependent T-test and Independent T-test parameters at a 95% confidence interval. Results: The difference in knowledge scores before and after being given education using disc media was 29,00. Meanwhile, education using leaflets obtained a difference in the average knowledge value of 21,50. There was an effect of nutritional education using disc media (p= 0,000) and leaflet media (p= 0,000) on increasing the knowledge of mothers of toddlers about visiting IHC. Conclusion: Nutrition education using disc media and leaflet media influences increasing the knowledge of mothers of toddlers about visiting IHC at the Sawah Lebar Bengkulu Health Center.

Resource recovery from palm kernel shell and its application for wastewater treatment: a circular economy approach

In this study, cationic Methylene Blue (MB) dye removal was investigated through a batch adsorption process using a ternary adsorbent developed from palm oil biomass, graphene oxide, and iron oxide (M-PKSGO). Box-Behnken Design (BBD) was applied for modelling and analyzing the effects of independent operational parameters including pH of initial MB dye solution, M-PKSGO dosage and adsorption time on dye removal and adsorption capacity. The experimental results show an adsorption capacity of 82.7 mg/g was achieved using 1 g/L of M-PKSGO with 135 min at pH 11. Hence, the M-PKSGO composite is proven as an effective adsorbent for cationic dye removal from wastewater. The utilization of biomass from the palm oil industries promotes a circular economy via resource recovery activities. Overall, this work brings to light the opportunities created by green adsorbents in promoting a circular economy and stimulates further research and development in this highly pertinent field.

Development of High-Entropy Shape-Memory Alloys: Structure and Properties

Amongst functional materials, shape-memory alloys occupy a special place. Discovered in the beginning of 1960th in XX century, these alloys attracted quite an attention because of the possibility to restore significant deformation amounts at certain stress–temperature conditions due to the martensitic diffusionless phase transformation involved in a process. It was possible to exploit not only so-called ‘shape-memory’ effect, but also superelasticity and high damping capacity. Over the years, more than 10 000 patents on shape-memory alloys were filed, appreciating not only the possibility to exploit energy transformation to ensure the response (feedback) at the change in independent thermodynamic parameters (temperature, stress, pressure, electric or magnetic field, etc.), but the significant work output as well. Applications ranged from different gadgets to automotive, aerospace industries, machine building, civil construction, etc. Unfortunately, the structural and functional fatigue restricted successful business application to medical sector with nitinol shape-memory alloy (different implants, stents, cardiovascular valves, etc.). Emerging high-entropy shape-memory alloys can be considered as a chance to overcome fatigue problems of existing industrial shape-memory alloys due to their specific structure that ensures superior resistance to irreversible plastic deformation.

High‐throughput experimental techniques for corrosion research: A review

AbstractHigh‐throughput experimental techniques can accelerate and economize corrosion evaluation, and thus, have great potential in the development of new materials for corrosion protection such as corrosion‐resistant metals, corrosion inhibitors, and anticorrosion coatings. This concise review highlights high‐throughput experimental techniques that have been recently applied for corrosion research, including (i) the high‐throughput preparation of metal samples in the form of thin films or bulk materials, (ii) high‐throughput experiments based on corrosive solutions with independent or gradient parameters, (iii) high‐throughput evaluation of changes in physicochemical properties, and (iv) high‐throughput corrosion evaluation by electrochemical methods. To advance automated and intelligent corrosion research, future directions for the development of the high‐throughput corrosion experimental and characterization techniques are also discussed.

A Novel Method for Vibrotactile Proprioceptive Feedback Using Spatial Encoding and Gaussian Interpolation.

The bidirectional communication between the user and the prosthesis is an important requirement when developing prosthetic hands. Proprioceptive feedback is fundamental to perceiving prosthesis movement without the need for constant visual attention. We propose a novel solution to encode wrist rotation using a vibromotor array and Gaussian interpolation of vibration intensity. The approach generates tactile sensation that smoothly rotates around the forearm congruently with prosthetic wrist rotation. The performance of this scheme was systematically assessed for a range of parameter values (number of motors and Gaussian standard deviation). Fifteen non-disabled subjects and one individual with congenital limb deficiency used vibrational feedback to control a virtual hand in the target-achievement control test. Performance was assessed by end-point error and efficiency as well as subjective impressions. The results showed a preference for smooth feedback and a higher number of motors (8 and 6 versus 4). With 8 and 6 motors, the standard deviation, determining the sensation spread and continuity, could be modulated through a broad range of values (0.1 - 2) without a significant performance loss. The overall average error and efficiency across these feedback configurations were ∼ 10% and ∼ 30%, respectively. For low values of standard deviation (0.1-0.5), the number of motors could be reduced to 4 without a significant performance decrease. The study demonstrated that the developed strategy provided meaningful rotation feedback. Moreover, the results indicate that the Gaussian standard deviation could be used as an independent parameter to encode an additional feedback variable. The proposed method is a flexible and effective approach to provide proprioceptive feedback while adjusting the trade-off between sensation quality and the number of vibromotors.

Optimization of laser beam parameters during processing of ASA 3D-printed plates

AbstractNew developments in manufacturing processes impose the need for experimental studies concerning the determination of beneficial process-related parameter settings and optimization of objectives related to quality and efficiency. This work aims to improve cutting geometry, surface texture, and arithmetic surface roughness average in the case of post-processing of filament material extrusion 3D-printed acrylonitrile styrene acrylate (ASA) thin plates by a low-power CO2 laser cutting apparatus. This material was selected owing to its unique properties for thin-walled customized constructions. Three parameters, namely focal distance, plate thickness, and cutting speed, were examined with reference to the Box-Behnken design of experiments (BBD) and regression modeling. Four responses were considered: mean kerf width, Wm (mm); down width, Wd (mm); upper width, Wu (mm); and average surface roughness Ra (μm) of cut surfaces. Different regression models were tested for their efficiency in terms of predicting the objectives with an emphasis on full quadratic regression. The results showed that a focal distance of 6.5 mm and 16 mm/s speed optimizes all quality metrics for the three plate thicknesses. The regression models achieved adequate correlation among independent process-related parameters and optimization objectives, proving that they can be used to improve the laser cutting process and support practical applications.

Восстановление пространственной функции распределения неоднородностей покрытия по функции распределения на шлифе

In the experimental studies of the structure of the special coating layer overlaid on metal applying gas-thermal spraying technique, one of the main methods is the study of polished specimen micrography. According to the computer analysis of microphotographs, it is possible to obtain the distribution function of inhomogeneities in the sample. However, since micrography is a flat image, the resulting function will be two-dimensional, whereas in a real sample, the distribution of defects is described by a three-dimensional function. In this paper, the problem of the space function recovery for the distribution of defects in a gas-thermal coating is viewed on the basis of the analysis of polished specimen micrography. The actual inclusion of an irregular shape is replaced by an effective three-axis ellipsoid. The problem is solved in the general form of reduction of the space function f of inhomogeneities distribution according to their distribution function f P on the cross - sectional plane P, which includes some integral transformation I. It is shown that in the special case of spherical particles, the inversion I^(-1) exists and is an integral transformation of the same type as I. The space distribution of spherical particles is also viewed, which does not depend on the longitudinal coordinate z, where particle sizes are limited at each point by a function R(x,y), depending on the coordinates. This distribution is suitable in its essense to the stationary spraying technology, when in deep layers near the substrate, the coating material melts completely and forms a single melt, while closer to the surface and edges, the parts that are not completely melted form inclusions of noticeable sizes. The reduction of the Fuller distribution law, used to optimize the granulometric composition of powder materials, is viewed as a second example. It is found that the reduction of the density of the ellipsoid distribution function to the section of a flat strip transfers the density of the distribution of centers as original, and the product of Fuller distributions times independent parameters is transformed into the product of distributions times the opposite degree parameters and also the previous values of the parameters of the ellipsoid

Ultralow Lattice Thermal Conductivity and Extraordinary Thermoelectric Performance in Highly Disordered CuIn7Se11 Layered Compound

AbstractSince the lattice thermal conductivity of thermoelectric materials is a relatively independent parameter, exploring semiconductor materials with intrinsically low lattice thermal conductivity is an important direction in the field of thermoelectric research. Herein, a high figure of merit ZT of 1.23 at 873 K and an exceptionally low lattice thermal conductivity of 0.18 W m−1 K−1 are realized in the n‐type CuIn7Se11 compound for the first time. The weak In─Se chemical bonds induce strong coupling between acoustic phonon and optical phonon with low frequency, leading to low sound velocity. The highly disordered configuration of Cu, In, and vacancies at In2, In3, and M sites, in conjunction with atomic‐scale slips and flips intensify phonon scattering. All these result in an intrinsically low thermal conductivity of the CuIn7Se11 compound. DFT calculation reveals that the density of states at the bottom of the conduction band is mainly contributed by the coupling between 4p orbitals of Se atoms and 5s orbitals of In atoms, featuring with a small effective mass. This small effective mass of charge carrier renders the CuIn7Se11 compound high carrier mobility of 435 cm2 V−1 s−1 at 300 K. Consequently, the CuIn7Se11 compound possesses an extraordinary thermoelectric performance

Intraepithelial CD8+ cytotoxic T-cells are the main component of the immune tumor microenvironment that drive prognosis in muscle-invasive bladder cancer

Abstract Introduction/Objective Quantity and the spatial relationship of individual immune cell (sub)types can provide prognostic information in muscle-invasive bladder cancer. Methods/Case Report To study the prognostic role of different immune cell subpopulations in muscle-invasive bladder cancer, we stained 521 muscle-invasive urothelial bladder carcinomas with 21 antibodies using BLEACH&STAIN multiplex fluorescence immunohistochemistry (mfIHC) in a tissue microarray format. A framework of neuronal networks was trained and used for image analysis. Results (if a Case Study enter NA) The identification of more than 300 different immune cell subpopulations and the characterization of their spatial relationship resulted in numerous spatial interaction patterns. 40 immune parameters showed prognostic significance in univariate analyses of which 15 were independent from pT, pN, and histologic grade. The strongest association to clinical outcome was identified for intraepithelial CD8+cytotoxic T-cells (tAUC: 0.70) compared to all other 15 independent prognosis parameters (AUC: ≤0.68, p=0.039). Deeper spatial analysis revealed an increased expression levels of TIM3, CTLA-4 and PD-1 on CD8+ T cells that were located in the intraepithelial compartment. Furthermore, the cell-to-cell interaction profile of intraepithelial CD8+ T-cells was dominated by intraepithelial dendritic cells as well as intraepithelial M1-macrophages. Conclusion The intraepithelial CD8+T-cells that showed the highest expression level of TIM-3, PD-1 and CTLA-4 and were strongly interacting with M1-macrophages as well as dendritic cells and represented the strongest prognostic parameter in muscle-invasive bladder cancer. This can be explained by the fact that tumor cell destruction by CD8+ cytotoxic T-lymphocytes through direct cell-to-cell-contacts represents the “terminal-end-route” of anti-tumor immunity. The strongest prognostic immune-factor – intraepithelial CD8+T-cells – can be measured straightaway in routine pathology as the CD8-labelling index using conventional bright field immunohistochemistry.

Compartmental Syndecan-1 (CD138) expression as a novel prognostic marker in triple-negative metaplastic breast cancer

BackgroundMetaplastic breast cancer (MpBC) is rare, aggressive, and mostly triple-negative (TN) subtype of BC. We aimed to investigate the potential prognostic significance of Syndecan-1 (SDC1/CD138) expression in this unique tumor. MethodsArchived charts of 50 TNBC patients [21 MpBC and 29 invasive ductal carcinoma (IDC)] were retrospectively evaluated. Corresponding paraffin blocks were used for immunohistochemical (IHC) staining of SDC1. Compartmental (epithelial membranous, stromal, and cytoplasmic) staining scores were expressed in quartiles (Q) and correlated with disease-free survival (DFS) and overall survival (OS). ResultsThe median follow-up period was 54.6 months (range: 2.2–112.7). MpBC patients showed significantly worse DFS and OS than IDC (p = 0.007 and 0.004, respectively). MpBC demonstrated significantly higher Q4 stromal and membranous SDC1 compared to IDC (p = 0.016 and 0.021, respectively), whereas IDC exhibited significantly higher cytoplasmic Q4 SDC1 than MpBC (p = 0.015). Stromal Q4 SDC1 expression was found to be an independent factor associated with MpBC relative to IDC (OR: 6.7, 95% CI: 1.24–36.90; p = 0.028). Stromal Q4 SDC1 expression was also an independent prognostic parameter for worse DFS and OS compared to Q1–3 in the whole cohort (HR: 4.2, 95% CI: 1.6–10.5; p = 0.003 and HR: 5.8; 95% CI: 2.2–15.3; p < 0.001, respectively). In MpBC, cytoplasmic Q1–3 SDC1 expression was an independent prognostic indicator for worse OS compared with their IDC counterparts (HR: 2.837, 95% CI: 1.048–7.682; p = 0.04). ConclusionThis study suggests, for the first time, that differential expression and localization of SDC1 may contribute to the pathogenesis and prognosis of TN-MpBC. Therefore, targeting SDC1 (CD138) could emerge as a novel therapeutic approach for this devastating disease.

Predicting the Clinical Course of Treatment with Midostaurin in Patients with Advanced Systemic Mastocytosis

Introduction: Advanced systemic mastocytosis (AdvSM) is characterized by presence of the KIT D816V mutation and variably other somatic mutations in the mast cell but also non-mast cell lineages, e.g. monocytes or eosinophils, in ≥80-90% of patients. Midostaurin is an approved multikinase/KIT inhibitor for treatment of AdvSM. The intricate landscape in assessment of response, resistance or progression, the occurrence of adverse events with its impact on dose modification and the recent approval of the specific KIT D816V inhibitor avapritinib contribute to complex decision-making. We here sought to characterize the most valuable baseline and on-treatment parameters for prediction of response and survival in midostaurin-treated patients with AdvSM. Methods: On basis of the ´German Registry on Disorders of Eosinophils and Mast Cells' (GREM), this retrospective-prospective analysis included 79 patients with AdvSM. Treatment was initiated between 2009 and 2022 accumulating into a 239 patient-years overall follow-up period. In addition to common serological, morphological, and molecular response parameters, we specifically examined the recurrent observation of an early flare-up in alkaline phosphatase (AP) levels, which was defined as a ≥25% AP increase within three months after treatment initiation, followed by a subsequent decline below baseline within six months. Results: Midostaurin was used in first-line (1L) or second and further lines (2L+; median 1 prior treatment line, range 0-4) in 63 (80%) and 16 (20%) patients, respectively. In 1L/2L+, median overall survival (OS) was 2.6/2.4 years. At baseline, the presence of additional somatic mutations in SRSF2 (n=36, 46%, HR 2.0 [1.2-3.6], P=0.014), ASXL1 (n=15, 19%, HR 2.0 [1.0-3.9], P=0.043), and EZH2 (n=6, 8%, HR 4.3 [1.7-11.3], P=0.003) significantly stratified patients according to their hazard ratio-weighted risk (median OS, 5.8 vs. 2.9 vs. 1.0 years; P&amp;lt;0.001). In 25 evaluable patients, the variant allele frequency in 72 additional somatic mutations remained stable (mean change 8%, absolute change &amp;gt;5% in 16/72 (22%) mutations). New mutations were identified in 9/25 (36%) patients during follow-up (Figure). Durable responses (complete, partial, clinical improvement) according to modified Valent criteria, IWG-MRT-ECNM criteria or Pure Pathological Response (PPR) criteria over a median duration between 12 and 24 months were achieved in 60%, 29% and 13% of patients, respectively. Within the first 12 months, duration of response was associated with an OS benefit ( P&amp;lt;0.001). An AP flare-up was observed in 22/79 (28%) patients, was paralleled by contemporaneous decrease of serum tryptase (median -35%, range -82 - 191%) and resulted into improved OS (median 5.8 vs. 2.2 years, P=0.007). Multivariable analysis identified the AP flare-up ( P=0.029), a ≥25% reduction of bone marrow (BM) mast cell (MC) infiltration ( P=0.046) and a ≥25% reduction of the KIT D816V expressed allele burden (EAB; P=0.034) at any time within the first 6 months as independent markers for improved OS (Table). Dynamic risk assessment of the independent parameters during follow-up allowed to abrogate a negative baseline risk-categorization. The initial dose was 200 mg/d (100 mg BID, including patients with a prespecified rapid dose escalation), 150 mg/d or 100 mg/d in 63/79 (80%), 2/79 (3%) and 14/79 (18%) patients, respectively. In the combined 150 mg and 100 mg cohorts, a dose increase to 200 mg/day was achieved in 9/16 (56%) patients. The 200 mg could be maintained at month 3, 6, 12, 24, and 36 in 50/63 (79%), 30/48 (63%), 23/34 (68%), 15/22 (68%) and 9/9 (100%) patients, respectively. Overall, 96 adverse events (AE) led to dose-adjustment (including temporary or permanent discontinuation). Hematologic and nonhematologic toxicities accounted for 24 (25%) and 36 (38%) of AEs with neutropenia (n=8, 8%) and nausea/emesis (n=22, 22%) being the most common AEs leading to dose-adjustment. Of note, the various dose levels had no impact on response or survival. Conclusions: Baseline mutations in SRSF2, ASXL1 and EZH2 conferred resistance to midostaurin. The various dose levels had no impact on response or survival. The on-treatment parameters AP flare-up, and a ≥25% reduction of BM MC infiltration and of KIT D816V EAB abrogated a negative baseline risk-categorization. No new safety signals occurred.

Gradient optimization method for tunnel resistivity and chargeability joint inversion based on deep learning

The water inrush hazards have become one of the bottleneck problems that constrain tunnel construction. Ahead geological prospecting is the major tool to avoid geo-hazards and ensure safe, economical and efficient tunnelling. This work proposes a novel deep learning-based electrical method, which jointly inverses resistivity and chargeability to estimate water-bearing structures and water volume. Specifically, we design an encoder–decoder network, with one shared encoder to extract features from input data, two encoders to output resistivity and chargeability models, respectively, and an elaborate collinear regularization on the two outputs to reduce solution multiplicity. Moreover, our input is first transformed to the gradient domain to address the spatial incorrespondence issue between observation data and the electrical model. Compared with traditional linear inversion methods, our proposed method demonstrates superiority in locating and delineating anomalous bodies. The ablation study also shows that joint inversion of two parameters could benefit and outperform independent parameter inversion.

Decontamination of levofloxacin from water using a novel chitosan–walnut shells composite: linear, nonlinear, and optimization modeling

Chitosan–walnut shells (Ch–W) composite was tested for the removal of levofloxacin from water. Various experimental factors were examined at different contact time intervals. The prepared composite exhibited maximum uptake capacity of 7.43 mg g−1 for levofloxacin with 0.5 g L−1 Ch–W dose at 45 min and pH = 7. Linear and nonlinear isotherm/kinetic models have been investigated, and the pertinency of the models was confirmed by correlation coefficients (R2) and error functions. Consequently, the adsorption of levofloxacin could be more accurately described by the nonlinear pseudo-second-order and Langmuir as well as Temkin models (R2 ˃ 0.98). Optimization modeling of levofloxacin was performed using a central composite design. The independent parameters; initial concentration, pH, and Ch–W dose, were selected while levofloxacin removal was the response. The maximum levofloxacin removal was 75.7% and 94.2% at concentrations of 4 mg L−1 and 2 mg L−1, respectively. Furthermore, 3D surface plots with the interaction effects of the investigated factors are presented. The developed model was reliable for further study and prediction owing to the closeness between the experimental and predicted values. The individual and interacting factors were found to be significant except for “concentration x dose” based on the ANOVA. The models confirmed the experimental results with R2, R2adjusted, and R2predicted values ˃ 0.939. The continuity adsorption cycles were tested for reuse and revealed that the removal of levofloxacin was decreased ~ 23% after six cycles. Hence, the prepared composite has the potential to remove antibiotics from water.

Using Working Parameters to Control Formation and Shape of Lotus-Type Pores

The development and final shape of lotus-type pores can be manipulated in advance during unidirectional solidification in this study. Independent parameters considered are solidification rate, ambient pressure, and factor accounting for solute concentration at a selected reference state in liquid deviated from that at the top surface. Lotus-type porous materials have contemporarily been used in heat sinks, energy including shock, vibration, and sound absorption in aircraft engines, etc., which strongly depend on directions, distributions, and pore shapes in solid. This model accounts for transient gas pressure in the pore affected by solute transfer, gas, capillary and hydrostatic pressures, and Sieverts’ law or Henry’s law at the bubble cap and top surface. Solute transport across the cap self-consistently accounts for solute convection at the cap based on a reference concentration deviated from that at the top free surface, the amount of solute rejected by the solidification front into the pore, and the convection-affected concentration at the solidification front. The resulting simultaneous systems of unsteady first-order ordinary differential equations are solved by a MATLAB code. The length of lotus-type pores is also interpreted by the conservation of the solute content in the system. The predicted final shapes of lotus-type pores agree with algebraic results previously confirmed by available experimental data.

Photocatalytic removal of crystal violet by C–MIL–53(Fe)@BiOI heterojunction: Process modeling and optimization

A facile synthesize protocol was utilized to prepare C–MIL–53(Fe)@BiOI photocatalyst with exceptional performance under 5-watt visible light irradiation. The prepared material was characterized for physio-chemical and optical properties. The EDX and N2 adsorption–desorption analysis revealed the heterojunction surface contained ∼ 71.48 % bismuth by weight, and a BET surface area of 12.96 m2/g. Box-Behnken design (BBD) was applied for the experimental study to optimize the process of crystal violet (CV) removal by low-powered LED irradiation. BBD also provided essential information on the effect of independent parameters i.e., pH, photocatalyst dose, CV concentration, reaction time, their interactions, and dye removal prediction under determined operating conditions. Screening tests indicated a significant improvement in catalytic performance for C–MIL–53(Fe)@BiOI composite compared to parent materials, MIL–53(Fe), and C–MIL–53(Fe). Maximum removal of 96.8 % was achieved for CV degradation at pH = 4, photocatalyst dosage = 0.55 g/L, CV concentration = 10 mg/L, and irradiation time = 38 min. Mechanistic study nominated OH. radicals as the most active species participate in CV degradation. C–MIL–53(Fe)@BiOI represents a viable material owing to the promising removal performance against cationic dye under low-energy irradiation.

Optimal risk-based design of reinforced concrete beams against progressive collapse

Risk analyses addressing consequences of progressive collapse have shown that, due to the small probabilities of element loss due to abnormal loads, structural strengthening for alternate load paths has negative cost-benefit for most buildings subjected to typical threats. This study addresses optimal risk-based design of reinforced concrete beams subjected to supporting column removal, with specific focus on reinforced concrete behavior. Nonlinear finite element analysis (NLFEA) is carried out, allowing both compressive arch and catenary actions to be predicted under large deflections. NLFEA results are compared to experimental data, showing good agreement and allowing quantification of model errors. Risk optimization results show that optimal beam designs change significantly with local damage probability, which is considered an independent parameter. More importantly, the study shows how different failure modes compete for limited construction and strengthening budgets. In case of intact structure, optimal design is governed by serviceability displacements, bending failure at midspan, and bending-shear failure at beam ends. Optimal design of the damaged structure is controlled by shear failure at beam ends and tensile rupture of steel rebar due to either catenary action or snap-through instability. Results highlight that, under significant column-loss probabilities, progressive collapse resistance is reached by larger beam depth, greater reinforcement area and reduced stirrup spacing. Such design measures against progressive collapse also provide greater safety margins against all failure modes of the intact structure.

Graphene-Based Composites for Thermoelectric Applications at Room Temperature

The thermoelectric materials that operate at room temperature represent a scientific challenge in finding chemical compositions with three optimized, independent parameters, namely electrical and thermal conductivity and the Seebeck coefficient. Here, we explore the concept of the formation of hybrid composites between carbon-based materials and oxides, with the aim of modifying their thermoelectric performance at room temperature. Two types of commercially available graphene-based materials are selected: N-containing reduced graphene oxide (NrGO) and expanded graphite (ExGr). Although the NrGO displays the lowest thermal conductivity at room temperature, the ExGr is characterized by the lowest electrical resistivity and a negative Seebeck coefficient. As oxides, we choose two perspective thermoelectric materials: p-type Ca3Co4O9 and n-type Zn0.995Al0.005O. The hybrid composites were prepared by mechanical milling, followed by a pelleting. The thermoelectric efficiency was evaluated on the basis of its measured electrical resistivity, Seebeck coefficient and thermal conductivity at room temperature. It was found that that 2 wt.% of ExGr or NrGO leads to an enhancement of the thermoelectric activity of Ca3Co4O9, while, for Zn0.995Al0.005O, the amount of ExGr varies between 5 and 20 wt.%. The effect of the composites’ morphology on the thermoelectric properties is discussed on the basis of SEM/EDS experiments.

Optimization and Validation of Sensitive UPLC-PDA Method for Simultaneous Determination of Thymoquinone and Glibenclamide in SNEDDs Formulations Using Response Surface Methodology

The development of analytical procedures capable of simultaneous determination of two or more drugs is in crucial demand due to the availability of different formulations that are composed of different APIs. The presented study aimed to optimize and validate a simple, accurate, and sensitive UPLC analytical method for the simultaneous determination of thymoquinone (TQ) and Glibenclamide (GB) using response surface methodology, and apply this method in pharmaceutical formulations. A 32 full design of experiment was utilized to study the impacts of the independent parameters (acetonitrile ACN concentration, A; and column temperature, B) on the drugs’ analytical attributes (viz, retention time, peak area, and peak asymmetry, in addition to the resolution between TQ and GB peaks). The results revealed that the independent parameters exhibited highly significant (p &lt; 0.05) antagonistic effects on retention times for TQ and GB peaks, in addition to the agnostic effect on GB peak symmetry (p-value = 0.001). Moreover, antagonistic impacts (p &lt; 0.05) on the resolution between TQ and GB peaks were found for both independent factors (A and B). The statistical software suggested 46.86% of ACN (A) and 38.80 °C for column temperature (B) for optimum analytical responses. The optimized green method was discovered to be acceptable in terms of selectivity, precision, accuracy, robustness, sensitivity, and specificity. Moreover, the optimized simultaneous method was successfully able to determine the contents of TQ and GB in self-nanoemulsifying drug delivery (SNEDD) formulation, in which the results showed that GB and TQ content within the prepared formulations were 1.54 ± 0.023 and 3.62 ± 0.031 mg/gm, respectively. In conclusion, the developed assay was efficient and valid in analyzing TQ and GB simultaneously in bulk and self-nanoemulsifying drug delivery system (SNEDDs) formulations.