Weight Determination Research Articles

Comparison of two proxies for the preconception weight using data from a pre-pregnancy cohort in Benin: Weight measured in the first trimester of pregnancy vs estimated by Thomas' formula.

Accurate determination of pre-pregnancy weight is essential for optimal pregnancy monitoring and antenatal care. Determining pre-pregnancy weight in limited-resources settings is challenging for both clinical practice and public health research. From a 2014-2017 pre-pregnancy cohort in Benin, we evaluated the agreement between the measured pre-pregnancy weight (MPPW) and two proxies: (i) the first trimester pregnancy weight (FTPW) and (ii) the estimated pre-pregnancy weight (EPPW) using Thomas & al. formula. We analysed data from 302 pregnant women with both pre-pregnancy weight measured within 3 months before conception and weight measured during the first trimester. Using segmented linear regression, we first assessed up to which gestational age the weight measured during the first trimester could reasonably estimate the MPPW. Then the Bland & Altman method was used to assess agreement between MPPW and the two proxies. Additional analyses were performed to assess the sensitivity of results to the timing of measurement of either MPPW or the two proxies. On average, FTPW did not feature significant difference with MPPW up to 13.03 (11.99-14.06) weeks of gestational age. FTPW, measured on average at 7 ± 2.4 weeks of gestation, and the EPPW showed similar Bland & Altman limits of agreement with the MPPW. However, while the FTPW slightly underestimated the MPPW by a mean of-0.16 (-0.08; +0.39) kg, the EPPW overestimated it by a mean of + 0.43 (+0.20; +0.66) kg. Minor differences in these results were observed when the MPPW was assessed earlier or within three months before pregnancy, or according to the gestational age at the time of the proxy's measurement. In conclusion, in Southern Benin and up to 12-14 weeks of pregnancy, the FTPW appeared to be a good proxy of the MPPW while using Thomas' formula did not enhance pre-pregnancy weight estimation.

Analyzing New Quality Productive Forces in New Energy Vehicle Companies Based on a New Multi-Criteria Decision Analysis Model

Assessing the new quality productive forces (NQPF) of new energy vehicle (NEV) companies is crucial for promoting the sustainable development of the NEV industry. This paper systematically evaluated and analyzed the NQPF of Chinese listed NEV companies from 2018 to 2022 using a novel multi-criteria decision analysis (MCDA) model. To address limitations in traditional MCDA models, such as unbalanced weight distribution, insufficient ranking differentiation, and incomplete identification of key influencing factors, this study introduced a new model, IDOCRIW-PROBID (integrated determination of objective criteria weights—preference ranking on the basis of ideal-average distance). First, an evaluation index system tailored to NEV companies’ NQPF was developed. Then, the IDOCRIW method was used to objectively assign weights to the indicators, enhancing the scientific rigor of the weight distribution. The PROBID method was employed to rank companies based on their NQPF, identifying differences between them. Additionally, an obstacle degree model was introduced to analyze key influencing factors, compensating for the traditional MCDA model’s limitations in this regard. The results showed, first, that the proposed IDOCRIW-PROBID model has a high degree of consistency with the classical Entropy-TOPSIS (technique for order of preference by similarity to ideal solution) model in terms of ranking the results (correlation coefficient = 0.91), and that IDOCRIW-PROBID offers higher differentiation compared to other MCDA models, validating its reliability and superiority. Second, during the study period, the development levels of NQPF in Chinese listed NEV companies varied significantly, with most companies at a low level of development and showing a downward trend, indicating that companies face considerable challenges in improving their NQPF. Third, the obstacle degree analysis revealed that R&D lease fees, R&D depreciation and amortization, and direct R&D investment were the primary factors hindering NQPF growth. This research provides theoretical support and decision-making insights for strategic optimization in NEV companies and informs government policy formulation.

A Novel Chemical Approach for the Development of Thioesterified Cellulose Derivatives

In this study, thioesterified cellulose was synthesized by grafting thiol moiety onto a-cellulose, which was first oxidized with different reactive oxygen species (ROS) such as H2O2, Fenton reagent (FR), and peroxyacetic acid in aqueous solution. The thioesterification was done by refluxing oxidized cellulose with ethanedithiol in a mixture of toluene and water (4:1) at 85°C for 6 h. The modification of cellulose was evidenced by FTIR, Raman, solid-state 13C CP MAS NMR spectroscopy, conductometric titration, thermogravimetric analysis, X-ray diffraction, zeta potential measurement, molecular weight determination, and SEM analysis. The characteristic absorption bands for −C=O and −C(=O)−S− bonds in FTIR and Raman spectra were suggestive of the modification of cellulose. 20 % FR was the most efficient in introducing the highest amount (158.93 μmol g−1) of the -COOH groups, while cleavage of cellulose backbone was found to take place as evidenced by the result of the degree of polymerization. The presence of new peaks in 13C CP MAS NMR spectra of thiol-functionalized cellulose ascertained the anchoring of thiol onto oxidized cellulose. Additionally, the significant decrease in the C6 signals for the amorphous region of thiol-modified cellulose provided information about successful modification. In addition, the degree of substitution was determined to be about 0.025. The efficacious functionalization was further supported by the measurement of zeta potential, wherein thioesterified cellulose exhibited the highest negative zeta potential due to increased hydrophobicity. This study would open up a new route for the development of important derivatives of cellulose, including cellulose dimer, containing the thioester group which is the backbone of many antibiotics and natural products.

Edge computing task scheduling method based on user’s social relations: a construction and solution for Smart City Library

In the realm of the development of a Smart City Library, the integration of robust edge computing is vital. The research suggests a novel task-scheduling model for edge computing, leveraging user’s social relationships. Analyzing these connections involves constructing a user’s social relationship graph by implementing mathematical convolution and the Jaccard similarity ratio. This precise quantification of social ties ensures secure and reliable task scheduling. An equipment connection graph of a user equipment service is also crafted based on Euclidean distance, aligning task scheduling with device-to-device (D2D) communication conditions. Combining a user’s social relationship graph and a user’s device-service device connection graph creates a task-device bipartite graph. On the other hand, the calculation of a task execution cost and edge weight determination finalize a scheduling model. Implementing the proposed method for constructing a model for edge computing task scheduling based on utilizing the Kuhn–Munkres (KM) algorithm demonstrates positive impacts, which are few delays and less energy consumption, on edge computing task scheduling. For instance, when the social threshold score changes from 02. To 0.6, the total task execution delay time increases from 23 to 32, which is the best when compared with other algorithms. The approach strengthens security and reliability while decreasing task execution delays and energy consumption. This research advances edge computing for Smart City Libraries, promising transformative implications.

An integrated environmental and economic assessment for the disposal of food waste from grocery retail stores towards resource recovery.

Food waste gives rise to many environmental problems. A large amount of food waste is produced by grocery retail stores. It is therefore important to apply efficient food waste treatment technologies with minimal environmental impact and investigate the optimal approach for food waste collection, transportation, and treatment. In the present study, a life cycle impact assessment (LCIA) was conducted to analyze different food waste disposal scenarios, including incineration, landfilling, composting, anaerobic digestion, and bioconversion. The impacts of the five scenarios on the environmental, economic, and social aspects were assessed. The results suggested that the landfilling scenario has the lowest net cost for the treatment of food waste, followed by the incineration scenario. The bioconversion treatment cost has the most significant positive effect on the net cost of the bioconversion scenario, and both the price and yield of compost have a significant negative effect on the net cost. The rankings of the five scenarios are the same under both weight determination methods, with the bioconversion scenario performing the best, followed by the composting scenario. The results of this study can help improve the disposal of food waste in grocery retail stores in the framework of sustainability and the circular economy.

Regulation of Rice Grain Weight by Fatty Acid Composition: Unveiling the Mechanistic Roles of OsLIN6 by OsARF12.

Fatty acids play a putative role as second messengers of phytohormones and regulate the rice grain weight. However, the inner mechanism is still unclear and needs to be further studied. In this study, we identified that oleic acid (C18:1) negatively correlates while linoleic acid (C18:2) positively correlates with rice grain weight. Field trials showed that 1000-grain weight was significantly reduced when treated with the fatty acid synthesis inhibitor, Firsocostat S enantiomer (FSE), at the heading and flowering stages. RNA-seq analysis revealed that FSE affects grain weight by modulating processes, such as glycolysis, sucrose metabolism, and hormone signaling. Notably, FSE inhibited the expression of OsLIN6, which is responsible for transporting C18:1 to the phosphatidylcholine pool for C18:2 synthesis. Compared with the wild type (WT), the OsLIN6 knockout mutant exhibited a lower grain weight, an increased C18:1 content, and a decreased C18:2 content. Importantly, OsARF12 was shown to bind to the OsLIN6 promoter and activate its expression. In summary, this study highlights the crucial role of the fatty acid synthesis gene, OsLIN6, which was regulated by OsARF12, in rice grain weight determination, thus establishing the molecular link between fatty acid synthesis and auxin signaling.

SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN LAPTOP DENGAN MENERAPKAN METODE SIMPLE ADDITIVE WEIGHTING (SAW)

This research aims to develop a Decision Support System (SPK) using the Simple Additive Weighting (SAW) method in selecting the best laptop based on various criteria. The criteria used in this research include price, RAM, internal memory, screen size, and processor. The SAW method was chosen because of its ability to handle multi-criteria decision making by giving weights to each criterion and calculating the final score of each alternative through data normalization. The results showed that the five best laptop alternatives with the highest scores were HP SPECTRE X360 with a score of 75.00, Xiaomi RedmiBook 15 with a score of 72.50, ASUS Zenbook S 13 OLED with a score of 67.50, Lenovo Thinkbook 14 with a score of 67.00, and Huawei MateBook D14 with a score of 67.00. From these results, HP SPECTRE X360 was declared the best choice. This study concludes that the SAW method is effective in helping decision making involving many criteria and alternatives. The weights of criteria such as price and processor show a significant influence on the final result. For further research, it is recommended to evaluate more deeply the determination of weights and normalization formulas to improve decision accuracy. Thus, this method can be adapted to a wider context and relevant in complex decision making

Development and Application of an Employee Moral Quotient (MQ) Evaluation Index System for Chinese Petroleum Enterprises

Recognizing the critical role of oil and gas resources as strategic assets and acknowledging the increasing emphasis on green development and social responsibility driven by international energy agreements and the “dual carbon” strategy, this study addresses the urgent need for a robust employee evaluation framework within China’s petroleum sector. While existing systems often prioritize competence-based indicators, they frequently overlook the crucial aspect of employee Moral Quotient (MQ). This research focuses on developing and validating a holistic, scientifically grounded, multi-dimensional, and dynamic MQ evaluation index system tailored specifically for Chinese petroleum enterprises. The study employed a mixed-methods approach, integrating a comprehensive literature review, semi-structured interviews with industry experts, and robust quantitative techniques, including the Analytic Hierarchy Process (AHP) and the entropy weight method. A two-round Delphi study, involving 18 experts from six provinces across China, ensured broad representation and facilitated the construction of the evaluation index system and the determination of indicator weights. The Delphi process achieved a high degree of expert consensus, evidenced by a 100% questionnaire response rate in both rounds, expert authority coefficients of 0.851 and 0.879, respectively, and Kendall’s coefficient of concordance of 0.243 and 0.247 (p < 0.01), respectively, demonstrating strong reliability and scientific validity. The resulting MQ evaluation index system comprises eight first-level indicators and thirty-three s-level indicators, encompassing key dimensions of employee morality relevant to the petroleum industry. This comprehensive system provides a robust and objective tool for employee selection, training, performance evaluation, and career development within Chinese petroleum enterprises, supporting informed decision-making in human resource management and fostering a culture of ethical conduct and sustainable development. Furthermore, the developed framework offers valuable insights and serves as a potential model for petroleum enterprises and other resource-intensive industries globally seeking to integrate MQ assessment into their human capital management strategies.

Comparison of Criteria Weight Determination Using MEREC and CRITIC Methods in Choosing The Best Student Accommodation with the MOORA Method Case Study: Coventry University

One of the challenges faced by IISMA Awardees and students in general in Coventry University is choosing a comfortable place to live. Although various student accommodations are provided, differences in facilities and considerations from other parties such as parents and friends make the selection process complicated. This study develops a decision support system to help students choose student accommodation objectively without any intervention from others and provides a comparison of the use of different combinations of methods as additional guidance in the decision-making process. Two methods, Method Based on the Removal Effects of Criteria (MEREC) and Criteria Importance Through Intercriteria Correlation (CRITIC), are used to weight the criteria. The Multi-Objective Optimization (MOORA) method is used to determine the best alternative after the weight calculation is known. The results using a combination of the MEREC-MOORA method and a combination of the CRITIC-MOORA method place Alternative 5 (A5) in first place, while the remaining alternatives show a similar ranking order. In this study, scenario testing was also carried out by deleting and adding criteria and alternatives which then provided ranking results with a positive correlation even though different combinations of methods were used in determining the ranking.

Preparation and performance evaluation of a low‐damage fracturing fluid for unconventional reservoirs

AbstractThe characteristics of tight matrix, underdeveloped natural fractures, small pore throat radius, poor pore connectivity, and abundant clay minerals result in a high potential for damage to the tight sandstone reservoirs of the Shaximiao Formation in the Qiulin Block in the Sichuan Basin. In order to reduce the retention damage caused by fracturing fluid to the tight sandstone reservoir, a low damage fracturing fluid system applicable to the target reservoir is developed in this work, and the indoor performance meets the requirements of on‐site fracturing operation. A low‐damage, heat‐resistant, shear‐stable amphiphilic polyacrylamide (PAAD) was prepared by free radical polymerization in aqueous solution using acrylamide, acrylic acid, and methacryloyloyloxyethyl dimethyloctadecyl ammonium bromide (DM18) as the monomers. The amphoteric polyacrylamide was characterized by Fourier infrared spectroscopy, nuclear magnetic resonance spectroscopy (1H NMR), light scattering molecular weight determination, and thermogravimetric analysis. Fracturing fluids were formulated with the amphiphilic hydrophobic associative polymer PAAD as a drag‐reducing agent, the fluorine‐containing and nonionic complex surfactant FD1 as a clean‐up additive, CT10‐4B as a bactericide and CT1‐12 as an emulsion breaker. The fracturing fluid formulation was preferred, and the performance of the fracturing fluid was evaluated in terms of temperature resistance and shear resistance, drag reduction, anti‐expansion, and core damage. The fracturing fluid showed good temperature and shear resistance, with a viscosity retention of 58.4% at 120°C and 170 s−1 shear for 1 h. At a loading of 0.1%, the drag reduction rate reaches 72.3%, the anti‐expansion rate of the gel‐breaking fluid is 86.04%, and the damage rate to the tight sandstone core is 16.25%. The results show that the fracturing fluid has good heat resistance and shear stability, as well as low damage and good resistance reduction and anti‐expansion properties. This work can provide new strategies for designing polymeric fracturing fluids with low damage, good heat resistance and shear stability.

Risk Based Weight Determination in Multiple Criteria Decision Making

Risk Based Weight Determination in Multiple Criteria Decision Making

Efficient multi-objective design method for optical coatings

The modern design of high-performance optical coatings demands a comprehensive consideration of multiple properties. We propose an efficient multi-objective optimization method utilizing an adaptive weighted sum method, exploring the lexicographic optimization and geometry-based adaptive weights determination method. Broadband negative dispersive mirrors have been designed that demonstrates notable improvements in performances of spectrum, group delay dispersion and electric field distribution. In addition, we employ Deep-learning method to enhance the optimization. The mapping between different design parameters and results has been learned, which helps the optimization to directly approach the optimal design. It extends the novel method to diverse designs utilizing the similar kinds of merit functions and subsequently accelerates the design by 120 times.

Optimization through combined compromise solution and weight determination through SHANNON-ENTROPY for multi criteria decision making in tribological testing of ultrahigh molecular weight polyethylene nano-composite in SBF environment and compression testing with it’s invitro analysis

Aluminium oxide and graphene reinforced with ultrahigh molecular weight polyethylene (UHMWPE) composites offer significant potential for prosthetic and implant applications due to their unique combination of properties. In this article, a series of aluminium oxide and graphene reinforced with ultrahigh molecular weight polyethylene (UHMWPE) composites were successfully fabricated through compression moulding. The average hardness of all the composites was observed to be higher than the UHMWPE (6.1 Hv). Composite U-15A-5 G was found to be the hardest with hardness of 10.5 Hv owing to strong bond between Al2O3 and UHMWPE. The compression strength of the U-15A-5 G material was about 81.936 MPa, which is 40.9% better than the pure UHMWPE. The compression modulus was about 330 MPa which is 22.2% higher than neat UHMWPE. Composite samples were tribo tested under simulated body fluid (SBF) lubricating environment. Using combined entropy-COCOSO method it was found that 50 mm/s speed, 200N load and 5% graphene are the optimum tribological parameters. with Cof of 0.04 and wear rate of 4.8745*10−4 mm3/Nm. Microstructure analysis indicated that long-range structured lamellar structures and microfibers were created between the crystals with the assistance of graphene layers. Apatite layer was also found on the sample. The apatite layer included phosphate and carbonate ions, which is good for in vitro bioactivity on polymeric films. This composite is a good candidate for prosthetics and implant components.

Research on Pattern Classification Based on Double Pseudo-Inverse Extreme Learning Machine

This research aims to address the limitations inherent in the traditional Extreme Learning Machine (ELM) algorithm, particularly the stochastic determination of input-layer weights and hidden-layer biases, which frequently leads to an excessive number of hidden-layer neurons and inconsistent performance. To augment the neural network’s efficacy in pattern classification, Principal Component Analysis (PCA) is employed to reduce the dimensionality of the input matrix and alleviate multicollinearity issues during the computation of the input weight matrix. This paper introduces an enhanced ELM methodology, designated the PCA-DP-ELM algorithm, which integrates PCA with Double Pseudo-Inverse Weight Determination (DP). The PCA-DP-ELM algorithm proposed in this study consistently achieves superior average classification accuracy across various datasets, irrespective of whether assessed through longitudinal or cross-sectional experiments. The results from both experimental paradigms indicate that the optimized algorithm not only enhances accuracy but also improves stability. These findings substantiate that the proposed methodology exerts a positive influence on pattern classification.

7866 Efficacy of Zinc and Myoinositol Supplementation on Metabolic Parameters In A Pediatric Population With Obesity: a Randomized Controlled Trial

Abstract Disclosure: V. Antoniotti: None. V. Mancioppi: None. A. Solito: None. C. Partenope: None. A. Petri: None. I. Rabbone: None. L. Scotti: None. F. Prodam: None. Background: In the last decades, pediatric obesity and cardiovascular-associated risks have been relevant challenges. The first treatment against obesity is a lifestyle change, nevertheless, no intervention seems to be effective in the evolution of the condition, especially in the long term. Thus, the interest in non-pharmaceutical as well as pharmaceutical compounds is growing. Inositol compounds have recently been studied in adults with obesity and insulin resistance but have never been investigated in the pediatric age where insulin resistance is a specific trait of puberty. Furthermore, zinc is critical for insulin secretion and growth. Methods. This is a double-blind, randomized, placebo-controlled study aiming to explore the efficacy of 3 months of combined administration of Zinc (5 mg/die) and Myo-inositol (2000 mg/die) in children and adolescents with obesity. 56 patients aged 10- 17.9 years (starting from Tanner 3) with obesity and insulin resistance have been enrolled. The treatment group received 5 mg of Zinc, 2000 mg of Myo-inositol, and 1000 mg of galactooligosaccharides from Pisum sativum, while the placebo group only received 1000 mg of galactooligosaccharides from Pisum sativum. All patients were subjected to an isocaloric diet following the Mediterranean diet style consisting of 55% carbohydrates, 30-35% fat, and 15% protein. Clinical and biochemical evaluations were carried out for the determination of weight, lipid profile, liver function, and glucose-insulin metabolism. Results. At the end of the study, BMI, fasting insulin, and insulin resistance (HOMA-IR) were significantly improved in both groups, while the treatment group also had an increase in HDL cholesterol (p=0.0426). On the other hand, the placebo group significantly improved its eating habits showing greater adherence to the Mediterranean diet than the treatment group. Moreover, subjects were divided according to the degree of obesity. Fasting insulin (p= 0.0137), HOMA-IR (p= 0.0273), and systolic blood pressure (p= 0.0349) showed a significant improvement in treated patients with severe obesity compared to treated patients with low-grade obesity. Thus, subjects with a higher level of obesity respond better to both treatment and lifestyle change than those with a lower grade of obesity. Also, the improvement of the lifestyle and the presence of fiber allow an increase in the metabolic framework in placebo subjects. Conclusions. Supplementation with Zinc and Myo-inositol could be used as a non-pharmacological compound for the control of complications related to obesity also in the pediatric age. Presentation: 6/1/2024

E-commerce and Mobile Application Development in the Sports Industry

In order to achieve user recommendations that best match their current contextual needs, the author proposes a mobile service QoS (Quality of Service) hybrid recommendation model based on sports user situational awareness. Cluster the users and service items covered by mobile users based on their location contextual information according to the classification principle of autonomous systems, forming a collaborative filtering and recommendation mechanism for mobile user service items; In response to the cold start problem of new users and new projects in traditional QoS recommendation methods, prediction and recommendation of missing QoS attribute preference values are based on User based and Item based CF; In response to the problem of difficult determination of mixed recommendation weights caused by massive data and uneven distribution of service QoS attribute values in mobile network environments, MapReduce based ant colony neural network weight training is used for CF mixed recommendation. Experimental results have shown that the Hadoop sports industry has improved the operational efficiency of algorithms and reduced the time for global QoS preference prediction; And by comparing the operation results of the 10% and 100% sub datasets, it can also be seen that the acceleration ratio of the algorithm will continuously improve with the increase of data volume, thereby improving the operational efficiency of the recommendation algorithm. It has been proven that the MapReduce based ant colony neural network weight training method significantly reduces the global computation time of the algorithm and improves the operational efficiency of the recommendation system.

PyGRF: An Improved Python Geographical Random Forest Model and Case Studies in Public Health and Natural Disasters

ABSTRACTGeographical random forest (GRF) is a recently developed and spatially explicit machine learning model. With the ability to provide more accurate predictions and local interpretations, GRF has already been used in many studies. The current GRF model, however, has limitations in its determination of the local model weight and bandwidth hyperparameters, potentially insufficient numbers of local training samples, and sometimes high local prediction errors. Also, implemented as an R package, GRF currently does not have a Python version which limits its adoption among machine learning practitioners who prefer Python. This work addresses these limitations by introducing theory‐informed hyperparameter determination, local training sample expansion, and spatially weighted local prediction. We also develop a Python‐based GRF model and package, PyGRF, to facilitate the use of the model. We evaluate the performance of PyGRF on an example dataset and further demonstrate its use in two case studies in public health and natural disasters.

Race, Ethnicity, and Geography as Determinants of Excessive Weight and Low Physical Activity in Pediatric Population: Protocol for Systematic Review and Meta-Analysis.

The rationale for the current study is the sparsity of data on the combined effect of the environmental and individual risks of obesity and sedentary lifestyle in children of different races/ethnicities from different regions. An effective weight management strategy is hard to design due to insufficient evidence. This work was initiated to study race, ethnicity, and geography as determinants of excessive weight and low physical activity in the pediatric population. To achieve this aim, we systematically review publications on daily length of physical activity of light, moderate, and vigorous intensity, as well as sedentary time and BMI and its dynamics in children of different races/ethnicities and geographies. The extracted data are stratified into six major geographic regions and six races/ethnicities. Then, a random-effects meta-analysis is used to calculate the pooled mean of each outcome measure. A ridge regression is constructed to explore age-related change in BMI. A Kruskal-Wallis H test is applied to compare the pooled duration of physical activity and sedentary time in the subgroups. Finally, we calculate paired correlation coefficients between BMI and physical activity/inactivity for each group. The findings can be further used in public health surveillance to clarify the epidemiology of obesity, to guide priority setting and planning, and to develop and evaluate public health policy and strategy.

Hydrogen energy storage technology selection through a cutting-edge probabilistic linguistic decision framework

As a viable alternative to traditional energy forms, hydrogen energy proves effective owing to its feasible cost and reduced pollution. The storage of such a renewable energy source is essential for ensuring energy security and promoting sustainability. However, earlier studies on energy storage technology (EST) selection cannot handle complex linguistic expressions and hesitation during the decision process. Motivated by the issue, a probabilistic linguistic decision approach is developed in the present study to model linguistic expressions in daily conversations effectively. Besides, a new integrated approach is introduced by considering the regret factor for expert weight determination, weighted Cronbach’s approach for criteria weight determination, and a novel ranking procedure with a weighted approximation for prioritizing ESTs. A case example is exemplified by considering nine criteria and six ESTs to clarify the usefulness and practicality of the proposed framework. The main novelty/benefits of the study are: (i) handle uncertainty better; (ii) capture interrelationship among criteria; (iii) reduce bias by methodically determining the importance of experts; and (iv) provide a personalized ranking of ESTs. Findings depict that total cost is the foremost driver for hydrogen EST selection, while pressure cylinder storage is the most viable technology. Finally, the benefits and usefulness of the study are realized through sensitivity and comparison analyses. This study can guide and assist energy officials, academicians, governments, and other stakeholders facing hydrogen EST selection issues.

Pharmaceutical Applications and Advances with Zetasizer: An Essential Analytical Tool for Size and Zeta Potential Analysis

: Zetasizer is an advanced device that measures various properties of particles or molecules suspended in a liquid medium. It is extensively used for evaluating the size of nanoparticles, colloids, and biomolecular particles, and for determining particle charge. There are several analytical techniques by which the size, zeta potential, and molecular weight can be determined, like Dynamic Light Scattering (DLS) that measures the size of particles in dispersed systems, which can range from sub-nanometers to several micrometers in diameter. Electrophoretic Light Scattering (ELS) analyzes the mobility and charge of particles, also known as the zeta potential. Static Light Scattering (SLS) determines the molecular weight of particles in a solution. The Zetasizer is part of the Zetasizer Advance range of benchtop systems available for laboratory use. The Zetasizer Ultra model offers unique measurement capabilities, such as Multi-angle Dynamic Light Scattering (MADLS) and particle concentration. These features offer a deeper understanding of samples, making the Zetasizer a vital instrument in numerous scientific and industrial applications. In this review, we have discussed Zetasizer’s principles for the determination of particle size, zeta potential, and molecular weight, along with its qualification and applications in different formulations.