Synergistic Relationship Research Articles

Green Corrosion Inhibitors for Iron Alloys: A Comprehensive Review of Integrating Data-Driven Forecasting, Density Functional Theory Simulations, and Experimental Investigation

This comprehensive review delves into the realm of green corrosion inhibitors for iron alloys, focusing on a thorough exploration guided by data-driven investigation, density functional theory (DFT) simulations, and experimental validation. Harnessing the potential of plant extracts, this study scrutinizes their effectiveness in mitigating corrosion in iron alloys through a multi-faceted approach. By integrating computational modeling with empirical experimentation, a deeper understanding of the inhibitive mechanisms is achieved, offering insights into their practical application. The review synthesizes findings from diverse studies, elucidating the pivotal role of DFT in predicting inhibitor behavior and optimizing their performance. Furthermore, experimental validation provides crucial validation of theoretical predictions, highlighting the synergistic relationship between simulation and real-world application. Through this journey of exploration, the review underscores the promise of green corrosion inhibitors derived from natural sources, paving the way for sustainable corrosion control practices in the realm of iron alloys.

AI APPLICATIONS IN RESERVOIR MANAGEMENT: OPTIMIZING PRODUCTION AND RECOVERY IN OIL AND GAS FIELDS

This paper explores various AI applications in reservoir management, highlighting how machine learning, data analytics, and advanced algorithms contribute to optimizing production and recovery in oil and gas fields. From predictive modeling to real-time monitoring and control, AI offers promising solutions to address the industry's evolving demands and enhance operational performance Reservoir management in oil and gas fields has historically presented challenges demanding continuous monitoring, analysis, and optimization for maximizing production and recovery. The integration of artificial intelligence (AI) technologies has ushered in a new era, providing reservoir engineers and operators with potent tools to augment decision-making processes and enhance overall efficiency. This paper delves into diverse AI applications within reservoir management, emphasizing the pivotal roles played by machine learning, data analytics, and advanced algorithms in optimizing production and recovery processes in oil and gas fields. The exploration encompasses predictive modeling, real-time monitoring, and control applications, showcasing how AI offers promising solutions to meet the dynamic demands of the industry while significantly improving operational performance. As we navigate through this technological evolution, the synergistic relationship between reservoir management and AI promises not only to address current challenges but also to lay the foundation for a more efficient and sustainable future in the oil and gas sector. The integration of artificial intelligence (AI) technologies has ushered in a new era, providing reservoir engineers and operators with potent tools to augment decision-making processes and enhance overall efficiency. The exploration encompasses predictive modeling, real-time monitoring, and control applications, showcasing how AI offers promising solutions to meet the dynamic demands of the industry while significantly improving operational performance. As we navigate through this technological evolution, the synergistic relationship between reservoir management and AI promises not only to address current challenges but also to lay the foundation for a more efficient and sustainable future in the oil and gas sector. Keywords: Reservoir Management, Oil And Gas Fields, Optimization, Recovery, Machine Learning, Data Analytics, Predictive Modeling.

Biosynthesis of CuFe2O4@Ag hybrid nanocomposite: Ultrasensitive detection and catalytic reduction of 4-nitrophenol

Due to the dearth of extremely capable, sensitive, and stable catalysts, the efficient detection and catalytic removal of 4-nitrophenol (4-NP) in industrial wastewater remains a serious challenge. The detection and determination of 4-nitrophenol (4-NP) presence in the environment is a matter of paramount importance because it is a high-priority hazardous pollutant that can affect people, animals, and plants. Here, we present a promising and economically viable green synthetic route for fabricating CuFe2O4 and CuFe2O4@Ag hybrid nanocomposites from the leaf extract of Senna didymobotrya. The UV–Vis, FTIR, XRD, FE-SEM, EDXA, BET and VSM analysis were performed to characterize the synthesis of CuFe2O4@Ag nanocomposite. To evaluate the electrocatalytic capacity of CuFe2O4@Ag, electrochemical sensing stratergy was performed with cyclic voltammetry (CV) and differential pulse voltammetry (DPV). The modified CuFe2O4@Ag glassy carbon electrode (GCE) (CuFe2O4@Ag/GCE) demonstrated a linear response in the range of 0.01-15 μg/ml (71 nm-107 μM) and the ability to detect 4-NP at low concentration (0.006 μg/ml (43 nM)). Due to the increased surface area of CuFe2O4@Ag/GCE by ̴ 1.5-fold, a greater cathodic current response (-16 μA/cm2) at a low potential of -0.81 V was observed compared to CuFe2O4/GCE alone for the detection of 4-NP. Additonally, CuFe2O4@Ag showed excellent reduction ability towards 4-NP using NaBH4 with an efficiency of 96.4 % which was higher than the CuFe2O4 (only 87.3 %) in 12 min due to the synergistic relationship among Ag NPs and CuFe2O4 nanostructures. The outcomes from this study shows that the bi-functional electrocatalyst holds vast potential for environmental remediation.

Synergistic mechanism of substrate hydrolysis and methanogenesis under “gradient anaerobic digestion” process

The destruction of the synergistic effect of the hydrolysis-methanogenesis process (HMP) of substrate may lead to the serious carbon loss of substrate and low biogas yield in traditional straw biogas engineering. In this study, the influence of substrate composition on anaerobic digestion (AD) was investigated under pilot conditions, and the relationship between HMP was further investigated under full-scale plants. The results showed that straw with a high content of soluble substances had the highest methane yield (264.25 L/kg VS) and degradation rate (59.32 %). At HRT = 20 d, the full-scale plant achieved the highest biogas yield (576.92 L/kg VS, 3.47 L/L/d) and degradation rate (67.50 %). Microbial analysis showed that substrate composition affected microbial community and methanogenic metabolic pathway. The analysis results of PICRUSt2 confirm that gradient AD guarantees the synergistic relationship of HMP. This study provides the theoretical basis and engineering guidance for improving the AD process in traditional straw biogas engineering.

Semantic-aware normalizing flow with feature fusion for image anomaly detection

In the field of computer vision, anomaly detection is a binary classification task used to identify exceptional instances within image datasets. Typically, it can be divided into two aspects: texture defect detection and semantic anomaly detection. Existing methods often use pre-trained feature extractors to singly capture semantic or spatial features of images, and then employ different classifiers to handle these two types of anomaly detection tasks. However, these methods fail to fully utilize the synergistic relationship between these two types of features, resulting in algorithms that excel in one type of anomaly detection task but perform poorly in the other type. Therefore, we propose a novel approach that successfully combines these two types of features into a normalizing flow learning module to address both types of anomaly detection tasks. Specifically, we first adopt a pre-trained Vision Transformer (ViT) model to capture both texture and semantic features of input images. Subsequently, using the semantic features as input, we design a novel normalizing flow model to fit the semantic distribution of normal data. In addition, we introduce a feature fusion module based on attention mechanisms to integrate the most relevant texture and semantic information between these two types of features, significantly enhancing the model’s ability to simultaneously represent the spatial texture and semantic features of the input image. Finally, We conduct comprehensive experiments on well-known semantic and texture anomaly detection datasets, namely Cifar10 and MVTec, to evaluate the performance of our proposed method. The results demonstrate that our model achieves outstanding performance in both semantic and texture anomaly detection tasks, particularly achieving state-of-the-art results in semantic anomaly detection.

Unraveling the influence of nitration on pore formation time in electroporation of cell membranes: a molecular dynamics simulation approach

Electroporation, the transient permeabilization of cell membranes induced by electric fields, is an essential technique in biomedicine, facilitating gene delivery, drug transport, and cancer therapy. Despite its wide application, the influence of nitration, a biological modification involving the addition of nitro groups to phospholipids, on electroporation dynamics remains understudied. Here, we employ molecular dynamics simulations to investigate the impact of nitration on pore formation during electroporation. By systematically varying nitration levels and electric field strengths, we explore the nuanced interplay between nitration and electroporation kinetics. Our simulations reveal that increasing nitration levels significantly accelerate pore formation, with notable reductions in pore formation times observed at higher nitration percentages and stronger electric fields. This phenomenon underscores the modulatory role of nitration in altering the dynamics of electroporation. Additionally, our study sheds light on the intricate mechanisms underlying this process, providing essential insights for optimizing electroporation protocols in gene therapy, drug delivery, plasma cancer treatment and related biomedical applications. These findings illuminate the synergistic relationship between nitration and electroporation, paving the way for future advancements in this vital field.

Does the miR-105–1-Kisspeptin Axis Promote Ovarian Cell Functions?

The objective of this study was to elucidate the intricate interplay among miR-105–1, kisspeptin, and their synergistic influence on basic ovarian granulosa cell functions. The effects of miR-105–1 mimics or miR-105–1 inhibitor, kisspeptin (0, 1, and 10 ng/ml), and its combinations with miR-105–1 mimics on porcine granulosa cells were assessed. The expression levels of miR-105–1, viability, proliferation (accumulation of PCNA, cyclin B1, XTT-, and BrdU-positive cells), apoptosis (accumulation of bcl-2, bax, caspase 3, p53, TUNEL-positive cells), proportion of kisspeptin-positive cells, and the release of steroid hormones and IGF-I were analyzed. Transfection of cells with miR-105–1 mimics promoted cell viability and proliferation, the occurrence of kisspeptin, and the release of progesterone and IGF-I; in contrast, miR-105–1 mimics inhibited apoptosis and estradiol output. MiR-105–1 inhibitor had the opposite effect. Kisspeptin amplified the expression of miR-105–1, cell viability, proliferation, steroid hormones, and IGF‐I release and reduced apoptosis. Furthermore, the collaborative action of miR-105–1 mimics and kisspeptin revealed a synergistic relationship wherein miR-105–1 mimics predominantly supported the actions of kisspeptin, while kisspeptin exhibited a dual role in modulating the effects of miR-105–1 mimics. These findings not only affirm the pivotal role of kisspeptin in regulating basic ovarian cell functions but also represent the inaugural evidence underscoring the significance of miR-105–1 in this regulatory framework. Additionally, our results show the ability of kisspeptin to promote miR-105–1 expression and the ability of miR‐105–1 to promote the occurrence and effects of kisspeptin and, therefore, indicate the existence of the self‐stimulating kisspeptin‐miR‐105–1 axis.

Bringing satellite and nanotechnologies together: unifying strengths against pollution and climate change

Nowadays, we witness remarkable technological progress alongside unprecedented challenges that threaten the delicate balance of our planet’s ecological system. Environmental contamination plays a central role in this, with rapid urbanization, industrialization, mining and agricultural practices intensifying the introduction of pollutants into the environment. This article highlights the potential synergy between two fields operating at vastly different scales: satellite technology and nanotechnology. This article delves into the offerings of each of these disciplines and examines how they can mutually contribute to the detection, prevention and mitigation of environmental pollution. Satellites play a crucial role in identifying and monitoring large-scale polluted areas, offering comprehensive insights into environmental challenges. They are indispensable in tracking air, water pollution levels, assessing land degradation, and monitoring changes in ocean health with relatively high spatial and temporal resolution. Nanotechnology leverages the unique properties of materials at sub-micron scale by offering amplified chemical reactivity and new optical, electronic, and magnetic attributes, enabling selective and sensitive sensors and rapid and efficient contaminant capture/degradation strategies. Emerging nanomaterials, along with nature-inspired and self-powered or self-sustaining designs, broaden capabilities for efficient solutions. Advanced nanocharacterization techniques deepen material understanding and quantification, while nanofabrication allows precise design of functional nano-devices. We believe the synergistic relationship between both fields can yield cooperative solutions, expediting effective measures and greatly influencing policy decisions. This article advocates for the collaboration between these two disciplines to foster impactful progress in facing global challenges.

A study on the visual defensive system of the Moluo Tower Village, Western China, based on a multi-dimensional model

AbstractScientifically validating the value of rural heritage and exploring construction wisdom has emerged as a significant challenge in the study of rural settlements worldwide. Rural heritage shows significant differences according to the natural environment, history, culture, and socio-economic factors. However, the assessment of the authenticity and effectiveness of heritage values have yet to be quantified due to the difficulty in obtaining evidence directly from samples. By constructing a multi-dimensional model of villages for quantitative analysis, we proposed a multifaceted spatial visibility analysis method based on individual and combined defense structures to empirically study the remains of eight towers in Moluo village, western China. This study demonstrated that towers serve as regional defense structures and are essential for constructing a comprehensive and effective visual defensive system (VDS) in Moluo village, a typical military defense village. The effectiveness and coverage of the towers vary depending on their location, height, type, and other factors. They form a complementary and synergistic relationship, playing a dual mechanism of public and private, local and overall defense. In summary, this method enables the feasible quantification and analysis of the functional value of rural heritage through landscape visual analysis. It provides a new perspective for studying the authenticity of heritage functions.

Exploring synergistic and individual causal effects of rare earth elements and renewable energy on multidimensional economic complexity for sustainable economic development

The ongoing global energy transition has highlighted the instrumental role of rare earth elements (REEs) in achieving green growth through the deployment of renewable energy technologies, establishing a hypothetical progressive synergistic association between them. Therefore, testing this hypothetical synergy and its potential causal effects on economic and technological proxies based on empirical foundations becomes imperative in addressing rampant climatic change. First, we employed Deep Learning algorithms to explore the combined effects of this synergy on Multidimensional Economic Complexity (MEC) as an index using panel data of the largest producers of REEs from 1990 to 2023. Second, Temporal Causal Modelling (TCM) was employed to unveil the directional causality between REEs and renewable energy, as well as their causal roles in driving economic complexities. The deep learning models accurately predicted the positive role of this synergistic relationship in promoting overall MEC. Then, the results of TCM reveal that REE production increases renewable energy use and reduces CO2 emissions. Renewable energy promotes Research and Technology complexities while REE production affects them indirectly through renewables. It implies that harnessing this synergy to enhance economic complexities can, in turn, bolster green economic and technological development while mitigating CO2 emissions and addressing pervasive climate change.

International Cooperation Towards Decarbonization, Originating from India – Case Analysis of Industrial Cooperation in the Semiconductor Sector between India, Japan, and Taiwan

Background: Climate change represents a global challenge that cannot be mitigated by any single nation. Notably, CO2 emissions from the Southern Hemisphere are escalating rapidly, currently constituting two-thirds of the global total. The proactive efforts of India, which leads this group, are noteworthy. Purpose: This study aims to elucidate the impact of international cooperation on decarbonization, with a specific focus on the semiconductor industry in India, a representative nation of the Global South. The current state of collaboration between Japan, Taiwan, and India serves as an exemplar of international efforts towards decarbonization. Furthermore, the strategic significance of industrial collaboration in the semiconductor sector among Japan, India, and Taiwan, and the potential for establishing a global semiconductor supply chain oriented towards decarbonization, are examined. Method: This study employs a case study methodology to scrutinize the current status of international collaboration in the semiconductor industry among India, Japan, and Taiwan. Result: The Japanese semiconductor industry, renowned for its advanced technical capabilities, necessitates the fortification of the global supply chain and technology transfer, with collaboration with India proving advantageous. Taiwan, actively expanding its presence in India, seeks to enhance the quality and efficiency of semiconductor manufacturing and to form diverse supply chains, thereby accelerating innovation through collaboration with Japan. A synergistic relationship in the semiconductor industry is being established among India, Japan, and Taiwan. Conclusion: India possesses the potential to assume a pivotal role in the construction of a global semiconductor supply chain, with Japan and Taiwan identified as optimal partners. This collaboration is anticipated to bolster manufacturing capabilities aimed at decarbonization, contribute to international decarbonization efforts, and drive technological advancements as part of global next-generation technology management.

Understanding of facial features in face perception: insights from deep convolutional neural networks.

Face recognition has been a longstanding subject of interest in the fields of cognitive neuroscience and computer vision research. One key focus has been to understand the relative importance of different facial features in identifying individuals. Previous studies in humans have demonstrated the crucial role of eyebrows in face recognition, potentially even surpassing the importance of the eyes. However, eyebrows are not only vital for face recognition but also play a significant role in recognizing facial expressions and intentions, which might occur simultaneously and influence the face recognition process. To address these challenges, our current study aimed to leverage the power of deep convolutional neural networks (DCNNs), an artificial face recognition system, which can be specifically tailored for face recognition tasks. In this study, we investigated the relative importance of various facial features in face recognition by selectively blocking feature information from the input to the DCNN. Additionally, we conducted experiments in which we systematically blurred the information related to eyebrows to varying degrees. Our findings aligned with previous human research, revealing that eyebrows are the most critical feature for face recognition, followed by eyes, mouth, and nose, in that order. The results demonstrated that the presence of eyebrows was more crucial than their specific high-frequency details, such as edges and textures, compared to other facial features, where the details also played a significant role. Furthermore, our results revealed that, unlike other facial features, the activation map indicated that the significance of eyebrows areas could not be readily adjusted to compensate for the absence of eyebrow information. This finding explains why masking eyebrows led to more significant deficits in face recognition performance. Additionally, we observed a synergistic relationship among facial features, providing evidence for holistic processing of faces within the DCNN. Overall, our study sheds light on the underlying mechanisms of face recognition and underscores the potential of using DCNNs as valuable tools for further exploration in this field.

Synergizing Sustainability and Financial Prosperity: Unraveling the Structure of Business Profit Growth through Consumer-Centric Strategies—The Cases of Kosovo and Albania

This research investigates the synergistic relationship between sustainability and financial prosperity in businesses, specifically focusing on the impact of consumers on profit growth in Kosovo and Albania. The study aims to understand consumers’ perceptions of their purchases, the factors influencing their choice of businesses, and the types of businesses that effectively support consumers in these countries. Data were collected through a survey completed by 200 consumers and 200 businesses. The analysis, utilizing multivariate analysis of variance, descriptive analysis, and reliability analysis with SPSS, reveals that consumers significantly influence the sustainability of business profit growth. Moving forward, it is recommended that businesses prioritize offering reasonable prices, quality products/services, easy access to products/services, clear information about products/services, and convenient locations. The research has profound implications for businesses, consumers, and countries and suggests the need for further exploration of the impact of consumers on profit growth in diverse contexts.

Dynamic Simulation and Tradeoffs and Synergies of Ecosystem Service Value in Metropolitan Suburbs Using the PLUS Model

INTRODUCTION: Due to rapid economic development and continuous human activities, land use changes in the suburbs of metropolitan areas are drastic, which in turn affects the balance of ecosystem functions. Analyzing and predicting the ecological service value characteristics and trade-offs in rapidly urbanizing regions is of great significance for promoting high-quality regional development.AIM: This study attempts to reveal the trade-off and synergistic characteristics between the internal values of ecosystem services in suburban metropolitan areas under the influence of rapid urbanization.METHODS: Based on the patch-generating land use simulation (PLUS) model, simulated the land use changes in Xinzheng City under multiple scenarios in 2030, combined with methods such as equivalent factor method and spatial autocorrelation analysis，estimating, and predicting the ecosystem service value and its trade-off synergy relationship in Xinzheng City from 1980 to 2030.RESULTS: The value of ecosystem services in Xinzheng City continues to decline, hydrological regulation and soil conservation are the most important ecosystem service functions, under the scenario of farmland protection, ESV shows a stable growth trend. The synergistic relationship between various functions of ESV is significant, the Shizu Mountain National Forest Park, Shuangji River and other high agglomeration areas, as well as the Airport Economic Zone and Nanlonghu Town and other low agglomeration areas, all show a synergistic relationship, with only a portion of the southern side of the main urban area of Xinzheng showing a balancing relationship.CONCLUSIONS: Our findings can scientifically identify the environmental advantages of ecological sustainable development in Xinzheng City, and transform them into development advantages, providing provide strong technical support for the spatial ecological restoration and ecological security pattern construction of metropolitan suburbs.

Aesthetic Potential and Safety Profile with Nanotextured Breast Implants With 1,000 Cases of Breast Augmentation as Evaluation of Single-Center Experience.

Nanotextured implants, an innovation in implant technology, are designed to enhance tissue interaction, reduce inflammation, and mitigate complications compared to traditional implants. The aim of the study was to evaluate the aesthetic potential and safety profile of nanotextured breast implants in breast augmentation from a single-surgeon experience. This prospective cohort study included 1,000 patients who underwent breast augmentation with nanotextured implants. Aesthetic outcomes were assessed using direct measurements and BCCT.core software, focusing on breast projection, symmetry, and upper pole fullness. Patient satisfaction was measured through BREAST-Q and overall satisfaction surveys. Safety profiles were evaluated based on the incidence of complications such as infection, hematoma, capsular contracture, and implant rupture. Statistical analyses were conducted to assess the outcomes. The study found significant improvements in aesthetic outcomes, with 92.8% of cases achieving substantial aesthetic enhancements. Patient satisfaction was high, with 96.4% reporting enhanced self-esteem and contentment. The safety profile was favorable, with a low incidence of complications (0.7% requiring revision surgery) and no major complications reported. Long-term follow-up data indicated sustained aesthetic outcomes and a stable safety profile. Insights into the synergistic relationship between nanotextured implants are defined by a significantly wider range of aesthetic options. The findings of this study underscore the positive impact of nanotextured breast implants on enhancing the aesthetic potential of breast augmentation. and contributing to a low incidence of complications.

Role of Hazelnut Husk Compost and Phosphate Solubilizing Bacteria in Improving Productivity and Quality Parameters of Wheat (Triticum aestivum L.) and Their Effects on Some Soil Biological Properties

ABSTRACT Implementing compost can assist in improving soil structure and enable nutrient mobilization to plants that initiate an appropriate soil balance. In a greenhouse experiment, the influence of hazelnut husk compost (HHC), both in single and in combination with the phosphate solubilizing bacteria (PSB) on the wheat yield, yield parameters, and soil biological properties were investigated. A significant increase in yield and its quality parameters was recorded when the soil was treated with different doses of compost and bacteria. It was observed copious improvements in the soil biological properties with these applications. The microbial biomass carbon and total organic carbon ratios (Cmic:Corg) ranged from 1.51 to 2.09, were also remarkable in the organic amended soils. However, in all the treatments, the co-inoculation of bacteria with compost enhanced all the parameters significantly more than their individual applications. The highest yield increase over control (22.12%) was recorded from the integrated application of organic source and bio-fertilizer. This reflects a synergistic relationship between the compost and bacteria that eventually enhanced wheat production. It is therefore concluded that using HHC and PSB in soil can unfold promising possibilities for better soil health, which in the long run can contribute to sustainable food production.

SARS-CoV-2 Mpro oligomerization as a potential target for therapy

The main protease (Mpro) of SARS-CoV-2 is critical in the virus's replication cycle, facilitating the maturation of polyproteins into functional units. Due to its conservation across taxa, Mpro is a promising target for broad-spectrum antiviral drugs. Targeting Mpro with small molecule inhibitors, such as nirmatrelvir combined with ritonavir (Paxlovid™), which the FDA has approved for post-exposure treatment and prophylaxis, can effectively interrupt the replication process of the virus. A key aspect of Mpro's function is its ability to form a functional dimer. However, the mechanics of dimerization and its influence on proteolytic activity remain less understood. In this study, we utilized biochemical, structural, and molecular modelling approaches to explore Mpro dimerization. We evaluated critical residues, specifically Arg4 and Arg298, that are essential for dimerization. Our results show that changes in the oligomerization state of Mpro directly affect its enzymatic activity and dimerization propensity. We discovered a synergistic relationship influencing dimer formation, involving both intra- and intermolecular interactions. These findings highlight the potential for developing allosteric inhibitors targeting Mpro, offering promising new directions for therapeutic strategies.

The double burden of malnutrition in individuals: Identifying key challenges and re-thinking research focus.

The 'double burden of malnutrition' is a global health challenge that increasingly affects populations in both low- and middle-income countries (LMICs). This phenomenon refers to the coexistence of undernutrition and overweight or obesity, as well as other diet-related non-communicable diseases, in the same population, household or even individual. While noteworthy progress has been made in reducing undernutrition in some parts of the world, in many of these areas, the prevalence of overweight and obesity is increasing, particularly in urban areas, resulting in greater numbers of people who were undernourished in childhood and have overweight or obesity in adulthood. This creates a complex and challenging situation for research experts and policymakers who must simultaneously address the public health burdens of undernutrition and overweight/obesity. This review identifies key challenges and limitations in the current research on the double burden of malnutrition in individuals, including the need for a more comprehensive and nuanced understanding of the drivers of malnutrition, the importance of context-specific interventions and the need for greater attention to the food environment and food systems. We advocate for the re-evaluation of research strategies and focus, with a greater emphasis on multidisciplinary and systems approaches and greater attention to the synergistic relationship between the biological, environmental, commercial and socio-economic determinants of malnutrition. Addressing these key challenges can enable us to better comprehend and tackle the multifaceted and dynamic issues of the double burden of malnutrition, particularly in individuals and work towards more effective and sustainable solutions.

Optimizing Auto Manufacturing: A Holistic Approach Integrating Overall Equipment Effectiveness for Enhanced Efficiency and Sustainability

In the face of pandemic-induced emergencies and unpredictable natural disasters, industries are compelled to implement rescue plans to mitigate unexpected risks. In this context, Overall Equipment Effectiveness (OEE) is considered as a key metric, followed by sustainability efforts to manage unforeseen risks, encompassing social, environmental, and economic aspects. OEE is considered as a lean tool to determine the efficiency of equipment or processes on par with the world class OEE standard, i.e., 85%. Performance, Availability and Quality as three main drivers of OEE. This research study explores the implementation of OEE in conjunction with sustainability principles in an auto sector manufacturing firm, aiming to enhance operational efficiency and sustainability practices. The research involves a 12-week initial session from April to June 2022, followed by an analysis of July to September 2022, resulting in an impressive OEE value of 48%. Notable improvements in Availability (89.75%), Performance (72.68%), and Quality (73.82%) contribute significantly. The analysis reveals enhancements in scrap rework (17%), training (16%), maintenance (13%), material availability (12%), and production capability (11%). Achievements include improvements in green profile (25%), health and safety (20%), and energy efficiency (25%), along with reductions in carbon dioxide emissions (21%), waste management (17%), and scrap (15%). This research underscores the commitment of the case study industry to sustainable development and economic growth, showcasing significant enhancements in product quality and efficiency. The integration of sustainability principles into OEE initiatives is pivotal for modern industrial optimization. The study results highlight the profound significance of this synergistic relationship, particularly within the blending section, driving substantial positive outcomes in manufacturing processes and operational excellence. The implementation of sustainability efforts not only mitigates risks and fosters growth for automotive manufacturers but also yields environmental benefits. Based on findings of this study, a roadmap for automotive manufacturers is devised to achieve robust OEE while concurrently reaping economic and environmental rewards by employing sustainability principles.

Molecular Dynamics Simulations of the Short-Chain Fluorocarbon Surfactant PFHXA and the Anionic Surfactant SDS at the Air/Water Interface.

The research and development of alternatives to long-chain fluorocarbon surfactants are desperately needed because they are extremely toxic, difficult to break down, seriously harm the environment, and limit the use of conventional aqueous film-forming foam fire extinguishing agents. In this study, mixed surfactant systems containing the short-chain fluorocarbon surfactant perfluorohexanoic acid (PFHXA) and the hydrocarbon surfactant sodium dodecyl sulfate (SDS) were investigated by molecular dynamics simulation to investigate the microscopic properties at the air/water interface at different molar ratios. Some representative parameters, such as surface tension, degree of order, density distribution, radial distribution function, number of hydrogen bonds, and solvent-accessible surface area, were calculated. Molecular dynamics simulations show that compared with a single type of surfactant, mixtures of surfactants provide superior performance in improving the interfacial properties of the gas-liquid interface. A dense monolayer film is formed by the strong synergistic impact of the two surfactants. Compared to the pure SDS system, the addition of PFHXA caused SDS to be more vertically oriented at the air/water interface with a reduced tilt angle, and a more ordered structure of the mixed surfactants was observed. Hydrogen bonding between SDS headgroups and water molecules is enhanced with the increasing PFHXA. The surface activity is arranged in the following order: PFHXA/SDS = 1:1 > PFHXA/SDS = 3:1 > PFHXA/SDS = 1:3. These results indicate that a degree of synergistic relationship exists between PFHXA and SDS at the air/water interface.