Systematic Exploration Research Articles

Systematic exploration of N, O coordination number on the catalytic performance for oxygen reduction and oxygen evolution

The reaction rate of oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) is an important factor restricting its application. In this work, we have systematic explorated of N, O coordination number on the catalytic performance for ORR and OER. Firstly, a variety of stability analysis methods show that most of the catalysts have excellent stability. Secondly, a series of excellent bifunctional catalysts are screened by volcanic maps, ORR overpotential (ηORR), OER overpotential (ηOER), and bifunctional index (BI), such as, RhN3O1-1 (ηORR = 0.28 V, ηOER = 0.34 V, BI = 0.62 V) and CoN3O1-1 (ηORR = 0.33 V, ηOER = 0.37 V, BI = 0.70 V). In particular, RhN3O1-1 has the smallest BI value, indicating that it has the best bifunctional catalytic activity. This study offers insights into how the coordination environment affects the activity of OER/ORR.

Survey on MapReduce Scheduler Algorithms in Hadoop Framework

Hadoop serves as a robust framework tailored for the storage and processing of vast data volumes across clusters. Its foundation lies in the Hadoop Distributed File System (HDFS) for data storage, complemented by the MapReduce paradigm for data processing. MapReduce, functioning as a parallel computing framework, operates adeptly on distributed clusters to manage large-scale data processing tasks. Within this framework, scheduling emerges as a pivotal aspect, influencing the overall system's performance and efficiency. The essence of scheduling in MapReduce lies in enhancing performance, minimizing response times, and optimizing resource allocation. This paper undertakes a systematic exploration of existing scheduling algorithms, offering a fresh classification and detailed examination of each category. Furthermore, the analysis delves into the core principles, objectives, as well as the strengths and weaknesses of these scheduling algorithms.

Sustainable-Driven Renovation of Existing Residential Buildings in China: A Systematic Exploration Based on Review and Solution Approaches

Under the backdrop of China’s national strategy to achieve carbon neutrality by 2060, efforts are underway across governmental, corporate, societal, and individual sectors to actively explore energy-saving renovations in existing buildings. Given that residential buildings constitute a significant proportion of the total energy consumption throughout the lifecycle of buildings in China, sustainable renovation of residential structures can contribute significantly to implementing China’s carbon emission reduction policies. While there exists a plethora of technological means in the market aimed at improving the energy performance of residential buildings, there still needs to be a more systematic discussion on the framework for sustainable renovation of existing Chinese residential buildings, with knowledge dissemination still needing to be more cohesive. In this context, this paper provides a comprehensive review of the field, utilizing bibliometric methods. Through a systematic review of selected peer-reviewed literature from the Web of Science and Scopus databases, the study focuses on the sustainable renovation of existing Chinese residential buildings, categorizing the process into three main stages: sustainable renovation, building performance simulation and suitability assessment. The paper also reviews the research methods adopted by previous researchers in the renovation, simulation and assessment stages, considering various optimization algorithms, variables, objectives, and software tools. Subsequently, the paper synthesizes a research framework comprising these three stages combined for different research objectives, aiming to assist policymakers, designers, and researchers in gaining a comprehensive understanding of the implementation status of sustainable renovation in existing Chinese residential buildings, identifying barriers to implementation, and formulating more efficient renovation policies and strategies for the future.

The pathways to lean manufacturing for circular economy: Implications for sustainable development goals

Purpose This study aims to address the critical challenge of implementing lean manufacturing (LM) in emerging economies, where sustainability complexities on the production floor hinder production efficiency and the transition towards a circular economy (CE). Addressing a gap in existing research, the paper introduces a path analysis model to systematically identify, prioritize and overcome LM implementation barriers, aiming to enhance performance through strategic removal. Design/methodology/approach The authors used a mixed-method approach, combining empirical survey data with literature reviews to pinpoint key LM barriers. Using the grey-based Decision-Making Trial and Evaluation Laboratory (DEMATEL) along with the Network Knowledge (NK) method, they mapped causal relationships and barrier intensities. This formed the basis for developing a path simulation algorithm, integrating heuristic considerations for practical decision-making. Findings This analysis reveals that the primary barriers to LM adoption is the negative perception and inadequate understanding of lean tools and CE principles. The study provides a strategic framework for managers, offering new insights into barrier prioritization and overcoming strategies to facilitate successful LM adoption. Research limitations/implications This research provides a strategic pathway for overcoming LM implementation barriers, empowering managers in emerging economies to enhance sustainability and competitive advantage through LM and CE integration. It emphasizes the significance of structured barrier management in the manufacturing sector. Originality/value This research pioneers a systematic exploration of LM implementation barriers in the CE context, making a significant contribution to the literature. It identifies, evaluates barriers and proposes a practical model for overcoming them, enriching sustainable manufacturing practices in emerging markets.

Numerical investigations of the extensive entanglement Hamiltonian in quantum spin ladders

Entanglement constitutes one of the key concepts in quantum mechanics and serves as an indispensable tool in the understanding of quantum many-body systems. In this work, we perform extensive numerical investigations of extensive entanglement properties of coupled quantum spin chains. This setup has proven useful for e.g. extending the Lieb–Schultz–Mattis theorem to open systems, and contrasts the majority of previous research where the entanglement cut has one lower dimension than the system. We focus on the cases where the entanglement Hamiltonian is either gapless or exhibits spontaneous symmetry breaking behavior. We further employ conformal field theoretical formulae to identify the universal behavior in the former case. The results in our work can serve as a paradigmatic starting point for more systematic exploration of the largely uncharted physics of extensive entanglement, both analytical and numerical.

Identification and validation of the surface proteins FIBG, PDGF-β, and TGF-β on serum extracellular vesicles for non-invasive detection of colorectal cancer: experimental study.

The absence of non-invasive biomarkers for the early diagnosis of colorectal cancer (CRC) has contributed to poor prognosis. Extracellular vesicles (EVs) have emerged as promising candidates for cancer monitoring using liquid biopsy. However, the complexity of EVs isolation procedures and the absence of clear targets for detecting serum-derived EVs have hindered the clinical application of EVs in early CRC diagnosis. In the discovery phase, we conducted a comprehensive 4D-DIA proteomic analysis of serum-derived EVs samples from 37 individuals, performing an initial screening of EVs surface proteins. In the technical validation phase, we developed an extraction-free CRC-EVArray microarray to assess the expression of these potential EVs surface proteins in a multi-centre study comprising 404 individuals. In the application phase, the authors evaluated the diagnostic efficacy of the CRC-EVArray model based on machine-learning algorithms. Through 4D-DIA proteomic analysis, the authors identified seven potential EVs surface proteins showing significantly differential expression in CRC patients compared to healthy controls. Utilizing our developed high-throughput CRC-EVArray microarray, we further confirmed the differential expression of three EVs surface proteins, FIBG, PDGF-β and TGF-β, in a large sample population. Moreover, we established an optimal CRC-EVArray model using the NNET algorithm, demonstrating superior diagnostic efficacy with an area under the curve (AUC) of 0.882 in the train set and 0.937 in the test set. Additionally, we predicted the functions and potential origins of these EVs-derived proteins through a series of multi-omics approaches. Our systematic exploration of surface protein expression profiles on serum-derived EVs has identified FIBG, PDGF-β, and TGF-β as novel diagnostic biomarkers for CRC. The development of CRC-EVArray diagnostic model based on these findings provided an effective tool for the large-scale CRC screening, thus facilitating its translation into clinical practice.

On the limitations of non-geometric fluxes to realize dS vacua

In this paper, we perform a systematic and analytical exploration of de Sitter conditions in type IIA compactifications with (non-)geometric fluxes along with the standard NS-NS and RR p-form fluxes. Exploiting the fact that the F-term scalar potential can be written as a bilinear form, we start by studying the most generic case. We find four conditions that the scalar fields and fluxes must satisfy to achieve de Sitter vacua. Particularizing to different configurations, we recover and extend previous results in the literature. We then impose an Ansatz in which the F-terms are proportional to the respective Kähler derivatives. In this set-up we are able to derive additional constraints and to classify the possible dS no-go scenarios in terms of eight axionic fluxes. Individually considering that any of these fluxes can be vanishing or non-vanishing leads to a total of 256 flux configurations. We find that 227 of these 256 possibilities result in a dS no-go scenario. The remaining 29 flux configurations, a priori, do not lead to dS no-go cases and would deserve further investigation.

Global research trends in CAR-T cell therapy for solid tumors: A comprehensive visualization and bibliometric study (2012–2023)

ABSTRACT CAR-T cell therapy has emerged as a significant approach for the management of hematological malignancies. Over the past few years, the utilization of CAR-T cells in the investigation and treatment of solid tumors has gained momentum, thereby establishing itself as a prominent area of research. This descriptive study involved the retrieval of articles about CAR-T cell therapy for solid tumors from the Web of Science Core Collection (WoSCC) database. Subsequently, bibliometric analysis and knowledge map analysis were conducted on these articles. The field under consideration is currently experiencing a period of swift advancement, as evidenced by the escalating number of publications in this domain each year. The United States holds an indisputable position as the foremost leader in this particular field, with the University of Pennsylvania emerging as the most active institution. The authors with the highest citation frequency and co-citation frequency are Carl H. June and Shannon L. Maude, respectively. The research hotspots in this field mainly focus on five aspects. Additionally, 10 emerging themes were identified. This study undertakes a comprehensive, systematic, and objective analysis and exploration of the field of CAR-T cell treatment for solid tumors, utilizing bibliometric methods. The findings of this study are expected to serve as a valuable reference and enlightenment for future research endeavors in this particular domain.

Optimal sizing of standalone hybrid renewable energy system based on reliability indicator: A case study

The economical electrification of rural settlements extensively utilizes renewable energy sources. In recent years, the deployment of solar photovoltaic, wind turbine, and biomass gasifier-based power generation technologies for electricity accessibility in remote regions has received greater prominence. This paper presents an optimal component design of a hybrid solar PV-wind and battery system along with a biomass gasifier to provide continuous electricity for rural communities of Uttarakhand, India. In this study, a recently developed population-based giza pyramids construction (GPC) methodology is applied to determine the optimal component sizing. The findings of the GPC method are contrasted with the particle swarm optimization (PSO) methodology to assess the effectiveness of the proposed method. The annualized system cost and levelized cost of energy of the hybrid renewable energy system have been minimized subject to technical reliability indicators such as loss of power supply probability and renewable fraction.Moreover, a systematic exploration of the system has been conducted to assess its optimal feasibility across different values of power loss probability ranging from (0% to 4%). The simulation indicates that the proposed method adeptly identifies globally optimal values, leading to a $177 decrease in annual cost and 8% reduction in average simulation time compared to PSO. The research reveals that the balanced point (relative attainment), where system reliability and cost-effectiveness intersect optimally, is achieved at a 3% power loss level, resulting in the most significant cost reduction of $199 compared to other probability levels.

Comprehensive Validation on Reweighting Samples for Bias Mitigation via AIF360

Fairness Artificial Intelligence (AI) aims to identify and mitigate bias throughout the AI development process, spanning data collection, modeling, assessment, and deployment—a critical facet of establishing trustworthy AI systems. Tackling data bias through techniques like reweighting samples proves effective for promoting fairness. This paper undertakes a systematic exploration of reweighting samples for conventional Machine-Learning (ML) models, utilizing five models for binary classification on datasets such as Adult Income and COMPAS, incorporating various protected attributes. In particular, AI Fairness 360 (AIF360) from IBM, a versatile open-source library aimed at identifying and mitigating bias in machine-learning models throughout the entire AI application lifecycle, is employed as the foundation for conducting this systematic exploration. The evaluation of prediction outcomes employs five fairness metrics from AIF360, elucidating the nuanced and model-specific efficacy of reweighting samples in fostering fairness within traditional ML frameworks. Experimental results illustrate that reweighting samples effectively reduces bias in traditional ML methods for classification tasks. For instance, after reweighting samples, the balanced accuracy of Decision Tree (DT) improves to 100%, and its bias, as measured by fairness metrics such as Average Odds Difference (AOD), Equal Opportunity Difference (EOD), and Theil Index (TI), is mitigated to 0. However, reweighting samples does not effectively enhance the fairness performance of K Nearest Neighbor (KNN). This sheds light on the intricate dynamics of bias, underscoring the complexity involved in achieving fairness across different models and scenarios.

Exploring Challenges and Solutions in Cloud Computing: A Review of Data Security and Privacy Concerns

As organizations transition towards cloud computing environments, the significance of security and privacy concerns escalates. This research report conducts a systematic exploration of diverse challenges and vulnerabilities inherent in cloud computing, with a particular emphasis on security and privacy issues. The study extensively evaluates potential threats ranging from data breaches to unauthorized access, and it evaluates the repercussions of these challenges on user trust and data integrity within cloud infrastructure. Moreover, it proposes and discusses effective strategies and solutions aimed at mitigating security risks and safeguarding user privacy in cloud computing. This paper contributes to the ongoing discourse on cloud security and privacy, offering both practitioners and researchers a valuable reference for navigating the dynamic landscape of cloud-based services.

Exploring Challenges and Solutions in Cloud Computing: A Review of Data Security and Privacy Concerns

As organizations transition towards cloud computing environments, the significance of security and privacy concerns escalates. This research report conducts a systematic exploration of diverse challenges and vulnerabilities inherent in cloud computing, with a particular emphasis on security and privacy issues. The study extensively evaluates potential threats ranging from data breaches to unauthorized access, and it evaluates the repercussions of these challenges on user trust and data integrity within cloud infrastructure. Moreover, it proposes and discusses effective strategies and solutions aimed at mitigating security risks and safeguarding user privacy in cloud computing. This paper contributes to the ongoing discourse on cloud security and privacy, offering both practitioners and researchers a valuable reference for navigating the dynamic landscape of cloud-based services.

Analysis of P-N Junction Length of Drain and Source in MOSFET Transistor Through TCAD Simulation

This paper delves into the convergence of mathematical and computer science principles within the realm of electronics engineering, particularly focusing on device-level transistor restructuring. A pressing challenge confronting the semiconductor sector is the persistent escalation of leakage current alongside technological advancements. At the heart of any chip or semiconductor device lies the transistor, serving as its fundamental building block. Among the various forms of leakage current, subthreshold leakage stands out as a dominant component in transistors. Within this study, a systematic exploration of the numerical association governing the physical configuration of MOSFET and leakage amount specifically the subthreshold one is undertaken. One innovative algorithm is crafted to facilitate the automated tracking of transistor structures, laying the groundwork for subsequent analyses. The simulation framework is meticulously constructed by leveraging mathematical formulations derived from the algorithmic outputs. Remarkably, the results obtained from the simulation conducted through TCAD software exhibit a remarkable proximity to established mathematical models. Given the ubiquity of Complementary Metal Oxide Semiconductor (CMOS) technology in contemporary semiconductor fabrication, it serves as the cornerstone for the simulation endeavours herein.

Navigating Through the Digital Workplace: Measuring Leader Digital Competence

AbstractIn a modern digital workplace, leaders must have the necessary skills to lead employees virtually. Despite its high practical and theoretical relevance, a consensus on crucial digital competencies for virtual leaders is lacking, hindering a systematic exploration of the leader’s role in facilitating technology use. In the present article, we propose a new concept and instrument to assess leader digital competence (LDC). After reviewing the literature, we establish three dimensions of LDC, centering around the leader’s ability and inclination to select, promote, and enable technology and digital media among their employees. We provide support for the scale's convergent, discriminant, criterion-related, and incremental validity using four independent samples (N1 = 156, N2 = 309, N3 = 201, N4 employee = 452, N4 leader = 93). Furthermore, results support the reliability and factor structure with the three proposed dimensions of the 10-item LDC scale. The findings demonstrate that the scale represents a psychometrically sound instrument, useful for further examining conditions for effectiveness in the virtual environment. Future research should aim to advance the understanding of antecedents and situational factors that influence the relevance of LDC and its impact on employee, team, and organizational outcomes.

Spatter-flow interaction behavior under protective gas flow parameters and scanning directions

Spattering is a common issue in selective laser melting (PBF-LB), causing contamination of the powder bed and negatively affecting product quality. One solution to this problem is introducing an inert gas flow that can remove spatter particles. The gas flow's characteristics and interaction with spatter particles depend on process parameters and involve a multi-physical field coupling process. However, current research mainly focuses on spattering behavior under single process conditions and lacks systematic exploration of gas flow characteristics and their interaction with spattering. This paper investigates the gas flow-spatter interaction process in PBF-LB, constructing a coupled model of Computational Fluid Dynamics (CFD) and Discrete Element Method (DEM). The model investigates the effects of inlet height, inlet flow velocity and laser scanning direction on the deposition and removal of spattered particles. The study reveals that flow velocity and inlet height affect the dynamic behavior of spattering by altering the flow distribution, while the laser scanning direction influences spatter dynamics by changing the direction of spatter ejects. The rate of spatter removal increases as the angle between the scanning direction and flow direction, and the flow velocity, increase. Conversely, it decreases as the inlet height increases. This work provides theoretical understanding of the interaction between gas flow characteristics and spattering, which is significant for optimizing printing process parameters and improving printing quality.

Controlling the Size of Hydrotalcite Particles and Its Impact on the Thermal Insulation Capabilities of Coatings.

This study focuses on the development of high-performance insulation materials to address the critical issue of reducing building energy consumption. Magnesium-aluminum layered double hydroxides (LDHs), known for their distinctive layered structure featuring positively charged brucite-like layers and an interlayer space, have been identified as promising candidates for insulation applications. Building upon previous research, which demonstrated the enhanced thermal insulation properties of methyl trimethoxysilane (MTS) functionalized LDHs synthesized through a one-step in situ hydrothermal method, this work delves into the systematic exploration of particle size regulation and its consequential effects on the thermal insulation performance of coatings. Our findings indicate a direct correlation between the dosage of MTS and the particle size of LDHs, with an optimal dosage of 4 wt% MTS yielding LDHs that exhibit a tightly interconnected hydrotalcite lamellar structure. This specific modification resulted in the most significant improvement in thermal insulation, achieving a temperature difference of approximately 25.5 °C. Furthermore, to gain a deeper understanding of the thermal insulation mechanism of MTS-modified LDHs, we conducted a thorough characterization of their UV-visible diffuse reflectance and thermal conductivity. This research contributes to the advancement of LDH-based materials for use in thermal insulation applications, offering a sustainable solution to energy conservation in the built environment.

Serum ferritin and the risk of myocardial infarction: A Mendelian randomization study.

The potential role of serum ferritin as a risk factor for myocardial infarction (MI) is controversial, necessitating a systematic exploration of the causal relationship between ferritin and MI through Mendelian randomization (MR) methods. Genetic data were derived from a genome-wide association study (GWAS), employing the inverse variance-weighted (IVW) method as the primary approach. Comprehensive sensitivity analyses were conducted to validate the robustness of the results. Evaluation of instrumental variables was performed using the F-statistic, and a meta-analysis was employed to assess the average gene-predicted effect between ferritin and MI. The MR study revealed a negative correlation between ferritin and MI. The odds ratios (ORs) in the IVW method were 0.83 [95% confidence interval (CI) = 0.72-0.97; P = .017] and 0.86 (95% CI = 0.72-1.02; P = .080). Additionally, meta-analysis consistently indicated a negative causal relationship between ferritin and MI, with no heterogeneity or horizontal pleiotropy, thereby indicating a negative correlation between ferritin levels and the risk of MI. The genetic evidence sheds light on the causal relationship between ferritin levels and MI risk, providing new perspectives for future interventions in acute myocardial infarction (AMI).

Enhancing methyl orange discoloration with TiO2/Fe2O3/AC composites: synergistic adsorption-photocatalysis

Fe2O3/TiO2/AC nanocomposites were successfully synthesized via a sol–gel method, and their efficacy in decomposing methyl orange via adsorption-photocatalytic processes was reported for the first time in this study. Systematic exploration of the physicochemical properties impacting photocatalytic activity was conducted through x-ray diffraction, scanning electron microscopy—energy-dispersive x-ray spectroscopy, and Fourier transform infrared spectroscopy (FTIR) characterizations. The study revealed that the discoloration of methyl orange resulted from a synergistic interplay between adsorption and photocatalysis. The incorporation of Fe2O3 nanoparticles significantly elevated photocatalytic activity by 70%, with additional enhancement observed upon the introduction of activated carbon (AC) particles. The synergistic effects of AC adsorption and TiO2/Fe2O3 photocatalysis exhibited remarkable efficiency in degrading methyl orange under visible light irradiation. The trial functioning of Fe2O3 and AC in the TiO2/Fe2O3/AC composite, which serves as an adsorbent, an electron trap, and a co-catalyst providing active sites, significantly improved photocatalytic activity. These findings not only contribute to understanding the crucial role of composite TiO2 in conjunction with metal and carbon-based co-catalysts but also hold valuable implications for advancing research in the development of adsorption-photocatalytic systems for wastewater treatment.

A mountaineering strategy to excited states: Accurate vertical transition energies and benchmarks for substituted benzenes.

In an effort to expand the existing QUEST database of accurate vertical transition energies [Véril et al.WIREs Comput.Mol.Sci. 2021, 11, e1517], we have modeled more than 100 electronic excited states of different natures (local, charge-transfer, Rydberg, singlet, and triplet) in a dozen of mono- and di-substituted benzenes, including aniline, benzonitrile, chlorobenzene, fluorobenzene, nitrobenzene, among others. To establish theoretical best estimates for these vertical excitation energies, we have employed advanced coupled-cluster methods including iterative triples (CC3 and CCSDT) and, when technically possible, iterative quadruples (CC4). These high-level computational approaches provide a robust foundation for benchmarking a series of popular wave function methods. The evaluated methods all include contributions from double excitations (ADC(2), CC2, CCSD, CIS(D), EOM-MP2, STEOM-CCSD), along with schemes that also incorporate perturbative or iterative triples (ADC(3), CCSDR(3), CCSD(T)(a) , and CCSDT-3). This systematic exploration not only broadens the scope of the QUEST database but also facilitates a rigorous assessment of different theoretical approaches in the framework of a homologous chemical series, offering valuable insights into the accuracy and reliability of these methods in such cases. We found that both ADC(2.5) and CCSDT-3 can provide very consistent estimates, whereas among less expensive methods SCS-CC2 is likely the most effective approach. Importantly, we show that some lower order methods may offer reasonable trends in the homologous series while providing quite large average errors, and vice versa. Consequently, benchmarking the accuracy of a model based solely on absolute transition energies may not be meaningful for applications involving a series of similar compounds.

Pyrolytic Conversion of Waste High-Density Polyethylene to Wax: Temperature Optimization and Characterization of Wax

The formation of wax from waste high-density polyethylene by thermal decomposition through pyrolysis is examined in this work. To get wax from waste high-density polyethylene, the thermal cracking reaction is run in a semi-batch pyrolysis reactor at 550 °C, 600 °C, and 650 °C. The yield percentage, melting point, specific gravity, and penetration degree of wax varied depending on the temperature. At 600 °C, waste high-density polyethylene yielded the highest wax output (87.25%) with a 0.7768 specific gravity, a 59 °C melting point, and an 81.8 mm penetration degree. The fourier transform infrared spectroscopy (FTIR) analysis concluded the presence of aliphatic hydrocarbon compounds (alkane, alkene, alcohol, and cycloalkane), which is also confirmed by gas chromatography-mass spectrometry (GC-MS) analysis. The innovation in this study lies in the systematic exploration and optimization of temperature conditions for wax production from waste high-density polyethylene (HDPE) bottles.