Efficient Mechanism Research Articles

Synthesis and characterization of MoS2-carbon based materials for enhanced energy storage applications

The article delves into the synthesis and characterization of MoS2-carbon-based materials, holding promise for applications in supercapacitors and ion batteries. The synthesis process entails the preparation of MoS2 and its carbon hybrids through exfoliation, hydrothermal treatment, and subsequent pyrolysis. Various analytical techniques were employed to comprehensively examine the structural, compositional, and morphological properties of the resulting materials. The article explores the electrochemical performance of these electrode materials in supercapacitors and ion batteries (LiB, SiB, KiB). Electrochemical measurements were conducted in aqueous electrolyte for supercapacitors and various aprotic electrolytes for ion batteries. Results highlight the impact of the synthesis process on electrochemical performance, emphasizing factors such as capacitance, rate capability, and charge/discharge cycle performance. Hydrothermally treated MoS2-carbon exhibited a specific capacitance of approximately 150 F g-1 in supercapacitors, attributed to its high surface area and efficient charge storage mechanisms. Additionally, for Li-ion battery materials without hydrothermal treatment showed impressive capacity retention of around 88% after 500 charge-discharge cycles, starting with an initial specific capacity of about 920 mAh/g. Long-term stability was demonstrated in both supercapacitors and lithium-ion batteries, with minimal capacitance degradation even after extensive charge-discharge cycles. This research underscores the potential of MoS2-based materials as effective energy storage solutions.

A dataset of drone-captured, segmented images for oil spill detection in port environments

The high incidence of oil spills in port areas poses a serious threat to the environment, prompting the need for efficient detection mechanisms. Utilizing automated drones for this purpose can significantly improve the speed and accuracy of oil spill detection. Such advancements not only expedite cleanup operations, reducing environmental harm but also enhance polluter accountability, potentially deterring future incidents. Currently, there’s a scarcity of datasets employing RGB images for oil spill detection in maritime settings. This paper presents a unique, annotated dataset aimed at addressing this gap, leveraging a neural network for analysis on both desktop and edge computing platforms. The dataset, captured via drone, comprises 1268 images categorized into oil, water, and other, with a convolutional neural network trained using an Unet model architecture achieving an F1 score of 0.71 for oil detection. This underscores the dataset’s practicality for real-world applications, offering crucial resources for environmental conservation in port environments.

Optimizing ironing parameters in material extrusion (MEX) technology: enhancing efficiency and performance

This study explores the optimization of ironing parameters in Material Extrusion (MEX) technology, specifically targeting improvements in the mechanical properties of polylactic acid (PLA) printed objects, with a focus on enhancing Ultimate Tensile Strength (UTS) and minimizing printing time. To achieve this, we employed a systematic experimental approach that varied flow rate, path speed, and spacing between passes, followed by a comprehensive statistical analysis using Analysis of Variance (ANOVA). Our findings indicate that path speed and spacing between passes significantly influence both UTS and printing time, while flow rate has a minor effect within the tested range. Optimal parameters were determined to be a flow rate of 14.34%, path speed of 30 mm/s, and spacing between passes of 0.3 mm, achieving a composite desirability of 0.970537. Validation using the response optimizer feature in Minitab software confirmed a strong correlation between predicted and experimental results. This research provides practical insights for optimizing MEX parameters, contributing to enhanced mechanical properties and efficiency in additive manufacturing processes.

VLFATRollout: Fully transformer-based classifier for retinal OCT volumes

Background and Objective:Despite the promising capabilities of 3D transformer architectures in video analysis, their application to high-resolution 3D medical volumes encounters several challenges. One major limitation is the high number of 3D patches, which reduces the efficiency of the global self-attention mechanisms of transformers. Additionally, background information can distract vision transformers from focusing on crucial areas of the input image, thereby introducing noise into the final representation. Moreover, the variability in the number of slices per volume complicates the development of models capable of processing input volumes of any resolution while simple solutions like subsampling may risk losing essential diagnostic details. Methods:To address these challenges, we introduce an end-to-end transformer-based framework, variable length feature aggregator transformer rollout (VLFATRollout), to classify volumetric data. The proposed VLFATRollout enjoys several merits. First, the proposed VLFATRollout can effectively mine slice-level fore-background information with the help of transformer’s attention matrices. Second, randomization of volume-wise resolution (i.e. the number of slices) during training enhances the learning capacity of the learnable positional embedding (PE) assigned to each volume slice. This technique allows the PEs to generalize across neighboring slices, facilitating the handling of high-resolution volumes at the test time. Results:VLFATRollout was thoroughly tested on the retinal optical coherence tomography (OCT) volume classification task, demonstrating a notable average improvement of 5.47% in balanced accuracy over the leading convolutional models for a 5-class diagnostic task. These results emphasize the effectiveness of our framework in enhancing slice-level representation and its adaptability across different volume resolutions, paving the way for advanced transformer applications in medical image analysis. The code is available at https://github.com/marziehoghbaie/VLFATRollout/.

Reconfigurable binary diffractive optical neural network based on chalcogenide phase change material Ge2Sb2Se4Te1

Diffractive optical neural networks (DONNs) possess unique advantages such as light-speed computing, low energy consumption, and parallel processing, which have obtained increasing attention in recent years. However, once conventional DONNs are fabricated, their function remains fixed, which greatly limits the applications of DONNs. Thus, we propose a reconfigurable DONN framework based on a repeatable and non-volatile phase change material Ge2Sb2Se4Te1(GSST). By utilizing phase modulation units made of GSST to form the network's neurons, we can flexibly switch the functions of the DONN. Meanwhile, we apply a binary training algorithm to train the DONN weights to binary values of 0 and π, which is beneficial for simplifying the design and fabrication of DONN while reducing errors during physical implementation. Furthermore, the reconfigurable binary DONN has been trained as a handwritten digit classifier and a fashion product classifier to validate the feasibility of the framework. This work provides an efficient and flexible control mechanism for reconfigurable DONNs, with potential applications in various complex tasks.

Harnessing the interfacial charge transfer in Mn-doped γ-Fe2O3@ZnO heterojunction for broad spectrum photocatalytic degradation of organic dyes

Developing affordable, sustainable, and environmentally friendly water remediation strategies is inevitable to achieve water security worldwide. To align with these challenges, this investigation successfully synthesized low-cost Mn-doped iron oxide and its heterojunction with Zinc Oxide (ZnO) as an efficient recovery mechanism for deteriorated water. Comprehensive characterization of Mn-doped γ-Fe2O3@ZnO heterojunction was performed using X-ray diffraction, Fourier transform infrared spectroscopy, Scanning electron microscopy, Energy dispersive X-ray, and Photoluminescence analysis. The electrical behaviors of all samples were characterized using Electrochemical impedance spectroscopy and Mott-Schottky analysis. This heterojunction showed distinct structural features along with a bandgap energy of 2.61 eV, confirming the formation of a heterojunction. The effectiveness of this heterojunction was assessed using Congo red (CR) and Crystal violet as model pollutants. The photodegradation capability using a small amount of as-synthesized photocatalyst (10 mg) reached 90.8 % for CR and 90.5 % for CV under natural sunlight for a duration of 105 min. The key findings of this report clearly demonstrated the effectiveness of the interfacial charge transfer mechanism of Mn-γ-Fe2O3@ZnO heterojunction towards the degradation of organic water contaminants.

Multifunctional Conductive MOFs Enhance the Photocatalytic Hydrogen Evolution Efficiency of S-Type Ni3(HITP)2/TiO2 Heterojunctions.

Despite their high surface area, remarkable porosity, and efficient charge transfer mechanisms, conductive MOFs have found limited utilization within the domain of photocatalysis. In this study, we synthesized a cutting-edge S-type Ni3(HITP)2/TiO2 heterojunction photocatalyst exhibiting outstanding light harvesting and prolonged lifetime of photogenerated electrons through an in situ synthesis approach. Compared with TiO2, the composite materials not only significantly increase the specific surface area by 4.07 times but also expand the visible light absorption edge from 400 to 1100 nm. The hydrogen production rate of Ti/Ni-3 reached 4.927 mmol·g-1·h-1, which is 4.51 times that of TiO2. The S-type interface charge transfer pathway of the Ni3(HITP)2/TiO2 composite material was inferred by band structure, in situ XPS, SPV, and free radical capture, which improves charge separation and extends the carrier lifetime to undergo directional migration driven by an IEF. This is the main reason for the improved photocatalytic performance of Ni3(HITP)2/TiO2 composite materials.

Social Movements and Environmental Activism: Interactive Mechanisms of Leadership, Organization and Resource Mobilization

The paper focuses on the interactive relationship between leadership, organizational structure, and resource mobilization in environmental protection activities, revealing their pivotal roles. Leadership, as the core driving force, guides the vision of environmental conservation and unleashes the potential of the team. The organizational system serves as the cornerstone for efficient operation, ensuring clear responsibilities, information flow, and resource sharing while fostering team and cultural development. Resource mobilization provides the material foundation for activities, enhancing their momentum and effectiveness through diversified funding and efficient allocation. These three elements are tightly interconnected, forming a synergistic effect that boosts the efficiency and sustainability of environmental protection activities. Strengthening leadership, optimizing organizations, and expanding resource channels are the crucial pathways to constructing an efficient environmental protection mobilization mechanism, holding profound significance for the cause of environmental protection.

Global health resource distribution during COVID-19 and in future pandemics: Here’s what people say

Abstract During the COVID-19 pandemic, countless individuals across the world needed novel and sophisticated health care resources simultaneously, leading to dramatic global shortages. How to distribute resources in such situations? This dilemma is neither new nor understudied: Its complexity is well known to medical ethicists, public health scientists and philosophers. Building on this knowledge, powerful global distribution frameworks were proposed already early into the pandemic. Despite this knowledge, no globally consensual regulations existed (and exist) on the political sphere. Worse still, numerous countries hoarded resources for themselves, increasing global inequalities and prolonging the pandemic for everyone. For future emergencies, it is thus necessary to develop immediately applicable strategies. Because public acceptance is key for such interventions, evidence on the public opinion towards allocation principles is strongly needed. In 2021, we asked representative samples of N = 2694 adults in England and Germany to rate seven COVID-19-specific global allocation principles. In line with literature on justice attitudes in general, extensions of bifactor(S-1) models showed that participants would have preferred a more equity- or equality-based global resource distribution during the pandemic, presenting themselves as more cosmopolitan than global leaders. Trying to understand these attitudes better, we tested preregistered hypotheses on relations with other constructs and found positive associations between equity- and equality-directed preferences and global human identification, among others. To ensure generalisability on future pandemics, we collected data from a second cohort in spring 2024. All results will be presented in detail. With their responses, European citizens call upon the development of fairer and more efficient global distribution mechanisms. This global public health perspective could inform preparations for future global emergencies. Key messages • European citizens would have preferred a more equity- or equality-based global distribution of scarce health care resources during the COVID-19 pandemic than global leaders organised. • Citizens’ global perspective on public health and their strong emphasis on global fairness should inform preparations for future pandemics and other global emergencies.

Establishing effective learning bridge cross multi-scale feature maps for object detection and semantic segmentation

ABSTRACT In the field of image object detection and semantic segmentation, improving the accuracy of object identification and segmentation is a primary goal. To achieve this, leveraging the potential of multi-scale information through feature map refinement and fusion has been widely recognized. However, existing feature fusion methods either design more complex feature pyramid networks, replace existing detectors, or incrementally introduce feature fusion modules, overlooking the effective approach of enhancing spatial information in deep feature maps. We propose a novel pluggable feature fusion paradigm termed ‘Effective Learning Bridge’. Our research introduces an efficient and adaptive learning mechanism that builds learning bridges between feature maps at different scales within the feature pyramid, thereby enhancing the spatial information of objects in deep feature maps. This mechanism is specifically designed for multi-scale feature maps and can be seamlessly integrated into any network incorporating feature maps. By altering the model’s backpropagation path, we successfully improve learning efficiency, which in turn enhances the accuracy of object detection and segmentation. Our proposed paradigm and method were extensively evaluated through experiments on SIMD, HRSID, and WHDLD datasets and benchmark models. The results unequivocally demonstrate the effectiveness of our approach in significantly improving the accuracy of object detection and semantic segmentation, as well as the overall learning efficiency of the model.

Investigating the potency of ethylenediurea (EDU) in alleviating the affliction of ambient ozone in heat labile tomato cultivars (Solanum lycopersicum L.).

Tomato is the second most valuable vegetable crop, and its susceptibility to tropospheric ozone (O3) varies on the cultivar. Eight tomato cultivars with documented O3 sensitivity were reevaluated using ethylenediurea (400ppm EDU) to determine the effectiveness of EDU in assessing O3 sensitivity under heavily O3-polluted tropical conditions. EDU helped in amending the growth, photosynthetic pigments, photosynthetic rate, stomatal conductance, and yield characteristics in the tomato cultivars. EDU reduced the lipid peroxidation and reactive oxygen species content, while enzymatic and non-enzymatic antioxidant responses differed across cultivars. The cultivar Superbug and Sel-7 (O3 susceptible) performed better by employing more biomass and yield and exhibiting more potent antioxidative defense machinery mainly non-enzymatic antioxidants after EDU treatment. The higher value of total antioxidative potential (TAP) in O3 susceptible cultivars suggested the adaptive resilience through EDU application against O3 stress. EDU application greatly enhanced the photosynthetic rate in O3 susceptible cultivars by increasing the stomatal conductance. Hence, both biophysical and biochemical responses were involved in protection against O3 provided by EDU. Kashi chayan and VRT02 (O3 tolerant) cultivars showed least response to EDU, due to their efficient inherent mechanisms in alleviating O3 stress. Thus, EDU may be used as an efficient biomonitoring tool against O3-sensitive cultivars.

Burp tax: Regulation and revenue potential in Indonesia

The increasingly urgent phenomenon of climate change requires concrete actions from various sectors, including the livestock sector which is a significant contributor to greenhouse gas emissions, particularly methane. This study explores the potential of implementing Burp Tax in Indonesia as a regulatory mechanism to reduce methane emissions from beef and dairy cattle farms. By referring to New Zealand's experience as a Burp Tax pioneer, this study analyzes the policy scheme that includes tax subjects, tax objects, tariffs, and fair and efficient collection mechanisms. In addition, this study identifies various obstacles that may be faced in the implementation of Burp Tax in Indonesia and provides recommendations to support the national commitment in achieving the Zero Emission target in 2045/2060. The research used qualitative methods with literature study data collection techniques. The research concludes that there is great potential for the imposition of Burp Tax, so that in addition to being a form of effort to reduce methane emissions from beef and dairy cattle farms, Burp Tax can also increase the portion of state revenue from the taxation sector. The specific tariff scheme is IDR30/KgCH4 with tax subjects in the beef and dairy cattle farming sector, including individuals with 30 cows and business entities. However, it is important to note that the imposition of Burp Tax may have an economic impact on livestock business actors and affect the price of meat and milk commodities. Therefore, further studies are needed regarding the potential and impact of Burp Tax implications in Indonesia. The results of this study are expected to make a significant contribution to the development of sustainable taxation policies that are responsive to the challenges of climate change.

A rolling bearing fault signal denoising algorithm that combines a new adaptive information entropy with a new wavelet threshold function

Abstract Mechanical fault diagnosis is of great significance to industrial automation, and extracting vibration fault signals is one of the important tasks in mechanical health monitoring and fault diagnosis. However, due to the complex working environment of rolling bearings, a large amount of noise makes it difficult to extract vibration fault signals. Denoising the vibration signal of bearings can remove interference noise, simplify the early identification of signal features, and thus improve diagnostic accuracy and mechanical maintenance efficiency. This paper proposes a rolling bearing fault signal denoising algorithm, which constructs a new feature extraction and denoising function. This method first decomposes the noisy signal into Intrinsic Mode Functions (IMFs) by Intrinsic Computing Expressive Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN). Secondly, a new adaptive information entropy threshold function is constructed to extract the noisy IMFs from it. Then, the noisy IMF signal is denoised by a new wavelet threshold function. Finally, the noise-free IMFs are reconstructed to reconstruct the denoised signal. To verify the actual performance of the algorithm, comparative experiments were conducted on a self-collected dataset and a public dataset, the results show that this method improves the continuity of signal reconstruction and can remove various types of vibration fault noise signals more effectively and accurately, \hl{thereby improving the fault detection accuracy by 2%-9%.

An Efficient Competitive Control Mechanism for Negative Information Spread in Online Social Networks

An Efficient Competitive Control Mechanism for Negative Information Spread in Online Social Networks

Mapping a food donation supply chain and the implications for social policies in Brazil

PurposeThe coronavirus disease 2019 (Covid-19) pandemic raised global alarms about hunger and food insecurity worldwide and the corresponding need for public policies, particularly in emerging countries. One possible solution for addressing this important topic is a food donation supply chain, which comprises food banks and involves their relationships with suppliers (donors), customers (people in need) and other potential stakeholders. The aim of this research is to understand how different agents in a food donation supply chain use different but interrelated capital dimensions (human, structural and relational) to leverage their resources and build knowledge in their relationships with other stakeholders.Design/methodology/approachWe conducted an abductive single case study with the food bank as a focal organisation in a direct supply chain. Data were collected by way of 15 semi-structured interviews with representatives from each tier in the chain (food bank, donor, social impact company and social movement), non-participant observation and secondary data (e.g. codes of practice, websites and media). All data sources were analysed individually and then cross-checked.FindingsOur findings show that tangible dimensions, such as those that are structural, are present upstream while intangible dimensions – those that are relational and human, for example – are prevalent downstream. The political dimension was identified as an important mechanism that allows stakeholder engagement and access to resources, funding and government incentives.Originality/valueBy applying a multi-tier approach, the present research explores how different stages in this supply chain use intellectual capital (IC) to better manage relationships. The heterogeneity of agents within the food donation supply chain (public, private and third sector), each of which has distinct levels of IC dimensions, can share their knowledge to improve efficiency and social protection mechanism policies. Since the focal organisation is a non-profit organisation, this study also contributes to IC theory.

Development of a xylose-inducible and glucose-insensitive expression system for Parageobacillus thermoglucosidasius

Inducible expression systems are pivotal for governing gene expression in strain engineering and synthetic biotechnological applications. Therefore, a critical need persists for the development of versatile and efficient inducible expression mechanisms. In this study, the xylose-responsive promoter xylA5p and its transcriptional regulator XylR were identified in Parageobacillus thermoglucosidasius DSM 2542. By combining promoter xylA5p with its regulator XylR, fine-tuning the expression strength of XylR, and reducing the glucose catabolite repression on xylose uptake, we successfully devised a xylose-inducible and glucose-insensitive expression system, denoted as IExyl*. This system exhibited diverse promoter strengths upon induction with xylose at varying concentrations and remained unhindered in the presence of glucose. Moreover, we showed the applicability of IExyl* in P. thermoglucosidasius by redirecting metabolic flux towards riboflavin biosynthesis, culminating in a 2.8-fold increase in riboflavin production compared to that of the starting strain. This glucose-insensitive and xylose-responsive expression system provides valuable tools for designing optimized biosynthetic pathways for high-value products and facilitates future synthetic biology investigations in Parageobacillus.Key points• A xylose-inducible and glucose-insensitive expression system IExyl* was developed.• IExyl* was applied to enhance the riboflavin production in P. thermoglucosidasius• A tool for metabolic engineering and synthetic biology research in Parageobacillus strains.

Optimization model for distributed energy trading based on a market value allocation mechanism in the electricity spot market

This study proposes a novel distributed energy trading market model with a value distribution mechanism to optimize the allocation and transactions of distributed energy resources (DERs). The framework incorporates a direct load management approach via an electricity aggregator agent, simplifying market processes and reducing transaction costs. A Nash bargaining model is employed to design a fair and efficient value distribution mechanism, promoting equitable benefit allocation among participants. The model integrates stochastic programming to account for uncertainties in real-time load and DER output, enhancing its robustness and applicability in real-world scenarios. The proposed mechanism quantifies each DER’s contribution using a market value contribution rate, serving as a foundation for the Nash bargaining model. This approach ensures individual rationality for both the aggregator and DERs while maximizing overall system benefits. Case studies validate the model’s effectiveness, demonstrating improvements in resource utilization and fair benefit allocation. This research contributes to the advancement of distributed energy markets, offering valuable insights for designing efficient and equitable market structures, ultimately promoting grid stability, renewable energy adoption, and the development of more sustainable and flexible energy systems.

Cytotoxicity of Orthodontic Archwires Used in Clinical Practice: In Vitro Study

This study investigates the cytotoxicity of various orthodontic archwires, which are essential in directing tooth movement through biomechanical forces. With advancements in material science, different archwire materials have been developed to balance mechanical performance with aesthetic and biological considerations. The study focuses on evaluating the biocompatibility and mechanical properties of stainless steel, nickel–titanium, and chromium–cobalt archwires, particularly their cytotoxic effects on oral cavity cells. In vitro cell culture experiments with fibroblasts, combined with scanning electron microscopy (SEM) analysis, were conducted to assess cell viability and morphology. The results revealed significant differences in cytotoxicity, with copper wires showing high toxicity and causing extensive cell death, while nickel–titanium and chromium–cobalt wires supported better cell viability and healthier cell morphology. These findings highlight the importance of selecting archwire materials that ensure mechanical efficiency without compromising cellular health, emphasizing the need for ongoing assessment of material biocompatibility in the oral environment.

Theoretical investigation on crash performance of bamboo-inspired energy absorption systems based on systematic experimental and numerical analysis

The need for energy-absorbing devices with adaptable crashworthiness is increasingly urgent in various engineering fields with diverse load environments. For this aim, bamboo-inspired modular energy absorption systems have recently been proposed to integrate mechanical efficiency, tunability, and robustness. However, their mechanical responses under varying load velocities have not been systematically studied, which limits the effective prediction and optimization of their crashworthiness. In this study, quasi-static and dynamic experiments were performed on bamboo-inspired systems, discretely assembled using 3D printed steel tube specimens. Matched finite element simulations were conducted using ABAQUS/Explicit. Specific energy absorption and efficiency of these systems respectively exceeded 12.9 J/g and 47 %, surpassing existing discretely assembled systems and rivaling typical cellular materials. Efficient deformation mode with self-locking capability was observed, and sharp impact peak was avoided when the crash velocity was less than 50 m/s. Building upon these findings, a modified spherical shell theory under crashing was developed to predict energy absorption performance of bamboo-inspired system based on the traveling plastic hinge model. The average relative error between the analytical solution and finite element results was found less than 6.5 %. The mean effective stress of the system could exceed 35 MPa under thickness-to-diameter ratio of 5 %. This work presents an efficient and effective approach for optimizing and estimating the crash performance of modular protective devices.

Hydrazine‐Catalysed Ring‐Opening Metathesis Polymerization Of Cyclobutenes

AbstractMaterials formed by the ring‐opening metathesis polymerization (ROMP) of cyclic olefins are highly valued for industrial and academic applications but are difficult to prepare free of metal contaminants. Here we describe a highly efficient metal‐free ROMP of cyclobutenes using hydrazine catalysis. Reactions can be initiated via in situ condensation of a [2.2.2]‐bicyclic hydrazine catalyst with an aliphatic or aromatic aldehyde initiator. The polymerizations show living characteristics, achieving excellent control over molecular weight, low dispersity values, and high chain‐end fidelity. Additionally, the hydrazine can be used in substoichiometric amounts relative to the aldehyde chain‐end while maintaining good control over molecular weight and low dispersity values, indicating that a highly efficient chain transfer mechanism is occurring.