Groundbreaking Method Research Articles

Biological removal of nitrogenous waste compounds in the biofloc aquaculture system: a review

Abstract Aquaculture has experienced significant global expansion and is considered one of the fastest-growing sectors in food production. However, there exist additional challenges that restrict the capacity to achieve maximum efficiency in aquaculture systems, such as issues over water quality and shortages of appropriate live feeds. Intensive aquaculture systems involve the use of protein-rich prepared feed for feeding the cultured animals. This may give rise to the discharge of nitrogenous compounds into the water, which can pose a risk to the environment when present in excessive quantities beyond the acceptable levels. In recent years, an innovative method called biofloc technology (BFT) has become a practical solution to this issue. Undoubtedly, BFT offers a groundbreaking method for nutrient disposal that eradicates the requirement for excessive water use or equipment maintenance. Three primary types of microorganisms are crucial in alleviating the adverse impacts of nitrogen compounds in this technique. Photoautotrophs participate in the processes of removal and absorption, whereas chemoautotrophs promote nitrification and conversion. Heterotrophs contribute to the absorption process. Biofloc predominantly consists of heterotrophic bacteria, alongside algae, protozoa, rotifers, and nematodes. While there have been reviews carried out on multiple aspects of biofloc technology, there exists a lack of literature that tackles this particular field of research progress. This article discusses every aspect and techniques of biological management used for removing nitrogenous waste compounds in biofloc aquaculture systems.

Flash sintering of metal-like ceramics: A short overview

Flash sintering was officially discovered about fifteen years ago as a ground-breaking method to consolidate ceramics in very short time at furnace temperature much lower than that used for conventional sintering. Since then, it has been applied to several ceramics and composites with the common feature of being characterized by a negative temperature coefficient for resistivity while few attempts have been made on materials with metal-like electronic conduction. In the present overview the results of the investigation carried out in the last years aiming at consolidating tungsten carbide and zirconium diboride by flash sintering are reported and the conditions for inducing a thermal runaway phenomenon like in other ceramics and understand the physical mechanisms behind the flash effect are analysed. The investigation allows to point out the importance of the fundamental processing variables like the applied pressure and voltage for obtain high density material. The key role of the starting particles’ surface chemistry is identified to generate specific microstructures and determine peculiar physical and mechanical properties in the flash sintered ceramic.

Statistical data pre-processing and time series incorporation for high-efficacy calibration of low-cost NO2 sensor using machine learning

Air pollution stands as a significant modern-day challenge impacting life quality, the environment, and the economy. It comprises various pollutants like gases, particulate matter, biological molecules, and more, stemming from sources such as vehicle emissions, industrial operations, agriculture, and natural events. Nitrogen dioxide (NO2), among these harmful gases, is notably prevalent in densely populated urban regions. Given its adverse effects on health and the environment, accurate monitoring of NO2 levels becomes imperative for devising effective risk mitigation strategies. However, the precise measurement of NO2 poses challenges as it traditionally relies on costly and bulky equipment. This has prompted the development of more affordable alternatives, although their reliability is often questionable. The aim of this article is to introduce a groundbreaking method for precisely calibrating cost-effective NO2 sensors. This technique involves statistical preprocessing of low-cost sensor readings, aligning their distribution with reference data. Central to this calibration is an artificial neural network (ANN) surrogate designed to predict sensor correction coefficients. It utilizes environmental variables (temperature, humidity, atmospheric pressure), cross-references auxiliary NO2 sensors, and incorporates short time series of previous readings from the primary sensor. These methods are complemented by global data scaling. Demonstrated using a custom-designed cost-effective monitoring platform and high-precision public reference station data collected over 5 months, every component of our calibration framework proves crucial, contributing to its exceptional accuracy (with a correlation coefficient near 0.95 concerning the reference data and an RMSE below 2.4 µg/m3). This level of performance positions the calibrated sensor as a viable, cost-effective alternative to traditional monitoring approaches.

System for Detecting Moving Objects Using 3D Li-DAR Technology

The “System for Detecting Moving Objects Using 3D LiDAR Technology” introduces a groundbreaking method for precisely identifying and tracking dynamic entities within a given environment. By harnessing the capabilities of advanced 3D LiDAR (Light Detection and Ranging) technology, the system constructs intricate three-dimensional maps of the surroundings using laser beams. Through continuous analysis of the evolving data, the system adeptly discerns and monitors the movement of objects within its designated area. What sets this system apart is its innovative integration of a multi-sensor approach, combining LiDAR data with inputs from various other sensor modalities. This fusion of data not only enhances accuracy but also significantly boosts the system’s adaptability, ensuring robust performance even in challenging environmental conditions characterized by low visibility or erratic movement patterns. This pioneering approach fundamentally improves the precision and reliability of moving object detection, thereby offering a valuable solution for a diverse array of applications, including autonomous vehicles, surveillance, and robotics.

A Comprehensive Review: Robot-Assisted Treatments for Gait Rehabilitation in Stroke Patients.

Robot-assisted gait training (RAGT) is at the cutting edge of stroke rehabilitation, offering a groundbreaking method to improve motor recovery and enhance the quality of life for stroke survivors. This review investigates the effectiveness and application of various RAGT systems, including both end-effector and exoskeleton robots, in facilitating gait enhancements. The selection process for this comprehensive analysis involved a meticulous review of the literature from databases such as PubMed, the Cochrane Library, and EMBASE, focusing on studies published between 2018 and 2023. Ultimately, 27 studies met the criteria and were included in the final analysis. The focus of these studies was on the various RAGT systems and their role in promoting gait and balance improvements. The results of these studies conclusively show that patients experience significant positive effects from RAGT, and when combined with other physiotherapy methods, the outcomes are notably superior in enhancing functional ambulation and motor skills. This review emphasizes RAGT's capability to deliver a more customized and effective rehabilitation experience, highlighting the importance of tailoring interventions to meet the specific needs of each patient.

Utilizing layout effects for analog logic locking

While numerous obfuscation techniques are available for securing digital assets in the digital domain, there has been a notable lack of focus on protecting intellectual property (IP) in the analog domain. This is primarily due to the relatively smaller footprint of analog components within an integrated circuit (IC), with the majority of the surface dedicated to digital elements. However, despite their smaller nature, analog components are highly valuable IP and warrant effective protection. In this paper, we present a groundbreaking method for safeguarding analog IP by harnessing layout-based effects that are typically considered undesirable in IC design. Specifically, we exploit the impact of length of oxide diffusion and well proximity effect on transistors to fine-tune critical parameters such as transconductance (gm\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$g_{m}$$\\end{document}) and threshold voltage (Vth\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$V_{th}$$\\end{document}). These parameters remain concealed behind key inputs, akin to the logic locking approach employed in digital ICs. Our research explores the application of layout-based effects in two commercial CMOS technologies, namely a 28 nm and a 65 nm node. To demonstrate the efficacy of our proposed technique, we implement it for locking an operational transconductance amplifier. Extensive simulations are performed, evaluating the obfuscation strength by applying a large number of key sets (over 50,000 and 300,000). The results exhibit a significant degradation in performance metrics, such as open-loop gain (up to 130 dB), phase margin (up to 50∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^\\circ $$\\end{document}), 3 dB bandwidth (approximately 2.5 MHz), and power consumption (around 1 mW) when incorrect keys are employed. Our findings highlight the advantages of our approach as well as the associated overhead.

Beyond Borders: CBAM’s Revolutionary Potential and Challenges in Achieving Carbon Neutrality

The Carbon Border Adjustment Mechanism (CBAM) is a ground-breaking method for attaining carbon neutrality in international trade as the world community struggles with the pressing need to address climate change. This research investigates how the CBAM might influence the dynamics of international trade and promote the shift to low-carbon, sustainable economies. A critical analysis has been conducted to explore the CBAM's transformational potential in bringing trade practices into line with climate goals by examining its economic, environmental, and political components and its implication practice, covering issues with trade equity, enforcement protocols, and possible geopolitical conflicts. The information has been gathered from several reliable sources, including the WTO, the European Commission for trade input, the World Bank, and the EPA for emission information. It has been compiled into a tabular format for simplicity of reading. The graphs have been plotted using Python (3.11.6) to improve visual comprehension. Stakeholders may help CBAM implement successfully by adopting these suggestions, maximising its revolutionary potential, and overcoming the difficulties in reaching carbon neutrality in international trade. By using an interdisciplinary approach, the study seeks to offer a thorough grasp of CBAM's function in becoming carbon neutral, revealing the complex obstacles that must be overcome for it to integrate into the world economy successfully.

Ge-doped Sn-Pb assisted by MACl triple metal cation perovskite solar cells with high open-circuit voltage

The potential to achieve the best photovoltaic properties by incorporating Ge into Sn-Pb-based perovskites (PVKs) is theoretically plausible. However, practical implementation is hindered by critical obstacles associated with Ge-containing precursors, such as poor solubility and extremely low stability. Furthermore, the application of Ge in PVKs is limited, particularly when combined with Sn-Pb-based PVK, owing to the instability of Sn and Ge within the same precursor. Herein, we report a groundbreaking method for inserting Ge into Sn-Pb PVK using volatile additives, resulting in high-performance Sn-Pb-Ge triple metal cation PVK solar cells (PSCs). Adding methylammonium chloride facilitates the dissolution of germanium iodide (GeI2) through anion exchange in the precursor, which evaporates during high-temperature annealing after the formation of the PVK films. The resulting low-bandgap PVK (1.228 eV), containing 3 % Ge, exhibits increased grain sizes and a significantly improved open-circuit voltage of 0.834 V, which can be attributed to a reduced energetic offset. The Sn-Pb-Ge triple metal cation PSC exhibits a power conversion efficiency of 20.7 % and enhanced stability.

Water Energy Food Nexus in Urbanizing Wetlands: Challenges and Solutions Explored through Choremes and Focus Maps

Addressing complex interactions within water, energy, and food (WEF) resources, innovative tools for in-depth analysis and decision-making are imperative. This study introduces chorematic focus maps (CFMs) as a groundbreaking method to visualize and tackle the WEF nexus’s complexities, focusing specifically on the Danube Delta Biosphere Reserve (DDBR). By merging geospatial analysis with on-site validation, this research reveals intricate interdependencies within the nexus and positions CFMs as an effective tool for stakeholders. This study adopts a methodological approach that focuses on identifying human activities and evaluating their impacts on the WEF nexus, with the goal of developing practical and grounded strategies for managing these essential resources. By testing this approach within the DDBR, the potential for wider application is demonstrated, offering a promising framework for addressing similar socio-environmental challenges across various regions. Future research directions include refining CFMs’ precision and practicality through extended fieldwork and stakeholder engagement, testing the framework’s adaptability across various locations and nexus dynamics. Additionally, incorporating cutting-edge technologies such as machine learning could provide deeper insights and reinforce CFMs’ role in decision support for the WEF nexus. Conclusively, this investigation into the WEF nexus through CFMs emphasizes the critical need for strategies that navigate the complexities of environmental management and resource optimization, marking CFMs as a significant tool for both decision-makers and researchers.

Dynamic Load Balancing in Cloud Computing: A Review and a Novel Approach

In cloud computing, load balancing is essential since it guarantees effective resource utilisation and reduces response times. It effectively distributes incoming workload across available servers. Load balancing involves dividing tasks among multiple systems or resources over the internet. By distributing traffic and workloads, load balancing ensures that no server or computer is overloaded, underutilized, or idle in a cloud computing environment. To enhance overall performance in the cloud, it optimises a number of variables, including execution time, response time, and system stability. To increase the effectiveness and reliability of cloud services, load balancing evenly distributes traffic, workloads, and computing resources across the environment. The proposed method, Enhanced Dynamic Load Balancing for Cloud Computing, adjusts load distribution and maintains a balanced system by considering variables like server capacity, workload distribution, and system load. By incorporating these factors and employing adaptive threshold adjustment, this approach optimizes resource allocation and enhances system performance. Experimental research shows that the proposed new novel approach is more effective and efficient than current load balancing techniques. In this context of cloud computing, this ground-breaking method offers a workable substitute for dynamic load balancing.

High-pressure phase transformations and lattice distortions in industrial AISI 1070 steel: Insights from Debye-Scherrer ring integration

In this study, the behavior of the industrially prominent AISI 1070 pearlitic steel under pressures up to 35 GPa was meticulously explored. Leveraging the 1D integration patterns from the Debye-Scherrer rings, we introduced a groundbreaking method to quantify dislocation densities, providing a novel lens to understand lattice distortions in such widely-used steels. At 5 GPa, the decomposition of cementite became evident, corroborated by changes in lattice parameters and dislocation densities. As the pressure neared to 10 GPa, the emergence of Hägg carbide was observed. The hallmark discovery was the phase transition from BCC to HCP around 20 GPa. This transformation bore witness to further distortions in the HCP structure with mounting pressures. Finally, molecular dynamic simulations revealed a mostly BCC to HCP transformation via an intermediary FCC phase at crystalline defects, elucidating observed orientations and phase transformation pathways.

Modeling and simulation of friction stir welding process: A neural approach

Friction Stir Welding (FSW) stands out as a groundbreaking method in solid-state joining for aluminum alloys, presenting an innovative way to achieve joints of exceptional quality. This research delves into the application of FSW for bonding, focusing on plates that are 6mm thick and made from aluminum alloys Al6063, Al5083, and AL6061, aiming to produce a variety of FSW joints. To evaluate the quality of these joints, the study compares mechanical properties such as tensile strength, safe bending strength, and bending toughness necessary for achieving a 90° bend. The investigation leverages welding data to formulate a neural model, starting with using a conventional feedforward neural model (CFNM). It tackles the limitations of CFNM, including its intensive training requirements and the challenge of dealing with unknown configurations, by proposing a new, more adaptable neural network model known as FNNM. When comparing the two models, it becomes evident that CFNM is constrained by a root mean square error (RMSE) of 7-15%, whereas FNNM marks a significant improvement with a minimal RMSE of 1-3%. This indicates that FNNM improves accuracy and effectively navigates the complexities of modeling with unknown parameters. Through this study, insightful contributions are made to understanding FSW in joining aluminum alloys and developing an advanced neural model capable of predicting the outcomes of welding with greater precision.

A coordinated restoration method of three-phase AC unbalanced distribution network with DC connections and mobile energy storage systems

In the rapidly changing domain of hybrid AC/DC urban distribution networks, this research unveils a groundbreaking method for the restoration of three-phase unbalanced systems by astutely harnessing the unique potential of DC line interconnections. At the heart of this innovation lies the synthesis of symmetrical Second Order Cone Programming (SOCP) with a sophisticated topology search technique, a union that offers a precise depiction of complex three-phase power flow with network restoration while simultaneously accelerating computational processes. Building upon this foundation, our approach places significant emphasis on the utilization of adaptable DC power control, coupled with the optimal deployment of mobile energy storage systems (MESSs), to ensure a harmonized power balance during critical interruptions. These strategies converge to prioritize the restoration of vital loads, especially those with high weighting factors, thereby significantly augmenting the network’s resilience, particularly in contexts vulnerable to disasters. The corroborative numerical results, as delineated in our study, highlight the distinct advantage and effectiveness of our methodology over prevailing practices in fortifying grid resilience against serious adversities.

Immunogenic cell death induced by low temperature plasma to activate antitumor immunity and enhance therapeutic efficiency

Low-temperature plasma (LTP) has emerged as a groundbreaking method for inducing immunogenic cell death (ICD) in cancer cells. ICD is a form of cellular demise that triggers an immune response against malignant cells. LTP has demonstrated remarkable potential in activating the immune system and augmenting antitumor responses. In this study, we delved into the capacity of LTP to induce ICD and explored its impact on the immune system. Additionally, we assessed the therapeutic effects of LTP-induced ICD on the immune system within a melanoma tumor model. The LTP treated B16F10 (LTP-B16) showed a notable surge in CD8+T cells accompanied by enhanced expression of granzyme B and IFN-γ, and reduction in Treg cells. This study ingeniously amalgamated LTP-induced ICD with cisplatin to synergistically impede tumor growth. These results offer fresh perspectives for the creation of tumor vaccines based on LTP as well as a new approach for therapeutic tumor immunotherapy.

Advanced Underwater Measurement System for ROVs: Integrating Sonar and Stereo Vision for Enhanced Subsea Infrastructure Maintenance

In the realm of ocean engineering and maintenance of subsea structures, accurate underwater distance quantification plays a crucial role. However, the precision of such measurements is often compromised in underwater environments due to backward scattering and feature degradation, adversely affecting the accuracy of visual techniques. Addressing this challenge, our study introduces a groundbreaking method for underwater object measurement, innovatively combining image sonar with stereo vision. This approach aims to supplement the gaps in underwater visual feature detection with sonar data while leveraging the distance information from sonar for enhanced visual matching. Our methodology seamlessly integrates sonar data into the Semi-Global Block Matching (SGBM) algorithm used in stereo vision. This integration involves introducing a novel sonar-based cost term and refining the cost aggregation process, thereby both elevating the precision in depth estimations and enriching the texture details within the depth maps. This represents a substantial enhancement over existing methodologies, particularly in the texture augmentation of depth maps tailored for subaquatic environments. Through extensive comparative analyses, our approach demonstrates a substantial reduction in measurement errors by 1.6%, showing significant promise in challenging underwater scenarios. The adaptability and accuracy of our algorithm in generating detailed depth maps make it particularly relevant for underwater infrastructure maintenance, exploration, and inspection.

Innovative compressive strength prediction for recycled aggregate/concrete using K-nearest neighbors and meta-heuristic optimization approaches

This paper presents a groundbreaking method for predicting the compressive strength (Fc) of recycled aggregate concrete (RAC) through the application of K-nearest neighbors (KNN) analysis. The task of designing mixture proportions to achieve the desired Fc can be remarkably intricate, owing to the intricate interplay among the components involved. Machine learning (ML) algorithms have exhibited considerable promise in tackling this complexity effectively. In pursuit of enhanced prediction accuracy, this research introduces a semi-empirical approach that seamlessly integrates strategies, including optimization techniques. This study incorporates two meta-heuristic methods, the Fire Hawk optimizer (FHO) and Runge–Kutta optimization (RUK) to enhance model accuracy. The research results reveal three separate models: KNFH, KNRK, and a single KNN model, each providing valuable insights for precise Fc prediction. Remarkably, the KNFH model stands out as a top performer, boasting an impressive R2 value of 0.994 and a meager RMSE value of 1.122. These findings not only validate the accuracy and reliability of the KNFH model but also highlight its effectiveness in predicting Fc outcomes. This approach holds great promise for precise Fc forecasting in the construction industry. Integrating meta-heuristic algorithms significantly improves model accuracy, leading to more reliable forecasts with profound implications for construction projects and their outcomes. This research marks a significant advancement in predicting Fc using ML, offering valuable tools for engineers and builders.

Docetaxel administered through a novel lymphatic drug delivery system (LDDS) improved treatment outcomes for lymph node metastasis

Recently, sentinel lymph nodes (LNs) have been recognized as a starting point of hematogenous metastasis; thus, an increase in the control rate of LN metastasis is expected to improve the survival rate. Although surgical treatment and radiation therapy are commonly used for the radical treatment of LNs, these treatments are associated with lymphedema, pain, and an extended hospital stay. In a recent mouse study, activation of metastatic tumors in distant organs was reported after removing LNs, with or without metastasis to the LNs. Thus, there is the necessity for cancer treatment that can replace LN removal. Here, we evaluated the treatment efficacy of lymphatic drug delivery system (LDDS) with osmotic pressure and viscosity escalated Docetaxel at the early stage of LN metastasis. MXH10/Mo/lpr mice were inoculated with mouse breast cancer cells into Subiliac LN to create the metastatic mouse model. Docetaxel was injected into mouse mammary carcinoma cells inoculated LN as a single shot (SS) or double shot (DS) to understand the therapeutic mechanism of a single shot or double shot intervention using an in vivo imaging system, histology, and qPCR. The results showed that the DS administration of docetaxel at 1,960 kPa (12 mPa∙s) had better therapeutic outcomes with increased complete response and improved survival with reduced adverse events. The results also revealed that administration of a DS of docetaxel enhances differentiation of T helper cells, and improves survival and therapeutic outcomes. From a safety perspective, LDDS-administered DS of low-concentration docetaxel without any other anticancer treatments to LNs a novel approach to cancer management of LN metastasis. We emphasize that LDDS is a groundbreaking method of delivering anticancer drugs specifically to cancer susceptible LNs and is designed to enhance the effectiveness of cancer treatment while minimizing side effects.

Overcoming the limitations of atomic-scale simulations on semiconductor catalysis with changing Fermi level and surface treatment

A new groundbreaking method for efficient optimization of doping concentration and cocatalyst materials for Fermi level engineering of wide band gap semiconductors.

Terahertz waveform synthesis in scattering systems via nonlinear ghost imaging

Terahertz Nonlinear Ghost Imaging introduces a groundbreaking method for object sampling at spatial-temporal levels, achieving super-resolution (i.e., beyond the diffraction limit). Our theoretical and experimental endeavour seeks to leverage this technique, enabling arbitrary field-level waveform manipulation through intricate propagation in scattering environments. This approach facilitates essential agile waveform adjustment, made possible through near-field interactions between terahertz sources and scattering media.

Research on the Evaluation of Art Teaching Based on Optimized Neural Network of Bottle Sea Sheath Algorithm

Abstract Today, the evolution of art teaching evaluation systems faces significant challenges and opportunities, particularly with the limitations of subjective judgments in traditional methods impacting the accuracy of teaching quality assessments. This study leverages advancements in artificial intelligence, specifically neural networks enhanced by the Taruhai Sheath algorithm, to propose a novel and more objective approach for evaluating art teaching. Our optimized neural network model demonstrates a remarkable 92% accuracy in art teaching evaluations, outperforming traditional methods by 15%. Furthermore, our analysis reveals that the quality of practical teaching is a critical factor in boosting students’ artistic creativity. By applying the Taruhai Sheath algorithm for neural network optimization, this research offers a groundbreaking method to elevate the objectivity and rigor of art education evaluations, promising a significant leap forward in art teaching quality.