Processing Nodes Research Articles

Analysis of Semi-open Queueing Network with Correlated Arrival Process and Multi-server Nodes

Analysis of Semi-open Queueing Network with Correlated Arrival Process and Multi-server Nodes

Causal Learning: Monitoring Business Processes Based on Causal Structures.

Conventional methods for process monitoring often fail to capture the causal relationships that drive outcomes, making hard to distinguish causal anomalies from mere correlations in activity flows. Hence, there is a need for approaches that allow causal interpretation of atypical scenarios (anomalies), allowing to identify the influence of operational variables on these anomalies. This article introduces (CaProM), an innovative technique based on causality techniques, applied during the planning phase in business process environments. The technique combines two causal perspectives: anomaly attribution and distribution change attribution. It has three stages: (1) process events are collected and recorded, identifying flow instances; (2) causal learning of process activities, building a directed acyclic graphs (DAGs) represent dependencies among variables; and (3) use of DAGs to monitor the process, detecting anomalies and critical nodes. The technique was validated with a industry dataset from the banking sector, comprising 562 activity flow plans. The study monitored causal structures during the planning and execution stages, and allowed to identify the main factor behind a major deviation from planned values. This work contributes to business process monitoring by introducing a causal approach that enhances both the interpretability and explainability of anomalies. The technique allows to understand which specific variables have caused an atypical scenario, providing a clear view of the causal relationships within processes and ensuring greater accuracy in decision-making. This causal analysis employs cross-sectional data, avoiding the need to average multiple time instances and reducing potential biases, and unlike time series methods, it preserves the relationships among variables.

Ferroelectricity in Ultrathin HfO2-Based Films by Nanosecond Laser Annealing.

Nonvolatile memory devices based on ferroelectric HfxZr1-xO2 (HZO) show great promise for back-end integrable storage and for neuromorphic accelerators, but their adoption is held back by the inability to scale down the HZO thickness without violating the strict thermal restrictions of the Si CMOS back end of line. In this work, we overcome this challenge and demonstrate the use of nanosecond pulsed laser annealing (NLA) to locally crystallize areas of an ultrathin (3.6 nm) HZO film into the ferroelectric orthorhombic phase. Meanwhile, the heat induced by the pulsed laser is confined to the layers above the Si, allowing for back-end compatible integration. We use a combination of electrical characterization, nanofocused scanning X-ray diffraction (nano-XRD), and synchrotron X-ray photoelectron spectroscopy (SXPS) to gain a comprehensive view of the change in material and interface properties by systematically varying both laser energy and the number of laser pulses on the same sample. We find that NLA can provide remanent polarization up to 2Pr= 11.6 μC/cm2 in 3.6 nm HZO, albeit with a significant wake-up effect. The improved TiN/HZO interface observed by XPS explains why device endurance goes beyond 107 cycles, whereas an identical film processed by rapid thermal processing (RTP) breaks already after 106 cycles. All in all, NLA provides a promising approach to scale down the ferroelectric oxide thickness for emerging HZO ferroelectric devices, which is key for their integration in scaled process nodes.

Smart-CX – Method of extraction of parasitic capacitances in ICs

This paper proposes a novel rule-based parasitic capacitance extraction methodology, integrated into Smart-CX, to enhance the accuracy of SPICE simulations and physical verification of ICs. This methodology is crucial during the microchip design phase, particularly in preparation for fabrication on mature to advanced process nodes. The proposed enhanced analytical method addresses the accuracy/time tradeoffs between numerical and analytical extraction techniques. This study validates the accuracy of the methodology by comparison of the extracted parasitic parameters with foundry-provided 2D-models. The parasitic capacitances that are extracted within this method include: area capacitances (Ca), coupling capacitances (Cc), including via-to-via capacitances (Cmv), contact-to-gate capacitances (Cmg), contact-to-active capacitances (Cmc) and fringe type capacitances with the top (Cft) and bottom (Cfb) conductive layers.

Efficient Visual-Aware Fashion Recommendation Using Compressed Node Features and Graph-Based Learning

In fashion e-commerce, predicting item compatibility using visual features remains a significant challenge. Current recommendation systems often struggle to incorporate high-dimensional visual data into graph-based learning models effectively. This limitation presents a substantial opportunity to enhance the precision and effectiveness of fashion recommendations. In this paper, we present the Visual-aware Graph Convolutional Network (VAGCN). This novel framework helps improve how visual features can be incorporated into graph-based learning systems for fashion item compatibility predictions. The VAGCN framework employs a deep-stacked autoencoder to convert the input image’s high-dimensional raw CNN visual features into more manageable low-dimensional representations. In addition to improving feature representation, the GCN can also reason more intelligently about predictions, which would not be possible without this compression. The GCN encoder processes nodes in the graph to capture structural and feature correlation. Following the GCN encoder, the refined embeddings are input to a multi-layer perceptron (MLP) to calculate compatibility scores. The approach extends to using neighborhood information only during the testing phase to help with training efficiency and generalizability in practical scenarios, a key characteristic of our model. By leveraging its ability to capture latent visual features and neighborhood-based learning, VAGCN thoroughly investigates item compatibility across various categories. This method significantly improves predictive accuracy, consistently outperforming existing benchmarks. These contributions tackle significant scalability and computational efficiency challenges, showcasing the potential transformation of recommendation systems through enhanced feature representation, paving the way for further innovations in the fashion domain.

Energy efficient routing for improving lifetime in MWSN: A clustering approach

A Mobile wireless sensor network (MWSN) consists of mobile sensor nodes, which can be deployed in any specific environment, and due to its’ mobility it can perform with rapid topological transformations of a network. The sensor nodes having limited battery power are used to collect specific data and this raw data is sent to a static sink node of the network. Under such a scenario, to avoid frequent disconnections due to topological change in the network and can avail more reliable data transmissions in energy awareness perspective, an energy efficient routing protocol for MWSNs to improve its lifetime is proposed in this paper by utilizing a clustering approach. A MWSN with random number of sensor nodes are initially considered and then, clustering algorithm K-means is used to find a predefined number of clusters with their initial cluster heads (CHs) and the centroid location of these clusters is also determined. The role of these CHs is to elect our DDBLACH (distance to sink and cluster centroid with battery level aware cluster head) nodes from each cluster, by sending and receiving intra-cluster messages among other member sensor nodes. A DDBLACH node is determined by using three factors, such as minimum distance from cluster centroid location, minimum distance from sink and the maximum battery level of the node from each cluster. These DDBLACH nodes are used to collect data from intra-cluster sensors and thereafter, send those towards sink node for further processing using tree based hierarchical routes. Finally, an energy efficient routing technique for MWSNs is proposed for data transmission from these DDBLACH nodes of clusters to a sink of the network. Here simulation results indicate the supremacy of our proposed scheme over other existing methods in various aspects, such as improving the presence of alive nodes and average network lifetime.

Development of an ultra-clean sample heating stage for thermal desorption spectroscopy

Control of surface molecular contamination (SMC) for components used in chemical vapor deposition (CVD), atomic layer deposition (ALD) and EUV photolithography is important to maintaining high yield and optimal tool operation at the latest process nodes in leading edge semiconductor manufacturing. High temperature thermal desorption spectroscopy (TDS) is a versatile tool for analyzing the cleanliness of surfaces, simulating thermal vacuum processes and studying the kinetics of desorption processes. A basic analysis of TD spectra allows for full characterization of volatile outgassing from surfaces, while detailed analysis can provide chemical information about the substrate surface.In fundamental studies, TDS is often carried out from low temperatures to room temperature or for small samples. However, for microelectronics applications, high temperature studies of large (100 mm or greater) samples are of greater interest due to direct applications for cleanliness testing and thermal vacuum simulation. A limitation for TDS sensitivity is the outgassing of sample stage materials, particularly when analyzing gases that may be present in the chamber background such as water, CO and CO2. Typical sample stages are often tested only for total pressure or at room temperature.In this study, we present a simple ultra-high vacuum (UHV) compatible sample heating stage for trace outgassing analysis of 100 mm samples at high temperatures. Simulation results are presented to support the feasibility of the concept. Experimental results verify the cleanliness of the stage via room temperature residual gas analysis (RGA) analysis and X-ray photoelectron spectroscopy (XPS) of stage components. Finally, use of this stage in a TDS analysis of a 100 mm Si witness wafer and comparison to room temperature RGA demonstrates operational capability.The sample heating stage is both shown to be clean at high temperature and capable of analyzing 100 mm wafers to higher sensitivity than room temperature RGA for all m/z at the 1 × 10−9 mbar level. Despite its high performance, the heating stage is also easily produced by any laser machining service, greatly improving the accessibility of UHV science for all researchers.

Hybrid ALM-DSP TDC in Intel Arria 10 FPGA

The article presents the first implementation of a time-to-digital converter (TDC) using a digital signal processing (DSP) block from an Intel field-programmable gate array (FPGA) device. The design and configuration capabilities of the DSP blocks of the leading FPGA manufacturers, AMD and Intel, are compared in the context of time/digital conversion. Next, three variants of TDCs are presented. The problem of ultra-wide bins observed in both the classic version of the converter based on the Adaptive Logic Modules (ALM) and the version built using only DSP blocks has been mitigated in a hybrid variant, combining both solutions. The proposed variant of the hybrid ALM-DSP TDC implemented in Arria 10 FPGA from Intel (20 nm process node) achieved a resolution of 2.1 ps, a single-shot-precision better than 6.36 ps and reduced the maximum differential nonlinearity (DNL) error from 74.5 ps to 12.7 ps.

Evaluation of fluoride emissions and pollution from an electrolytic aluminum plant located in Yunnan province

The monitoring and evaluation of fluoride pollution are essentially important to make sure that concentrations do not exceed threshold limit, especially for surrounding atmosphere and soil, which are located close to the emission source. This study aimed to describe the atmospheric HF and edaphic fluoride distribution from an electrolytic aluminum plant located in Yunnan province, on which the effects of meteorological conditions, time, and topography were explored. Meanwhile, six types of solid waste genereted from different electrolytic aluminum process nodes were characterized to analyze the fluoride content and formation characteristics. The results showed that fluoride in solid waste mainly existed in the form of Na3AlF6, AlF3, CaF2, and SiF4. Spent electrolytes, carbon residue, and workshop dust are critical contributors to fluoride emissions in the primary aluminum production process, and the fluorine content is 17.14 %, 33.30 %, and 31.34 %, respectively. Unorganized emissions from electrolytic aluminum plants and solid waste generation are the primary sources of fluoride in the environment, among which the edaphic fluoride content increases most at the sampling sites S1 and S7. In addition, the atmospheric HF concentration showed significant correlations with wind speed, varying wildly from March to September, with daily average and hourly maximum HF concentrations of 4.32 μg/m3 and 9.0 μg/m3, respectively. The results of the study are crucial for mitigating fluorine pollution in the electrolytic aluminum industry.

Moore's Law Scaling and Chemical Mechanical Planarization (CMP)

Since the invention of the first integrated circuit in 1958, chemistry and materials have played a critical role in semiconductor process technologies, which has led to an exponential growth of transistors packed into circuits. Decades ago, Gordon Moore posited there would be a doubling of components every two years (revised from his original doubling every one-year prediction). This observation became known as Moore’s law.As Denard scaling took on more complexity, the number of new materials used to build semiconductors increased at a torrid pace. The number of elements in the periodic table, used to fabricate devices, increased roughly five-fold from the 1980’s to the 2000’s.In the earlier years, lithography, etch, thin films, implant, and diffusion were foundational technologies used to fabricate a silicon wafer into hundreds of microchips. In the late 1980’s a new technology Chemical Mechanical Planarization (CMP) was invented. It was developed out of necessity to reduce topography, critical for lithography’s depth of focus requirements. Without this key invention, patterning and building layer upon layer from front end transistors to back-end interconnects would not have been possible.Intel implemented CMP into manufacturing in the early 1990’s on the 0.8 micron technology node which manufactured the 486 microprocessor. CMP was required to planarize dielectric oxide used to insulate the aluminum metal interconnects. Since then, the number of CMP applications over the last three decades has exploded, enabling the scaling of Moore’s Law.CMP is not only used to improve lithography depth of focus. CMP also enables a method of metal interconnect and contact formation called damascene. Planarization is used to create dielectric isolation trenches, discrete plugs for insulation, aid advanced patterning architectures, build through silicon vias (TSVs), and enable complex wafer and chip bonding for die-to-die interconnects for disaggregated products.Transistor scaling alone is no longer sufficient to meet device performance, power, and cost requirements. Complex material and architectural changes are required. And novel CMP applications continue to be added at each new technology node. This author will make an argument that CMP is critical to push scaling and enable future process nodes. Figure 1

Simulation of different structured gate-all-around FETs for 2 nm node

This paper compares different types of Gate All Around (GAA) FET structures using TCAD simulation, including Lateral Nanosheet, Lateral Nanowire, Vertical Nanosheet, and Vertical Nanowire. The increase in electrostatic control and reduced short channel effects are key benefits to adopting GAAFET structures to meet scaling requirements for next generation process nodes. To understand channel geometry impacts on performance, the channel effective width (Weff) is swept around the projected dimensions, including ratio of height and width parameters. The performance is evaluated using the key device metrics such as on-state current (Ion), off-state current (Ioff), and threshold voltage (Vt) for transfer characteristics, and drain-induced barrier lowering (DIBL), subthreshold slope (SS), and gate induced drain leakage (GIDL) for short-channel effects. It is observed that thinner channel geometries, as often seen implemented in Nanosheet structures, have major benefits across SCE and Ioff metrics compared to more symmetrically square shaped channels. Additionally, stacking channels as a means to increase Weff appears to be an attractive option for increasing performance without significant increase in SCEs observed. For bulk technology the ratio between height and width of a Nanosheet structure can be optimized to reduce parasitic channel influence, so that optimal Ion/Ioff ratio is achieved.

Empathy-related abnormalities among women with premenstrual dysphoric disorder: clinical and functional magnetic resonance imaging study.

Empathy refers to the cognitive and emotional reactions of an individual to the experiences of another. Women with premenstrual dysphoric disorder (PMDD) report severe social difficulties during the luteal phase of their menstrual cycle. This clinical and functional magnetic resonance imaging study aimed to explore affective and cognitive empathy in women with PMDD, during the highly symptomatic luteal phase. Overall, 32 women with PMDD and 20 healthy controls participated in the study. The neuroimaging data were collected using a highly empathy-engaging movie. First, we characterised the synchrony of neural responses within PMDD and healthy groups, using the inter-individual correlation approach. Next, using network cohesion analysis, we compared connectivity within and between brain networks associated with affective and cognitive empathy between groups, and assessed the association of these network patterns with empathic measures. A consistent, although complex, picture of empathy abnormalities was found. Patients with PMDD showed decreased neural synchrony in parietal and frontal key nodes of cognitive empathy processing (theory-of-mind network), but higher neural synchrony in the anterior insula and anterior cingulate cortex, a part of the salience network, implicated in affective empathy. Positive correlations between cognitive perspective-taking scores and neural synchrony were found within the theory-of-mind network. Interestingly, during highly emotional moments, the PMDD group showed increased functional connectivity within this network. Similar to major depression, individuals with PMDD show enhanced affective empathy and reduced cognitive empathy. These findings echo clinical observations reported when women with PMDD have a dysregulated emotional response to negative stimuli.

Queuing‐based energy‐efficient processing algorithm for smart transportation through V2V communication

SummaryApplications of intelligent systems installed in vehicles require substantial computational processing for various tasks. These intensive computations result in high energy consumption and power demands within vehicles. Computational offloading based on Vehicle‐to‐Vehicle (V2V) communication in vehicular fog computing (VFC) has been proposed as a promising solution to enhance energy efficiency in transportation applications. In this paper, the primary objective is addressing this concern by identifying the optimal nearby vehicle that minimizes energy consumption for the offloading and execution of computational tasks. Therefore, a decision‐making and intelligent task offloading mechanism based on queueing theory is proposed. By modeling the problem environment based on queueing theory and modeling the behavior of distributed tasks with discrete‐time Markov chain, the proposed solution can predict the future behavior of vehicles in selecting the most energy‐efficient processing node. Therefore, this paper investigates three energy decision parameters based on queueing theory extracted from the Markov model to enhance the performance of the proposed algorithm. Experimental results demonstrate that the computational energy parameter achieves the most significant improvement. The proposed algorithm outperforms previous methods, improving energy‐efficient system performance by 6.25% and 2.67%, and reducing delivery failure rate by 6.52% and 2.72%. It also decreases overall transportation system processing energy consumption by 0.05% for 100–500 vehicle arrival rates, resulting in an average total processing energy consumption of 0.48%.

Using Deep Learning Neural Networks to Predict Violent vs. Nonviolent Extremist Behaviors

ABSTRACT Recent analyses of radicalization processes have shown that extremist attitudes and violent behavior may be related in some cases, but are rarely collinear. It therefore benefits analysts of political violence to leverage tools that assist in the distinction of characteristics that might move an individual towards violence (vs. nonviolence) in support of their beliefs. To this end, the current study explores the efficacy of deep learning neural networks for classifying extremists as potentially violent or nonviolent based on dozens of common predictors derived from various perspectives on radicalization. Specifically, this study uses 337 predictors from the Profiles of Individual Radicalization in the U.S. dataset to populate a neural network with two hidden layers composed of four processing nodes. The model correctly predicted whether an individual engaged in violence (or not) in 94.2 percent of cases, on average. Analyses further identified several predictors that were most important in classifying violent and nonviolent cases. These analyses demonstrate neural networks may be effective tools in the study of radicalization and extremism, particularly regarding the disaggregation of salient outcomes.

Human–Robot Collaborative Manufacturing Cell with Learning-Based Interaction Abilities

This paper presents a collaborative manufacturing cell implemented in a laboratory setting, focusing on developing learning-based interaction abilities to enhance versatility and ease of use. The key components of the system include 3D real-time volumetric monitoring for safety, visual recognition of hand gestures for human-to-robot communication, classification of physical-contact-based interaction primitives during handover operations, and detection of hand–object interactions to anticipate human intentions. Due to the nature and complexity of perception, deep-learning-based techniques were used to enhance robustness and adaptability. The main components are integrated in a system containing multiple functionalities, coordinated through a dedicated state machine. This ensures appropriate actions and reactions based on events, enabling the execution of specific modules to complete a given multi-step task. An ROS-based architecture supports the software infrastructure among sensor interfacing, data processing, and robot and gripper controllers nodes. The result is demonstrated by a functional use case that involves multiple tasks and behaviors, paving the way for the deployment of more advanced collaborative cells in manufacturing contexts.

Non‐orthogonal multiple access‐based task processing and energy optimization in vehicular edge computing networks

SummaryVehicular edge computing (VEC) is envisioned as a promising approach to process explosive vehicle tasks, where vehicles can choose to upload tasks to nearby edge nodes for processing. However, since the communication between vehicles and edge nodes is via wireless network, which means the channel condition is complex. Moreover, in reality, the arrival time of each vehicle task is stochastic, so efficient communication methods should be designed for VEC. As one of the key communication technologies in 5G, non‐orthogonal multiple access (NOMA) can effectively increase the number of simultaneous transmission tasks and enhance transmission performance. In this article, we design a NOMA‐based task allocation scheme to improve the VEC system. We first establish the mathematical model and divide the allocation of tasks into two processes: the transmission process and the computation process. In the transmission process, we adopt the NOMA technique to upload the tasks in batches. In the computation process, we use a high response‐ratio strategy to determine the computation order. Then we define the optimization objective as maximizing task completion rate and minimizing task energy consumption, which is an integer nonlinear problem with lots of integer variables and cannot be solved directly. Through further analysis, we design a heuristics algorithm which we name as the AECO (average energy consumption optimization) algorithm. By using the AECO, we obtain the optimal allocation strategy by constantly adjusting the optimal variables. Simulation results demonstrate that our algorithm has a significant number of advantages.

Near-Unity Indistinguishability of Single Photons Emitted from Dissimilar and Independent Atomic Quantum Nodes

Generating indistinguishable photons from independent nodes is an important challenge for the development of quantum networks. In this work, we demonstrate the generation of highly indistinguishable single photons from two dissimilar atomic quantum nodes. One node is based on a fully blockaded cold Rydberg ensemble and generates on-demand single photons. The other node is a quantum repeater node based on a Duan-Lukin-Cirac-Zoller quantum memory and emits heralded single photons after a controllable memory time that is used to synchronize the two sources. We demonstrate an indistinguishability of 94.6±5.2% for a temporal window including 90% of the photons. This advancement opens new possibilities for interconnecting quantum repeater and processing nodes with high-fidelity Bell state measurement without sacrificing its efficiency. Published by the American Physical Society 2024

LATA: learning automata-based task assignment on heterogeneous cloud computing platform

A cloud computing environment is a distributed system where idle resources are accessible across a wide area network, such as the Internet. Due to the diverse specifications of these resources, computational clouds exhibit high heterogeneity. Task scheduling, the process of dispatching cloud applications onto processing nodes, becomes a critical challenge in such environments. Ensuring high utilization in this heterogeneous environment entails identifying suitable machines or virtual machines capable of efficiently executing jobs, constituting a multi-objective optimization problem. This paper proposes a dynamic Learning Automata-based Task Assignment algorithm, named LATA, to address this challenge. In the algorithm, each application is represented as a Directed Acyclic Graph, with tasks as nodes and data dependencies as edges. Initially, tasks are grouped based on their data dependencies to consolidate independent tasks into one group. Subsequently, a variable-structure learning automaton is assigned to each group of tasks to identify appropriate task-machine combinations. The primary objectives of LATA include minimizing makespan and energy consumption by facilitating efficient task placement to achieve load balance and maximize resource utilization. Additionally, an enhancement is proposed, involving the use of a different grouping policy prior to task assignment to further improve performance. Computer simulation results demonstrate the superior performance of the proposed algorithms in highly heterogeneous environments compared to state-of-the-art algorithms. Notably, total execution time and energy consumption decrease by up to 50% and 37%, respectively.

Suppression of excitatory synaptic transmission in the centrolateral amygdala via presynaptic histamine H3 heteroreceptors.

The central histaminergic system has a pivotal role in emotional regulation and psychiatric disorders, including anxiety, depression and schizophrenia. However, the effect of histamine on neuronal activity of the centrolateral amygdala (CeL), an essential node for fear and anxiety processing, remains unknown. Here, using immunostaining and whole-cell patch clamp recording combined with optogenetic manipulation of histaminergic terminals in CeL slices prepared from histidine decarboxylase (HDC)-Cre rats, we show that histamine selectively suppresses excitatory synaptic transmissions, including glutamatergic transmission from the basolateral amygdala, on both PKC-δ- and SOM-positive CeL neurons. The histamine-induced effect is mediated by H3 receptors expressed on VGLUT1-/VGLUT2-positive presynaptic terminals in CeL. Furthermore, optoactivation of histaminergic afferent terminals from the hypothalamic tuberomammillary nucleus (TMN) also significantly suppresses glutamatergic transmissions in CeL via H3 receptors. Histamine neither modulates inhibitory synaptic transmission by presynaptic H3 receptors nor directly excites CeL neurons by postsynaptic H1, H2 or H4 receptors. These results suggest that histaminergic afferent inputs and presynaptic H3 heteroreceptors may hold a critical position in balancing excitatory and inhibitory synaptic transmissions in CeL by selective modulation of glutamatergic drive, which may not only account for the pathophysiology of psychiatric disorders but also provide potential psychotherapeutic targets. KEY POINTS: Histamine selectively suppresses the excitatory, rather than inhibitory, synaptic transmissions on both PKC-δ- and SOM-positive neurons in the centrolateral amygdala (CeL). H3 receptors expressed on VGLUT1- or VGLUT2-positive afferent terminals mediate the suppression of histamine on glutamatergic synaptic transmission in CeL. Optogenetic activation of hypothalamic tuberomammillary nucleus (TMN)-CeL histaminergic projections inhibits glutamatergic transmission in CeL via H3 receptors.

Optimization to identify the adapted product design and product adaptation process with initial evaluation of information quality in branches of AND-OR tree based on information entropy

Adaptable products are used to satisfy new customer requirements through changes of products such as upgrading of modules during their utilization stages. Compared with the creation of a new product from scratch, the process to adapt an existing product to a new one can result in savings for the customer. In this research, a new optimization approach to identify the adapted product design and product adaptation process is developed with initial evaluation of information quality in branches of AND-OR tree based on information entropy. In this approach, various candidates of design configurations and operation processes for adaptation of a product are modelled by an AND-OR tree with design and process nodes. Design and process nodes are further associated with design and process parameters. Optimization to identify the best adapted design and adaptation process is conducted at two levels: configuration/process optimization level and parameter optimization level. Information entropy is employed to evaluate the information quality of partial configuration/process candidates modelled as branches in the AND-OR tree to eliminate the branches that are unlikely to lead to the optimal solution for improvement of the optimization efficiency. A numerical example and an engineering application are developed to demonstrate the effectiveness of the newly introduced approach.