Control Nodes Research Articles

Shifting priorities and expectations in the new world of work. Insights from millennials and generation Z

In the contemporary world of work and modern business, the well-being of a company is directly linked with the well-being of its employees, and in this context, the proactive promotion of work-life balance has become more important than ever because it meets the new ideal and is an ongoing challenge through the lens of the younger generation’s age groups. By unpacking worklife balance into five drivers, the present paper aims to explore the recent trends, priorities, and expectations of the modern workforce. The target population was represented by two generations of employees, Millennials and Generation Z, and the research hypotheses were tested using structural equation in SmartPLS. The results of this research aim to enhance the current knowledge base, offering a distinct perspective along with significant findings for employers, managers, recruiters, policymakers, and other nodes of control who can gain insights regarding work-related priorities and expectations.

Investigation of the Durability of the Regulating Junction of the Christmas Tree Throttle

Objective: In the research work, the issue of ensuring the stability of its details wasconsidered by studying the voltage distribution mode in the throttle control node. SolidWorks software was used to perform force analysis of the improved throttle. Theoretical Framework: The research draws upon theories and simulations related to regulatory constructions and wear prevention. Methodology: In the research work, the performance criteria of the improved throttle have been studied. The force analysis of the proposed throttle's regulating node was carried out using modern methods and Solidworks simulations. Results and Discussion: The analysis of the wear occurring at the tip of the improved throttle shows that the wear resistance of this equipment is ensured, and the wear of the saddle-cutter pair, which has a hardness of HRC=55-66, amounts to 0.040...0.050 mm after 500 forward-backward cycles intended for the throttle. This is also twice smaller than the allowable limit. The processing of the conducted force analyses has proven that the performance of the improved throttle is ensured. Research Implications: The structural force analysis of the improved throttle shows that its performance is ensured. An analytical expression has been obtained to determine the maximum value of the radial stresses occurring at the tip of the throttle. It has been found that the safety factor for bending stress in the central zone of the throttle tip is equal to 2.5. Originality/Value: Based on simulations and analytical calculations, the radial stresses generated at the tip of the nozzle were investigated, and the longevity of the improved nozzle was enhanced based on new approaches.

The Prognostic Importance of Radiologic Extranodal Extension in Hypopharyngeal Carcinoma.

Extranodal extension (ENE) had been included in the latest cancer staging system in hypopharyngeal squamous cell carcinoma (HypoSCC). However, the impact of ENE on HypoSCC survival and treatment outcomes are still unclear. Records from all HypoSCC patients diagnosed at the National Taiwan University Hospital from January 2007 to December 2018 were reviewed. All patients were divided into two groups, with or without ENE. Clinical features, pathological factors, and survival rates between the two groups were reviewed. We analyzed data from 388 HypoSCC patients, 125 (32.22%) with and 263 (67.78%) without ENE. The 5-year overall survival of the HypoSCC patients with radiological ENE, pathological ENE, and without ENE were 22.9%, 40.3%, and 55.5%. From the multivariate analysis, primary T3/T4 classification (p = 0.001) and radiological ENE (p < 0.001) were independent risk factors for disease-free and overall survival (OS). Finally, upfront neck dissection may significantly benefit disease-free survival (DFS) and neck nodal control in ENE+ (p = 0.002 and p = 0.007, respectively) or ENE- patients (p = 0.003 and p = 0.02, respectively). More than one-third of HypoSCC patients have ENE, with significantly lower OS and DFS. The upfront neck dissection could provide better DFS and neck nodal control.

External beam radiotherapy followed by image-guided adaptive brachytherapy in locally advanced cervical cancer: a multicenter retrospective analysis.

To evaluate oncological outcomes and toxicities in patients with locally advanced cervical cancer (LACC) treated with concurrent chemoradiotherapy followed by image-guided adaptive brachytherapy at two Italian centres. A retrospective analysis was conducted on 122 patients with LACC treated between 2010 and 2022. Primary endpoints were local control (LC), pelvic control (PC), and nodal control (NC). Secondary endpoints included disease-free survival (DFS), metastasis-free survival (MFS), overall survival (OS), and late toxicity. Correlations between patient characteristics and oncological outcomes were conducted. Brachytherapy planning was CT and MRI-based in 88 (72.1%) and 34 patients (27.9%), respectively. The mean total dose (EQD2) delivered to high-risk clinical target volume was 82Gy. Overall treatment time was ≤ 50days and > 50days in 48 (39.3%) and 74 patients (60.7%), respectively. At a mean follow up of 101months, 3 and 5-year LC rates were 87% and 85%, respectively. Five-year PC and NC rates were 77% and 85.1%. Five-year DFS and OS were 61% and 65.4%, respectively, with significant correlations between these outcomes and FIGO stage and nodal status at diagnosis. Gastrointestinal, genitourinary and vaginal adverse effects were the most reported late toxicities and 8 (6.5%) grade 3-5 events were observed. 32 patients (26.2%) had vaginal stenosis and it was significantly related to 3D imaging used for brachytherapy planning. The study confirmed the efficacy and safety of chemoradiotherapy and IGABT for LACC. Full implementation of MRI treatment planning and interstitial techniques could further enhance personalized treatment and outcomes.

Selective collaboration in distributed FxLMS active noise control systems

This paper addresses the selection of the collaboration configuration on distributed active noise control (ANC) systems. ANC systems aim to cancel out acoustic noise within a listening area. In distributed systems, the control task is delegated among multiple acoustic control nodes that generate the control signals by filtering a noise reference signal. The coefficients of each node filter are iteratively calculated using the filtered-X LMS algorithm. The stability is achieved when the adaptive filters computed in each node converge to finite values. However, acoustic coupling among nodes could lead to instability (i.e., divergence). Collaboration among selected nodes may avoid this phenomenon, although not just any collaboration configuration guarantees network stability. On the other hand, a collaborative distributed system presents two drawbacks: the stability assessment is computationally expensive, and communication requirements increase with the number of collaborations among nodes. In this paper, we propose and discuss several methods to establish a collaboration configuration that ensures system stability. The optimal configuration, which is characterized by the minimal number of necessary collaborations between nodes, can be identified through exhaustive search. However, this approach incurs a high computational cost, particularly in networks with many nodes. To address this challenge, we introduce several heuristic methods aimed at efficiently obtaining stable configurations.

Acoustography by Beam Engineering and Acoustic Control Node: BEACON.

Acoustic manipulation has emerged as a valuable tool for precision controls and dynamic programming of cells and particles. However, conventional acoustic manipulation approaches lack the finesse necessary to form intricate, configurable, continuous, and 3D patterning of particles. Here, this study reports acoustography by Beam Engineering and Acoustic Control Node (BEACON), which delivers intricate, configurable patterns by guiding particles along custom paths with independent phase modulation. Leveraging analytical methods of orbital angular momentum beam via iterative Wirtinger hologram algorithm, this study accomplish acoustography by facilitating orbital angular momentum traps, enabling continuous 2D and 3D acoustic manipulation of microparticles in any desired geometry, with phase modulation independent of intensity. Utilizing on-chip acoustography, the BEACON platform markedly increases the space-bandwidth product to 31000 while attaining an enhanced resolution with a pixel size of ≈25µm, surpassing the typical resolution of over 200µm in previous holographic particle manipulation methods. The capabilities of BEACON are demonstrated in creating intricate triple helical tracing structures using microdroplets (20µm in diameter) and those carrying DNA to validate the effectiveness of the acoustography and phase control methods. This study offers new particle manipulation opportunities, paving the way for next-generation biomedical systems and the future of contact-free precision manufacturing.

A modular circuit coordinates the diversification of courtship strategies.

Mate recognition systems evolve rapidly to reinforce the reproductive boundaries between species, but the underlying neural mechanisms remain enigmatic. Here we leveraged the rapid coevolution of female pheromone production and male pheromone perception in Drosophila1,2 to gain insight into how the architecture of mate recognition circuits facilitates their diversification. While in some Drosophila species females produce unique pheromones that act to arouse their conspecific males, the pheromones of most species are sexually monomorphic such that females possess no distinguishing chemosensory signatures that males can use for mate recognition3. We show that Drosophila yakuba males evolved the ability to use a sexually monomorphic pheromone, 7-tricosene, as an excitatory cue to promote courtship. By comparing key nodes in the pheromone circuits across multiple Drosophila species, we reveal that this sensory innovation arises from coordinated peripheral and central circuit adaptations: a distinct subpopulation of sensory neurons has acquired sensitivity to 7-tricosene and, in turn, selectively signals to a distinct subset of P1 neurons in the central brain to trigger courtship. Such a modular circuit organization, in which different sensory inputs can independently couple to parallel courtship control nodes, may facilitate the evolution of mate recognition systems by allowing novel sensory modalities to become linked to male arousal. Together, our findings suggest how peripheral and central circuit adaptations can be flexibly coordinated to underlie the rapid evolution of mate recognition strategies across species.

A novel controllability method on complex temporal networks based on temporal motifs

SummaryComplex temporal networks have become instrumental in modeling dynamic systems across various disciplines, presenting unique challenges and opportunities in understanding and influencing their behavior. Controllability, a fundamental aspect of network dynamics, plays a pivotal role in manipulating these systems towards desired states. Temporal motifs are important patterns in temporal complex networks that have many applications in solving problems related to this type of networks. In this paper, a novel method for controlling temporal complex networks using temporal motifs is proposed. First, the most important effective temporal motifs in the controllability processes of complex networks have been identified and it has been shown that the network can be fully controlled using these temporal motifs. Then, an algorithm for extracting temporal motifs is proposed. This algorithm has been proposed to identify effective temporal motifs in network controllability to optimally identify control nodes. To increase the efficiency of extracting temporal motifs, a method for predicting the temporal motif‐based link has been proposed, which predicts temporal motifs. The results of the simulation of the proposed method based on temporal motifs and its implementation on real‐world temporal complex networks demonstrates that its performance was better than the conventional controllability methods.

Minimizing Costs in Fog Nodes Through Effective Resource and Task Scheduling

Background: This system comprises the fog control node, access node, and Internet of Things (IoT) application. This study aims to reduce scheduling-related cloud costs. A user scheduling method and an economical task are carried out for this reason. The algorithm for job scheduling is constructed to determine the best strategy to handle several jobs in a fog access node. Reallocation technology ultimately reduces delays in both time and service. For analytical objectives, extensive simulations were run and performance metrics were compared with those of other currently in use methods. It was discovered that the suggested technique lowers task latency and offers extreme cost-effectiveness by improving the concurrent capability in the fog node, users, and task scheduling with improved performance statistics that made it possible. Materials and Methods: The research paper explores fog computing, a three-layered architecture consisting of IoT devices, fog computing, and cloud computing. The fog computing layer sits between cloud networks and Internet of Things (IoT) devices, providing fast access to these devices and handling computation locally at the network edge. Results: The research paper focuses on reducing cloud costs in scheduling. Although the proposed system showed many similarities in execution times compared to the diverse early completion-time method, it consistently lags behind the heterogeneous method by 5 to 10%. Conclusion: This research aims to reduce the cloud scheduling costs for Internet of Things (IoT) devices. A user-based scheduling algorithm and economical task allocation are implemented. A constraint-based and user-defined strategy is recommended.

Energy efficient and reliable data transmission in WBAN using multivariate gradient divergence African buffalo optimization

SummaryWireless body area network (WBAN), a popular radio communication technique, tracks patients' health conditions remotely using small, low‐power, portable sensors positioned on the body to sense vital signs and send data to a control node. To sustain long‐term health monitoring, WBANs require significant energy from the nodes. This research proposes an energy efficient and reliable data transmission using multivariate gradient divergence African buffalo optimization (EEMGDABO) to minimize the delay of critical node data transmission by identifying the optimal path to the control node. The protocol comprises two key processes: winsorized correlative segmented symbolic regression with reinforcement learning (WCS‐SRRL) and the walrus optimization algorithm (WaOA) for accurate priority‐based classification of health data (normal, abnormal, and critical). The second process involves transferring these priority‐based data with reduced energy consumption and delay while increasing throughput by determining the best path from the critical node to the destination (hospital/doctor) using EEMGDABO, which calculates node distance and energy fitness value. Implemented in MATLAB with a health monitoring WBAN dataset, the proposed protocol demonstrates 23% less energy consumption, 12.33% less delay, and 23.44% higher network lifetime compared to existing optimization approaches.

Controllable Weyl nodes and Fermi arcs from Floquet engineering triple fermions

Controllable Weyl nodes and Fermi arcs from Floquet engineering triple fermions

Seismic design parameters of double-layer diamatic domes

The effect of past earthquakes (e.g., the 1995 Kobe and 2016 Kumamoto earthquakes) on the space structures revealed the fact that the assumption of the invulnerability of these structures is incorrect despite their light self-weight and high degree of redundancy, and space structures may suffer severe damage during strong ground motions. Therefore, special attention should be paid to the design of this type of structure in high seismic-risk regions. Nevertheless, the existing seismic design codes have not provided the seismic design parameters (i.e., the seismic response modification factors), including the behavior factor, R, and the displacement amplification factor, Cd for space structures. This research aims to extract the seismic design parameters of double-layer diamatic domes (DLDDs) to answer the challenges that structural engineers face in the design of this type of structure. For this purpose, 16 DLDD models with different rise-to-span ratios (RSRs) and span lengths were considered. After designing the models and performing the nonlinear time history analysis using the simultaneous effect of the three transitional components of ground motions, the target displacement at the control node was determined. Then, the pushover analysis was carried out to derive the capacity curve of the structures and calculate the seismic response modification factors in both horizontal and vertical directions. The results indicate that the domes with RSRs of 1/7 and 1/5 should be designed such that they remain in the elastic state in the horizontal direction. Then, with the growth of RSR, the value of R increases and reaches 1.792. Finally, equations were presented in this study for the period, behavior factor, and displacement amplification factor of DLDD structures in both horizontal and vertical directions using the nonlinear regression analysis.

BROADBAND SEISMIC NODE BASED ON LOW-NOISE HIGH-SENSITIVITY MOLECULAR-ELECTRONIC SENSORS

The developed wideband compact digital seismic module designed for use in ground nodal seismic systems with deployment assistance from unmanned aerial vehicle (UAV) is discussed. The operation frequency band is 1–300 Hz, which is by far wider than that of the seismic nodes based on using standard (10Hz) electrodynamic geophones. Such improvement was achieved through the use of highly sensitive seismic sensors with molecular electronic transfer (MET) that allow for covering a low-frequency part of the spectrum that is needed for broadband processing and receiving information on deep subsurface formation. Low self-noise of the MET seismic sensors used for this seismic station has been proven in special tests. According to the test results, measurements of the self-noise of the developed sensor turned out to be 27-30 dB lower than the self-noise of similar sensors with additional mass. Besides of the 3 mutually orthogonal wideband MET sensors the developed module consists of a digital electronics block enabling autonomous registration and featuring wireless data transmission capability; a battery compartment providing autonomous operation of the seismic module; a device for attaching the seismic module to UAVs and lowering it to the ground. The developed device ensures data time synchronization with satellites, records data at frequencies up to 1000 samples per second with 24 bits resolution and stores the recorded information on the internal 32 Gb storage. A distinctive feature of the presented seismic node is the implementation of quality control of seismic data using a drone. This function is implemented on a Raspberry Pi single-board computer installed on board of the UAV, which has the ability to retrieve portion of information from the node for data quality control and initiate calibration procedure.

Light energy harvested flexible wireless sensing for disinfection sterilization in food storage

Cold chain logistics involves all aspects of food storage, transport, and sales. This study focuses on innovating cold storage disinfection within this context. Regular disinfection reduces microorganism growth, improves food quality, and ensures safety. Traditional methods lack automation, accurate monitoring, and responsive control. Conventional sensors are unsuitable for curved bottles and need frequent power changes. This paper proposes a light energy harvested flexible wireless sensing for disinfection sterilization in food storage (FLWDS). FLWDS comprises five parts: light energy harvesting and power supply node (LEPN), sensing node, control node, monitoring node, and host computer. A predictive control model improves response speed and accuracy. Bluetooth technology ensures real-time communication between nodes. FLWDS collects light energy inside the cold store as a supplementary energy source and can be used in curved structures with a response time of less than 1 s. The maximum communication distance is 40 m. The flexible level sensing system can be designed according to the induction pad’s length, with a measurement accuracy of 0.3 mm, sampling every 4.8 ms, and a resolution of 0.2 mm. FLWDS provides self-powered flexible wireless sensing, ensuring accurate liquid level measurement, improved food quality, reduced pollution, and increased sustainability.

A swarm anomaly detection model for IoT UAVs based on a multi-modal denoising autoencoder and federated learning

The widespread application of unmanned aerial vehicle (UAV) swarms has posed unique challenges for anomaly detection. Multi-modal noise from multi-source heterogeneous sensors during UAV swarm communication affects data quality, and limited data sharing between different UAV organisations restricts training a unified anomaly detection model. To address these problems, this study proposes a UAV swarm anomaly detection model based on a multi-modal denoising autoencoder and federated learning (L-MDAE). First, L-MDAE simulates noise by adding perturbations to the original data during UAV swarm communication. Second, according to the characteristics of UAV data noise, this study designs a new MSE loss function (normalised mean square error, NMSE) based on the normalised correlation coefficient. Furthermore, heterogeneous neural networks with NMSE are constructed to enhance the multi-modal noise–removal capability of the model. Finally, this study considers the UAV control node as the client and the ground control station as the server. Using a federated learning mechanism, L-MDAE is trained on a client dataset, and its parameters are integrated and distributed on the server. In this way, each UAV can effectively detect abnormal data using L-MDAE. Experimental results on five datasets, including ALFA, TLM and ITS, demonstrate that L-MDAE outperforms baseline and related models. When using ALFA, L-MDAE achieved an accuracy of 0.9919 and a swarm anomaly detection accuracy of 0.9901, approximately 2% higher than that of the baseline model.

A practically efficient algorithm for identifying critical control proteins in directed probabilistic biological networks

Network controllability is unifying the traditional control theory with the structural network information rooted in many large-scale biological systems of interest, from intracellular networks in molecular biology to brain neuronal networks. In controllability approaches, the set of minimum driver nodes is not unique, and critical nodes are the most important control elements because they appear in all possible solution sets. On the other hand, a common but largely unexplored feature in network control approaches is the probabilistic failure of edges or the uncertainty in the determination of interactions between molecules. This is particularly true when directed probabilistic interactions are considered. Until now, no efficient algorithm existed to determine critical nodes in probabilistic directed networks. Here we present a probabilistic control model based on a minimum dominating set framework that integrates the probabilistic nature of directed edges between molecules and determines the critical control nodes that drive the entire network functionality. The proposed algorithm, combined with the developed mathematical tools, offers practical efficiency in determining critical control nodes in large probabilistic networks. The method is then applied to the human intracellular signal transduction network revealing that critical control nodes are associated with important biological features and perturbed sets of genes in human diseases, including SARS-CoV-2 target proteins and rare disorders. We believe that the proposed methodology can be useful to investigate multiple biological systems in which directed edges are probabilistic in nature, both in natural systems or when determined with large uncertainties in-silico.

Activation of the G protein-coupled sulfakinin receptor inhibits blood meal intake in the mosquito Aedes aegypti.

Little is known about the blood-feeding physiology of arbovirus vector Aedes aegypti although this type of mosquito is known to transmit infectious diseases dengue, Zika, yellow fever, and chikungunya. Blood feeding in the female A. aegypti mosquito is essential for egg maturation and for transmission of disease agents between human subjects. Here, we identify the A. aegypti sulfakinin receptor gene SKR from the A. aegypti genome and show that SKR is expressed at different developmental stages and in varied anatomical localizations in the adult mosquito (at three days after eclosion), with particularly high expression in the CNS. Knockingdown sulfakinin and sulfakinin receptor gene expression in the female A. aegypti results in increased blood meal intake, but microinjection in the thorax of the sulfakinin peptide 1 and 2 both inhibits dose dependently blood meal intake (and delays the time course of blood intake), which is reversible with receptor antagonist. Sulfakinin receptor expressed ectopically in mammalian cells CHO-K1 responds to sulfakinin stimulation with persistent calcium spikes, blockable with receptor antagonist. These data together suggest that activation of the Gq protein-coupled (i.e., calcium-mobilizing) sulfakinin receptor inhibits blood meal intake in female A. aegypti mosquitoes and could serve as a strategic node for the future control of A. aegypti mosquito reproduction/population and disease transmission.

A Cooperative Intrusion Detection System for the Internet of Things Using Convolutional Neural Networks and Black Hole Optimization.

Maintaining security in communication networks has long been a major concern. This issue has become increasingly crucial due to the emergence of new communication architectures like the Internet of Things (IoT) and the advancement and complexity of infiltration techniques. For usage in networks based on the Internet of Things, previous intrusion detection systems (IDSs), which often use a centralized design to identify threats, are now ineffective. For the resolution of these issues, this study presents a novel and cooperative approach to IoT intrusion detection that may be useful in resolving certain current security issues. The suggested approach chooses the most important attributes that best describe the communication between objects by using Black Hole Optimization (BHO). Additionally, a novel method for describing the network's matrix-based communication properties is put forward. The inputs of the suggested intrusion detection model consist of these two feature sets. The suggested technique splits the network into a number of subnets using the software-defined network (SDN). Monitoring of each subnet is done by a controller node, which uses a parallel combination of convolutional neural networks (PCNN) to determine the presence of security threats in the traffic passing through its subnet. The proposed method also uses the majority voting approach for the cooperation of controller nodes in order to more accurately detect attacks. The findings demonstrate that, in comparison to the prior approaches, the suggested cooperative strategy can detect assaults in the NSLKDD and NSW-NB15 datasets with an accuracy of 99.89 and 97.72 percent, respectively. This is a minimum 0.6 percent improvement.

Target control of linear directed networks based on the path cover problem

Securing complete control of complex systems comprised of tens of thousands of interconnected nodes holds immense significance across various fields, spanning from cell biology and brain science to human-engineered systems. However, depending on specific functional requirements, it can be more practical and efficient to focus on a pre-defined subset of nodes for control, a concept known as target control. While some methods have been proposed to find the smallest driver node set for target control, they either rely on heuristic approaches based on k-walk theory, lacking a guarantee of optimal solutions, or they are overly complex and challenging to implement in real-world networks. To address this challenge, we introduce a simple and elegant algorithm, inspired by the path cover problem, which efficiently identifies the nodes required to control a target node set within polynomial time. To practically apply the algorithm in real-world systems, we have selected several networks in which a specific set of nodes with functional significance can be designated as a target control set. The analysed systems include the complete connectome of the nematode worm C. elegans, the recently disclosed connectome of the Drosophila larval brain, as well as dozens of genome-wide metabolic networks spanning major plant lineages. The target control analysis shed light on distinctions between neural systems in nematode worms and larval brain insects, particularly concerning the number of nodes necessary to regulate specific functional systems. Furthermore, our analysis uncovers evolutionary trends within plant lineages, notably when examining the proportion of nodes required to control functional pathways.

3D criteria to improve real-time pressure control in water distribution network system architecture

ABSTRACT Real-time control (RTC) methods have been developed for over one decade to regulate service pressure and reduce leakage using pressure control valves in water distribution networks (WDN). The present study investigates control node selection frameworks and RTC system architecture for the field-oriented control of a rural WDN. The unique topology of the case-study area, the small size of the area, and high-pressure variety increased the considerations of the RTC system. A computer code was developed based on the method of characteristics for the hydraulic simulation of the network. The code could analyze unsteady flows through sloped pipes, implement pressure-based analysis, and regulate control valves based on the target node. Leakage reduction, pressure fluctuation reduction, and cavitation prevention were used as three criteria and constraints in the selection of control nodes. It was found that the optimal strategy would reduce the real water loss from 25 to 10%. Furthermore, key parameters in remote sensing were evaluated to minimize the number of sensors in order to further simplify the control algorithm and RTC system architecture through an artificial neural network (ANN) approach. The control function with a convergence rate of 99% was introduced.