Competitive Scheme Research Articles

The use ADDIE model to improve the competence of the higher education task force in obtaining competitive funding for the independent campus program

PurposeThis research aims to improve the competence of the task force of vocational higher education study programs in preparing a proposal for the Vocational Higher Education Strengthening Program-Private University (VHESP-PU) of the Independent Campus Program.Design/methodology/approachThis type of research is R&D by applying the ADDIE model procedures. The research subjects were 30 lecturers assigned as study program task forces from 15 private universities in North Sumatra. The techniques to collect data are tests and questionnaires. Data analysis techniques use quantitative descriptive statistics.FindingsThe application of the ADDIE Model was proven effective in achieving training objectives based on the mean comparison analysis between the pre-test and post-test, showing an increase. The maximum score achieved by participants also increased by 9.52% from 84 to 92. Participants' evaluation of the implementation of the training was also dominant in the “Good” and “Very Good” categories. The training learning achievements that participants have obtained will help prepare higher-quality VHESP-PU proposals for each fiscal year to optimize training sustainability.Research limitations/implicationsThe fact that only 15 universities participated in the training underscores the urgent need for ongoing training using the ADDIE model. This will ensure that more participants from private universities in North Sumatra can benefit from this practical approach to proposal preparation.Practical implicationsUsing the ADDIE model to prepare VHESP-PU proposals is an innovative approach that can inspire similar applications in other fields. This research demonstrates that the ADDIE model, usually associated with instructional design, curriculum or learning materials, can also effectively prepare funding proposals using a competition scheme.Originality/valueThe use of the ADDIE model in preparing VHESP-PU proposals is an innovation because ADDIE is usually more related to developing instructional design, curriculum or learning materials rather than funding proposals using a competition scheme.

GRRIS: A Real-time Intrasite Observation Scheduling Scheme for Distributed Survey Telescope Arrays

The distributed telescope array offers promise for conducting large-sky-area, high-frequency time-domain surveys. Multiple telescopes can be deployed at each observation site, so intrasite observation task scheduling is crucial for enhancing observation efficiency and quality. Efficient use of observable time and rapid response to special situations are critical to maximize scientific discovery in time-domain surveys. Besides, the competing scientific priorities, time-varying observation conditions, and capabilities of observation equipment, lead to a vast search space of the scheduling. So with the increasing number of telescopes and observation fields, balancing computational time with solution quality in observation scheduling poses a significant challenge. Informed by the seminal contributions of earlier studies on a multilevel scheduling model and global scheduler for a time-domain telescope array, this study is devoted to further exploring the site scheduler. Formulating the observation scheduling of multiple telescopes at the site as a cooperative decision-making problem, this paper proposes GRRIS, a real-time intrasite observation scheduling scheme for the telescope array using graph and reinforcement learning (RL). It employs a graph neural network to learn node features that can embed the spatial structure of the observation scheduling. An algorithm based on multi-agent RL is designed to efficiently learn the optimum allocation policy of telescope agents to field nodes. Through numerical simulations with real-world scenarios, GRRIS can achieve up to a 22% solution improvement over the most competitive scheme. It offers better scalability and subsecond decision speed, meeting the needs of observation scheduling control for future distributed telescope arrays.

MXene/rGO piezoresistive sensor based on Longan leaves’ hierarchical microstructure for human-motion detection

Flexible pressure sensors exhibit great application potential in wearable devices, human-computer interaction, medical monitoring and other fields and thereby have attracted much attention. However, a common challenge in piezoresistive sensors is the trade-off between sensing range and sensitivity, which limits their versatility across various applications. By employing Longan leaf as a hierarchical microstructure template, the sensing range and sensitivity can be significantly improved at low cost, based on fractal theory. MXene was used as the sensing material, and a systematic study was conducted concerning the effect of MXene concentration on the sensor's sensitivity and detection range, which revealed that the sensitivity firstly increased and then decreased with MXene concentration ranging from 5 mg/mL to 30 mg/mL. Furthermore, based on the solution method to prepare composite sensing layer, MXene and rGO were mixed in proportion to obtain a sensing network that can coordinate with each other. When the volume ratio was 1:1, and the concentration of MXene was up to 15 mg/mL, the fabricated sensor demonstrated high sensitivity (47.36 kPa−1) and wide detection range (>100 kPa) at the same time. In addition, the sensor was successfully used to monitor various human joint activities and physiological signals, which was also expected to provide a competitive scheme for wearable flexible electronic devices in human-motion detection.

RNA-Independent Regulatory Functions of lncRNA in Complex Disease.

During the metagenomics era, high-throughput sequencing efforts both in mice and humans indicate that non-coding RNAs (ncRNAs) constitute a significant fraction of the transcribed genome. During the past decades, the regulatory role of these non-coding transcripts along with their interactions with other molecules have been extensively characterized. However, the study of long non-coding RNAs (lncRNAs), an ncRNA regulatory class with transcript lengths that exceed 200 nucleotides, revealed that certain non-coding transcripts are transcriptional "by-products", while their loci exert their downstream regulatory functions through RNA-independent mechanisms. Such mechanisms include, but are not limited to, chromatin interactions and complex promoter-enhancer competition schemes that involve the underlying ncRNA locus with or without its nascent transcription, mediating significant or even exclusive roles in the regulation of downstream target genes in mammals. Interestingly, such RNA-independent mechanisms often drive pathological manifestations, including oncogenesis. In this review, we summarize selective examples of lncRNAs that regulate target genes independently of their produced transcripts.

Spectrum: Speedy and Strictly-Deterministic Smart Contract Transactions for Blockchain Ledgers

Today, blockchain ledgers utilize concurrent deterministic execution schemes to scale up. However, ordering fairness is not preserved in these schemes: although they ensure all replicas achieve the same serial order, this order does not always align with the fair, consensus-established order when executing smart contracts with runtime-determined accesses. To preserve ordering fairness, an intuitive method is to concurrently execute transactions and re-execute any order-violating ones. This in turn increases unforeseen conflicts, leading to scaling bottlenecks caused by numerous costly aborts under contention. To address these issues, we propose Spectrum, a novel deterministic execution scheme for smart contract execution on blockchain ledgers. Spectrum preserves the consensus-established serial order (so-called strict determinism) with high performance. Specifically, we leverage a speculative deterministic concurrency control to execute transactions in speculation and enforce an agreed-upon serial order by aborting and re-executing any mis-speculated ones. To overcome the scaling bottleneck, we present two key optimizations based on speculative processing: operation-level rollback and predictive scheduling, for reducing both the overhead and the number of mis-speculations. We evaluate Spectrum by executing EVM-based smart contracts on popular benchmarks, showing that it realizes fair smart contract execution by preserving ordering fairness and outperforms competitive schemes in contended workloads by 1.4x to 4.1x.

Hybrid-Supervised Dual-Search: Leveraging Automatic Learning for Loss-Free Multi-Exposure Image Fusion

Multi-exposure image fusion (MEF) has emerged as a prominent solution to address the limitations of digital imaging in representing varied exposure levels. Despite its advancements, the field grapples with challenges, notably the reliance on manual designs for network structures and loss functions, and the constraints of utilizing simulated reference images as ground truths. Consequently, current methodologies often suffer from color distortions and exposure artifacts, further complicating the quest for authentic image representation. In addressing these challenges, this paper presents a Hybrid-Supervised Dual-Search approach for MEF, dubbed HSDS-MEF, which introduces a bi-level optimization search scheme for automatic design of both network structures and loss functions. More specifically, we harness a unique dual research mechanism rooted in a novel weighted structure refinement architecture search. Besides, a hybrid supervised contrast constraint seamlessly guides and integrates with searching process, facilitating a more adaptive and comprehensive search for optimal loss functions. We realize the state-of-the-art performance in comparison to various competitive schemes, yielding a 10.61% and 4.38% improvement in Visual Information Fidelity (VIF) for general and no-reference scenarios, respectively, while providing results with high contrast, rich details and colors. The code is available at https://github.com/RollingPlain/HSDS_MEF.

Quantifying the effect of competition on the functional assembly of bryophyte and lichen communities: A process‐based model analysis

Abstract Environmental filtering and competition are two fundamental processes that shape plant community assembly in terms of functional composition, that is, the distribution of trait values. Understanding the role of these two processes in the assembly of non‐vascular vegetation, such as bryophytes and lichens, is important since these communities provide essential ecosystem services in many regions around the world, and these depend on functional composition. Responses of non‐vascular communities to environmental selection pressures have been explored in a range of experimental and modelling studies. However, it is still largely unknown to what extent competition affects the distribution of functional traits of non‐vascular communities. Moreover, it remains poorly explored which traits that are associated with competition are key for shaping community functional assembly. Here, we integrated a field transplantation experiment with a process‐based model to disentangle the effects of environmental filtering and competition on the assembly of non‐vascular communities. Following the validation of environmental selection in the model using field observations, we performed a simulation experiment to understand the impacts of competition on trait distributions in non‐vascular communities growing in two temperate locations that differ in microclimatic conditions (a shaded and an open location). Our results suggest that the functional composition is likely a result of weak competition or may not depend on competitive exclusion at all while environmental filtering plays an essential role. Plant height seems to be a key trait for competition. However, no single‐trait competition scheme could consistently explain the observed functional composition of the studied non‐vascular communities. Synthesis. The presented model provides a new trait‐based approach for simulating the functional assembly of non‐vascular communities. Environmental filtering appears to be more essential than competition for predicting trait distributions of non‐vascular communities under temperate climatic conditions and we recommend caution in associating competition to a single trait while analysing community functional assembly.

Longitudinal vibration estimation of a mine hoist using a hybrid signal fusion method combining UKF, ND and improved DE

The monitoring of cage longitudinal vibration can directly indicate the operational status of mine hoists. However, it is always challenging to collect the sensor signals of moving cages with high dynamic characteristics in real time from complex working environments using traditional monitoring methods. In this study, a more practical hybrid signal fusion approach is proposed to realize estimation of cage longitudinal vibration from a low sampling rate acceleration acquisition signal and a low cost encoder signal for state estimation. A nonlinear differentiator is applied to extract encoder differential signals and expand observation variables. An unscented Kalman observer based on nonlinear mine hoist model is designed to estimate the unknown state. To overcome the influence of the uncertain parameters, an improved differential evolution (DE) algorithm combining parameter adaptive method, reverse learning competition scheme and multiple parallel populations strategy is proposed to find unknown parameters of the observation model and autotune the parameters of the algorithms by using low sampling rate acceleration. Sensor data of the simulated experiment platform were collected and processed by the xPC system to validate the effectiveness of the proposed strategy. The experimental results showed that the improved DE (IDE) algorithm had a faster mean time for parameter tuning and the smallest fitness value compared to the standard DE, the particle swarm optimization algorithm and the genetic algorithm. Moreover, the longitudinal vibration estimation system, after parameter tuning by the IDE optimization algorithm, could achieve the purpose of signal estimation, with a smaller estimation error and a better estimation effect.

Peatland-VU-NUCOM (PVN 1.0): using dynamic plant functional types to model peatland vegetation, CH4, and CO2 emissions

Abstract. Despite covering only 3 % of the planet’s land surface, peatlands store 30 % of the planet’s terrestrial carbon. The net greenhouse gas (GHG) emissions from peatlands depend on many factors but primarily soil temperature, vegetation composition, water level and drainage, and land management. However, many peatland models rely on water levels to estimate CH4 exchange, neglecting to consider the role of CH4 transported to the atmosphere by vegetation. To assess the impact of vegetation on the GHG fluxes of peatlands, we have developed a new model, Peatland-VU-NUCOM (PVN). The PVN model is a site-specific peatland CH4 and CO2 emissions model, able to reproduce vegetation dynamics. To represent dynamic vegetation, we have introduced plant functional types and competition, adapted from the NUCOM-BOG model, into the framework of the Peatland-VU model, a peatland GHG emissions model. The new PVN model includes plant competition, CH4 diffusion, ebullition, root, shoot, litter, exudate production, belowground decomposition, and aboveground moss development under changing water levels and climatic conditions. Here, we present the PVN model structure and explore the model's sensitivity to environmental input data and the introduction of the new vegetation competition schemes. We evaluate the model against observed chamber data collected at two peatland sites in the Netherlands to show that the model is able to reproduce realistic plant biomass fractions and daily CH4 and CO2 fluxes. We find that daily air temperature, water level, harvest frequency and height, and vegetation composition drive CH4 and CO2 emissions. We find that this process-based model is suitable to be used to simulate peatland vegetation dynamics and CH4 and CO2 emissions.

Competing alliances in a four-species cyclic ecosystem

We consider a four-species cyclic community with species ui where i=1,…,4. For each i, species ui exhibit a crowding effect, competing with members of its own species (intraspecies competition). Furthermore, there is interspecies competition as ui competes with ui+1 (mod 4). We also allow for species mobility via Fickian diffusion. A feature of this cyclic competition scheme is that there are two alliances of non-competing species, d13, consisting only of species u1 and u3, and d24, consisting only of species u2 and u4. We focus on the parameter regime where these two alliances are the only stable states.We consider the invasion problem, where these two states are initially adjacent, and derive conditions where one state displaces the other. We focus primarily on the standstill problem, where both alliances are evenly matched, dividing the parameter space into two regions - one where d13 displaces d24 and one where d24 displaces d13. We concentrate on three specific parameter regimes: (i) moderate competition - the interspecies and intraspecies competitions are roughly of equal strength, (ii) strong competition - interspecies competition is significantly larger than intraspecies competition, and (iii) slow competitors - the species comprising one alliance are significantly less mobile than the species comprising the other alliance. We employ asymptotic and perturbation methods to determine the outcome of the invasion (winning alliance) and compare our analytic results (generally very positively) with both numerical computations and, when parameters are such that the problem can be reduced to the two-species problem, with previously obtained results.

A Provably Secure and Lightweight Access Control Protocol for EI-Based Vehicle to Grid Environment

The Energy Internet (EI) presents a novel paradigm for renewable energy distribution that utilizes communication and computing technologies to revolutionize the conventional Intelligent Transportation Systems (ITSs) and power grid into a structure that provides assistance to open innovation. The EI offers sustainable and bidirectional transmission for the improvement and analysis of energy convention among Electric Vehicles (EVs) and service providers. To ensure efficient, reliable, and secure operation, EI must be safe from security attacks. Therefore, efficient and secure key negotiation is a significant issue for the EI-based Vehicle-to-Grid (V2G) architecture. Thus, to ensure the system’s security, we have devised a robust scheme to facilitate a secure key agreement between the entities involved for the secure and efficient renewable energy distribution for the EI-based energy vehicles in V2G. The devised scheme’s robustness is solicited through the widely-accepted formal Random or Real (RoR) model. In addition, the informal security analysis is conducted on the devised scheme, which is evident that the designed scheme achieves all the required security features of EI-based ITS. Moreover, the performance evaluation results endorse that the designed scheme achieves the desired security and minimizes the communication, computation, and energy overhead by 29.33%, 27.94% and 28.08% in comparison to the existing competitive schemes.

Competitive learning to generate sparse representations for associative memory

One of the most well established brain principles, Hebbian learning, has led to the theoretical concept of neural assemblies. Based on it, many interesting brain theories have spawned. Palm’s work implements this concept through multiple binary Willshaw associative memories, in a model that not only has a wide cognitive explanatory power but also makes neuroscientific predictions. Yet, Willshaw’s associative memory can only achieve top capacity when the stored vectors are extremely sparse (number of active bits can grow logarithmically with the vector’s length). This strict requirement makes it difficult to apply any model that uses this associative memory, like Palm’s, to real data. Hence the fact that most works apply the memory to optimal randomly generated codes that do not represent any information. This issue creates the need for encoders that can take real data, and produce sparse representations - a problem which is also raised following Barlow’s efficient coding principle. In this work, we propose a biologically-constrained network that encodes images into codes that are suitable for Willshaw’s associative memory. The network is organized into groups of neurons that specialize on local receptive fields, and learn through a competitive scheme. After conducting auto- and hetero-association experiments on two visual data sets, we can conclude that our network not only beats sparse coding baselines, but also that it comes close to the performance achieved using optimal random codes.

Measurement of differential mode group delay in few-mode fibers based on Fresnel reflection peaks.

Mode-division multiplexing communication based on few-mode fibers (FMFs) is the most competitive scheme to address the bandwidth exhaustion in single-mode fibers. The differential mode group delay (DMGD) among different modes in FMFs is an important aspect in characterizing FMFs, and the convenient, fast, and accurate measurement of DMGD in different modes of FMFs is a crucial task. This paper presents a single-ended measurement method for DMGD in FMFs based on Fresnel reflection peaks. Experimental measurements were conducted to determine the DMGD of a 3.689km six-mode fiber and a 9.7km three-mode fiber, and the results were compared with the traditional time-of-flight method. The findings demonstrate that our proposed method based on Fresnel reflection peaks exhibits good accuracy in measuring the DMGD of FMFs. Moreover, this method enables single-ended measurement and offers simplicity and practicality.

Pemberdayaan Komunitas UMKM Makmur Bersama Untuk Meningkatkan Kualitas dan Legalitas Digitalisasi Ekonomi Masyarakat Kelurahan Karangbesuki

Along with the development of the economic sector and the rapid pace of the information sector, the position of MSMEs in market competition schemes is often left behind by businesses with larger capital. With more modern marketing methods and legal activities that more clearly affect the relationship of trust between sellers and consumers. So Empowerment and Training is needed on how to build a business that has legality and quality so that it can survive in business competition and can develop into a more modern one. This time the community service raised this problem and collaborated with Makmur Bersama MSME Community partners. Partners are located in Karangbesuki Village, Sukun District, Malang City, around the Malang State University Campus. This community service proposes a program of empowerment and assistance in obtaining business licensing starting from Business Identification Numbers (NIB), Home Industry Product Numbers (P-IRT), and Product Halal Certification.

Parity violating scalar-tensor model in teleparallel gravity and its cosmological application

The parity violating model based on teleparallel gravity is a competitive scheme for parity violating gravity, which has been preliminary studied in the literature. To further investigate the parity violating model in teleparallel gravity, in this paper, we construct all independent parity-odd terms that are quadratic in torsion tensor and coupled to a scalar field in a way without higher-order derivatives. Using these parity-odd terms, we formulate a general parity violating scalar-tensor model in teleparallel gravity and obtain its equations of motion. To explore potentially viable models within the general model, we investigate the cosmological application of a submodel of the general model in which terms above the second power of torsion are eliminated. We focus on analyzing cosmological perturbations and identify the conditions that preserve the parity violating signal of gravitational waves at linear order while avoiding the ghost instability.

Intelligent prediction of lower extremity loadings during badminton lunge footwork in a lab-simulated court.

Introduction: Playing badminton has been reported with extensive health benefits, while main injuries were documented in the lower extremity. This study was aimed to investigate and predict the knee- and ankle-joint loadings of athletes who play badminton, with "gold standard" facilities. The axial impact acceleration from wearables would be used to predict joint moments and contact forces during sub-maximal and maximal lunge footwork. Methods: A total of 25 badminton athletes participated in this study, following a previously established protocol of motion capture and musculoskeletal modelling techniques with the integration of a wearable inertial magnetic unit (IMU). We developed a principal component analysis (PCA) statistical model to extract features in the loading parameters and a multivariate partial least square regression (PLSR) machine learning model to correlate easily collected variables, such as the stance time, approaching velocity, and peak accelerations, with knee and ankle loading parameters (moments and contact forces). Results: The key variances of joint loadings were observed from statistical principal component analysis modelling. The promising accuracy of the partial least square regression model using input parameters was observed with a prediction accuracy of 94.52%, while further sensitivity analysis found a single variable from the ankle inertial magnetic unit that could predict an acceptable range (93%) of patterns and magnitudes of the knee and ankle loadings. Conclusion: The attachment of this single inertial magnetic unit sensor could be used to record and predict loading accumulation and distribution, and placement would exhibit less influence on the motions of the lower extremity. The intelligent prediction of loading patterns and accumulation could be integrated to design training and competition schemes in badminton or other court sports in a scientific manner, thus preventing fatigue, reducing loading-accumulation-related injury, and maximizing athletic performance.

BSAPM: BlockChain based secured authentication protocol for large scale WSN with FPGA implementation

According to state-of-the-art, several multiple base station-based authentication and key agreement protocols exist. Still, most of the protocols are computationally inefficient and suffer from several potential attacks. In this research work, we consider WSN architecture, where multiple base stations are located in a distributed way. After that, we proposed a blockchain-based authentication and key agreement protocol for establishing secure communication. This protocol utilizes a smart contract facility during the registration of the sensor node. Our work also stores records of the sensor node in blockchain network. Most importantly, we have verified the proposed protocol using FPGA implementation, which further confirms the correctness of the generated session keys. BSAPM is demonstrated in terms of security threats using the Scyther tool, and we have also shown formal security analysis using the Real-or-Random(ROR) model. The informal security analysis demonstrates the protection against security threats. BSAPM is better in terms of communication, computation overheads, and functionality features when compared to the related other competitive schemes.

Enhancing Security in ZigBee Wireless Sensor Networks: A New Approach and Mutual Authentication Scheme for D2D Communication.

The latest version of ZigBee offers improvements in various aspects, including its low power consumption, flexibility, and cost-effective deployment. However, the challenges persist, as the upgraded protocol continues to suffer from a wide range of security weaknesses. Constrained wireless sensor network devices cannot use standard security protocols such as asymmetric cryptography mechanisms, which are resource-intensive and unsuitable for wireless sensor networks. ZigBee uses the Advanced Encryption Standard (AES), which is the best recommended symmetric key block cipher for securing data of sensitive networks and applications. However, AES is expected to be vulnerable to some attacks in the near future. Moreover, symmetric cryptosystems have key management and authentication issues. To address these concerns in wireless sensor networks, particularly in ZigBee communications, in this paper, we propose a mutual authentication scheme that can dynamically update the secret key value of device-to-trust center (D2TC) and device-to-device (D2D) communications. In addition, the suggested solution improves the cryptographic strength of ZigBee communications by improving the encryption process of a regular AES without the need for asymmetric cryptography. To achieve that, we use a secure one-way hash function operation when D2TC and D2D mutually authenticate each other, along with bitwise exclusive OR operations to enhance cryptography. Once authentication is accomplished, the ZigBee-based participants can mutually agree upon a shared session key and exchange a secure value. This secure value is then integrated with the sensed data from the devices and utilized as input for regular AES encryption. By adopting this technique, the encrypted data gains robust protection against potential cryptanalysis attacks. Finally, a comparative analysis is conducted to illustrate how the proposed scheme effectively maintains efficiency in comparison to eight competitive schemes. This analysis evaluates the scheme's performance across various factors, including security features, communication, and computational cost.

Formulation and Analysis of a Cubic B-Spline-Based Time Integration Procedure for Structural Seismic Response Analysis

In this study, an explicit time integration scheme based on cubic B-spline interpolation strategy and momentum corrector is extended for structural seismic response analysis. The acquisition strategy for the required seismic load is provided. The cubic B-spline-based scheme with moment corrector is generalized for the nonlinear hysteresis system. The analysis shows that the explicit scheme has more desirable stability and accuracy properties than other competitive schemes. The effectiveness of the scheme is demonstrated by the linear and nonlinear numerical examples.

Modeling and Control of Modular Multilevel Matrix Converter for Low-Frequency AC Transmission

The modular multilevel matrix converter (M3C) is the core component in low-frequency AC (LFAC) transmission, which is a competitive scheme for offshore wind power integration. In this paper, the M3C control strategy with the reduced switching frequency SM voltage balancing method is proposed. First, based on the conventional αβ0 and dq transformations, the M3C mathematical model is derived. Then, the dual-loop control structure with outer loop and inner loop controllers commonly used in voltage source converters is applied to the M3C. The inner loop controller consists of the current tracking controller in the dq reference frame and the circulating current suppressing controller in the αβ0 reference frame; the outer loop controller is proposed for the offshore wind farm LFAC integration scenario. Additionally, according to the operating characteristics of full-bridge sub-modules (FBSMs), three characteristic variables are defined and a reduced switching frequency SM voltage balancing method based on the nearest level control (NLC) is proposed. Finally, time-domain simulations in PSCAD/EMTDC demonstrate the feasibility of the proposed control strategy.