Contracting Factor Research Articles

Examining the relationship between channel disintermediation and chemical fertilizer application: Empirical evidence from smallholder farmers in central China

AbstractDigital advancements have reshaped the sales channels for industrial products like chemical fertilizers, allowing for the elimination of certain intermediaries. Utilizing survey data from 1374 rice‐growing smallholder farmers across counties in central China, this study explored the impact of disintermediation in chemical fertilizer sales channels on application rates among smallholder farmers. Benchmark regression analyses indicated that channel disintermediation significantly decreased chemical fertilizer application across various scenarios. Additionally, the alignment of contractual factors and technical services between chemical suppliers and farmers, facilitated by disintermediation, reduced chemical fertilizer application. Heterogeneity analysis revealed that the reduction in chemical fertilizer application due to channel disintermediation was more pronounced among farmers with higher digital literacy or lower production capacity. These findings highlighted the importance of encouraging chemical suppliers with direct‐to‐consumer channels to enhance their technical services and increase supervision over the quality of their products and services, thereby building trust among farmers and facilitating their adaptation to changes in the sales channel structure.

Application of Local Search Particle Swarm Optimization Based on the Beetle Antennae Search Algorithm in Parameter Optimization

Intelligent control algorithms have been extensively utilized for adaptive controller parameter adjustment. While the Particle Swarm Optimization (PSO) algorithm has several issues: slow convergence speed requiring a large number of iterations, a tendency to get trapped in local optima, and difficulty escaping from them. It is also sensitive to the distribution of the solution space, where uneven distribution can lead to inefficient contraction. On the other hand, the Beetle Antennae Search (BAS) algorithm is robust, precise, and has strong global search capabilities. However, its limitation lies in focusing on a single individual. As the number of iterations increases, the step size decays, causing it to get stuck in local extrema and preventing escape. Although setting a fixed or larger initial step size can avoid this, it results in poor stability. The PSO algorithm, which targets a population, can help the BAS algorithm increase diversity and address its deficiencies. Conversely, the characteristics of the BAS algorithm can aid the PSO algorithm in finding the optimal solution early in the optimization process, accelerating convergence. Therefore, considering the combination of BAS and PSO algorithms can leverage their respective advantages and enhance overall algorithm performance. This paper proposes an improved algorithm, W-K-BSO, which integrates the Beetle Antennae Search strategy into the local search phase of PSO. By leveraging chaotic mapping, the algorithm enhances population diversity and accelerates convergence speed. Additionally, the adoption of linearly decreasing inertia weight enhances algorithm performance, while the coordinated control of the contraction factor and inertia weight regulates global and local optimization performance. Furthermore, the influence of beetle antennae position increments on particles is incorporated, along with the establishment of new velocity update rules. Simulation experiments conducted on nine benchmark functions demonstrate that the W-K-BSO algorithm consistently exhibits strong optimization capabilities. It significantly improves the ability to escape local optima, convergence precision, and algorithm stability across various dimensions, with enhancements ranging from 7 to 9 orders of magnitude compared to the BAS algorithm. Application of the W-K-BSO algorithm to PID optimization for the Pointing and Tracking System (PTS) reduced system stabilization time by 28.5%, confirming the algorithm’s superiority and competitiveness.

When royalties impede technology transfer

AbstractWe use data on over 800 early-stage technology transaction negotiations to model the impact of different types of risk on whether the transaction was executed and then test for contractual factors that may ameliorate these risks. Our data highlight the importance of project risk in determining which negotiations result in a signed contract. We find that transactions aiming to sell early-stage technology to large corporates are less likely to be executed when the buyer is large, and the contract contains royalties, holding constant five different types of risk involved in the transaction. Other risk-reducing contract modes do not appear to increase the probability of an executed contract. Our results support the view that technology sellers’ reliance on royalties may reflect organisational preferences or capabilities which may not be economically or managerially optimal. We also find that ‘people risk’ matters more than ‘technological’, ‘market’, ‘appropriation’ and ‘freedom-to-operate’ risks.

Trends and determinants of clinical staff retention in the English NHS: a double retrospective cohort study

ObjectivesTo investigate how demographic, contractual and organisational factors are related to the retention of hospital workers in the English NHS. The study will specifically examine the trends in age-retention profiles.DesignA...

Fractal Interpolation Function based Thin Wire Antennas

This paper presents an approach for the design of wire antennas based on fractal interpolation functions (FIFs). The interpolation points and the contraction factors of the FIFs are chosen as free parameters to modify the antenna geometry. The proposed structures’ gain and radiation pattern can be optimized using FIF parameters. Producible prefractal antennas obtained in the intermediate iterations of fractal generation have compact sizes compared to classical counterparts. The error in prefractal geometry and the original fractal is bounded, and can be determined in terms of the finest producible detail’s dimensions. The emerging structures have multiband behavior due to their self-similar and symmetric nature. To illustrate the approach, we have provided finite element based simulations for several prefractal antennas. |S11|, the gain, the radiation efficiency, the radiation patterns, and feed point impedances for the demonstrated antennas are calculated numerically. The results indicate that produced antennas can be used in applications that require limited mechanical size, multiple operating bands, and controlled radiation patterns.

Period-dependent pricing methods of multi-type vehicles for BOT highway projects with pavement rehabilitation effects

In the planning stage of a BOT highway project, the future traffic demand plays a pivotal role on designing contract factors. Considering that the traffic demand generally has variations with time, the constant toll during the concession period obviously is not a good choice. And the different toll charges classified by vehicles types definitely affect the traffic composition and maintenance cost. To address this practical issue, this study proposes the mixed integer nonlinear programming models to select the period-dependent toll charges for multi-type vehicles and highway capacity, by considering both the objectives of the government and private firms. Then two solution algorithms are provided to solve our proposed models. Finally, numerical experiments are conducted to assess the applicability and efficiency of our models and algorithms.

A Synergetic Effect of the Integration of Lean, Sustainable Construction Practices and Alliance Contract on Operation Performance in the Indian Construction Industry

By pioneering a comprehensive analysis of lean integration within the construction sector, this research provides novel insights into optimizing project outcomes. The construction industry is generally known for its frequent deadline slip-ups, budget overruns, and issues with upholding proper quality. It is usual practice to integrate different management techniques which are successful in the production industry in order to avoid such issues. Several researchers adopted sustainable construction management, lean management and alliance contract practices individually on construction sites to improve the profit, productivity, quality and transparency of the project. In this study, the synergetic integration of sustainable construction management, lean management and alliance contract practices have been studied. A questionnaire survey was conducted among 345 practitioners, resulting in 309 responses after screening. These respondents, comprising various roles in the AEC sector, provided insights into their experience, project involvement, and business sectors. The study identified 23 factors associated with lean practices, sustainable construction, and alliance contracts, examining their relationships and benefits through Structural Equation Modeling (SEM). The results demonstrate significant effects of lean practice, sustainable construction, and alliance contract factors on the benefits derived from their integration. Measures of reliability and validity affirm the robustness of these relationships. The discussion underscores the benefits of integration, including enhanced efficiency, reduced environmental impact, improved collaboration, shared risk and reward, superior project outcomes, and competitive advantage. Overall, this research elucidates the potential advantages of integrating these methodologies within construction projects.

In Vivo Wound Healing Potential and Molecular Pathways of Amniotic Fluid and Moringa Olifera-Loaded Nanoclay Films.

Cutaneous wounds pose a significant health burden, affecting millions of individuals annually and placing strain on healthcare systems and society. Nanofilm biomaterials have emerged as promising interfaces between materials and biology, offering potential for various biomedical applications. To explore this potential, our study aimed to assess the wound healing efficacy of amniotic fluid and Moringa olifera-loaded nanoclay films by using in vivo models. Additionally, we investigated the antioxidant and antibacterial properties of these films. Using a burn wound healing model on rabbits, both infected and non-infected wounds were treated with the nanoclay films for a duration of twenty-one days on by following protocols approved by the Animal Ethics Committee. We evaluated wound contraction, proinflammatory mediators, and growth factors levels by analyzing blood samples. Histopathological changes and skin integrity were assessed through H&E staining. Statistical analysis was performed using SPSS software (version 2; Chicago, IL, USA) with significance set at p < 0.05. Our findings demonstrated a significant dose-dependent increase in wound contraction in the 2%, 4%, and 8% AMF-Me.mo treatment groups throughout the study (p < 0.001). Moreover, macroscopic analysis revealed comparable effects (p > 0.05) between the 8% AMF-Me.mo treatment group and the standard treatment. Histopathological examination confirmed the preservation of skin architecture and complete epidermal closure in both infected and non-infected wounds treated with AMF-Me.mo-loaded nanofilms. RT-PCR analysis revealed elevated concentrations of matrix metalloproteinases (MMPs) and vascular endothelial growth factor (VEGF), along with decreased levels of tumor necrosis factor-alpha (TNF-α) in AMF-Me.mo-loaded nanofilm treatment groups. Additionally, the antimicrobial activity of AMF-Me.mo-loaded nanofilms contributed to the decontamination of the wound site, positioning them as potential candidates for effective wound healing. However, further extensive clinical trials-based studies are necessary to confirm these findings.

Dynamic-Projection-Integrated Particle-Filtering-Based Identification of Friction Characteristic Curve for Train Wheelset on Slipping Fault Condition

This paper proposes a dynamic-projection-integrated particle-filtering-based identification strategy for the friction characteristic curve of a train wheelset under the slipping fault condition. This strategy aims to achieve the identification of the fault friction characteristic curve (FFCC) in the early slipping fault stage. First, a multi-dimensional integrated particle-filtering (MDIPF)-based parameters correction method is proposed. The MDIPF constructs an error particle state transition model encompassing multi-dimensional parameters, which integrates inter-particle correlation to facilitate error fusion during the state transition process. Then, a dynamic projection domain (DPD)-based particle refinement method is proposed. The DPD constructed the contraction factors to dynamically fine-tune the particle projection domain. Finally, a multi-level evaluation-based identification method for the FFCC is proposed. And the dynamic-projection-integrated particle-filtering-based identification strategy is validated, which can actualize the rapid and accurate identification of the FFCC.

“To Be or Not to Be” of a Polymer Nanogel—Unravelling the Relationship of Product Properties vs. Synthesis Conditions Governing the Radiation Crosslinking of Poly(acrylic acid) Using GPC/SEC—MALLS

In this paper, a state-of-the-art multi-detection gel permeation chromatography/size exclusion chromatography (GPC/SEC) system including multi-angle laser light scattering (MALLS) is applied to monitor radiation-induced synthesis of internally crosslinked nanostructures from poly(acrylic acid) (PAA). The aim is to demonstrate that this modern tool yields a more detailed picture of reaction mechanism and product structure than the techniques used to date. The prevailing intramolecular crosslinking narrows the molecular weight distribution from Mw/Mn = 3.0 to 1.6 for internally crosslinked structures. A clear trend from over 0.7 to 0.5 in the Mark–Houwink exponent and a decrease in Rg/Rh from 1.7 to 1.0 point to the formation of nanogels, more rigid and less permeable than the starting coils. Changes in the coil contraction factor (g′ = [η]irradiated/[η]linear) as a function of the radical density revealed the existence of two modes in intramolecular crosslinking, the initial one (up to 0.075 radicals per monomer unit) where the compactness of products changes strongly with progressing crosslinking and a second one where further compacting is suppressed by the lower flexibility of the partially crosslinked chain segments. This indicates a transition from soft, still internally crosslinkable nanogels to more rigid structures, less prone to further intramolecular loop formation. Our findings provide means for the tailored design of new PAA nanomaterials.

Risk Mechanism Analysis of Rural Infrastructure Investment PPP Model and Prevention Path Selection

In view of the frequent risk problems in the current PPP mode of rural infrastructure investment, this paper discusses the mechanism of risk generation from four aspects: environment, contract, behavior, ability and cognitive factors, and puts forward risk prevention paths, including establishing and improving institutional guarantee, decision-making mechanism, dynamic risk adjustment mechanism, dynamic incentive measures, supervision and risk monitoring system.

An Application Study of Improved Iris Image Localization Based on an Evolutionary Algorithm

This study aims to enhance the localization of the inner and outer circles of the iris while addressing issues of excessive invalid computations and inaccuracies. To achieve this objective, diverse methods are employed to improve the process to varying extents. Initially, the image undergoes pre-processing operations, including grayscale conversion, mathematical morphological transformation, noise reduction, and image enhancement. Subsequently, the accurate localization of the inner and outer edges is achieved by applying algorithms such as Canny edge detection and the Hough transform, allowing for the determination of their corresponding center and radius values within the iris image. Lastly, an improvement is made to the particle swarm optimization algorithm by combining various algorithms, namely LinWPSO, RandWPSO, contraction factor, LnCPSO, and AsyLnCPSO, employing mechanisms such as simulated annealing and the ant colony algorithm. Through dual validation on the CASIA-Iris-Syn dataset and a self-built CASIA dataset, this approach significantly enhances the precision of iris localization and reduces the required iteration count.

Demystifying the catalytic role of retention strategies and psychological contract in the health-care sector

PurposeThis paper aims to explore the role of retention strategies and psychological contract (PC) factors in private hospitals.Design/methodology/approachThe current study is an empirical research. The study was conducted by receiving responses from a sample size of 190 respondents who participated in the Doctors and Nurses’ survey.FindingsThe study results show the relationship between PC and the retention strategies of employees in hospitals. The research found that employees try to be committed to the hospital due to the image of the hospital and career development opportunities. Creating a sense of commitment among the employees towards their job and organisation, making to feel pride in the job, creating an attitude among the employees, a willingness to advocate the benefits and advantages and satisfaction among the employee towards the job and organisation.Research limitations/implicationsWith a population of more than one million employees (Doctors and Nurses) in the Indian health-care sector, the sample needs to be more adequate. However, these limitations present scope for future studies on this topic bearing on the aspiration for generalisation of the findings on the entire population.Practical implicationsThe study has strong practical implications in strengthening the relationships by identifying the factors of PC and influencing the retention strategies of health-care professionals in the hospital.Originality/valueThis research explores the changing dynamics of the Indian health-care sector. Health-care professionals may perceive and react differently to the retention strategies and policies of the HR system in the health-care sector and have more expectations of PC fulfilment that enhance their capability to use professional skills and resources in the realisation of their organisational goals.

A matrix computing method for visualizing switchless controlled 3D hexagonal‐annular rotary braiding process architecture

AbstractNumerous process architectures for fabric molding are investigated because fiber braided structure is closely related to the performance of fiber‐reinforced composites. However, existing methods for calculating process architectures have to deal with the coordinates of each fiber when modeling the three‐dimensional braiding method without individually controlled switches. The amount of data grows geometrically as the number of fibers and braiding steps increases. A methodology that enables simple and efficient computation of a model of the relationship between braiding parameters and fiber structure is urgently needed. This paper proposes a novel algorithm to trace the yarn carrier trajectories through machine simulation with Euler rotation‐matrix operations. To predict the real fabric architecture, a contraction factor is introduced to optimize the yarn trajectory while considering the volume of the yarn. The optimized fabric architecture is explored and the braiding process architecture with different braiding parameters is simulated according to the algorithm. Based on the exploration of the relationship between the braiding parameters and the process structure, a hexagonal‐annular braiding machine was established, which can fabricate fabrics with different cross‐sectional shapes. The simulation results were verified by the braiding experiments.

Rolling Bearing Fault Diagnosis Based on Support Vector Machine Optimized by Improved Grey Wolf Algorithm.

This study targets the low accuracy and efficiency of the support vector machine (SVM) algorithm in rolling bearing fault diagnosis. An improved grey wolf optimizer (IGWO) algorithm was proposed based on deep learning and a swarm intelligence optimization algorithm to optimize the structural parameters of SVM and improve the rolling bearing fault diagnosis. A nonlinear contraction factor update strategy was also proposed. The variable coefficient changes with the shrinkage factor α. Thus, the search ability was balanced at different early and late stages by controlling the dynamic changes of the variable coefficient. In the early stages of optimization, its speed is low to avoid falling into local optimization. In the later stages of optimization, the speed is higher, and finding the optimal solution is easier, balancing the two different global and local optimization capabilities to complete efficient convergence. The dynamic weight update strategy was adopted to perform position updates based on adaptive dynamic weights. First, the dataset of Case Western Reserve University was used for simulation, and the results showed that the diagnosis accuracy of IGWO-SVM was 98.75%. Then, the IGWO-SVM model was trained and tested using data obtained from the full-life-cycle test platform of mechanical transmission bearings independently researched and developed by Nanjing Agricultural University. The fault diagnosis accuracy and convergence value of the adaptation curve were compared with those of PSO-SVM (particle swarm optimization) and GWO-SVM diagnosis models. Results showed that the IGWO-SVM model had the highest rolling bearing fault diagnosis accuracy and the best diagnosis convergence.

Performance Optimization of Multipair Massive MIMO Polarized Relay Systems

In this paper, we analyze the challenges faced by multipair massive Multiple-input-multiple-output (MIMO) relay channels in 5G wireless communication systems, where high path loss and severe shadow fading between different user nodes can cause poor channel quality. To overcome these limitations, we propose a polarization selection scheme for antenna arrays combining multipair massive MIMO relay and beamforming to improve MIMO relay channel quality. Specifically, our main goal is to exploit the potential of polarization diversity to maintain and improve the link quality while maintaining the compact size of the MIMO antenna array. To achieve this goal, we introduce a dynamic weighted particle swarm optimization algorithm with contraction factor (CF-DWPSO) to select the polarization direction. In addition, we employ distributed beamforming to effectively suppress or eliminate inter-pair interference. The performance of the simulation analysis shows that CF-DWPSO combined with beamforming provides significant performance improvement of the multipair massive MIMO polarized relay channels, which further indicates that it is of great necessity for improving the performance of this system to optimize the polarization selection by combining beamforming techniques.

Master Curve for the Dimensions of Statistical Network Polymers

AbstractThe graph diameter D is correlated with the mean‐square radius of gyration Rg2 and is a useful measure to design and control the network architecture. The fraction d of segments located in the diameter chain, defined by d = D/ns, where ns is the total number of segments in the polymer, is investigated for various statistical network polymers. It is found that the relationship between d and the cycle rank r of the well‐developed statistical networks follows a master curve. As a convenient measure to define the well‐developed networks, the network maturity index (NMI) is proposed. The random crosslinked networks with NMI &gt; 3 are considered well‐developed, but a larger value of NMI is required for the nonrandom, heterogeneous networks. With the help of prior knowledge of the relationship between Rg2 and D, the master curve for the relationship between the contraction factor g and r can also be determined.

Optimization of Square-shaped Bolted Joints Based on Improved Particle Swarm Optimization Algorithm

The bolted joint is widely used in heavy-duty CNC machine tools, which has huge influence on working precision and overall stiffness of CNC machine. The process parameters of group bolt assembly directly affect the stiffness of the connected parts. The dynamic model of bolted joints is established based on the fractal theory, and the overall stiffness of joint surface is calculated. In order to improve the total stiffness of bolted assembly, an improved particle swarm optimization algorithm with combination of time-varying weights and contraction factor is proposed. The input parameters are preloading of bolts, fractal dimension, roughness, and object thickness. The main goal is to maximize the global rigidity. The optimization results show that improved algorithm has better convergence, faster calculation speed, preferable results, and higher optimization performance than standard particle swarm optimization algorithm. Moreover, the global rigidity optimization is achieved.

Optimal polynomial smoothers for multigrid V‐cycles

AbstractThe idea of using polynomial methods to improve simple smoother iterations within a multigrid method for a symmetric positive definite system is revisited. A two‐level bound going back to Hackbusch is optimized by a very simple iteration, a close cousin of the Chebyshev semi‐iterative method, but based on the Chebyshev polynomials of the fourth instead of first kind. A full V‐cycle bound for general polynomial smoothers is derived using the V‐cycle theory of McCormick. The fourth‐kind Chebyshev iteration is quasi‐optimal for the V‐cycle bound. The optimal polynomials for the V‐cycle bound can be found numerically, achieving about an 18% lower error contraction factor bound than the fourth‐kind Chebyshev iteration, asymptotically as the number of smoothing steps . Implementation of the optimized iteration is discussed, and the performance of the polynomial smoothers is illustrated with numerical examples.

The effect of contractual and behavioral CEO attributes on aggressive tax avoidance: case of German-listed firms in HDAX

Purpose This paper aims to examine the effect of contractual factors and noncontractual factors on tax avoidance (TA). Design/methodology/approach The sample comprises 400 firm-year observations of 67 companies listed on the HDAX during the period 2008–2017. The generalized least square panel regression is applied. Findings The study results confirm a significant effect of long-term chief executive officer (CEO) compensation incentives and CEO attributes on TA. Findings exhibit a significant impact of foreign CEO on TA, whereas an insider CEO mitigates TA. The results hold for several robustness tests, with lag effective tax rate as dependent variable and with splitting foreign CEO into European and non-European origin. Research limitations/implications First, the sample is limited to 400 firm-year observations and to the German context. For shareholders, the study provides first evidence on relationships between the geographical and internal versus external labor market for CEOs and TA. For researchers, the findings underline the importance of integrating behavioral approaches like place attachment theory and the rooting theory in the theory of TA. Originality/value To the best of the authors’ knowledge, this is the first study to examine the impact of both contractual determinants and behavioral determinants on TA in the German context as an emerged economy with a dualistic corporate governance. This study contributes to the existing literature regarding the scientific debates about the impact of CEOs and CEO attributes on TA. It also analyses the balance between the place attachment theory and the rooting theory in the face of the compensation outcomes of agency theory.