Initial Phase Research Articles

Research on the dynamics of a heavy mechanized bridge in the deployment phase of the lifting frame

This article presents a dynamic model of the TMM-3M heavy mechanized bridge during the frame lifting stage, which is driven by a hydraulic system, constituting the initial phase of the bridge erection process. The model is constructed as a multi-body dynamic system, taking into account the elastic deformation of the rear outriggers, front tires, and front suspension system. The research model integrates a mechanical system controlled by hydraulic cylinders, with pressure being considered as a variable reacting to external loads during the system's operation. Lagrangian equations of the second kind are utilized to establish a system of differential equations describing the oscillations of the system and form the basis for investigating the dynamics of the frame lifting process. The system of differential equations is solved numerically using MATLAB simulation software based on the Runge-Kutta algorithm. The study has revealed laws regarding the displacement and velocity of components within the system, evaluating the stability of the TMM-3M heavy mechanized bridge during operation. This research paves the way for a comprehensive understanding of the working process of the TMM-3M heavy mechanized bridge, aiming for practical improvements to minimize deployment or retrieval time, reduce the number of deployment team members, enhance the automation of the operation process to reduce the workload for operators

Standard practices in cardiac monitoring: training needs of intensive care unit nurses

BackgroundEnforcing practice standards for cardiac monitoring in intensive care units (ICUs) has been shown to reduce misdiagnoses and inappropriate interventions. Continuous professional development (CPD) programs are committed to aligning clinical practices with recommended standards. The crucial initial phase in CPD development involves assessing the training needs of the targeted population.ObjectiveTo assess the training needs of ICU nurses in cardiac monitoring. The overarching goal was to formulate a focused Continuous Professional Development (CPD) program geared towards implementing standard practices in cardiac monitoring.MethodsThis study employed a generic qualitative approach with a descriptive design, utilizing interviews and focus groups from July to September 2018. Involving 16 ICU nurses. Content analysis was employed, encompassing transcription, fluctuant and iterative reading, unitization, categorization, coding, description, and interpretation.ResultsAll nurses recognized cardiac monitoring’s importance in the ICU but reported barriers to its effective implementation which were related to factors that could addressed by a CPD as insufficient knowledge and skills. Training needs were identified in both clinical and technical aspects, with recommendations for practical and theoretical activities and e-learning strategies. Barriers related to organizational aspects (equipment and communication within the healthcare team) were also mentioned.ConclusionICU nurses presented clear and specific training needs related to cardiac monitoring as knowledge, skills, and competencies. Other organizational aspects were also reported as barriers. Addressing these learning needs through targeted CPD aligned with organizational initiatives can contribute to enhancing the quality of cardiac monitoring practices in ICUs.

Functional connectivity key feature analysis of cognitive impairment patients based on microstate brain network.

Mild cognitive impairment (MCI) is the initial phase of Alzheimer's disease (AD). The cognitive decline is linked to abnormal connectivity between different regions of the brain. Most brain network studies fail to consider the changes in brain patterns and do not reflect the dynamic pathological characteristics of patients. Therefore, this paper proposes a method for constructing brain networks based on microstate sequences. It also analyzes the microstate temporal parameters and introduces a new feature, the brain homeostasis coefficient (Bhc), to quantify the stability of patient brain connections. The results showed that microstate class B parameters were higher in the MCI than in the HC group. Additionally, the Bhc values in most channels of the MCI and AD groups were lower than those of the HC group, with the most significant differences observed in the right frontal lobe. These differences were statistically significant (P < 0.05). The findings indicate that connectivity in the right frontal lobe may be most severely disrupted in patients with cognitive impairment. Furthermore, the Montreal Cognitive Assessment score showed a strong positive correlation with Bhc. This suggests that Bhc could be a novel biomarker for evaluating cognitive function in patients with cognitive impairment.

Controlling nonlinear collapse of ellipticity and orientation of a co-variant vector optical field.

A vector optical field with inhomogeneous spatial polarization distribution offers what we believe to be a new paradigm to form controllable filaments. However, it is challenging to steer multiple performances (e.g. number, orientation, and interval) of filaments in transparent nonlinear media at one time. Herein, we theoretically self-design and generate a kind of believed to be novel ellipticity and orientation co-variant vector optical field to interact with Kerr medium to solve this issue. The collapsing behaviors of such a new hybrid vector optical field reveal that, by judiciously adjusting the inherent topological charge and initial phase of incident optical field, we are able to give access to stable collapsing filamentation with tunable numbers, orientations and interval. Additionally, the collapsing patterns presented are immune nearly to the extra random noise. The relevant mechanism behind the collapse of the vector optical field is elucidated as well. The findings in this work may have huge potential in optical signal processing, laser machining, and other related applications.

Modular system innovation for increasing milkfish production on traditional ponds in Usto village, Mare district, Bone

One of the milkfish cultivation techniques (Chanos chanos) to increase pond productivity is a modular system. Modular system is a pond cultivation technique with a tiered plot model, where connecting between ponds is made, the purpose of which is to make milkfish easily move between ponds. The purpose of this study is to analyze the effectiveness of modular system innovation techniques (tiered plots) in increasing the production of bait milkfish in traditional ponds in Usto Village, Mare District, Bone. The research was conducted from August to the end of November 2023, at the location of the 2023 Matching Fund - Kedaireka program in a traditional pond in Usto Village, Mare District, Bone Regency, South Sulawesi Province. The research method used is a quantitative research method with a type of experimental research. Primary data measurements include; water quality measurement, including; temperature, salinity, pH and dissolved oxygen. While the parameters of the organization include the length of the fish from stocking to harvest. The data analysis method used is descriptive statistical analysis, in order to obtain an overview of the success of modular system techniques in traditional ponds. The results of the study obtained that the modular system technique can increase fish growth quickly, with the process of transferring fish periodically between ponds, making fish experience different conditions and physiological conditions that feel new. The modular system provides an average fish growth rate of 4.5 cm within 3 months or an average of 0.15 cm per day, with the highest rate occurring in rearing pond I, which is during the initial phase of post-stunting maintenance, which is an average growth rate of 6.2 cm per month, or 0.21 per day. Thus, this modular system technique can be used to increase the productivity of traditional ponds.

Mechanical power during mechanical ventilation

Mechanical ventilation provides lifesaving support for patients with acute respiratory failure. However, the pressures and volumes required to maintain gas exchange can cause ventilator-induced lung injury. The current approach to mechanical ventilation involves attention to both tidal volume and airway pressures, in particular plateau pressures and driving pressures. The ventilator provides energy to overcome airway resistance and to inflate alveolar structures. This energy delivered to the respiratory system per unit time equals mechanical power. Calculation of mechanical power provides a composite number that integrates pressures, volumes, and respiratory rates. Increased levels of mechanical power have been associated with tissue injury in animal models. In patients, mechanical power can predict outcomes, such as ICU mortality, when used in multivariable analyses. Increases in mechanical power during the initial phase of ventilation have been associated with worse outcomes. Mechanical power calculations can be used in patients on noninvasive ventilation, and measurements of mechanical power have been used to compare ventilator modes. Calculation of mechanical power requires measurement of the area in a hysteresis loop. Alternatively, simplified formulas have been developed to provide this calculation. However, this information is not available on most ventilators. Therefore, clinicians will need to make this calculation. In summary, calculation of mechanical power provides an estimate of the energy requirements for mechanical ventilation based on a composite of factors, including airway resistance, lung elastance, respiratory rate, and tidal volume.&#x0D; &#x0D; Key words: mechanical ventilation, mechanical power, ventilator-induced lung injury, energy, work

The longer, the farther? The internationalization of Chinese firms

Purpose This study aims to explore the internationalization of multinational enterprises (MNEs) from China and aims to examine the relationship between Chinese MNEs’ duration of internationalization and export intensity, and the contingent roles of the home country government. Design/methodology/approach By extending the springboard theory with institutional and cost-benefit analyses, the authors elaborate a two-phase framework of internationalization to explain how Chinese MNEs develop their international business under the influences of the home country government. Furthermore, the authors apply the Heckman two-stage method based on a panel data set of 19,994 firm-year observations of Chinese listed firms in 2008–2018 to test the hypotheses. Findings The research findings demonstrate an inverted U-shape relationship between the duration of internationalization and the export intensity of MNEs from China. The export intensity of MNEs from China increases during the initial phase of internationalization, and decreases during the subsequent. A further study reveals that the inverted U-shape of Chinese non-SOEs is steeper than that of SOEs, and this moderating effect is more salient after the Belt and Road Initiative. These results highlight the influence of the home government through state ownership and policies on the inverted U-shaped relationship. Originality/value This study helps to refine the understanding of Chinese MNEs’ global expansion by addressing time as an explicit dimension and revealing the mechanism of state ownership and the home country governmental policy in the dynamic internationalization process.

Demographic Dynamics in Organizational Stress and Coping Stress Perceptions: A Comprehensive Analysis

This study aims to assess both the stress levels of employees and identify the sources of stress. Data for the research were collected through questionnaires distributed to 60 employees via online surveys. The instruments utilized for data collection included the personal information form, organizational stress scale, and stress coping scale. The initial phase of data analysis focused on revealing the demographic characteristics of the workers. Subsequently, analyses were conducted to explore potential correlations between organizational stress, stress-coping mechanisms, and demographic variables. While socio-demographic variables are commonly treated as control variables in organizational stress research, this study distinguishes itself by placing them at the forefront. Its objective is to comprehend the influence of gender, age, educational background, marital status, income levels, titles, and total work experience on individuals' perspectives regarding organizational stress and stress coping. Examining survey data from Assist Company, a subsidiary of Turk Telekom, the study uncovers that male employees tend to display greater resilience to organizational stress. Additionally, unmarried workers demonstrate a higher efficacy in coping with stress compared to their married counterparts. The paper concludes with implications for managerial applications, theoretical insights, and suggestions for future research directions. In conclusion, the study recommends that firms consider implementing stress coping methods, techniques, and programs. Furthermore, it suggests organizations identify the specific factors within their structure that contribute to stress among employees.

Phase stability and initial phase high-temperature corrosion behavior of non-stoichiometric lanthanum cerium oxide thermal barrier coatings

Three different compositions of lanthanum cerium oxide thermal barrier coatings with varied cerium oxide (1.8CeO2, 1.9CeO2 and 2.0CeO2) compositions were synthesized and plasma sprayed to achieve free-standing coatings. The phase stability studies showed that the coatings are stable at 1400 °C after 100 h. The high-temperature corrosion was performed in the presence of sulfate-vanadate (50 wt%Na2SO4+50 wt%V2O5) at 1000 °C for 4 h. The result depicts LaVO4 as the major corrosive product formed in all three compositions along with CeVO4, LaCeVO4 and CeO2. The hot corrosion test result gives information that the composition of CeO2 greatly influences the hot corrosion resistance. The increase in CeO2 percentage increases the resistance against the infiltration of molten sulfate-vanadate compounds. This investigation provides a feasible way to understand the coating composition for better hot corrosion resistance in sulfate-vanadate mixtures.

HGSMDA: miRNA-Disease Association Prediction Based on HyperGCN and Sørensen-Dice Loss.

Biological research has demonstrated the significance of identifying miRNA-disease associations in the context of disease prevention, diagnosis, and treatment. However, the utilization of experimental approaches involving biological subjects to infer these associations is both costly and inefficient. Consequently, there is a pressing need to devise novel approaches that offer enhanced accuracy and effectiveness. Presently, the predominant methods employed for predicting disease associations rely on Graph Convolutional Network (GCN) techniques. However, the Graph Convolutional Network algorithm, which is locally aggregated, solely incorporates information from the immediate neighboring nodes of a given node at each layer. Consequently, GCN cannot simultaneously aggregate information from multiple nodes. This constraint significantly impacts the predictive efficacy of the model. To tackle this problem, we propose a novel approach, based on HyperGCN and Sørensen-Dice loss (HGSMDA), for predicting associations between miRNAs and diseases. In the initial phase, we developed multiple networks to represent the similarity between miRNAs and diseases and employed GCNs to extract information from diverse perspectives. Subsequently, we draw into HyperGCN to construct a miRNA-disease heteromorphic hypergraph using hypernodes and train GCN on the graph to aggregate information. Finally, we utilized the Sørensen-Dice loss function to evaluate the degree of similarity between the predicted outcomes and the ground truth values, thereby enabling the prediction of associations between miRNAs and diseases. In order to assess the soundness of our methodology, an extensive series of experiments was conducted employing the Human MicroRNA Disease Database (HMDD v3.2) as the dataset. The experimental outcomes unequivocally indicate that HGSMDA exhibits remarkable efficacy when compared to alternative methodologies. Furthermore, the predictive capacity of HGSMDA was corroborated through a case study focused on colon cancer. These findings strongly imply that HGSMDA represents a dependable and valid framework, thereby offering a novel avenue for investigating the intricate association between miRNAs and diseases.

Quantum Circuit-Based Proxy Blind Signatures: A Novel Approach and Experimental Evaluation on the IBM Quantum Cloud Platform

Abstract This paper presents a novel approach to proxy blind signatures in the realm of quantum circuits, aiming to enhance security while safeguarding sensitive information. The main objective of this research is to introduce a Quantum Proxy Blind Signature (QPBS) protocol that utilizes quantum logical gates and quantum measurement techniques. The QPBS protocol is constructed by initial phase, proximal blinding message phase, remote authorization and signature phase, remote validation and de-blinding phase. This innovative design ensures a secure mechanism for signing documents without revealing the content to the proxy signer, providing practical security authentication in a quantum environment under the assumption that the CNOT gates are securely implemented. Unlike existing approaches, our proposed QPBS protocol eliminates the need for quantum entanglement preparation, thus simplifying the implementation process. To assess the effectiveness and robustness of the QPBS protocol, we conduct comprehensive simulation studies in both ideal and noisy quantum environments on the IBM quantum cloud platform. The results demonstrate the superior performance of the QPBS algorithm, highlighting its resilience against repudiation and forgeability, key security concerns in the realm of proxy blind signatures. Furthermore, we have established authentic security thresholds (82.102%) in the presence of real noise, thereby emphasizing the practicality of our proposed solution.

Smartphone Fast Charger Analysis Based on RLC Circuit Transient Process

In today's era, the widespread adoption of mobile terminals, particularly smartphones, has been facilitated by the rapid advancements in information and communication technology. However, a notable drawback of smartphones is their high-power consumption, necessitating faster charging solutions to enhance user convenience. This paper focuses on the RLC circuit of fast chargers. Initially, it introduces the fundamental principles and associated protocols of fast charging technology, elucidating the operation of integrated circuits within fast chargers and highlighting the advantages of fast charging compared to conventional slow charging methods. Moreover, the paper conducts an in-depth analysis of the transient processes within the pivotal series RLC circuit in fast chargers, encompassing zero-input responses and sinusoidal AC excitation responses. The transient processes are categorized into overdamped, critically damped, or underdamped states based on circuit element parameters, with initial conditions and power supply phase determining whether the circuit transitions directly into a steady-state process when stimulated by sinusoidal AC. This research contributes to a deeper understanding of the RLC circuit within smartphone fast chargers and serves as a valuable reference for advancing the development of fast charging solutions.

Lower limb electromyographic characteristics and implications of taekwondo roundhouse kick "hit" and "miss" actions.

To compare the muscular characteristics of "hit" and "miss" actions in roundhouse kicks among taekwondo athletes, and explore the similarities, differences, and implications for training, motion tests were conducted on ten taekwondo athletes using Noraxon32 and VICON. The results showed no significant differences (p > 0.05) in integrated electromyography (EMG) during the initiation and kicking phases between "miss" and "hit" actions. However, during the retraction phase, significant differences (p < 0.05) were observed in the left rectus femoris, left peroneus longus, right biceps femoris, right semitendinosus, and right tibialis anterior muscles. The tibialis anterior muscle of the swinging leg was activated first in the "hit" action, while the biceps femoris was activated first in the "miss" action. The supporting-side rectus femoris was activated first in the "hit" action, whereas it was the biceps femoris in the "miss" action. In both techniques, the gluteus maximus was the last muscle to be activated. The "miss" action had a longer cycle, and the duration of muscle work was longer than in the "hit" action. During the retraction phase of the front leg roundhouse kick, the muscles worked more than during the kicking phase, with the erector spinae and tibialis anterior being the core force-producing muscles in both techniques, characterized by high EMG values and long activation times. In the "miss" action, the thigh muscles drove the calf muscles, while the "hit" action exhibited the opposite pattern. "Hit" actions had a faster cycle compared to "miss," with greater force generation in "miss." The hip flexors and knee extensors of the kicking leg were the core force-producing muscles during the kicking process, determining the effectiveness and completion of the action.

Switchable Two-Dimensional AND and Exclusive OR Operation Based on Dual-Wavelength Metasurfaces.

Logic operation serves as the foundation and core element of computing networks; it will bring huge vitality to advanced information processing with its adaptation in the optical domain. As fundamental logic operations, AND and exclusive OR (XOR) operations serve a multitude of purposes, such as their ability to cooperate in enabling image processing and interpretation. Here, we propose and experimentally demonstrate a wavelength multiplexed AND and XOR function based on metasurfaces. By combining two cosine gratings with distinct frequencies and an initial phase difference of π/2, we extract the similarities and differences between two input images simultaneously by illuminating them with 445 and 633 nm wavelengths. Additionally, we explore its potential in information encryption, where overall security is enhanced by distributing distinct parts of initial information and encoded keys to different receivers. This design possesses the benefits of convenient mode switching and high-quality imaging, facilitating advanced applications in pattern recognition, machine vision, medical diagnosis, etc.

Poised for growth: Exploring the relationship between accelerator program design and startup performance

AbstractResearch SummaryAccelerator programs provide valuable market feedback and education to participants that may improve startup performance. However, it is unclear whether the average effect of accelerator participation on startup performance post acceleration is positive, and if so, how this effect varies with accelerator program design. We analyze data from 8580 startups that made it past the initial selection stage at 408 accelerators in 176 countries between 2013 and 2019. We compare accelerated and non‐accelerated startups and find a positive average effect of accelerator participation on startup performance post acceleration. Moreover, we find that this effect varies substantially with program design, and depends on venture stage, industry, and founder expertise. Our findings highlight the impact of program design on the benefits that startups derive from accelerator participation.Managerial SummaryThe purpose of this study is to determine whether participation in startup accelerator programs generally leads to improved startup performance post acceleration and how the benefits of participation might vary with the design of these programs. We analyze data from 8580 startups that applied to and passed the initial selection phase at 408 accelerators in 176 countries over multiple cohorts between 2013 and 2019. Our results indicate that on average, startups that participate in accelerators perform better than those that are also selected but not accelerated. Furthermore, we find that the benefits that startups gain from being accelerated vary with the design of the program. Our findings highlight the importance of accelerator program design in influencing the extent of improvement in startup performance post acceleration.

Smoking behavior among Asian Americans during the initial phase of the COVID-19 pandemic: The influence of pandemic stressors and depression.

Heightened levels of distress among Asian Americans during the initial phases of the pandemic may be associated with current smoking behavior. In this study, we examine differences in current smoking among Asian Americans from two different ethnic backgrounds before and during the COVID-19 pandemic. We analyzed cross-sectional survey data (n=202) from Chinese and South Asian adults in Chicago, collected between February and May 2020. We conducted logistic regression models to estimate the relationship between exposure to the COVID-19 pandemic and current smoking. We tested whether the association varied by Asian American ethnic group, unemployment, racial discrimination, and depression symptoms. We found that current smoking increased from 28% to 48% among Asian Americans (i.e. Chinese and South Asians) during the pandemic. We found a statistically significant interaction between the COVID-19 period indicator variable and current smoking by Asian American ethnic groups (p=0.014), such that current smoking was lower for Chinese compared to South Asians before COVID-19, but was comparable for both groups during the pandemic. We also found a statistically significant interaction between the period indicator variable and current smoking by racial discrimination (p=0.047) and depression symptoms (p=0.02). Results from these interactions suggest that Asian Americans who experienced racial discrimination and depression during the pandemic may be more likely to be current smokers compared to their pre-pandemic counterparts. The findings of the study highlight the need for culturally tailored smoking cessation interventions for Asian American communities that address pandemic-related stressors such as discrimination that may trigger cigarette use.

Sensors for Digital Transformation in Smart Forestry.

Smart forestry, an innovative approach leveraging artificial intelligence (AI), aims to enhance forest management while minimizing the environmental impact. The efficacy of AI in this domain is contingent upon the availability of extensive, high-quality data, underscoring the pivotal role of sensor-based data acquisition in the digital transformation of forestry. However, the complexity and challenging conditions of forest environments often impede data collection efforts. Achieving the full potential of smart forestry necessitates a comprehensive integration of sensor technologies throughout the process chain, ensuring the production of standardized, high-quality data essential for AI applications. This paper highlights the symbiotic relationship between human expertise and the digital transformation in forestry, particularly under challenging conditions. We emphasize the human-in-the-loop approach, which allows experts to directly influence data generation, enhancing adaptability and effectiveness in diverse scenarios. A critical aspect of this integration is the deployment of autonomous robotic systems in forests, functioning both as data collectors and processing hubs. These systems are instrumental in facilitating sensor integration and generating substantial volumes of quality data. We present our universal sensor platform, detailing our experiences and the critical importance of the initial phase in digital transformation-the generation of comprehensive, high-quality data. The selection of appropriate sensors is a key factor in this process, and our findings underscore its significance in advancing smart forestry.

Phase Retrieval for Radar Constant–Modulus Signal Design Based on the Bacterial Foraging Optimization Algorithm

Optimizing the energy spectrum density (ESD) of a transmitted waveform can improve radar performance. The design of a time–domain constant–modulus signal corresponding to the transmitted waveform ESD is practically important because constant–modulus signals can maximize transmission power and meet the hardware requirements of radar transmitters. Here, we present a time–domain signal design under dual constraints of energy and constant modulus. The mutual information (MI)–based waveform design method is used to design transmitted waveform ESD under the energy constraint. Then, the bacterial foraging optimization algorithm (BFOA) is proposed to design the time–domain constant–modulus signal. We use minimum mean square error (MMSE) in the frequency domain as the cost function. The BFOA monotonously decreases the MMSE with increasing iterations, which makes the ESD of the time–domain constant–modulus signal close to the MI–based optimal waveform ESD. The simulation results indicate that the proposed algorithm has advantages, including insensitivity to initial phases, rapid convergence, smaller MI loss, and MMSE compared with the iterative reconstruction algorithm.

Development of Rock Classification Systems: A Comprehensive Review with Emphasis on Artificial Intelligence Techniques

At the initial phases of tunnel design, information on rock properties is often limited. In such instances, the engineering classification of the rock is recommended as a primary assessment of its geotechnical condition. This paper reviews different rock mass classification methods in the tunnel industry. First, some important considerations for the classification of rock are discussed, such as rock quality designation (RQD), uniaxial compressive strength (UCS) and groundwater condition. Traditional rock classification methods are then assessed, including the rock structure rating (RSR), rock mass rating (RMR), rock mass index (RMI), geological strength index (GSI) and tunnelling quality index (Q system). As RMR and the Q system are two commonly used methods, the relationships between them are summarized and explored. Subsequently, we introduce the detailed application of artificial intelligence (AI) method on rock classification. The advantages and limitations of traditional methods and artificial intelligence (AI) methods are indicated, and their application scopes are clarified. Finally, we provide suggestions for the selection of rock classification methods and prospect the possible future research trends.

Unlocking the Secondary Critical Raw Material Potential of Historical Mine Sites, Lousal Mine, Southern Portugal

A steady supply of mineral raw materials is vital for the transition to a low-carbon, circular economy. The number of active mines in Europe has severely declined over the last century and half, giving rise to many abandoned mining waste sites and corresponding geological heritage. Also, the rise in minerals demand for large-scale deployment of renewable energy requires the continued and steady availability of key minerals. The supply risk associated with unpredicted geopolitical events needs to be eliminated/mitigated. Historical mine waste sites are the answer but evaluating mine waste is a lengthy and costly exercise. The study, undertaken in the Lousal Mine, used small unmanned aerial systems (sUASs) to model and determine mine waste volumes by generating orthomosaic maps with quick, inexpensive, and reliable results. Calculated mine waste volumes between 308,478 m3 and 322,455 m3 were obtained. XRD and p-XRF techniques determined the mineralogy and chemistry of waste, which varied from mineralization and host rocks with hydrothermal alteration and numerous neogenic sulphates (mostly gypsum, rhomboclase, ferricopiapite, coquimbite, and jarosite) related with supergene processes and weathering. The study shows the viability of using these sUASs to successfully model historical mine waste sites in an initial phase and for future monitoring programs.