bluuebunny/crossref_metadata_2025 huggingface dataset:
Learning from the Life Cycle Assessment of Power-to-hydrogen Systems
Authors: Gibon Thomas
Published: October 2024

In a decarbonisation context, understanding the potential environmental implications of power-to-gas pathways is key. Life cycle assessment (LCA) is widely recognized as a tool of choice to quantify the environmental impacts of a product or service. Here we assess various hydrogen production routes, including steam methane reforming (SMR) and electrolysis. Results show that the environmental footprint of hydrogen production is intricately linked to factors such as the electricity source and methane leakage. While “green” hydrogen widely inherits the properties of its electricity source, the life cycle greenhouse gas emissions of “blue” hydrogen are significantly influenced by the upstream natural gas supply chain. The study underscores the importance of stringent certification for ensuring low-carbon hydrogen production. Comparisons with other sectors, such as transportation, highlight the unique challenges and opportunities posed by hydrogen production methods. Looking ahead, future analyses should prioritize application-level assessments and quantify the environmental profiles of power-to-gas systems amid evolving contexts and changing energy landscapes.
Retinal Vein Occlusion
Authors: Chang Andrew , Kotecha Aachal
Published: October 2024

This animated video article provides an overview of the pathogenesis, symptoms, risk factors, and unmet needs in retinal vein occlusion.
Research on time characteristic sea surface temperature prediction based on decomposition
Authors: Chen Jing , Xu Wentao , Jiang Hongbin , Chen Xiao
Published: October 2024

The effective prediction of sea surface temperature has become a hot topic of scientific research. The hidden characteristics and time characteristics of sea surface temperature are studied, and a new sea surface temperature prediction model SLA based on decomposition is proposed in this paper. Firstly, the trend term and periodic seasonal term of sea surface temperature were decomposed by STL decomposition algorithm, which improved the problem of single characteristics of sea surface. Secondly, on the basis of the calculation method of the improved LSTM model and the attention mechanism, the problem that the prediction of sea surface temperature cannot be remembered in the long term and the influence weight of historical information is ignored is solved. Finally, the sea surface temperature of the South China Sea (112.125E,10.125N) was taken as the research object, and the experimental verification was carried out in three data sets. The experimental results show that the SLA model proposed in this paper has good prediction accuracy.
Solving inverse boundary value problem of Poisson equation by LS-SVM
Authors: Huang Minghao , Wu Ziku , Han Xiaoming
Published: October 2024

In this paper, a new method based on least squares support vector machines (LS-SVM) is presented for solving the inverse boundary value problem of Poisson equation. The closed form analytical solution is obtained by optimizing the regression parameters. The core problem is to transform the parametric regression problem into a quadratic programming problem. To demonstrate the efficiency of the proposed algorithm, numerical experiments are conducted. The proposed method is found to be feasible for the inverse boundary value problem of Poisson equation.
Research on assembly constraints reconstruction based on Unreal Engine
Authors: Tang Lei , Wang Daiyong , Mou Zixin , Li Ling , Long Yang , Li Dong
Published: October 2024

In the process of digital twin modeling, the assembly constraints of the model in the Unreal Engine usually requires manual intervention. To realize the intelligence and high efficiency of digital twin modeling, an Unreal Engine based assembly constraints reconstruction method is proposed. The method explores the high-fidelity assembly constraints mapping from the modeling software to the Unreal Engine, divides the model in the modeling software into two parts: the entity structure and the assembly constraints, and reconstructs the assembly constraints in the Unreal Engine, to realize the transformation from the modeling software model to the Unreal Engine model. Finally, the proposed method is applied to the digital twin engineering of various institutions to verify its rapidity and effectiveness in practical application scenarios.
Research on parameter deviation modelling of dual LCC wireless power transfer system
Authors: Yang Kaiying
Published: October 2024

In this paper, parameter deviation modelling analysis is performed based on the dual LCC compensation network of wireless power transfer (WPT) system. Firstly, the mutual inductive coupling model of the dual LCC compensation circuit is derived based on the fundamental wave analysis method; secondly, the parameter deviation is introduced into the circuit model for the errors existing in the inductive and capacitive components of the compensation network circuit of the actual wireless charging system, and the relation between the deviation and the amplitude of the branch currents, the phase angle and the output characteristics of system are obtained through the establishment of derivation model and the resistive deviation correction term is introduced; finally, the parameter deviation is verified through the simulation. The establishment of parameter deviation model increases the accuracy of the whole circuit model and provides the theoretical basis to reduce the negative influence of parameter deviation on transmission characteristics. The value of parameter deviation can be inverted through the phase angle, thus providing a basis for adjusting the resonant state of the compensation circuit.
Analysis of the influence of nonlinear energy sink parameters applicable to helicopter main reduction systems
Authors: Dai Zhixiong , Feng Zhizhuang , Qian Feng , Lu Xing , Xing Longtao
Published: October 2024

In response to the vibration problem of helicopter fuselage caused by continuous rotor periodic excitation force, a nonlinear energy sink is considered to be installed on the main reduction transmission channel to reduce the vibration level of the fuselage. This article conducts an analysis of the influence of nonlinear energy sink structure parameters under harmonic excitation force, establishes a coupled dynamic model of helicopter main reduction/absorber/fuselage, and uses harmonic balance method and stability analysis theory to analyze the periodic solution and stability of the system. Using the vibration amplitude of the system as the evaluation criterion, the influence of damping, stiffness, and mass ratio on the vibration suppression effect of the nonlinear energy sink was explored. The results showed that as the damping of the nonlinear energy sink increased, the resonance frequency of the coupled system shifted to the left, and the vibration amplitude of the main reduction system increased; As the stiffness of the nonlinear energy sink increases, the vibration amplitude of the coupled system will first decrease and then increase, gradually approaching the vibration amplitude of the system without coupled dampers; The influence of mass ratio on system amplitude follows the same trend as the influence of stiffness. By optimizing, the structural parameters of the nonlinear absorber with good vibration suppression effect are ultimately obtained.
Critical lines dynamic identification for grid-connected wind power system under N-k contingency based on Oscillating equations
Authors: Li Lijuan , Lv Zhiqiang , He Hao , Ouyang Wenzhao , Liu Renai
Published: October 2024

As the penetration rate of wind power and other renewable energy sources increases, power systems are more prone to experiencing multiple component failures, known as N-k contingency Identifying and then closely monitoring and protecting critical components during N-k contingency can effectively prevent widespread cascading failures. The primary task in identifying critical components is to model the power system; A refined model better captures the time-varying characteristics of electrical components. Therefore, a dynamic model of line power flow decay and oscillation based on swing equations is established; it introduces time-varying saturated cut-set and transfer margin indices to identify critical lines. Simulations on the IEEE-118 nodes system, along with MATCASC cascading failure simulations, demonstrate that targeting identified critical lines as deliberate attack points in the grid results in a normalized remaining load demand decrease to 50% of the original.
Research on ultra-high temperature sensor and cold end compensation error
Authors: Wang Zhenting , Liu Baowei , Qi Na , He Tianhao , Wang Yuanwei , Wang Mingqian
Published: October 2024

An ultra high temperature sensor and cold end compensation error are proposed. Aiming at the ultra-high temperature and relatively complex harsh environment of spacecraft, a sensor probe structure which can withstand the high temperature of 2500°C is presented in this paper. According to the practical application characteristics of the sensor with high measurement accuracy, the author proposed a high-precision cold end compensation method. Through this compensation technology, the ultra-high temperature sensor maintains high-precision temperature measurement in the full temperature range. The simulation analysis of the sensor structure and temperature test show that the sensor can not only realize temperature measurement at ultra-high temperature, but also control the nonlinear error to 0.6% of the full scale. The accuracy of the whole temperature range is controlled within 0.92% of the full scale, which meets the practical requirements.
Chaotic dynamics of a delayed fractional order MLNCML system
Authors: Liu Liyan
Published: October 2024

We present a delayed fractional order spatiotemporal dynamical system using the mixed linear-nonlinear coupling connections between lattices. In the simulation experiments, the dynamical characteristics of the proposed model are looked into and analyzed by using bifurcation diagrams and metric entropy density. The proposed model has fewer periodic windows in its bifurcation diagrams, large key space and strong ergodicity due to the delay. These excellent properties are of great significance in encryption. Moreover, the parameter μ breaks its fixed range in the traditional logistic map, which can contribute to enlarge the key space of cryptosystem. Through numerical simulations and security analysis, it is found that the proposed model is applied for encryption.
Design and realization of multifunctional energy meter calibration station under extreme conditions
Authors: Chen Qian , Chen Yunpeng , Liu Jianbo , Yang Mei , Sun Xiao Jie , Zhao Gege , Cao Baohua
Published: October 2024

This paper designs a multifunctional calibration station that can be installed inside the temperature box in order to adapt to some of the special test requirements, which can be used for the testing of most types of energy meters on the market. Extreme environments such as high and low temperatures can easily cause cracks in equipment welds, aging of material insulation, and functionally cause sensitive components to drift affecting the accuracy of testing during the life cycle of the calibrator. This multifunctional calibration station designed and implemented in this paper is capable of maintaining its functionality without being destroyed under repeated experiences of extreme conditions. In this paper, a multifunctional calibration station is designed and realised. In order to verify the influence of the calibrator on the calibration error of the energy meter after repeatedly experiencing extreme conditions, a comparison test of 40 cycles was carried out by comparing the calibrator with the one placed at room temperature, and the difference between the two calibrators’ measurement results did not show a regular growth trend with the increase in the number of measurement cycles, which proves that the calibrator is able to keep its function undamaged under the repeated experience of extreme conditions.
Numerical analysis of flexural behaviour of UHPC-CFTS columns as enclosure supports for electrical substations
Authors: Yan Yan , Gao Xian , Chen Xilong
Published: October 2024

This study establishes a finite element analysis model of ultra-high-performance concrete (UHPC) encased concrete filled steel tube (CFST) composite columns under combined compression and bending. Appropriate constitutive models and element types are selected as the basis. The finite element model simulates the test results of steel-concrete composite columns, and the applicability of the model is verified by comparison with existing test data of UHPC encased CFSTs. On the basis of demonstrating model reliability, the full-range load-deformation curves of UHPC encased CFSTs with different concrete strengths under compression-bending are compared. The failure modes and material stress distributions are analyzed to elucidate the influence mechanism of the UHPC on the flexural performance of UHPC encased CFSTs under combined compression and bending. The results show that compared to normal concrete, UHPC can effectively improve the flexural capacity of UHPC encased CFST columns. Owing to the addition of steel fibers, the failure mode of UHPC encased CFSTs under combined compression and bending is more integral compared to normal concrete. The research results of this study can provide references for the performance evaluation and design of UHPC encased CFSTs in the structural enclosures for electrical equipment in high/low voltage substations.
Development and application of carbon dioxide direct mineralization technology and emission reduction device
Authors: Li Dongfang , Zheng Zhang , Huang Zengyang
Published: October 2024

This study analyzes the principles and methods of carbon dioxide mineralization fixation to address the challenges posed by global climate and ecological changes, achieve carbon neutrality goals, and solve carbon utilization issues in the development of the fluorosilicon new material industry. The research focuses on the direct mineralization reaction system of carbon dioxide flue gas and the development of a carbon dioxide emission reduction device utilizing wet mineralized gas treatment technology. To implement alkaline waste management for industrial solid waste. The research shows that the wet carbon dioxide mineralization and emission reduction device can make the alkaline solution better mineralize carbon dioxide, avoid the gas discharge too fast and affect the reaction rate. At the same time, the solution is mixed and the exhaust linkage is carried out to further improve the reaction rate, and provide a reference for the development of large-scale carbon dioxide utilization technology suitable for high energy consumption industries.
Research on coupling analysis and decoupling control of two-degree-of-freedom PTZ system
Authors: Gao Jianshu , Zhao Lei
Published: October 2024

With the development of technology, especially in areas such as robotics, aerospace, photography, and videography, gimbal systems, as an important stabilization device, have been widely used. However, the two-degree-of-freedom gimbal system also faces a series of challenges in practical applications. Among them, one of the most critical issues is the internal coupling phenomenon in the system. The coupling phenomenon refers to the mutual influence and a constraint relationship between different degrees of freedom in the system, which may increase the complexity of the system’s dynamic characteristics and make the design and implementation of control strategies difficult. This study aims at explore more advanced and practical decoupling control strategies through in-depth analysis of the coupling characteristics of the two-degree-of-freedom gimbal system, providing new ideas and methods for the further optimization and application of the gimbal system.
Research on Calibration device of gas leakage alarm system and uncertainty analysis
Authors: Liu Xing , Qin Maoyuan , Yang Zhongzhi , Xiang Yang , Liu Wei
Published: October 2024

This paper studies the calibration device and method of the gas leakage alarm system, and evaluates the uncertainty of the measurement results. Firstly, it analyzes the important components, parameters, and working principles of the gas leakage alarm system. Then, it designs a set of inhalation-type gas leakage alarm system calibration devices to test the indication error, repeatability, and response time of the alarm system, and evaluates the uncertainty of the calibration device. The study concludes that the relative expanded measurement uncertainty of the device calibrating the alarm system is 2.6%, which meets the accuracy requirements of national standards for calibration devices. The main factors affecting the measurement results are the resolution and repeatability of the alarm system being tested. By improving the performance of the alarm system sensor, the measurement results of the alarm system can be made closer to the true value, reducing misjudgment and improving the accuracy of the system. And during calibration, the measurement capability of the device can be enhanced by using higher grade gas standard substances.
Study on bearsing capacity and rigidity of surface throttling bearing
Authors: Song Lihua , Kang Junyuan
Published: October 2024

Based on the lubrication principle and Reynolds theory, the influence of the pressure field and structural parameters on the bearing characteristics of the surface throttle bearing is analyzed and studied by using the finite difference method. The results show that the surface throttling bearing has obvious static bearing characteristics when the shaft is not rotating; The dynamic pressure effect of oil wedge load can be seen obviously when the shaft rotates. With the increase of radius gap h0, the bearing capacity and rigidity will decrease significantly. The increase of oil supply pressure will reduce bearing capacity and rigidity. With the increase of length-diameter ratio

L

¯

, the rigidity will be improved.
An improved relative integral capacity-K-means clustering method for capacity pre-sorting of decommissioned power batteries
Authors: Xu Xuntao , Wang Shunli , Liu Donglei , Fernandez Carlos , Blaabjerg Frede
Published: October 2024

Capacity sorting is the primary prerequisite for the stepwise utilization of decommissioned power batteries. This study proposes an improved relative integral capacity-K-means clustering (RIC-KMC) method for preliminary sorting of decommissioned power battery capacity. Firstly, according to the electrochemical aging characteristics of decommissioned power batteries, combined with the ampere-time integration algorithm, a new lossless extraction method of the capacity characteristics of Lithium-ion batteries based on the segment voltage is proposed. Second, to minimize the interference of environmental factors and sampling errors on the charging capacity, the relative amount of charging capacity is introduced. Finally, to complete the capacity sorting before the ladder utilization, an improved RIC-KMC method is proposed, which combines the electrochemical aging feature values of decommissioned power batteries extracted from the segment voltage charging capacity with the K-means clustering algorithm. Using three sets of battery charging and discharging data to validate the sorting algorithm, the capacity sorting errors are reduced by 0.926%, 12.381%, and 10.185%, respectively, which verification the validity of the proposed RIC-KMC method. This study provides a new solution for capacity pre-sorting of decommissioned power batteries for stepwise utilization.
Dyeing scheduling optimization in a multi-machine system with resource constraints
Authors: Gao Shaofeng , Zhang Biyue , Xiao Lei , Zhou Qihong
Published: October 2024

Water consumption is one of the most important concerns for a textile dyeing company in the era of global energy revolution. Different from the traditional production scheduling which focuses on the production efficiency improvement, the dyeing scheduling emphasizes the constraints of resource consumption. Besides, different from the traditional production scheduling where the task relevance is seldomly considered, the colour depth heavily impacts the water consumption and the dyeing scheduling results, which aggravates the uncertainty and complexity when modelling the dyeing scheduling optimization. Therefore, how to select proper dyeing orders among a large number of orders and schedule them for the purpose of generating more profit is challenging. To address the above issue, an optimization model with objective of maximizing profit and consideration of water consumption, delivery due time, and machine capacity, is built, and genetic algorithm is adopted to solve the problem. A numeric experiment is conducted to validate the effectiveness of the proposed method.
Research on deep reinforcement learning-based complex crowd navigation algorithm using risk perception and path selection
Authors: Zhao Tianxiang , Jiang Yang , Chang Jiajun , Zhao Bin
Published: October 2024

Aiming at the Frozen Robot Problem and the poor effect of dynamic obstacle avoidance in traditional navigation methods when encountering dynamic obstacles, a deep reinforcement learning navigation method based on risk perception and path selection is proposed. The core of this method lies in CP (Collision Probability) module and IOU (Intersection over Union) module. The CP module calculates the CP between the robot and the nearby dynamic obstacles in real time, so that the robot can avoid the dangerous obstacles first. At the same time, the intersection-merging ratio module calculates the “pass ability” of the area near the robot in real time, and guides the robot to choose a safer area to pass through. Compared with the DRL method without these two modules, the proposed method achieves the highest navigation success rate in all simulation test environments, and the maximum improvement rate is 11%. At the same time, it still has advantages in navigation time and other indicators.
Optimal inventory decisions of infrequently needed spare parts for maintenance with inaccurate demand forecasts and limited storage space
Authors: Liu Xuanyi , Xu Zhenqi , Zheng Meimei
Published: October 2024

In modern manufacturing workshops, maintaining adequate inventory for spare parts is an effective strategy to satisfy maintenance requirements. However, due to the stochastic nature of failures, spare parts often have characteristics of infrequent usage and inaccurate forecasted demand. Additionally, the multitude diversity of spare parts and limited storage space in warehouse poses challenges to inventory decisions regarding the types and quantity of spares. Thus, this paper investigates the inventory decision optimization problem of infrequently needed spare parts for maintenance within limited storage space and inaccurate forecasted demand. This paper constructed an integer programming model based on chance constraints to address the uncertainty of the storage space occupied by the spare parts. The main contributions of this paper are as follows. First, we investigate the inventory decision optimization problem within limited storage space and inaccurate forecasted demand, which are rarely considered in the optimization problem of the spare parts for maintenance. Second, we proposed a method for simplifying the computation of chance constraints based on a transformation of the Central Limit Theorem. Third, we proposed a heuristic iterate algorithm to search the optimal inventory decision by minimizing the overall downtime and storage costs. Using data from the workshop of an automotive enterprise, a case study is conducted by comparison with the Genetic Algorithm.
Failure analysis and life extension of mechanical products under multiple service conditions
Authors: Chen Shangong , Ren Jiadong , Shi Peng , He Hongdou , Hao Xiaobing , Huang Guoyan
Published: October 2024

During the service of engineering machinery products, the interaction of mechanical load, service environment and other conditions leads to the diversity and uncertainty of failure modes. Mechanism analysis is difficult to accurately calculate the dynamic changes of performance. Most existing machine learning methods only focus on the remaining useful life, without considering the internal mechanism and connection of multiple failure modes. In this paper, a resistance degradation model is established based on the data of multiple service cycles. According to the service data under different working conditions, a dynamic resistance degradation model based on classification and regression tree (CART) and a product service life enhancement strategy based on working condition sequence optimization are proposed. Firstly, the characteristics of different working conditions are analysed. Secondly, the data of multiple sensors are fused, and CART is used to fit the degradation model. Finally, a product service life optimization problem is designed to increase the life. Experiments show that the proposed method can fit the resistance degradation process, and can significantly improve the life of mechanical products.
High-accuracy gearbox fault detection using deep learning on vibrational data
Authors: Arshad Mayo Sohaib , Rehman Saud , Cai Zhiqiang
Published: October 2024

In the face of escalating demands for reliability in industrial machinery, this study introduces an advanced deep-learning model aimed at the early detection of faults in rotating gear systems. Addressing the need for timely fault diagnosis to prevent operational disruptions and financial setbacks, our work utilizes a novel neural network approach applied to vibrational data from Kaggle’s Gearbox Fault Diagnosis Dataset. The methodology involves data preprocessing steps, including normalization and one-hot encoding, followed by training a sequential neural network with multiple dense layers and dropout for regularization. The model, trained using the Adam optimizer and evaluated over 50 epochs, significantly outperforms traditional machine learning techniques, achieving a remarkable 98.63% accuracy in distinguishing between healthy and compromised gear conditions. This research marks a significant stride in predictive maintenance, providing a robust foundation for future development toward prognostic health management systems that anticipate maintenance needs and ensure uninterrupted industrial productivity.
Television and Academic Achievement: Evidence from the Digital Television Transition in the UK
Authors: Nieto Castro Adrián
Published: October 2024

This paper exploits exogenous variation in the transition date from an analogue to digital television signal in the UK across more than 32,000 geographical units to examine the causal impact of television on academic performance and potential mechanisms. Using a large administrative dataset on the universe of students in public education in England, I show that the switchover increased pupil test scores and that the effect is driven by economically disadvantaged students. Using TV market data and a child survey dataset, I investigate possible mechanisms. I show that the digital transition considerably increased TV viewing time and displaced forms of socialisation associated with risky behaviour for disadvantaged children.
A review and future trends of precision livestock over dairy and beef cow cattle with artificial intelligence
Authors: Michelena Álvaro , Fontenla-Romero Óscar , Luis Calvo-Rolle José
Published: October 2024

The demand for meat and dairy products is expected to rise significantly in the current demographic and economic growth context. Concurrently, various factors, including financial crises and reduced profitability, have decreased the number of livestock farms. Consequently, the livestock industry is undergoing intensification, with an increased number of cows per farm. This shift makes the management of animals increasingly complex. Amid these challenges, the society’s growing concern for animal health and welfare and the quality of consumed products underscores the importance of systems that aid in the individual and herd-level monitoring and control within livestock operations. In this scenario, precision livestock farming has become essential with the emergence of new sensors and technologies and the evolution of artificial intelligence. Against this backdrop, this paper reviews systems and techniques used in precision livestock farming to detect anomalies caused by reproductive processes and diseases in dairy and beef cattle. The article analyzes sensors and technologies employed, presents key studies conducted, and discusses the most commonly used artificial intelligence techniques in detecting prevalent anomalies.
Construction of a comprehensive meteorological risk index for wheat Fusarium head blight prediction based on more than a half-century of monitoring data
Authors: Xu Min , Xu Yifei , Xu Jingwei , Xu Meng , Yang Rongming , Liu Xin
Published: October 2024

Fusarium head blight (FHB) represents a critical threat to wheat production globally, not only reducing yields but also contaminating crops with harmful mycotoxins. This study aimed to elucidate new spatiotemporal patterns of FHB incidence and to develop a comprehensive meteorological risk index to enhance scientific prevention and control of the disease. Through the analysis of annual and decadal variations from 1965 to 2023, the study assessed FHB trends across four agricultural regions (I, II, III, and IV) in Jiangsu Province, located in the middle and lower reaches of the Yangtze River—a hotspot for FHB in China. Key findings include: Since 1965, Regions I and III consistently exhibited higher FHB incidence rates compared to Regions II and IV. Post-2000, there was a notable increase in years with high incidence rates, with Region III overtaking Region I as the region with the highest incidence. Since 2010, occurrences of FHB reaching the most severe grade (Grade 5) have surpassed those in previous decades across all regions. The study also revealed a stronger correlation between meteorological factors (cumulative precipitation, number of days with rainfall ≥ 0.1 mm, total rainy days with ≥ 2 and ≥3 consecutive days of rain, total rainy days with both average daily air temperature ≥ 15 °C and daily rainfall ≥ 0.1 mm, days with average daily relative humidity ≥ 85%, cumulative sunshine hours, and cumulative cloudy days) and the FHB incidence rates during the heading-flowering-grain filling period in Regions I, II, and III, compared to the heading-flowering period alone. This indicates that optimal temperature and high humidity during the grain filling stage significantly contribute to the final FHB incidence rates. Despite the less apparent correlation between temperature changes and disease rates, the significant warming trend observed since 2000 has likely fostered conditions conducive to the proliferation of FHB. The comprehensive meteorological risk index, constructed to incorporate key meteorological factors during the heading-flowering-filling period, showed a strong correlation with actual disease incidences. The index demonstrated fitting accuracy rates of 84.7% for Region I, 72.9% for Region II, 83.1% for Region III, and 90.9% for Region IV, underscoring its effectiveness in predicting FHB occurrences. This tool offers both convenience and practicality, providing valuable insights for strategically managing FHB risks based on local weather conditions.
The Impact of Paraspinal Muscle Degeneration on Oswestry Disability Index Subsections Two Years After Spinal Surgery for Degenerative Lumbar Spondylolisthesis
Authors: Hambrecht Jan , Köhli Paul , Duculan Roland , Lan Ranqing , Chiapparelli Erika , Guven Ali E. , Evangelisti Gisberto , Burkhard Marco D. , Tsuchiya Koki , Shue Jennifer , Sama Andrew A. , Cammisa Frank P. , Girardi Federico P. , Mancuso Carol A. , Hughes Alexander P.
Published: October 2024

Study Design.

Secondary analysis of prospective study.

Objective.

To investigate the impact of fatty infiltration (FI) in the paraspinal muscles (PM) on postoperative Oswestry Disability Index (ODI) subsection-scores in patients undergoing elective lumbar surgery for degenerative lumbar spondylolisthesis (DLS).

Background.

DLS can increase FI in the PM like the multifidus (MF), erector spinae (ES), and psoas (PS), leading to greater spinal disability and higher ODI-scores. It is unclear if increased PM FI affects all ODI subsections equally.

Methods.

This study reviewed data from a single-center cohort of DLS-patients who underwent elective lumbar surgery. Overall ODI and the subsection scores were prospectively assessed before and 2 years post-surgery. FI of the PS, MF, and ES was measured using T2-weighted MRI images. The relationship between PM FI and postoperative ODI subsections were analyzed, using multivariable linear regression analysis.

Results.

Overall, 229 patients (59% female, mean age of 67±8 y) were included with an overall baseline ODI of 50[40-62] and a postoperative ODI of 16.[2-34] The highest preoperative subsection-scores were seen in pain intensity (4[2-4]), changing degree of pain (4[3-4]), lifting (4[1-5]), and standing (4[3-4]). PM-measurements showed a mean FI of 41±10% for ES, 58±15% for MF, and 6±5% for PS. Patients with increased ES FI were more likely to show higher postoperative scores in all ODI subsections and in the overall ODI (Est=0.45, 95%CI 0.20-0.71, P=0.004). Increased MF FI was significantly associated with higher postoperative ODI subsection scores in standing (Est=0.02, 95%CI 0.01-0.03, P=0.033) and walking (Est=0.02, 95%CI 0.01-0.03, P=0.017).

Conclusion.

Increased erector spinae fatty infiltration is significantly associated with higher ODI scores across all subsections 2 years after lumbar surgery, while higher multifidus fatty infiltration is linked to greater disability in standing and walking. These findings underscore the need to maintain paraspinal muscle health to improve surgical planning, improve rehabilitation outcomes, and reduce postoperative disability.
Silicone on the Move: Migratory Siliconoma After Retinal Surgery Report of a Case and Review of the Literature
Authors: Finkelstein Mitchell J. , Pukhalskaya Tatsiana , North Jeffrey A. , Mully Thaddeus , Wesche W. Allen , Tetzlaff Michael T.
Published: October 2024

Silicone granulomas, or “siliconomas,” are the common foreign-body inflammatory responses to injected silicone material. In rare cases, siliconomas develop remotely from the original site of injection, secondary to silicone migration. If a history of silicone injection is not noted, such lesions risk misdiagnosis (possibly as infection or malignancy). Intraocular silicone oil is commonly used in retinal detachment surgery, with occasional reports of granulomatous responses after its application in this context. Here, we report a 66-year-old man who developed a periocular silicone granuloma years after scleral buckle surgery for a detached retina and summarize the literature of similar reports of migratory siliconoma.
The Effect of Mindfulness-Based Stress Reduction on Pediatric Oncology Nurses’ Stress Level, and Pediatric Oncology Patients’ Psychosocial Symptoms and Care Satisfaction
Authors: Semerci Remziye , Sarıbudak Tuğba P. , Çalışkan Behice B. , Altınel Dalmış Nazife , Yıldız Güldem , Kapancı Meryem
Published: October 2024

Studies investigating the effects of relaxation practices on nurses and patient outcomes are limited. This study aimed to evaluate the impact of Mindfulness-Based Stress Reduction (MBSR) on pediatric oncology nurses’ stress levels, as well as the psychosocial symptoms and care satisfaction of pediatric oncology patients. A non-randomized prospective study with a pretest-posttest design was conducted, involving 112 children and 8 pediatric oncology nurses between November 2021 and August 2022. The study was conducted in 3 stages. First, the children’s nursing care satisfaction and psychosocial symptoms were evaluated during the first 3 months before the MBSR intervention. Second, the nurses participated in the MBSR program for 2 months. Third, the children’s nursing care satisfaction and psychosocial symptoms were assessed again 3 months after the MBSR intervention. The results showed that children who received nursing care post-MBSR had lower scores in the Psychosocial, Hopelessness, and Communication Difficulty subscales compared to those who received care pre-MBSR. Additionally, these children had higher scores in Evaluation of Quality of Care from the Children’s Point of View, Psychosocial, Physical, and Information subscales. There was a significant reduction in the stress levels of nurses between the pre-MBSR and post-MBSR assessments. MBSR is an effective method for reducing pediatric oncology nurses’ perceived stress levels and should be utilized to support both pediatric oncology nurses and patients in enhancing their psychosocial functioning.
Academic Coaching in Entry-level Doctor of Physical Therapy Education
Authors: Black Kayla , Feda Jessica , Reynolds Breanna , Cutrone Guillermo , Gagnon Kendra
Published: October 2024

Background and Purpose.

Learners face a variety of academic and personal challenges that may impact performance in Doctor of Physical Therapy (DPT) programs. Academic coaching has emerged as a student support model within undergraduate medical education but has not yet been evaluated in DPT education. The purpose of this method/model presentation was to describe the development, implementation, and evaluation of 2 coaching programs.

Method/Model Description and Evaluation.

Two hybrid DPT programs implemented a coaching approach to support students. The coaching framework was centered around an individualized relationship between the learner and faculty coach to promote reflection, growth, agency, and persistence in educational and professional development. Faculty members were prepared and supported to serve as coaches. Coaches met with their assigned students at regular intervals throughout the program, including clinical experiences. The coaching programs were assessed through program exit surveys and an alumni survey.

Outcomes.

Graduates from both programs reported that the coaching program was effective and supported a sense of belonging or connectedness within their DPT program. They reported several specific activities that were part of the coaching program structure that supported its effectiveness, including group-based orientation activities and 1:1 meetings with their coach. Graduates also provided suggestions for program improvement including increased structure and more opportunities to connect with their peers and coaches in a nonacademic setting.

Discussion and Conclusion.

Two DPT programs successfully implemented coaching as their student support model. With appropriate preparation, planning, and support, coaching programs have the potential to provide individualized and meaningful support for DPT students.
Ergonomics in bronchoscopy
Authors: Di Felice Christopher , Alunilkummannil Jojo , Holden Van
Published: October 2024

Purpose of review

This study examines ergonomic considerations in bronchoscopic procedures, surveying existing research, injury rates, contributing factors, and practical ergonomic controls.

Recent findings

The field of ergonomics examines the relationship between workers and their workplace to enhance productivity and minimize injuries. Bronchoscopists may face ergonomic hazards due to extended periods of maintaining fixed positions, repetitive actions, and the design of both tools and procedure spaces. Studies on the ergonomics of bronchoscopy have revealed a wide range of musculoskeletal issues among practitioners. Those new to the field, including trainees and early-career professionals, may be particularly vulnerable to injuries compared to their more experienced counterparts. Implementing a systematic approach, such as that proposed by the National Institute for Occupational Safety and Health, could help reduce physical stress, discomfort, and the likelihood of musculoskeletal disorders for bronchoscopists.

Bronchoscopists increasingly face ergonomic issues in their practice. Despite available measures to address these problems, the field lacks uniform ergonomic guidelines for bronchoscopy. To safeguard bronchoscopists’ health and safety from the outset of their careers, more studies and training focused on ergonomics in bronchoscopy are needed.
Long-term Outcomes Following Non-operative Management of Acute Appendicitis
Authors: Telesnicki Teagan , Nantais Jordan , De Mestral Charles , de Buck van Overstraeten Anthony , Gomez David
Published: October 2024

Objective:

To describe long-term re-presentations and interventions following non-operative management (NOM) of acute appendicitis (AA).

Summary Background Data:

Trial data suggest NOM of AA carries a substantial risk of subsequent appendectomy, although NOM is increasingly offered to patients. Population-based data is required to understand the real-world long-term course of patients undergoing NOM of AA.

Methods:

This population-based cohort study included all adult patients in Ontario, Canada who presented to any emergency department (ED) with AA between 2004-2019. Patients who did not undergo a procedure on index ED presentation or hospital admission were defined as NOM and followed for five years. The cumulative incidence of composite re-presentation or intervention (ED re-presentation, re-admission, emergency, or scheduled appendicitis procedure) was calculated accounting for competing risk of death.

Results:

Of 156,362 patients identified with AA, 13,200 underwent NOM. The cumulative incidence of composite re-presentation or intervention was 33% at 1-year (95%CI 32-33%) and 36% at 5-years (95%CI 36-37%). At 5-years, the incidence of appendicitis-specific ED re-presentation or hospital re-admission was 16% (95%CI 15-16%), the incidence of an emergency procedure for AA was 12% (95%CI 12-13%), and the incidence of scheduled surgery was 21% (95%CI 20-21%). In a subgroup of patients with uncomplicated AA, composite re-presentation or intervention was 28% at 1-year (95%CI 27-29%) and 32% at 5-years (95%CI 32-33%).

Conclusions:

Real-world estimates of emergency re-presentation with or without urgent surgery following NOM of AA were lower than previously described. Scheduled appendectomy made up an important proportion of long-term interventions following NOM.
Escape motility of multicellular magnetotactic prokaryotes
Authors: Yang Xinyi , Prakash Manu , Brumley Douglas R.
Published: October 2024

Microorganisms often actively respond to multiple external stimuli to navigate toward their preferred niches. For example, unicellular magnetotactic bacteria integrate both oxygen sensory information and the Earth’s geomagnetic field to help them locate anoxic conditions in a process known as magneto-aerotaxis. However, for multicellular magnetotactic prokaryotes (MMPs), the colonial structure of 4–16 cells places fundamental constraints on collective sensing, colony motility and directed swimming. To investigate how colonies navigate environments with multiple stimuli, we performed microfluidic experiments of MMPs with opposing magnetic fields and oxygen gradients. These experiments reveal unusual back-and-forth excursions called ‘escape motility’, in which colonies shuttle along magnetic field lines, punctuated by abrupt—yet highly coordinated—changes in collective ciliary beating. Through cell tracking and numerical simulations, we demonstrate that escape motility can arise through a simple magneto-aerotaxis mechanism, which includes the effect of magnetic torques and chemical sensing. At sufficiently high densities of MMPs, we observe the formation of dynamic crystal structures, whose stability is governed by the magnetic field strength and near-field hydrodynamic interactions. The results shed light on how some of the earliest multicellular organisms navigate complex physico-chemical landscapes.
Paenibacillus suaedae sp. nov. and Paenibacillus violae sp. nov., isolated from the roots of Suaeda japonica Makino and Viola mandshurica W. Becker with plant growth-promoting potential
Authors: Kim Inhyup , Park Sunho , Jung Yonghee , Woo Haejin , Lee Hyunji , Yook Subin , Seo Taegun
Published: October 2024

Two novel Gram-positive strains, chi10T and PFR10T, were isolated from the roots of Suaeda japonica Makino and Viola mandshurica W. Becker, respectively. The strains are facultatively aerobic, rod-shaped, motile via peritrichous flagella and endospore-forming. Strains chi10T and PFR10T showed the highest 16S rRNA gene sequence similarity to Paenibacillus alvei DSM 29T (99.0%) and Paenibacillus planticolens LMG 31457T (99.3%). Furthermore, the phylogenetic and phylogenomic analyses demonstrated that strains chi10T and PFR10T were closely related to the genus Paenibacillus. The digital DNA–DNA hybridization (dDDH) values for strains used in the phylogenetic analysis and strain chi10T ranged from 19.6 to 28.4%, while for strain PFR10T, the values ranged from 19.8 to 53.5%, according to the Genome-to-Genome Distance Calculator 3.0, which were all less than 70%. Additionally, the dDDH value between strains chi10T and PFR10T was 22.0%, confirming that they are different species of the genus Paenibacillus. The average nucleotide identity between chi10T and PFR10T and other species of the genus Paenibacillus were 68.4–84.7% and 67.8–93.6%, respectively. In addition, the major polar lipids of chi10T and PFR10T consisted of diphosphatidylglycerol, phosphatidylglycerol and phosphatidylethanolamine. The major fatty acid profile of strain chi10T was iso-C15 : 0 and anteiso-C15 : 0, whereas the major fatty acid profile of strain PFR10T was iso-C15 : 0, anteiso-C15 : 0 and iso-C16 : 0. The sole respiratory quinone in strains chi10T and PFR10T was menaquinone-7. Phylogenomic, phenotypic and genome analyses strongly indicated that chi10T and PFR10T could be two novel Paenibacillus species for which the names Paenibacillus suaedae sp. nov. (type strain chi10T = KACC 23258T = TBRC 17803T) and Paenibacillus violae sp. nov. (type strain PFR10T = KACC 23263T = TBRC 17804T) are proposed, respectively.
The impact of perceived knowledge on marketing agility in the context of big data: role of deployment level
Authors: Haverila Matti , Haverila Kai Christian , McLaughlin Caitlin
Published: October 2024

Purpose

Against the backdrop of dynamic capabilities theory, this research examines the relationship between knowledge and marketing agility in the context of big data marketing analytics (BDMA). The relevant knowledge constructs under investigation are business/marketing, relational, technological and technology management. The level of BDMA deployment is also examined to determine its impact on these relationships.

Design/methodology/approach

A survey was used to gather data from marketing professionals working in firms with at least limited experience in big data (BD) deployment in the United States and Canada. The results were analyzed using partial least squares structural equation modeling (PLS-SEM) with a sample of 236 responses.

Findings

The results indicate that marketing professionals perceived the knowledge and marketing agility constructs differently than the previous research on IT professionals. The knowledge construct was perceived as a two-dimensional construct consisting of broad knowledge skills and specific technical knowledge skills. Only the broad knowledge skills construct was significantly related to the marketing agility construct, with progressively high predictive validity and relevance when the deployment of BDMA progresses.

Originality/value

The paper's originality stems from the different conceptualizations of the knowledge and marketing agility constructs due to the use of a novel sample of marketing professionals in this study. The research also contributes to the dynamic capabilities theory by emphasizing the critical role of vital knowledge when aiming to enhance marketing agility.