bluuebunny/crossref_metadata_2025 huggingface dataset:
Effectiveness of hydrotherapy on pain and functional status of shoulder joint among individuals undergone intra-articular injections
Authors: Srinivasan Anandh , Kumar S. Senthil
Published: October 2024

Background

The shoulder complex with its varied pathology responds differently from patient to patient and so, the best option for progression is a choice of rehabilitation. In the present post-COVID scenario, many patients are referred to acute and sub-acute shoulder pain and dysfunction. It is very difficult to predict the outcome of therapy. Some patients recover even without any modalities. Recurrent or adverse symptoms are also possible. Gender, other demographic features, physiological response, and progressive outcome need to be considered in rehabilitation. This study is intended to evaluate the short-term effects of hydrotherapy post-intra-articular injections of shoulder dysfunctions.

Aims and objectives

To evaluate the effectiveness of hydrotherapy during early rehabilitation of individuals who have undergone intra-articular injections of shoulder joint on pain and function.

Materials and methods

In this prospective study, we have studied a total of 59 cases within the age group of 40–70 years with idiopathic frozen shoulder, acromio-clavicular joint dysfunction, and grade-1 untreated rotator cuff injuries. Group A patients were treated with conventional shoulder exercises and group B patients were treated in a hydrotherapy pool. The chief aim was to obtain earlier pain relief with controlled movement patterns and earlier relief from functional disability. The Constant-Murley score (CMS–100 points scale) composing pain and functional parameters was the outcome measure.

Results

After confounding all the demographic factors, it was found that the experimental group B treated by hydrotherapy revealed a statistically extremely significant difference (p value < 0.0001) at the end of the first week as compared to the conventional group. The treatment protocol was continued for 5 weeks. More than 90% of patients in the hydrotherapy group felt it easier and exercise participation progression was best. Better progression and improvement of shoulder function were achieved in the hydrotherapy group compared to the conventional group.

Conclusion

From this study, we conclude that hydrotherapy during early management of shoulder dysfunctions post-intra-articular injections is safer and provides excellent relief from pain as early as the first week. Functional progression is better and avoids complications related to mobilization.
Translation, cross-cultural adaptation, and psychometric testing of the STarT musculoskeletal tool into Yoruba language among persons with low back pain
Authors: Mbada Chidozie Emmanuel , Ariyo Olaide Elizabeth , Fasuyi Francis , Sonuga Ademola , Fatoye Clara Toyin , Gebrye Tadesse , Oyewole Olufemi Oyeleye , Fatoye Francis
Published: October 2024

Background

The STarT Musculoskeletal (MSK) tool is a validated tool used to stratify patients with musculoskeletal disorder, as a guide to applying intervention and prognosticating outcomes. Only few translations and cultural adaptations of it exist. The availability of the tool in local and indigenous languages may help improve comprehensibility and usage among patients. This study was aimed to translate and cross-culturally adapt the STarT MSK tool into the Yoruba language, and to determine its psychometric properties.

Methods

The first stage of this study involved translation of the English STarT MSK into the the Yoruba language following the Beaton criteria. A total of 55 respondents with low- back pain attending a University Teaching Hospital participated in the validity testing, while 25 patients responded in the reliability test of the tool. The Quadruple Visual Analogue Scale (QVAS) and the Fear Avoidance Belief Questionnaire (FABQ) were used for the convergent and the discriminant validity of the tool.

Results

The mean age of the respondents was 52.13 ± 13.21 years. The Yoruba version of the STarT MSK (STarT MSK–Y) had an acceptable concurrent validity (r = 0.993; p = 0.001). The discriminant validity of STarT MSK–Y with FABQ yielded correlation co-efficient scores of r = 0.287; p = 0.034 and r = 0.033; p = 0.810 for FABQ-Work and FABQ-physical activities. The result indicated that STarT MSK–Y had fair discriminant validity with FABQ-work and a weak correlation with the FABQ-physical activities. The convergent validity of STarT MSK–Y indicated significant correlations with all domains and global score of the QVAS (r = 0.727; p = 0.001). The test- retest reliability and internal consistency (Cronbach’s alpha = α) of the STarT MSK–Y yielded ICC = 1.00 and α = 0.97 for the global score of the items, respectively. The factor loading for five items were satisfactory ranging from 0.46 to 0.83.

Conclusion

The STarT MSK–Y has acceptable validity and reliability and can be used as a valid assessment tool among Yoruba-

speaking patients with low back pain.
Geophysical archaeology for the study of complex historical-period burial spaces: An example from the Fort Lewis military cemetery, Colorado
Authors: Sturm Jennie O.
Published: October 2024

Geophysical methods have long been used to locate and map unmarked graves in cemeteries and burial spaces. Despite their prevalence, geophysical cemetery surveys can be extremely complex, particularly when dealing with historical-period cemeteries. Not only are there interpretive challenges from site formation processes and noncultural features, but there are also cultural challenges when seeking to study a cemetery within its historical context. Geophysical archaeology is a specialization that can guide this type of work by underscoring the cultural context for geophysical data. Beyond simply locating unmarked graves or other “anomalies,” geophysical archaeology emphasizes increasing the interpretive ability of geophysical data by situating these surveys in cultural theory. Using the example of the Fort Lewis military cemetery, which also served the Fort Lewis Indian Boarding School, in southwest Colorado, this paper highlights how geophysical archaeology was used to identify specific features about the cemetery, including the original fence line, number of likely graves, and two different burial contexts (casketed burials and exhumed graves).
Quality control on vibrator ground force in land vibroseis acquisition
Authors: Qi Yongfei , Wei Zhouhong , Wang Rong , Li Guofa
Published: October 2024

Many quality control (QC) methods are used to monitor the output force from seismic vibrators. These QC tools usually provide the vibrator output force in three aspects — fundamental force, phase, and harmonic distortion — to measure the quality of the ground force compared with the predetermined sweep. As the vibrator works on challenging earth surfaces, such as unconsolidated ground surfaces, the peak harmonic distortion, a key vibrator QC parameter, always exceeds its QC threshold. This necessitates a vibrator reshoot and thereby a degradation of productivity. The purpose of this paper is to demonstrate that in unconsolidated ground surface areas a new setting of the harmonic cutoff frequency on a vibrator controller is recommended. This new setting reduces the unnecessary harmonics around 200 Hz so that the peak harmonic distortion is controlled in the required QC range. Therefore, unnecessary vibrator reshoots can be avoided, leading to improved productivity.
Revolutionizing Cloud Security: A Novel Framework for Enhanced Data Protection in Transmission and Migration
Authors: Dasari RakeshNag , Babu G. Rama Mohan
Published: October 2024

This research introduces a novel security framework specifically tailored to enhance data protection during cloud transmission and migration. Our study addresses critical gaps in existing security models by proposing a multi-dimensional system that incorporates advanced encryption techniques, dynamic access control, and continuous security auditing. Notably, this framework excels in ensuring cloud data integrity, confidentiality, and availability—core aspects often compromised under conventional methods. Comparative analysis with existing models in simulated cloud environments reveals that our framework significantly enhances threat detection accuracy, response speed, and resource management efficiency. The findings highlight the system’s capability to reduce security vulnerabilities while optimizing operational overhead, presenting a substantial improvement over traditional security solutions. This innovative approach, marked by improved scalability and flexibility, is poised to revolutionize cloud data security practices across various industries, prompting further research into robust cloud computing security methodologies.
Fuzzy based Decision-Making Algorithm for Solving Big Data Issues in Smart Cities
Authors: Li Weining , Zhu Hui
Published: October 2024

To better provide urban services and build an increasingly sustainable architecture, big data can be used to make more efficient use of current assets while enhancing the caliber of services offered to local inhabitants. However, there are several challenges to incorporating big data into existing infrastructure. Therefore, this research aims to determine the problems associated with Big Data’s effectiveness in developing intelligent towns and to investigate the connections between those difficulties. The 14 issues with Big Data were found through a literature study, and the precision was checked by feedback from professionals. Next, we employ a combined approach based on fuzzy interpretation, Structured simulation, and the Fuzzy Making Decisions Trial and Assessment Laboratories to decipher the connections between our identified problems incorporating Big Data into the development of smart cities is hampered, as shown by the analysis of links between challenges, primarily by the heterogeneous inhabitants in developed cities and the lack of connectivity. The findings of this study will provide creative city practitioners and policy planners with the information they need to successfully tackle these obstacles, clearing the way for the widespread adoption of smart city technologies. This research is a first step towards creating an interpretive structural model of the difficulties brought on by Big Data in cutting-edge urban planning. The study attempts, in part, to use this paradigm to better understand the relationship among the highlighted issues.
Simulation of Motion Nonlinear Error Compensation of CNC Machine Tools With Multi-Axis Linkage
Authors: Li Xianyi
Published: October 2024

In order to solve the problem of nonlinear error for a dual rotary table five-axis CNC machine tool due to the linkage of rotary and translational axes, the simulation of motion nonlinear error compensation for a multi-axis linkage CNC machine tool is proposed. The adjacent points in the tool position file are selected as the tool position points for building the model, and then the nonlinear error model resolved by the harmonic function is established according to the error distribution in the classical post-processing. The nonlinear error between the two tool position points is quickly predicted by the analytical expression of this model, and the real-time error compensation of the intermediate interpolation points is realized. Finally, MALTLAB simulation analysis is performed on the tool position file of an impeller part machining to verify the effectiveness of the proposed algorithm. The experimental results show that it can be seen from the distribution curve of the nonlinear error that it is about 10% after compensation as before compensation, thus verifying the effectiveness of the nonlinear error compensation mechanism. The correctness of the nonlinear error analysis and compensation method and the effectiveness of post-processing are verified.
The Security and Protection System of Electromechanical Equipment in Smart Campus using the Improved Data Mining Algorithm
Authors: He Anyuan
Published: October 2024

In order to improve the maintenance and management efficiency of campus electromechanical equipment and reduce or even avoid the safety risks brought by campus electromechanical equipment, this work uses the data mining algorithm to design the security and protection system of campus electromechanical equipment. First, this work constructs the campus electromechanical equipment classification model using the Bayesian algorithm of data mining algorithm and designs a simulation experiment to verify the effect of the classification model. Then, the security and protection system for the campus electromechanical equipment is designed. It includes the system business process, system function design, system core module’s function design and system implementation. Finally, a simulation experiment is designed to verify the system’s performance. The results show that: (1) Bayesian algorithm is superior to the K-Nearest Neighbor (KNN) algorithm in both classification effect and running time. (2) When the browser concurrency in the system increases, the server processor and memory usage also increases, but the value meets the expected requirements. It shows that the system has a certain browser concurrency-bearing capacity. Moreover, as the browser concurrency of the system increases, the response time of the test also increases, but the value meets the expected requirements. This work aims to improve the maintenance efficiency of campus electromechanical equipment and provide a reference for the safety protection work of electromechanical equipment in other enterprises or units.
Edge Computing Method for False Data Injection Attack Detection in Electromechanical Transient Simulation Grid
Authors: Yang Gang , Zhang Ying , Zhao Lili , Zhang Limin , Zhang Na
Published: October 2024

Because edge computing is close to the terminal, it has significant advantages of low latency and high-security realtime control and has huge application prospects in the current popular smart grid. Because its edge side is close to the terminal, it can also effectively avoid the risk of information leakage when control instructions or communication data are transmitted to remote cloud center servers over a long distance. However, edge devices have a greater probability of encountering false data injection attacks (FDIAs) from illegal terminals because of their proximity to terminals. The transient electromechanical situation of the smart grid due to FDIAs is analyzed under edge computing. Due to the characteristics that the status values of grid nodes have temporal correlation before and after and spatial correlation between nodes, the Long Short-Term Memory (LSTM) for training time series data is selected to predict the status values of grid nodes in advance. The FDIA detection method is also proposed based on the LSTM network. By calculating the predicted value at each historical moment and the root means square error (RMSE) of the system state estimation at that moment, the detection threshold of the scheme is calculated through the cumulative distribution function of RMSE. Simulation experiments are conducted on the Institute of Electrical and Electronics Engineers (IEEE)-14 node standard system. The detection rate is as high as 99.71%, which verifies the effectiveness of the proposed FDIA detection scheme. This paper studies the security threat of FDIA to the stable power system operation and provides theoretical analysis and practical reference for other power grid security protection strategies.
Identifying Crop Distress and Stress-Induced Plant Diseases Using Hyper Spectral Image Analysis
Authors: Min Byungchan
Published: October 2024

The purpose of this study is to determine whether hyper spectral images can be used to identify plant diseases and crop pressure from aerial photographs. With extensive research on the prevalent methods used closer to the problem, this study offers a potent strategy for identifying crop distress and illnesses using this efficient imaging technique. To identify the spectral fingerprints of common indications and symptoms of plant diseases and crop strains, this study evaluates the available hyper spectral photo datasets. After that, the data are examined using two learning algorithms—the highly randomized trees and the Random Woodland set of rules—to create predictions that are entirely dependent on the results that are discovered. In the end, a benchmarked set of test statistics is used to assess the prediction accuracy. The results of this study show that hyper spectral photo evaluation has a strong and promising utility for crop stress and disease identification. Hyper spectral light evaluation is a method for identifying plant diseases caused by strain on crops. By gathering and evaluating high-dimensional spectral reflection data from satellite or aircraft structures, details regarding the physiological homes of flowers can be identified. The health of plants, illnesses brought on by stress, and agricultural productivity predictions can all be made using these facts. Hyper spectral recordings can also be used to create actions that reduce agricultural losses and enhance the health of plants that are prone to disease.
The Defect Identification System of Electromechanical Equipment on the Edge Side of the Power Grid under Edge Computing
Authors: Han Haian , Hu Fan
Published: October 2024

With the development of the industrial Internet of Things, modern industrial systems have developed towards intelligence. Electromechanical Equipment (EE) is essential, and its defect identification is fundamental. Firstly, this research introduces the basic content of Gated Recurrent Unit (GRU), Variational Auto-Encoder (VAE) in Deep Learning (DL), and Edge Computing (EC) to explore the construction of a defect identification system for EE on the edge side of the power grid. Secondly, combined with the advantages of GRU and VAE, a GRU-VAE defect recognition model is proposed. Then, the EC architecture is introduced, and the EE defect identification system based on the GRU-VAE algorithm is constructed. The EC intelligent EE defect identification service system is designed with this as the core. Finally, simulation experiments are carried out using different data sets to verify the performance of the GRU-VAE model. The results show that the GRU-VAE model has higher precision and recall than the separate GRU model and VAE model, and the corresponding F1 value is also higher. The F1 value can reach 0.997 on aperiodic data and 0.966 on periodic data. In addition, the optimal thresholds of different datasets are analyzed, and the relationship between the length of the time window and the model’s performance is studied. When the time window length is 15, the model performance is optimal. This research on the defect identification system of EE on the edge side of the power grid based on EC and DL can provide a new path and inject new vitality into the defect detection of EE.
Research and Empirical Evidence of Machine Learning based Financial Statement Analysis Methods
Authors: Fan Yaotang
Published: October 2024

This study presents a novel approach called FinAnalytix which merging machine learning’s prowess in pattern recognition with financial statement analysis. This integrated algorithm combines deep neural networks and recurrent neural networks for predictive accuracy in stock return analysis, alongside logistic regression and random forest models for robust fraud detection in financial statements. The empirical evidence demonstrates FinAnalytix effectiveness in identifying abnormal financial patterns and predicting market reactions to earnings announcements. The study utilizes extensive data from listed companies, ensuring a comprehensive and practical application. FinAnalytix represents a significant advancement in the field, providing a dual approach to financial analysis for enhancing investment strategies through accurate stock return forecasts and bolstering financial integrity by detecting fraudulent activities. The simulation of the study based on the financial data of 100 sample listed companies. This research not only bridges the gap between traditional financial analysis and modern machine learning techniques but also offers a powerful tool for investors and regulatory bodies in navigating the complex financial landscape.
Research on the Application of Artificial Intelligence-based Cost Estimation and Cost Control Methods in Green Buildings
Authors: Zhang Yan
Published: October 2024

In the research titled Comprehensive AI-Driven Cost Dynamics Model (AICD-CDM) for Sustainable Green Building Projects, we delve into the burgeoning field of artificial intelligence to revolutionize cost estimation and control in green building construction. This study introduces AICD-CDM, a novel framework that integrates several advanced machine learning techniques, including Linear Regression (LR), Artificial Neural Networks (ANN), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting (LGBoost), and Natural Gradient Boosting (NGBoost), to address the multifaceted challenges of cost prediction and management in sustainable building projects. By leveraging the distinct strengths of these methods, the AICD-CDM model offers a multi-dimensional approach to cost estimation, providing not only point predictions but also probabilistic forecasts to better manage uncertainties inherent in green building projects. The model’s capability to process complex, non-linear relationships between a multitude of cost-influencing factors makes it exceptionally adept at handling the intricate dynamics of sustainable construction. Furthermore, the integration of AI techniques ensures enhanced accuracy, adaptability, and computational efficiency, making the AICD-CDM an invaluable tool for decision-makers in the green building sector. This research not only contributes to the field of construction management by introducing a sophisticated cost control mechanism but also aligns with global sustainability goals by promoting efficient resource allocation and cost optimization in green buildings. The findings and methodologies of this study have the potential to set new benchmarks in the application of AI in sustainable construction management.
Research on Supply Chain Optimization and Management Based on Deep Reinforcement Learning
Authors: Yunxiang Gao , Zhao Wang
Published: October 2024

This research introduces a groundbreaking approach to supply chain optimization and management, termed as Deep Reinforcement Learning based Supply Chain Optimization and Management (DRL-SCOM). At the core of this approach is the utilization of advancements in Deep Reinforcement Learning (DRL), specifically through the integration of Randomized Ensembled Double Q-learning (REDQ) and Trust Region Policy Optimization (TRPO). DRL-SCOM is designed to effectively tackle the inherent complexities and dynamic challenges that are characteristic of supply chain management. One of the key strengths of DRL-SCOM lies in its use of REDQ, which plays a crucial role in mitigating the overestimation bias commonly associated with traditional Q-learning methods. This results in more accurate value estimation and policy improvement, a critical factor in the effective management of supply chains. Additionally, the integration of TRPO into the framework brings the advantage of safe and stable policy updates. Such stability is vital for maintaining the robustness required in the fluctuating environment of supply chain operations. The combination of REDQ and TRPO in DRL-SCOM creates a powerful synergy. REDQ’s ensembled learning approach, when fused with TRPO’s trust-region method, enables the framework to efficiently navigate the complex and high-dimensional decision space typical of supply chains. This allows for real-time optimization of decisions while staying within operational constraints. The DRL-SCOM methodology shows significant potential in addressing various aspects of supply chain management, from demand forecasting and inventory management to logistics, adeptly handling the nonlinearities and uncertainties that are prevalent in these areas. Thus, the DRL-SCOM framework emerges as an innovative solution, pushing the frontiers of traditional supply chain management. It paves the way for a more agile, responsive, and intelligent system, equipped to adapt to changing market demands and operational challenges. This approach represents a significant stride towards transforming supply chain management into a more advanced, data-driven, and adaptive field.
The Application of Deep Learning in Sports Competition Data Prediction
Authors: Chen Ji , Cui Pengtao
Published: October 2024

In order to predict sports competition data, the author needs to implement the structure and related processes of the relevant competition victory and defeat prediction system, and specifically introduce and plan the implementation of each functional module. The data collection and storage module adopts Alibaba Cloud servers and combines Python to remotely and automatically collect data on a scheduled basis, according to the actual situation of game wins and losses, data cleaning and filtering are carried out, and multiple encoding forms are used to vectorize the data in order to find the best model. The data is divided according to the standard training and testing sets, and multiple classifiers are used for model training and saved locally for direct use next time; Test the above model using the training set; Compare the advantages and disadvantages of each vectorized encoding and classifier based on the final performance evaluation module. Based on the relevant experimental results, a detailed analysis was conducted to compare the advantages and disadvantages of each model, proving that introducing word vectors (word embeddings) into the competition data analysis system is worthwhile. We have obtained an excellent performance prediction model with a highest accuracy P of 0.825, a recall R of 0.729, and a corresponding F1 value of 0.774. For a prediction model that only knows the initial lineup allocation as a prerequisite, this already has sufficient practical guidance significance.
Upper Limb Rehabilitation Robots Training Analysis based on Multi-sensor Trajectory Data and Human-computer Interaction
Authors: Liu Yanyu , Lao Xianqian
Published: October 2024

Upper limb rehabilitation robots have important practical significance in helping patients recover their motor function, but traditional training methods often lack real-time and accurate evaluation of patient movement status and intention and have low interactive participation. Therefore, the study proposes a training system for upper limb rehabilitation robots based on multi-sensor trajectory data and human-computer interaction. The system is designed from three aspects: inertial sensor trajectory tracking, Kinect sensor image restoration, and interaction system design. A polynomial joint zero value constraint algorithm is introduced to correct errors, and variable parameter pixel filtering combined with a weighted average moving algorithm is used to improve image quality. The training results showed that the robot trajectory tracking accuracy significantly improved, and the improvement in motion trajectory error was greater than 20%. Compared with traditional training methods, interactive training had a recognition accuracy of over 85% in rehabilitation actions, and the system had better stability. This rehabilitation robot can effectively meet the feasibility and effectiveness of improving the interaction system, providing new technological means for intelligent medical services.
The Application of Big Data Technology in the Analysis of Commercial Circulation Data in Emerging Industries
Authors: Jia Xiaoqin , Zhang Li , Jia Xiaoqin
Published: October 2024

Inside the generation of rapid technological development, rising industries are increasingly counting on big facts to pressure growth and innovation. This examine explores the transformative effect of huge facts generation on the analysis of commercial circulate statistics in those burgeoning sectors. By way of harnessing numerous and voluminous datasets, organisations inside rising industries can uncover crucial insights, optimise delivery chains, are expecting marketplace developments, and enhance patron reviews. The paper begins by using outlining the unique challenges and possibilities that emerging industries face within the virtual landscape, emphasising the want for strong facts-driven techniques. We delve into the methodologies of large facts analytics, together with data acquisition, garage, processing, and visualization strategies tailor-made for the nuanced requirements of these industries. A crucial examination of case research wherein huge facts has been efficaciously carried out offers realistic insights into its effectiveness and barriers. The examine similarly investigates the role of advanced analytics, system studying, and AI in refining data evaluation techniques, providing a complete view of the way those technologies synergistically make contributions to strategic selection-making. Moral issues, especially regarding records of privateness and safety, are also addressed, acknowledging the responsibilities that accompany the utilization of huge information. The paper concludes by projecting future traits in big statistics programs within rising industries, which includes the capacity for predictive analytics and the integration of IoT gadgets. This research now not only underscores the significance of big statistics in revolutionising business flow however also serves as a guiding framework for industry leaders and stakeholders trying to navigate the complexities of the digital age in rising markets.
Design and Practice of Virtual Experimental Scenes Integrating Computer Vision and Image Processing Technologies
Authors: Ma XinHai , Qi YunSon
Published: October 2024

In this work, we introduce a novel framework, GAN-based Image Processing (GAN-IP), to design and manipulate digital experimental settings that effectively combine computer vision and image processing technology. Using the skills of Generative Adversarial Networks (GANs), GAN-IP creates and complements virtual scenes and affords rich, adaptive surroundings for analysing and creating computationally smart and perceptive algorithms. GAN-IP solves the pressing troubles of variability and absence of data through synthesising realistic pictures and scenarios, enabling simulations of the diverse environments in which computer vision structures have to function. Our approach improves the fidelity and sort of digital scenes and introduces a way to mechanically alter and evoke a pleasant image, enabling more accurate and powerful computer vision and prescient models. Through extensive experimentation, GAN-IP demonstrates remarkable improvements in the performance of computer vision tasks, including object detection, segmentation, and recognition in complicated virtual environments. This research lays the foundation for future studies in this field and provides an adaptive tool for researchers and practitioners to simulate and test superior computer imaginative and prescient image processing technologies.
Larvas de Odonata (Insecta) em um trecho do rio Marambaia, ilha da Marambaia, Rio de Janeiro, Brasil: distribuição e preferência por substratos
Authors: Souza Karoline Oliveira de , Bernardo Antônio Igor Vieira , Vieira Arize Duarte , Joaquim Junior Evaldo Alves , Almeida Gisele Luziane de , Andrade Jéssica Furtado de , Gouvêa Leandro Ferreira , Souza Rachel Amaro de , Avelino-Capistrano Fernanda
Published: October 2024

Odonata é uma importante ordem de insetos hemimetabólicos com grande representação popular. As larvas são aquáticas e podem colonizar ambientes lóticos e lênticos. O objetivo deste estudo foi verificar a distribuição de larvas de Odonata em diferentes substratos, caracterizar o padrão de distribuição destas larvas e verificar a preferência por micro-habitat dos indivíduos em relação aos substratos em um trecho do Rio Marambaia, na Ilha da Marambaia, Rio de Janeiro. A Ilha da Marambaia está localizada na Baía de Sepetiba sendo uma importante área de Mata Atlântica preservada, que se encontra sob jurisdição do corpo de Fuzileiros Navais. Coletas mensais foram realizadas entre os meses de julho/2018 a junho/2019, em três áreas de 100 m, ao longo de um trecho contínuo de 1 km. Coletas manuais foram realizadas em cinco diferentes substratos: areia, folhiço de fundo, folhiço retido, rocha fixa e rocha rolada. Foram coletadas 154 larvas, identificado e distribuído em seis famílias e seis gêneros: Limnetron (Förster, 1907) (Aeshnidae) (n = 14), Argia (Rambur, 1842) (Coenagrionidae) (n = 105), Brechmorhoga (Kirby, 1894) (Libellulidae) (n = 5), Progomphus (Selys, 1854) (Gomphidae) (n = 7), Heteragrion (Selys, 1862) (Megapodagrionidae) (n = 18), Perilestes (Selys, 1862) (Perilestidae) (n = 5). O trecho com maior abundância foi o Trecho 2. Argia foi o gênero mais abundante (68,2% das amostras) e a sua preferência foi pelo substrato rocha; Heteragrion, Limnetron e Perilestes demonstraram ter preferência por substratos com folhiço; Brechmorhoga e Progomphus por substratos com areia, corroborando com dados citados na literatura.
An Efficient Optimization Model and Tabu Search–Based Global Optimization Approach for the Continuous p-Dispersion Problem
Authors: Lai Xiangjing , Lin Zhenheng , Hao Jin-Kao , Wu Qinghua
Published: October 2024

Continuous p-dispersion problems with and without boundary constraints are NP-hard optimization problems with numerous real-world applications, notably in facility location and circle packing, which are widely studied in mathematics and operations research. In this work, we concentrate on general cases with a nonconvex multiply connected region that are rarely studied in the literature due to their intractability and the absence of an efficient optimization model. Using the penalty function approach, we design a unified and almost everywhere differentiable optimization model for these complex problems and propose a tabu search–based global optimization (TSGO) algorithm for solving them. Computational results over a variety of benchmark instances show that the proposed model works very well, allowing popular local optimization methods (e.g., the quasi-Newton methods and the conjugate gradient methods) to reach high-precision solutions due to the differentiability of the model. These results further demonstrate that the proposed TSGO algorithm is very efficient and significantly outperforms several popular global optimization algorithms in the literature, improving the best-known solutions for several existing instances in a short computational time. Experimental analyses are conducted to show the influence of several key ingredients of the algorithm on computational performance.

History: Accepted by Erwin Pesch, Area Editor for Heuristic Search & Approximation Algorithms.

Funding: This work was supported by the National Natural Science Foundation of China [Grants 72122006, 71821001, and 72471100]

Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0089 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0089 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .
Extractivism and Climate Justice in a Context of Political Contestation in Zimbabwe
Authors: Bhatasara Sandra , Nyamwanza Admire M.
Published: October 2024

This chapter explores the nexus between extractivism and climate justice in Zimbabwe. The country has been going through a protracted political crisis, particularly so after the post-November 2017 “military assisted transition”. Available data shows that the extractive sector plays a strong economic role in different countries,many of which face challenges such as resource dependency and weak governance. Selected key sectors—mining and energy—are at the core of extractivism in Zimbabwe, leaving the country stuck in the fossil energy age. This comes at a time when the world is calling for green transitions to cleaner and sustainable renewable energy and the country has made commitments to a low carbon development strategy.The new lithium frontier is expanding and mutating and this can facilitate clean energy and mobility transition while also creating green job opportunities. However, as we will illustrate in the following chapter, green jobs and green energy are, so far, a fantasy.This chapter utilizes various case studies to explicate what can be conceived as violent extractivism and extractivist banditry in both old and new mineral frontiers, showing how a political rhetoric on green transition is playing out and how climate justice remains an illusion for local communities.
How Green and Just? Transition to Renewable Energy in Turkey
Authors: Özen Hayriye
Published: October 2024

his study examines the transition to renewable energy in Turkey under the AKP government. It seeks to explore from a political ecology perspective to what extent this transition was just and green. Taking into account the broader political economic structure and associated power relations, it demonstrates that the main driving force behind this transition is not green energy production, but to open new natural resources and areas to capital accumulation within the framework of the AKP government’s recently formulated economic policies. The study also shows how mainstream green discourse serves the AKP in legitimizing the opening up of new elements of nature to an exploitative form of renewable energy development that is neither fair nor green. Analysis of the Turkish case thus shows how new environmental and social crises could arise from the “green” transition advocated by the mainstream green discourse
The (In-)Justice League and the Battle of the Climate Narratives: An Ethnographic Study of Climate Policy Skepticism in the Norwegian Paradox
Authors: Wilhelmsen Marthe Elden
Published: October 2024

This ethnographic study explored the justice perceptions of climate policy skeptics in Southern Norway. The data was generated through fieldwork from 2022–2024. The participants viewed themselves as marginalized truth-tellers in a moralized society, feeling excluded from the dominant climate discourse. Their climate skepticism was often driven by a sense of injustice, challenging the established narrative. This study underscores the significance of social identity and perception of exclusion in shaping climate skepticism. In this case, the injustice perception was linked to defending their privileges and themselves from change and responsibility. People’s different normative perceptions of climate justice and who we can trust determines their justice perception and feeling of injustice. To have a constructive conversation about climate policies, we need to understand what lies behind the climate skeptical claims.
Climate of Regression: Public Climate Attitudes and Radical Right Anti-Climate Mobilization in the Battle Around the Green Transition in Germany
Authors: Richter Christoph , Salheiser Axel , Patz Janine , Marschner Noah
Published: October 2024

In the past, climate regressive movements and narratives have rather enjoyed a niche existence in public and political discourse in Germany. Recently, the more offensive climate policy under participation of the Green Party and the heated, populist debates about specific climate protection measures have increased public conflict concerning climate protection. The radical right, alongside parties of the democratic spectrum, successfully mobilizes fears of decline and loss against climate protection measures. Currently, there are indications that the formally high level of support for climate protection measures in Germany is decreasing. Against this background, we analyze a survey data set (N=8642) obtained in 2022/23 and investigate which of the numerous factors known from international research on climate perception and resistance to climate protection (including socio-demographic, socio-economic, political and cultural characteristics, as well as media use, institutional trust and exposure of climate risks) influence the perception of the climate crisis and the support for climate policies in Germany. In line with international research findings, our research points to a strong impact of political and cultural values, shaping attitudes towards climate change perception and action.
RASO: an Ontology on Requirements for the Development of Adaptive Systems.
Authors: Peçanha Cássio Capucho , Duarte Bruno Borlini , Falbo Ricardo de Almeida , Souza Vitor E. Silva
Published: October 2024

There is growing interest in software that can adapt their behavior todeal with deviations between their outcome and their requirements at runtime.A systematic mapping of the literature on self-adaptation approaches based on requirements models revealed over 200 papers on this subject. However, there is still a lack of a formal and explicit representation of the concepts in this domain, which can lead to problems in communication, learning, problem-solving and interoperability. To make a clear and precise description of this domain, this paper proposes RASO: the Requirements-based Adaptive Systems Ontology. RASOwas built using a well-established Ontology Engineering method, is grounded on a foundational ontology and reuses concepts from other software-related ontologies.The ontology was evaluated by mapping constructs from the most referenced approaches from the literature to its concepts, thus creating a path for interoperability among them.
Fundamentals of shade matching
Authors: Mackenzie Louis , Parmar Dipesh , Bonsor Stephen J
Published: October 2024

With a move in contemporary restorative dentistry towards the provision of tooth-coloured restorations, the ability of the clinical team to achieve imperceptible shade matching between the (direct or indirect) restorative material and the dental hard tissues is highly desirable. The present article provides an overview of the physics of light, and how colours are perceived by the human eye. It then discusses the factors that affect the optical properties of natural teeth and dental restorative materials alike, and provides tips on how the clinician can achieve best results when ‘taking the shade.’

CPD/Clinical Relevance: The art of shade matching is a fundamental clinical skill in restorative dentistry to ensure that the best possible aesthetic outcome is achieved.
Tracheostomy tube changes by speech-language pathologists: a service review of an expanded scope of practice
Authors: Russell Scott M , Highsmith Lindsey ,   Grady Memorial Health System Group, Belagaje Samir , Henriquez Oswaldo , Moore Charles
Published: October 2024

Background/Aims

Patients in acute care who have received a tracheotomy require efficient management to adequately progress through the acute care setting. This progression is predicated on efficient tracheostomy weaning through tracheostomy downsizing, capping and decannulation. The objective of this retrospective study was to review the outcomes of tracheostomy tube changes by speech-language pathologists.

Methods

A retrospective cohort analysis was performed of adult patients who received a tracheostomy tube change during their acute care hospitalisation by speech-language pathologists during a 6-month span in 2022. A total of 38 consecutive referrals undergoing an initial tracheostomy tube change had their electronic medical records reviewed. Success was defined as the placement of the tracheostomy tube into the tracheal lumen with confirmation of placement with no adverse events. Complications were recorded at the time of the tracheostomy tube change.

Results

Speech-language pathologists performed 38 (100%) initial tracheostomy tube changes successfully at the bedside without complications. The primary referral services were trauma and neurology. The primary diagnoses were polytrauma with a traumatic brain injury and multiple trauma without a traumatic brain injury or spinal cord injury. The mean Glasgow Coma Scale score and number of intubations were 12.4 and 1.4 respectively. A total of 34 (89%) tracheostomy tube changes resulted in the downsizing of the tracheostomy tube, while the remainder of tracheostomy tube changes (n=4; 11%) maintained the same size but were cuffless. All the tracheostomy tube changes were completed without complications, and all patients were noted as stable after 24 hours.

Conclusions

The procedural success corroborates the data reported by an earlier study in the literature and further validates the efficacy of a competent and experienced speech-language pathologists' ability to perform low-risk tracheostomy tube changes in the acute setting. This study demonstrates real world data from a single large trauma centre of tracheostomy tube changes performed by speech-language pathologists. Such data better defines the emerging role expansion of speech-language pathologists in the acute care setting.

Implications for practice

This study has the potential to expand the discussion around speech-language pathologists changing tracheostomy tubes in the acute care setting for a low-risk cohort. The authors have outlined the requisite training that may serve as a resource for speech-language pathologists to develop procedural competency.
Phobia: Supporting children and young people with specific fears
Authors: Thornton Stephanie
Published: October 2024

Research suggests that around 10% of children and teenagers have a specific phobia that significantly affects their mental health, functioning and development. Stephanie Thornton looks at the incidence, causes and interventions.
Paramedics at risk? How responding to a terror attack affects mental wellbeing
Authors: Tew Joanna , Ward Richard
Published: October 2024

Background:

Terror attacks have a significant impact on first responders’ wellbeing, with an associated risk of post-traumatic stress disorder (PTSD).

Aims:

To improve understanding of how responding to terror attacks affects paramedics’ mental health.

Methodology:

A literature search was carried out on databases using the PRISMA strategy. Critical appraisal tools were used alongside thematic analysis to appraise and synthesise the literature.

Findings:

Ten papers were identified. Themes arising regarding risks to responders included: the level of exposure, with the amount related to the risks of developing PTSD and psychopathology; and level of preparedness, with responders carrying out unfamiliar tasks and working outside the scope of practice as well as lacking education on the psychological impact of terror attacks.

Conclusion:

If paramedics are deployed into terror attack warm zones, policy needs to consider the associated psychological risk. Training should incorporate stress management techniques and education on the extreme emotions these practitioners may experience. More research specifically with the paramedic population is required to understand the risk posed to them and how this may be managed.
Historical perspectives of heat-related illness part 2: the present and future
Authors: Hannides Michael , Walter Edward
Published: October 2024

Part one of this series on heat-related illness described how the disease has been known and written about for thousands of years. Since then, there have been some improvements in our understanding of its risks, mechanisms and treatments. However, heat-related illness remains a significant problem, and can be fatal or cause ongoing disability even after the acute episode has passed. This narrative review, the second article in a two-part series describes current understanding of heat-related illness and offers an insight into what we may learn in the future about its cause and potential new treatments and strategies.
Both sides of the picture
Authors: Cochrane Alice
Published: October 2024

In this column, Alice Cochrane reflects on some of the more difficult aspects of her job, and the importance of sharing this to convey an honest portrayal of her NQP experience