bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparison of Advanced Multivariable Control Techniques for Axial-Piston Pump
Authors: Mitov Alexander , Slavov Tsonyo , Kralev Jordan
Published: August 2024

This article is devoted to a comparison of two advanced control techniques applied to the same plant. The plant is a certain type of axial-piston pump. A linear-quadratic (LQR) controller and an H-infinity (H∞) controller were synthesized to regulate the displacement volume of the pump. The classical solution to such a problem is to use a hydro-mechanical controller (by pressure, flow rate, or power) but, in the available sources, there are solutions that implement proportional-integral-derivative (PID), LQR, model predictive control (MPC), etc. Unlike a classical solution, in our case, the hydro-mechanical controller is replaced by an electro-hydraulic proportional valve, which receives a reference signal from an industrial microcontroller. It is used as the actuator of the pump swash plate. The pump swash plate swivel angle determines the displacement volume, respectively, and the flow rate of the pump. The microcontroller is capable of embedding various control algorithms with different structures and complexities. The developed LQR and H∞ controllers are compared in the simulation and real experiment conditions. For this purpose, the authors have developed a laboratory experimental test bench, enabling a real-time function of the control system via USB/CAN communication. Both controllers are compared under different pump loading modes. Also, this paper contributes an uncertain model of an axial-piston pump with proportional valve control that is obtained from experimental data. Based on this model, the robust stability of the closed-loop system is investigated by comparing the structured singular value (μ). The investigations show that both control systems achieved robust stability. Moreover, they can tolerate up to four times larger uncertainties than modeled ones. The system with the H∞ controller attenuates approximately at least 30 times the disturbances with frequency up to 1 rad/s while the system with the LQR controller attenuates at least 10 times the same disturbances.
Ethical Dilemmas in Contemporary Igbo Christian Marriages: Navigating Modernity and Cultural Identities
Authors: Okoye Kanayochukwu Michael , Ugwu Ndidiamaka Vivian
Published: August 2024

This study explores the ethical dilemmas in contemporary Igbo Christian marriages as couples navigate the interaction between modernity and cultural identities. Marriage in traditional Igbo society is entrenched in strong cultural and religious values. However, Christianity and modernity have brought new dynamics to this institution in contemporary times. This paper, therefore, examines the influence of Christianity and modernity on Igbo traditional marriage, discussing ethical dilemmas arising from these influences. It specifically addresses areas where the traditional Igbo practices often clash with Christian doctrine and modern ideals, such as Igbo communalism, gender roles, family obligations, and marital expectations. The study also highlights strategies for resolving these dilemmas, including effective communication, cultural adaptability, and external support systems. Utilising a qualitative case study and descriptive–analytical methods, the research provides invaluable insights into the emerging dilemmas in Igbo marriage, offering a nuanced understanding of how individuals and communities can navigate these ethical complexities in a fast-shifting world. This work contributes to the broader discussions on cultural identities, religious practices, and ethical challenges in modern times.
Monitoring of Woody Biomass Quality in Italy over a Five-Year Period to Support Sustainability
Authors: Gasperini Thomas , Leoni Elena , Duca Daniele , De Francesco Carmine , Toscano Giuseppe
Published: August 2024

Biomass continues to play a key role as an alternative to fossil fuels. Woody biomass produces lower greenhouse gas emissions than fossil fuels. However, in order to consider biomass as ‘green energy’, a number of factors should be taken into account, including the characterization of the quality of the resource. Therefore, monitoring quality parameters, such as moisture, ash, N content, is essential to assess the sustainability of biomass for energy production. This paper presents the results of laboratory analyses performed on wood chip samples from four Italian regions over a five-year period (2019–2023). In particular, all quality parameters defined by ISO 17225-9 for industrial wood chips were assessed. Data were analyzed using descriptive, parametric, non-parametric statistics, and multivariate analysis. An interest in quality monitoring has been observed, indicated by an increase in the number of samples received from suppliers and an enhancement in the average values of quality parameters. Moreover, an overall decrease in moisture and N content has been observed, while ash content and heating value have undergone non-linear variations. Statistically significant quality differences between samples from different regions may be the result of different practices, such as outdoor or indoor storage, climate differences, different biomass growth conditions.
Ionospheric and Meteorological Anomalies Associated with the Earthquake in Central Asia on 22 January 2024
Authors: Lukianova Renata , Daurbayeva Gulbanu , Siylkanova Akgenzhe
Published: August 2024

On 22 January 2024, at 18 UT, a strong earthquake (EQ), Mw = 7, occurred with the epicenter at 41°N, 79°E. This seismic event generated a complex response, the elements of which correspond to the concept of lithosphere–atmosphere–ionosphere coupling through electromagnetic processes. While flying over the EQ area on the night-ide of the Earth, the tandem of low-orbiting Swarm satellites observed small-scale irregularities in the plasma density with an amplitude of ~1.5 × 104 el/cm3, which are likely associated with the penetration of the coseismic electric field into the ionosphere. The local anomaly was detected against the background of a global increase in total electron content, TEC (although geomagnetic indices remained quiet), since the moment of EQ coincided with the ionospheric response to a solar flare. In the troposphere, specific humidity decreased while latent heat flux and aerosol optical depth increased, all exhibiting the co-located disturbances that can be attributed to the effect of increased air ionization rates, resulting in greater electrical conductivity in the near-Earth boundary layer. Anomalies started developing over the epicenter the day before and maximized on the day of the main shock and aftershocks.
Non-Dominated Sorting Genetic Algorithm II (NSGA2)-Based Parameter Optimization of the MSMGWB Model Used in Remote Infrared Sensing Prediction for Hot Combustion Gas Plume
Authors: Li Yihan , Hu Haiyang , Wang Qiang
Published: August 2024

The Multi-Scale Multi-Group Wide-Band (MSMGWB) model was used to calculate radiative transfer in strongly non-isothermal and inhomogeneous media such as the remote infrared sensing of aircraft exhaust system and jet plume scenario. In this work, the reference temperature was introduced into the model as an independent variable for each spectral subinterval group. Then, to deal with the exceedingly vast parameter sample space (i.e., the combination of spectral subinterval grouping results, reference temperatures and Gaussian quadrature schemes), an MSMGWB model’s parameter optimization process superior to the exhaustive approach employed in previous studies was established, which was consisted of the Non-dominated Sorting Genetic Algorithm II method (NSGA2) and an iterative scan method. Through a series of 0-D test cases and two real 3-D remote infrared imaging results of an aircraft exhaust system, it was observed that the MSMGWB model established and optimiazed in current work demonstrated notable improvements in both accuracy and computational efficiency.
FMCW Laser Fuze Structure with Multi-Channel Beam Based on 3D Particle Collision Scattering Model under Smoke Interference
Authors: Guo Zhe , Yang Bing , Wu Kaiwei , Liang Yanbin , Hao Shijun , Huang Zhonghua
Published: August 2024

In the environment of smoke and suspended particles, the accurate detection of targets is one of the difficulties for frequency-modulated continuous-wave (FMCW) laser fuzes to work properly in harsh conditions. To weaken and eliminate the significant influence caused by the interaction of different systems in the photon transmission process and the smoke particle environment, it is necessary to increase the amplitude of the target echo signal to improve the signal-to-noise ratio (SNR), which contributes to enhancing the detection performance of the laser fuze for the ground target in the smoke. Under these conditions, the particle transmission of photons in the smoke environment is studied from the perspective of three-dimentional (3D) collisions between photons and smoke particles, and the modeling and Unity3D simulation of FMCW laser echo signal based on 3D particle collision is conducted. On this basis, a laser fuze structure based on multiple channel beam emission is designed for the combined effect of particle features from different systems and its impact on the target characteristics is researched. Simulation results show that the multiple channel laser emission enhances the laser target echo signal amplitude and also improves the anti-interference ability against the combined effects of multiple particle features compared with the single channel. Through the validation based on the laser prototype with four-channel beam emitting, the above conclusions are supported by the experimental results. Therefore, this study not only reveals the laser target properties under the 3D particle collision perspective, but also reflects the reasonableness and effectiveness of utilizing the target characteristics in the 3D particle collision mode to enhance the detection performance of FMCW laser fuze in the smoke.
Pneumatic Bellow Actuator with Embedded Sensor Using Conductive Carbon Grease
Authors: Moreno David , Narvaez Diana , Newell Brittany
Published: August 2024

The present work demonstrates the manufacturing process of a pneumatic bellow actuator with an embedded sensor, utilizing a novel manufacturing approach through the complete use of additive manufacturing techniques, such as direct ink writing (DIW) and traditional fused deposition modeling (FDM) methods. This study is innovative in its integration of a dielectric electroactive polymer (DEAP) structure with sensing electrodes made of conductive carbon grease (CCG), showcasing a unique application of a 3D-printed DEAP with CCG electrodes for combined DEAP sensing and pneumatic actuation. Initial experiments, supported by computational simulations, evaluated the distinct functionality of the DEAP sensor by itself under various pressure conditions. The findings revealed a significant change in capacitance with applied pressure, validating the sensor’s performance. After sensor validation, an additive manufacturing process for embedding the DEAP structure into a soft pneumatic actuator was created, exhibiting the system’s capability for dual sensing and actuation, as the embedded sensor effectively responded to applied actuation pressure. This dual functionality represents an advancement in soft actuators, especially in applications that require integrated and responsive actuation and sensing capabilities. This work also represents a preliminary step in the development of a 3D-printed dual-modality actuator (pneumatic and electrically activated DEAP) with embedded sensing.
Spatial Augmented Reality for Expanding the Reach of Individuals with Tremor beyond Their Physical Limits
Authors: Wang Kai , Wu Mengjing , Sun Zhi , Huang Qun
Published: August 2024

Tremor is a prevalent neurological disorder characterized by involuntary shaking or trembling of body parts. This condition impairs fine motor skills and hand coordination to varying degrees and can even affect overall body mobility. As a result, tremors severely disrupt the daily lives and work of those affected, significantly limiting their physical activity space. This study developed an innovative spatial augmented reality (SAR) system aimed at assisting individuals with tremor disorders to overcome their physical limitations and expand their range of activities. The system integrates eye-tracking and Internet of Things (IoT) technologies, enabling users to smoothly control objects in the real world through eye movements. It uses a virtual stabilization algorithm for stable interaction with objects in the real environment. The study comprehensively evaluated the system’s performance through three experiments: (1) assessing the effectiveness of the virtual stabilization algorithm in enhancing the system’s ability to assist individuals with tremors in stable and efficient interaction with remote objects, (2) evaluating the system’s fluidity and stability in performing complex interactive tasks, and (3) investigating the precision and efficiency of the system in remote interactions within complex physical environments. The results demonstrated that the system significantly improves the stability and efficiency of interactions between individuals with tremor and remote objects, reduces operational errors, and enhances the accuracy and communication efficiency of interactions.
A Hierarchical Matrix Factorization-Based Method for Intelligent Industrial Fault Diagnosis
Authors: Li Yanxia , Zhou Han , Liu Jiajia , Tan Xuemin
Published: August 2024

Data-driven fault diagnosis, identifying abnormality causes using collected industrial data, is one of the challenging tasks for intelligent industry safety management. It is worth noting that practical industrial data are usually related to a mixture of several physical attributes, such as the operating environment, product quality and working conditions. However, the traditional models may not be sufficient to leverage the coherent information for diagnostic performance enhancement, due to their shallow architecture. This paper presents a hierarchical matrix factorization (HMF) that relies on a succession of matrix factoring to find an efficient representation of industrial data for fault diagnosis. Specifically, HMF consecutively decomposes data into several hierarchies. The intermediate hierarchies play the role of analysis operators which automatically learn implicit characteristics of industrial data; the final hierarchy outputs high-level and discriminative features. Furthermore, HMF is also extended in a nonlinear manner by introducing activation functions, referred as NHMF, to deal with nonlinearities in practical industrial processes. The applications of HMF and NHMF to fault diagnosis are evaluated by the multiple-phase flow process. The experimental results show that our models achieve competitive performance against the considered shallow and deep models, consuming less computing time than deep models.
A Comprehensive Review of LiDAR Applications in Crop Management for Precision Agriculture
Authors: Farhan Sheikh Muhammad , Yin Jianjun , Chen Zhijian , Memon Muhammad Sohail
Published: August 2024

Precision agriculture has revolutionized crop management and agricultural production, with LiDAR technology attracting significant interest among various technological advancements. This extensive review examines the various applications of LiDAR in precision agriculture, with a particular emphasis on its function in crop cultivation and harvests. The introduction provides an overview of precision agriculture, highlighting the need for effective agricultural management and the growing significance of LiDAR technology. The prospective advantages of LiDAR for increasing productivity, optimizing resource utilization, managing crop diseases and pesticides, and reducing environmental impact are discussed. The introduction comprehensively covers LiDAR technology in precision agriculture, detailing airborne, terrestrial, and mobile systems along with their specialized applications in the field. After that, the paper reviews the several uses of LiDAR in agricultural cultivation, including crop growth and yield estimate, disease detection, weed control, and plant health evaluation. The use of LiDAR for soil analysis and management, including soil mapping and categorization and the measurement of moisture content and nutrient levels, is reviewed. Additionally, the article examines how LiDAR is used for harvesting crops, including its use in autonomous harvesting systems, post-harvest quality evaluation, and the prediction of crop maturity and yield. Future perspectives, emergent trends, and innovative developments in LiDAR technology for precision agriculture are discussed, along with the critical challenges and research gaps that must be filled. The review concludes by emphasizing potential solutions and future directions for maximizing LiDAR’s potential in precision agriculture. This in-depth review of the uses of LiDAR gives helpful insights for academics, practitioners, and stakeholders interested in using this technology for effective and environmentally friendly crop management, which will eventually contribute to the development of precision agricultural methods.
Advanced Sensing System for Sleep Bruxism across Multiple Postures via EMG and Machine Learning
Authors: Gul Jahan Zeb , Fatima Noor , Mohy Ud Din Zia , Khan Maryam , Kim Woo Young , Rehman Muhammad Muqeet
Published: August 2024

Diagnosis of bruxism is challenging because not all contractions of the masticatory muscles can be classified as bruxism. Conventional methods for sleep bruxism detection vary in effectiveness. Some provide objective data through EMG, ECG, or EEG; others, such as dental implants, are less accessible for daily practice. These methods have targeted the masseter as the key muscle for bruxism detection. However, it is important to consider that the temporalis muscle is also active during bruxism among masticatory muscles. Moreover, studies have predominantly examined sleep bruxism in the supine position, but other anatomical positions are also associated with sleep. In this research, we have collected EMG data to detect the maximum voluntary contraction of the temporalis and masseter muscles in three primary anatomical positions associated with sleep, i.e., supine and left and right lateral recumbent positions. A total of 10 time domain features were extracted, and six machine learning classifiers were compared, with random forest outperforming others. The models achieved better accuracies in the detection of sleep bruxism with the temporalis muscle. An accuracy of 93.33% was specifically found for the left lateral recumbent position among the specified anatomical positions. These results indicate a promising direction of machine learning in clinical applications, facilitating enhanced diagnosis and management of sleep bruxism.
Tool State Recognition Based on POGNN-GRU under Unbalanced Data
Authors: Tong Weiming , Shen Jiaqi , Li Zhongwei , Chu Xu , Jiang Wenqi , Tan Liguo
Published: August 2024

Accurate recognition of tool state is important for maximizing tool life. However, the tool sensor data collected in real-life scenarios has unbalanced characteristics. Additionally, although graph neural networks (GNNs) show excellent performance in feature extraction in the spatial dimension of data, it is difficult to extract features in the temporal dimension efficiently. Therefore, we propose a tool state recognition method based on the Pruned Optimized Graph Neural Network-Gated Recurrent Unit (POGNN-GRU) under unbalanced data. Firstly, design the Improved-Majority Weighted Minority Oversampling Technique (IMWMOTE) by introducing an adaptive noise removal strategy and improving the MWMOTE to alleviate the unbalanced problem of data. Subsequently, propose a POG graph data construction method based on a multi-scale multi-metric basis and a Gaussian kernel weight function to solve the problem of one-sided description of graph data under a single metric basis. Then, construct the POGNN-GRU model to deeply mine the spatial and temporal features of the data to better identify the state of the tool. Finally, validation and ablation experiments on the PHM 2010 and HMoTP datasets show that the proposed method outperforms the other models in terms of identification, and the highest accuracy improves by 1.62% and 1.86% compared with the corresponding optimal baseline model.
Disturbances in Electrodermal Activity Recordings Due to Different Noises in the Environment
Authors: Bari Dindar S. , Aldosky Haval Y. Y. , Tronstad Christian , Martinsen Ørjan G.
Published: August 2024

Electrodermal activity (EDA) is a widely used psychophysiological measurement in laboratory-based studies. In recent times, these measurements have seen a transfer from the laboratory to wearable devices due to the simplicity of EDA measurement as well as modern electronics. However, proper conditions for EDA measurement are recommended once wearable devices are used, and the ambient conditions may influence such measurements. It is not completely known how different types of ambient noise impact EDA measurement and how this translates to wearable EDA measurement. Therefore, this study explored the effects of various noise disturbances on the generation of EDA responses using a system for the simultaneous recording of all measures of EDA, i.e., skin conductance responses (SCRs), skin susceptance responses (SSRs), and skin potential responses (SPRs), at the same skin site. The SCRs, SSRs, and SPRs due to five types of noise stimuli at different sound pressure levels (70, 75, 80, 85, and 90 dB) were measured from 40 participants. The obtained results showed that EDA responses were generated at all levels and that the EDA response magnitudes were significantly (p < 0.001) influenced by the increasing noise levels. Different types of environmental noise may elicit EDA responses and influence wearable recordings outside the laboratory, where such noises are more likely than in standardized laboratory tests. Depending on the application, it is recommended to prevent these types of unwanted variation, presenting a challenge for the quality of wearable EDA measurement in real-world conditions. Future developments to shorten the quality gap between standardized laboratory-based and wearable EDA measurements may include adding microphone sensors and algorithms to detect, classify, and process the noise-related EDA.
Beehive Smart Detector Device for the Detection of Critical Conditions That Utilize Edge Device Computations and Deep Learning Inferences
Authors: Kontogiannis Sotirios
Published: August 2024

This paper presents a new edge detection process implemented in an embedded IoT device called Bee Smart Detection node to detect catastrophic apiary events. Such events include swarming, queen loss, and the detection of Colony Collapse Disorder (CCD) conditions. Two deep learning sub-processes are used for this purpose. The first uses a fuzzy multi-layered neural network of variable depths called fuzzy-stranded-NN to detect CCD conditions based on temperature and humidity measurements inside the beehive. The second utilizes a deep learning CNN model to detect swarming and queen loss cases based on sound recordings. The proposed processes have been implemented into autonomous Bee Smart Detection IoT devices that transmit their measurements and the detection results to the cloud over Wi-Fi. The BeeSD devices have been tested for easy-to-use functionality, autonomous operation, deep learning model inference accuracy, and inference execution speeds. The author presents the experimental results of the fuzzy-stranded-NN model for detecting critical conditions and deep learning CNN models for detecting swarming and queen loss. From the presented experimental results, the stranded-NN achieved accuracy results up to 95%, while the ResNet-50 model presented accuracy results up to 99% for detecting swarming or queen loss events. The ResNet-18 model is also the fastest inference speed replacement of the ResNet-50 model, achieving up to 93% accuracy results. Finally, cross-comparison of the deep learning models with machine learning ones shows that deep learning models can provide at least 3–5% better accuracy results.
From Near-Sensor to In-Sensor: A State-of-the-Art Review of Embedded AI Vision Systems
Authors: Fabre William , Haroun Karim , Lorrain Vincent , Lepecq Maria , Sicard Gilles
Published: August 2024

In modern cyber-physical systems, the integration of AI into vision pipelines is now a standard practice for applications ranging from autonomous vehicles to mobile devices. Traditional AI integration often relies on cloud-based processing, which faces challenges such as data access bottlenecks, increased latency, and high power consumption. This article reviews embedded AI vision systems, examining the diverse landscape of near-sensor and in-sensor processing architectures that incorporate convolutional neural networks. We begin with a comprehensive analysis of the critical characteristics and metrics that define the performance of AI-integrated vision systems. These include sensor resolution, frame rate, data bandwidth, computational throughput, latency, power efficiency, and overall system scalability. Understanding these metrics provides a foundation for evaluating how different embedded processing architectures impact the entire vision pipeline, from image capture to AI inference. Our analysis delves into near-sensor systems that leverage dedicated hardware accelerators and commercially available components to efficiently process data close to their source, minimizing data transfer overhead and latency. These systems offer a balance between flexibility and performance, allowing for real-time processing in constrained environments. In addition, we explore in-sensor processing solutions that integrate computational capabilities directly into the sensor. This approach addresses the rigorous demand constraints of embedded applications by significantly reducing data movement and power consumption while also enabling in-sensor feature extraction, pre-processing, and CNN inference. By comparing these approaches, we identify trade-offs related to flexibility, power consumption, and computational performance. Ultimately, this article provides insights into the evolving landscape of embedded AI vision systems and suggests new research directions for the development of next-generation machine vision systems.
CAWE-ACNN Algorithm for Coprime Sensor Array Adaptive Beamforming
Authors: Liu Fulai , Zhou Wu , Qin Dongbao , Liu Zhixin , Wang Huifang , Du Ruiyan
Published: August 2024

This paper presents a robust adaptive beamforming algorithm based on an attention convolutional neural network (ACNN) for coprime sensor arrays, named the CAWE-ACNN algorithm. In the proposed algorithm, via a spatial and channel attention unit, an ACNN model is constructed to enhance the features contributing to beamforming weight vector estimation and to improve the signal-to-interference-plus-noise ratio (SINR) performance, respectively. Then, an interference-plus-noise covariance matrix reconstruction algorithm is used to obtain an appropriate label for the proposed ACNN model. By the calculated label and the sample signals received from the coprime sensor arrays, the ACNN is well-trained and capable of accurately and efficiently outputting the beamforming weight vector. The simulation results verify that the proposed algorithm achieves excellent SINR performance and high computation efficiency.
PolyMeme: Fine-Grained Internet Meme Sensing
Authors: Arailopoulos Vasileios , Koutlis Christos , Papadopoulos Symeon , Petrantonakis Panagiotis C.
Published: August 2024

Internet memes are a special type of digital content that is shared through social media. They have recently emerged as a popular new format of media communication. They are often multimodal, combining text with images and aim to express humor, irony, sarcasm, or sometimes convey hatred and misinformation. Automatically detecting memes is important since it enables tracking of social and cultural trends and issues related to the spread of harmful content. While memes can take various forms and belong to different categories, such as image macros, memes with labeled objects, screenshots, memes with text out of the image, and funny images, existing datasets do not account for the diversity of meme formats, styles and content. To bridge this gap, we present the PolyMeme dataset, which comprises approximately 27 K memes from four categories. This was collected from Reddit and a part of it was manually labelled into these categories. Using the manual labels, deep learning networks were trained to classify the unlabelled images with an estimated error rate of 7.35%. The introduced meme dataset in combination with existing datasets of regular images were used to train deep learning networks (ResNet, ViT) on meme detection, exhibiting very high accuracy levels (98% on the test set). In addition, no significant gains were identified from the use of regular images containing text.
Balance Assessment Using a Handheld Smartphone with Principal Component Analysis for Anatomical Calibration
Authors: Anthony Evan C. , Kam Olivia K. , Klisch Stephen M. , Hazelwood Scott J. , Berg-Johansen Britta
Published: August 2024

Most balance assessment studies using inertial measurement units (IMUs) in smartphones use a body strap and assume the alignment of the smartphone with the anatomical axes. To replace the need for a body strap, we have used an anatomical alignment method that employs a calibration maneuver and Principal Component Analysis (PCA) so that the smartphone can be held by the user in a comfortable position. The objectives of this study were to determine if correlations existed between angular velocity scores derived from a handheld smartphone with PCA functional alignment vs. a smartphone placed in a strap with assumed alignment, and to analyze acceleration score differences across balance poses of increasing difficulty. The handheld and body strap smartphones exhibited moderately to strongly correlated angular velocity scores in the calibration maneuver (r = 0.487–0.983, p < 0.001). Additionally, the handheld smartphone with PCA functional calibration successfully detected significant variance between pose type scores for anteroposterior, mediolateral, and superoinferior acceleration data (p < 0.001).
Direct Multi-Target Teaching Interface for Autonomous Handling of Multi-Stack Logistics in a Warehouse
Authors: Choi Haegyeom , Jeong Jaehyun , Park Taezoon , Lee Donghun
Published: August 2024

This study presents a framework for enabling autonomous pick–place operations, addressing the need for efficiency in complex logistics environments using a direct multi-target teaching interface. First, tag and segmentation information were combined to recognize products in a complex warehouse, and a camera was installed on the rack to allow workers to remotely see the work environment, allowing workers to view the work environment in real time through a tablet. Workers can access the camera view showing the rack containing the target product through a swiping action and select the target product through direct teaching action. When the target product is finally selected, an optimal path is created through task planning, and an autonomous pick–place operation is performed based on the generated path. As a result of conducting a usability evaluation using the SUS (System Usability Scale) with six users on the interface that enables these tasks, it was confirmed that high user satisfaction was achieved with an average of 77.5 points. In conclusion, the proposed interface enhances operational efficiency and provides a user-friendly solution for complex warehouse tasks.
Innovative Air-Preconditioning Method for Accurate Particulate Matter Sensing in Humid Environments
Authors: Kunić Zdravko , Mršić Leo , Đambić Goran , Ražov Tomislav
Published: August 2024

Smart cities rely on a network of sensors to gather real-time data on various environmental factors, including air quality. This paper addresses the challenges of improving the accuracy of low-cost particulate matter sensors (LCPMSs) which can be compromised by environmental conditions, such as high humidity, which is common in many urban areas. Such weather conditions often lead to the overestimation of particle counts due to hygroscopic particle growth, resulting in a potential public concern, although most of the detected particles consist of just water. The paper presents an innovative design for an indicative air-quality measuring station that integrates the particulate matter sensor with a preconditioning subsystem designed to mitigate the impact of humidity. The preconditioning subsystem works by heating the incoming air, effectively reducing the relative humidity and preventing the hygroscopic growth of particles before they reach the sensor. To validate the effectiveness of this approach, parallel measurements were conducted using both preconditioned and non-preconditioned sensors over a period of 19 weeks. The data were analyzed to compare the performance of the sensors in terms of accuracy for PM1, PM2.5, and PM10 particles. The results demonstrated a significant improvement in measurement accuracy for the preconditioned sensor, especially in environments with high relative humidity. When the conditions were too severe and both sensors started measuring incorrect values, the preconditioned sensor-measured values were closer to the actual values. Also, the period of measuring incorrect values was shorter with the preconditioned sensor. The results suggest that the implementation of air preconditioning subsystems in LCPMSs deployed in smart cities can provide a cost-effective solution to overcome humidity-related inaccuracies, thereby improving the overall quality of measured air pollution data.
Exploring Social Skills in Students of Diverse Cultural Identities in Primary Education
Authors: Tomé-Fernández María , Aranda-Vega Eva María , Ortiz-Marcos José Manuel
Published: August 2024

Social skills are crucial to the personal development and academic success of elementary school students. Through competencies such as empathy, conflict resolution, leadership, and self-esteem, students learn to interact effectively and adapt to diverse social situations. However, the manifestation of these skills can be conditioned by factors such as race, ethnicity, or religion of the students, as well as by the environment in which they live. Therefore, it is vitally important to actively promote these skills within the school environment to ensure healthy growth and a successful future in society. In this context, the research focuses on the analysis of the social skills of intercultural students in Andalusia. Specifically, we investigated whether the White race, Castilian ethnicity, and Christian religion have any influence on these skills. To meet this objective, a quasi-experimental design was used with a non-probabilistic purposive sampling that included a sample of N = 803 intercultural students aged 6 to 12 years from schools in all Andalusian provinces. The results indicate that students of predominantly White, Castilian ethnicity, and Christian religion exhibit greater social skills compared to those of Gypsy ethnicity, Islamic religion, or no religious affiliation. These findings underscore the significant influence that culture and social environment exert on the development of social skills, which are fundamental for facilitating integration and promoting peaceful coexistence among different cultural groups.
Psychogeography of Refugee Youth from Ukraine in Weimar, Germany: Navigating the Sense of Belonging in the Context of Liminality
Authors: Kunchuliya Mariam , Eckardt Frank
Published: August 2024

This study looks at the sense of belonging among the youth who fled Russia’s full-scale invasion of Ukraine and currently reside in Weimar, Germany. Having fled the war in a time of transition to a more independent stage of life, refugee youth are finding themselves in a challenging context of liminality: both in terms of age and environment. Experiencing a feeling of uncertainty about their positioning in life and a new society, refugee youth are especially prone to feeling excluded and lost, which creates further challenges for their well-being. While the sense of belonging cannot be strictly defined, it is considered a vital factor for mental and physical well-being, as well as a core sign of social integration. To understand how to help newcomers foster their sense of belonging, this study tracks senses of (non)belonging among refugee youth following a weak theory and psychogeographic approach. The results demonstrate the ‘dialectic’ battle of opposites: how right-wing city rallies and pro-Russian symbolism in Weimar are triggering a sense of alienation and detachment on the one hand, and how signs of solidarity with Ukraine and connecting to local social groups invite engagement with the city, its politics and hence create a sense of agency, welcoming and belonging on the other. The results of the study have important applicability for human geography as well as the development of the theory on the sense of belonging among refugee youth in the context of liminality.
Mann-Type Inertial Accelerated Subgradient Extragradient Algorithm for Minimum-Norm Solution of Split Equilibrium Problems Induced by Fixed Point Problems in Hilbert Spaces
Authors: Khonchaliew Manatchanok , Khamdam Kunlanan , Petrot Narin
Published: August 2024

This paper presents the Mann-type inertial accelerated subgradient extragradient algorithm with non-monotonic step sizes for solving the split equilibrium and fixed point problems relating to pseudomonotone and Lipschitz-type continuous bifunctions and nonexpansive mappings in the framework of real Hilbert spaces. By sufficient conditions on the control sequences of the parameters of concern, the strong convergence theorem to support the proposed algorithm, which involves neither prior knowledge of the Lipschitz constants of bifunctions nor the operator norm of the bounded linear operator, is demonstrated. Some numerical experiments are performed to show the efficacy of the proposed algorithm.
On Some Formulas for Single and Double Integral Transforms Related to the Group SO(2, 2)
Authors: Shilin I. A. , Choi Junesang
Published: August 2024

We present a novel proof, using group theory, for a Meijer transform formula. This proof reveals the formula as a specific case of a broader generalized result. The generalization is achieved through a linear operator that intertwines two representations of the connected component of the identity of the group SO(2,2). Using this same approach, we derive a formula for the sum of three double integral transforms, where the kernels are represented by Bessel functions. It is particularly noteworthy that the group SO(2,2) is connected to symmetry in several significant ways, especially in mathematical physics and geometry.
Exploring the Role of Autonomous Trucks in Addressing Challenges within the Trucking Industry: A Comprehensive Review
Authors: Hasiri Ali , Kermanshah Amirhassan
Published: August 2024

The trucking industry, a vital part of the economic structure, faces numerous challenges such as greenhouse gas emissions, labor-related issues, fluctuating fuel costs, and safety concerns. These challenges intensify as the industry expands to meet growing demand. The advent of artificial intelligence has led to the development of autonomous trucks, which are seen as a promising solution to these ongoing issues. This study is the first comprehensive review of literature on autonomous trucks, organized by theme and research method. Studies are initially categorized based on the timeline of the issues investigated, divided into two main subcategories: foundational aspects of autonomous truck implementation and practical implementation and economic analysis of autonomous trucks. Research on the foundational aspects of autonomous trucks is further divided into four categories: (1) Acceptance surveys, (2) Identification of barriers, (3) Core technologies for autonomous trucks implementation, and (4) Predictions of adoption rates. Research on practical and economical aspects of autonomous trucks falls into three subcategories: (1) Infrastructure, (2) Systemic performance optimization, and (3) Cost estimation. To enhance the accuracy of this review, a more detailed classification was conducted on two specific subcategories: core technologies for autonomous truck implementation and systemic performance optimization. Additionally, the studies were also categorized based on their research methods and assumptions, which include accurate descriptions of autonomous technology, data collection methods, assumptions about the study environment, the fuel type of autonomous trucks, and approach to analysis: simultaneous or separate. This comprehensive review of the literature offers a roadmap for researchers, aiding them in identifying unique and novel research topics, thereby propelling the advancement of autonomous trucks as a viable solution to numerous challenges in the trucking industry.
Life-Threatening Conditions in Children with Bocavirus Infection-Case Series and Mini Review of the Literature
Authors: Tătăranu Elena , Galos Felicia , Anchidin-Norocel Liliana , Axinte Roxana , Filip Florin , Axinte Sorin , Tătăranu Adrian , Terteliu Monica , Diaconescu Smaranda
Published: August 2024

In this study, we present four cases of Human Bocavirus (HBoV) infection in children aged between 1 month and 4 years. Among these cases, two siblings were hospitalized with similar symptoms. Among the four pediatric cases of patients with HBoV infection, three were associated with acute respiratory failure and spontaneous pneumothorax, and two of these presented with subcutaneous emphysema. The presented patients were young children, aged between 1 month and 4 years, two of whom were siblings, suggesting a possible intrafamilial transmission of HBoV1 infection. These cases highlight the importance of considering HBoV as a differential diagnosis in pediatric patients with respiratory and gastrointestinal symptoms. Early recognition and appropriate medical care are important in treating HBoV infection in young children.
Cytological Features of Inflammatory Mammary Carcinoma in Dogs
Authors: Pîrvu Adina-Mihaela , Caniatti Mario , Pieri Marta , Roccabianca Paola , Militaru Manuella
Published: August 2024

Background: Inflammatory mammary carcinoma (IMC) is the most aggressive and malignant type of mammary carcinoma. As in humans, canine IMC resembles mastitis clinically. However, IMC is highly aggressive with high incidence of metastases and common recurrence after surgery, leading to guarded prognosis and low survival rate. Given the complex morphology of canine mammary tumours, cytological examination is not performed routinely, and IMC diagnosis relies on the association of clinical features and histopathology. The purpose of this study is to describe the characteristics of canine IMC cytology, in an attempt to find possible cytological features that allow differentiation of IMC from other mammary tumour types. Methods: We analysed preoperative cytological samples from 25 dogs with IMC, later confirmed by corroborating clinical and histopathological examinations. Results: Distinct cytological features of canine IMC included scarce cellular cohesiveness, ballooning aspect of neoplastic cells, frequent multinucleation, irregularly dispersed and ropy chromatin pattern, and squamous metaplasia in some individualised cells or those in small groups. Conclusions: Our results indicate that cytological examination can contribute to the diagnosis of IMC and might help differentiate it from other mammary carcinomas, even when clinical data is not available, which is common in cytological routine.
Antibiotic Resistance and Aquatic Systems: Importance in Public Health
Authors: Lajqi Berisha Njomza , Poceva Panovska Ana , Hajrulai-Musliu Zehra
Published: August 2024

This review focuses on the crucial role of aquatic ecosystems in preserving biodiversity and the biosphere, as well as the connection between antimicrobial resistance (AMR) and these ecosystems. It provides an in-depth analysis of the link between ecological well-being and public health, helping readers understand this complex subject. Aquatic ecosystems are essential for maintaining biodiversity and ecological balance. Additionally, they act as primary reservoirs and pathways for the spread of antimicrobial resistance (AMR). These ecosystems risk antibiotic contamination through various sources, such as the release of antibiotics from animal and human waste, improper disposal of unused medications, and pharmaceutical industry waste management practices. The presence of antibiotic residues in these environments significantly speeds up the development of bacterial resistance. The global prevalence of antimicrobial resistance (AMR) is evident in freshwater bodies, tributaries, sewage waters, and wastewater treatment facilities. Antimicrobial resistance (AMR) is now a significant public health threat, compromising the effectiveness of many previously successful treatments against various pathogens. One notable and alarming aspect of antimicrobial resistance (AMR) is its rapid development, often occurring within 5–10 years after introducing antimicrobial drugs to the market. This acceleration is closely tied to bacteria’s ability to thrive and adapt in the presence of antimicrobial agents and their residues in the environment. The implications of antimicrobial resistance (AMR) include treatment failures with long-term effects and a continuous increase in healthcare costs. This review comprehensively examines the intricate relationship between aquatic habitats, antibiotics, and the global challenge of antimicrobial resistance (AMR). It emphasizes the critical role of these ecosystems in preserving ecological diversity. It raises awareness about AMR’s urgent public health issue, laying a foundation for understanding its extensive consequences.
Effect of Stocking Density on Growth Performance of Juvenile Gibel Carp (Carassius gibelio) and Economic Profit of Land-Based Recirculating Aquaculture System
Authors: Li Huacheng , Liu Jieya , Gu Xiao , Li Luyi , Yu Liqin , Tang Rong , Wang Chunfang , Li Li , Li Dapeng
Published: August 2024

The land-based recirculating aquaculture system (RAS) has been widely applied to fish farming as a new eco-friendly culture model. This system consists of circular culture tanks on land integrated with water treatment and recycling systems. This study investigated the growth performance of juvenile gibel carp (Carassius gibelio) cultured at high stocking density (HSD, 0.3 kg/m3) and low stocking density (LSD, 0.15 kg/m3) conditions in RAS, and evaluated the comprehensive economic profit of RAS. The body weight, body length, weight gain rate, and condition factor of gibel carp in the LSD group were significantly higher than those in the HSD group (p < 0.05). The feed conversion ratio increased significantly in the HSD group (p < 0.05). A histological analysis revealed a significantly higher density of white muscle fibers in the LSD group (p < 0.05). Relative mRNA expression levels showed that ubiquitin–proteasome system (UPS)-related genes, ub, psma2, and mafbx, were significantly expressed in the HSD group, while the s6k1 expression was elevated in the LSD group (p < 0.05). The mRNA expression levels of keap1 and hsp70 in the dorsal muscle were significantly higher in the HSD group (p < 0.05). Throughout the rearing period, the water temperature remained consistent between the two density groups. The pH value gradually decreased and the dissolved oxygen levels in the HSD group were generally lower than in the LSD group. The nitrite nitrogen (NO2−-N) content was higher in the HSD group. Compared to the LSD group, the return on investment was significantly lower in the HSD group. In conclusion, the water quality and growth rates of juvenile gibel carp were better in the LSD group. An appropriate stocking density improved the growth performance and aquaculture economic efficiency.