bluuebunny/crossref_metadata_2025 huggingface dataset:
Revocable and Fog-Enabled Proxy Re-Encryption Scheme for IoT Environments
Authors: Lin Han-Yu , Chen Pei-Ru
Published: September 2024

As technology advances rapidly, a diverse array of Internet of Things (IoT) devices finds widespread application across numerous fields. The intelligent nature of these devices not only gives people more convenience, but also introduces new challenges especially in security when transmitting data in fog-based cloud environments. In fog computing environments, data need to be transmitted across multiple devices, increasing the risk of data being intercepted or tampered with during transmission. To securely share cloud ciphertexts, an alleged proxy re-encryption approach is a commonly adopted solution. Without decrypting the original ciphertext, such a mechanism permits a ciphertext intended for user A to be easily converted into the one intended for user B. However, to revoke the decryption privilege of data users usually relies on the system authority to maintain a user revocation list which inevitably increases the storage space. In this research, the authors come up with a fog-based proxy re-encryption system with revocable identity. Without maintaining the traditional user revocation list, the proposed scheme introduces a time-updated key mechanism. The time-update key could be viewed as a partial private key and should be renewed with different time periods. A revoked user is unable to obtain the renewed time-update key and hence cannot share or decrypt cloud ciphertexts. We formally demonstrate that the introduced scheme satisfies the security of indistinguishability against adaptively chosen identity and chosen plaintext attacks (IND-PrID-CPA) assuming the hardness of the Decisional Bilinear Diffie–Hellman (DBDH) problem in the random oracle model. Furthermore, compared with similar systems, the proposed one also has lower computational complexity as a whole.
Comparative Approach to De-Noising TEMPEST Video Frames
Authors: Vizitiu Alexandru Mădălin , Sandu Marius Alexandru , Dobrescu Lidia , Focșa Adrian , Molder Cristian Constantin
Published: September 2024

Analysis of unintended compromising emissions from Video Display Units (VDUs) is an important topic in research communities. This paper examines the feasibility of recovering the information displayed on the monitor from reconstructed video frames. The study holds particular significance for our understanding of security vulnerabilities associated with the electromagnetic radiation of digital displays. Considering the amount of noise that reconstructed TEMPEST video frames have, the work in this paper focuses on two different approaches to de-noising images for efficient optical character recognition. First, an Adaptive Wiener Filter (AWF) with adaptive window size implemented in the spatial domain was tested, and then a Convolutional Neural Network (CNN) with an encoder–decoder structure that follows both classical auto-encoder model architecture and U-Net architecture (auto-encoder with skip connections). These two techniques resulted in an improvement of more than two times on the Structural Similarity Index Metric (SSIM) for AWF and up to four times for the SSIM for the Deep Learning (DL) approach. In addition, to validate the results, the possibility of text recovery from processed noisy frames was studied using a state-of-the-art Tesseract Optical Character Recognition (OCR) engine. The present work aims to bring to attention the security importance of this topic and the non-negligible character of VDU information leakages.
Improving Performance of Cluster Heads Selection in DEC Protocol Using K-Means Algorithm for WSN
Authors: Juwaied Abdulla , Jackowska-Strumillo Lidia
Published: September 2024

Wireless sensor networks (WSN) have found more and more applications in remote control and monitoring systems. Energy management in the network is crucial because all nodes in the WSN are energy constrained. Therefore, the design and implementation of WSN protocols that reduce energy depletion in the network is still an open scientific problem. In this paper, we propose a new clustering protocol that combines DEC (deterministic energy-efficient clustering) protocol with K-means clustering, called DEC-KM (deterministic energy-efficient clustering protocol with K-means). DEC is a very energy-efficient clustering protocol that outperforms its predecessors, such as LEACH and SEP. K-means ensures more effective clustering and shorter data transmission distances within the network. The shorter distances improve the network’s lifetime and stability and reduce power consumption. Additional heuristic rules in DEC-KM ensure improved cluster head selection, taking into account node energy level and position and minimising the risk of premature cluster head exhaustion. The simulation results for the DEC-KM protocol using MATLAB show that cluster heads have shorter distances to nodes in cluster areas than for the original DEC protocol. The proposed protocol ensures reduced energy consumption, outperforms the standard DEC, and extends the stability period and lifetime of the network.
Radar Target Radar Cross-Section Measurement Based on Enhanced Imaging and Scattering Center Extraction
Authors: Tan Xin , Wang Chaoqi , Fang Yang , Wu Bai , Zhao Dongyan , Hu Jiansheng
Published: September 2024

Accurate measurement of a Radar Cross-Section (RCS) is a critical technical challenge in assessing the stealth performance and scattering characteristics of radar targets. Traditional RCS measurement methods are limited by high costs, sensitivity to environmental conditions, and difficulties in distinguishing local scattering features of targets. To address these challenges, this paper proposes a novel RCS measurement method based on enhanced imaging and scattering center extraction. This method integrates sub-aperture imaging with image fusion techniques to improve imaging quality and enhance the detail of target scattering characteristics. Additionally, an improved sequence CLEAN algorithm is employed to effectively suppress sidelobe effects and ensure the accuracy of scattering center extraction. Experimental results demonstrate that this method achieves higher precision in RCS measurement of complex targets and is particularly effective in environments with strong interference, where it successfully separates the scattering contributions of the target from those of the interference sources. This method offers a new technological approach for precise RCS measurement of radar stealth targets in the future.
Object Detection and Classification Framework for Analysis of Video Data Acquired from Indian Roads
Authors: Padia Aayushi , T. N. Aryan , Thummagunti Sharan , Sharma Vivaan , K. Vanahalli Manjunath , B. M. Prabhu Prasad , G. N. Girish , Kim Yong-Guk , B. N. Pavan Kumar
Published: September 2024

Object detection and classification in autonomous vehicles are crucial for ensuring safe and efficient navigation through complex environments. This paper addresses the need for robust detection and classification algorithms tailored specifically for Indian roads, which present unique challenges such as diverse traffic patterns, erratic driving behaviors, and varied weather conditions. Despite significant progress in object detection and classification for autonomous vehicles, existing methods often struggle to generalize effectively to the conditions encountered on Indian roads. This paper proposes a novel approach utilizing the YOLOv8 deep learning model, designed to be lightweight, scalable, and efficient for real-time implementation using onboard cameras. Experimental evaluations were conducted using real-life scenarios encompassing diverse weather and traffic conditions. Videos captured in various environments were utilized to assess the model’s performance, with particular emphasis on its accuracy and precision across 35 distinct object classes. The experiments demonstrate a precision of 0.65 for the detection of multiple classes, indicating the model’s efficacy in handling a wide range of objects. Moreover, real-time testing revealed an average accuracy exceeding 70% across all scenarios, with a peak accuracy of 95% achieved in optimal conditions. The parameters considered in the evaluation process encompassed not only traditional metrics but also factors pertinent to Indian road conditions, such as low lighting, occlusions, and unpredictable traffic patterns. The proposed method exhibits superiority over existing approaches by offering a balanced trade-off between model complexity and performance. By leveraging the YOLOv8 architecture, this solution achieved high accuracy while minimizing computational resources, making it well suited for deployment in autonomous vehicles operating on Indian roads.
Nondestructive Detection of Corky Disease in Symptomless ‘Akizuki’ Pears via Raman Spectroscopy
Authors: Yang Yue , Yang Weizhi , Zhang Hanhan , Xu Jing , Jin Xiu , Zhang Xiaodan , Ye Zhengfeng , Tang Xiaomei , Liu Lun , Heng Wei , Jia Bing , Liu Li
Published: September 2024

‘Akizuki’ pear (Pyrus pyrifolia Nakai) corky disease is a physiological disease that strongly affects the fruit quality of ‘Akizuki’ pear and its economic value. In this study, Raman spectroscopy was employed to develop an early diagnosis model by integrating support vector machine (SVM), random forest (RF), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and convolutional neural network (CNN) modeling techniques. The effects of various pretreatment methods and combinations of methods on modeling results were studied. The relative optimal index formula was utilized to identify the SG and SG+WT as the most effective preprocessing methods. Following the optimal preprocessing method, the performance of the majority of the models was markedly enhanced through the process of model reconditioning, among which XGBoost achieved 80% accuracy under SG+WT pretreatment, and F1 and kappa both performed best. The results show that RF, GBDT, and XGBoost are more sensitive to the pretreatment method, whereas SVM and CNN are more dependent on internal parameter tuning. The results of this study indicate that the early detection of Raman spectroscopy represents a novel approach for the nondestructive identification of asymptomatic ‘Akizuki’ pear corky disease, which is of paramount importance for the realization of large-scale detection across orchards.
Development of a Gait Analysis Application for Assessing Upper and Lower Limb Movements to Detect Pathological Gait
Authors: Taishaku Atsuhito , Yamada Shigeki , Iseki Chifumi , Aoyagi Yukihiko , Ueda Shigeo , Kondo Toshiyuki , Kobayashi Yoshiyuki , Sahashi Kento , Shimizu Yoko , Yamanaka Tomoyasu , Tanikawa Motoki , Ohta Yasuyuki , Mase Mitsuhito
Published: September 2024

Pathological gait in patients with Hakim’s disease (HD, synonymous with idiopathic normal-pressure hydrocephalus; iNPH), Parkinson’s disease (PD), and cervical myelopathy (CM) has been subjectively evaluated in this study. We quantified the characteristics of upper and lower limb movements in patients with pathological gait. We analyzed 1491 measurements of 1 m diameter circular walking from 122, 12, and 93 patients with HD, PD, and CM, respectively, and 200 healthy volunteers using the Three-Dimensional Pose Tracker for Gait Test. Upper and lower limb movements of 2D coordinates projected onto body axis sections were derived from estimated 3D relative coordinates. The hip and knee joint angle ranges on the sagittal plane were significantly smaller in the following order: healthy > CM > PD > HD, whereas the shoulder and elbow joint angle ranges were significantly smaller, as follows: healthy > CM > HD > PD. The outward shift of the leg on the axial plane was significantly greater, as follows: healthy < CM < PD < HD, whereas the outward shift of the upper limb followed the order of healthy > CM > HD > PD. The strongest correlation between the upper and lower limb movements was identified in the angle ranges of the hip and elbow joints on the sagittal plane. The lower and upper limb movements during circular walking were correlated. Patients with HD and PD exhibited reduced back-and-forth swings of the upper and lower limbs.
Fibre Refractometry for Minimally Invasive Sugar Content Measurements within Produce
Authors: Zentile Mark A. , Offermans Peter , Young David , Zhang Xu U.
Published: September 2024

A minimally invasive needle refractometer is presented for sugar content measurements within produce. A passive sampling cap structure was developed that improves the reliability of the device by avoiding interfering back reflections from the flesh of the produce. It is explained that factory calibration may not be needed for this type of refractometer, potentially reducing production costs. Also demonstrated is an iterative method to correct for temperature variations without the need for an integrated model for how the refractive index changes with temperature for different levels of sugar concentration. The sensor showed a typical standard deviation of 0.4 °Bx for a 10-s-long measurement and was validated against a prism refractometer, showing an average offset of (0.0±0.1) °Bx. In addition, the potential for using the device to investigate sugar distributions within a single fruit sample is demonstrated.
Machine Learning-Empowered Real-Time Acoustic Trapping: An Enabling Technique for Increasing MRI-Guided Microbubble Accumulation
Authors: Wu Mengjie , Liao Wentao
Published: September 2024

Acoustic trap, using ultrasound interference to ensnare bioparticles, has emerged as a versatile tool for life sciences due to its non-invasive nature. Bolstered by magnetic resonance imaging’s advances in sensing acoustic interference and tracking drug carriers (e.g., microbubble), acoustic trap holds promise for increasing MRI-guided microbubbles (MBs) accumulation in target microvessels, improving drug carrier concentration. However, accurate trap generation remains challenging due to complex ultrasound propagation in tissues. Moreover, the MBs’ short lifetime demands high computation efficiency for trap position adjustments based on real-time MRI-guided carrier monitoring. To this end, we propose a machine learning-based model to modulate the transducer array. Our model delivers accurate prediction of both time-of-flight (ToF) and pressure amplitude, achieving low average prediction errors for ToF (−0.45 µs to 0.67 µs, with only a few isolated outliers) and amplitude (−0.34% to 1.75%). Compared with the existing methods, our model enables rapid prediction (<10 ms), achieving a four-order of magnitude improvement in computational efficiency. Validation results based on different transducer sizes and penetration depths support the model’s adaptability and potential for future ultrasound treatments.
Application of Sensor Technology in Wheelchair Sports for Real-Time Data Collection during Training and Competition and for Assessment of Performance Parameters: A Systematic Review and Future Directions
Authors: Weizman Yehuda , Bäumker Lena , Fuss Franz Konstantin
Published: September 2024

This review reports on the use of sensors in wheelchair sports to monitor and analyze performance during match and training time. With rapid advancements in electronics and related technologies, understanding performance metrics in wheelchair sports is essential. We reviewed nine studies using various sensor types, including electric motors, inertial measurement units, miniaturized data loggers with magnetic reed switches, and smartphones with inbuilt accelerometers and gyroscopes, operating at frequencies from 8 Hz to 1200 Hz. These studies measured parameters such as angular and translational velocities, distance, number of starts/pushes, and other performance indicators in sports such as basketball, rugby, tennis, and racing. Despite differences in sport types and methodologies, most studies found sensor-derived data effective for assessment of performance. Future developments and research in this field should focus on multi-sensor systems that could provide real-time match analysis and deeper insights into performance metrics. Overall, sensor technologies show significant potential for improving wheelchair sport performance diagnostics, contributing to better athlete training and future wheelchair design, and enhancing competitive outcomes. This review emphasizes the need for continued innovation and standardization in applying sensor technologies in wheelchair sports.
Multi-AUV Kinematic Task Assignment Based on Self-Organizing Map Neural Network and Dubins Path Generator
Authors: Li Xin , Gan Wenyang , Pang Wen , Zhu Daqi
Published: September 2024

To deal with the task assignment problem of multi-AUV systems under kinematic constraints, which means steering capability constraints for underactuated AUVs or other vehicles likely, an improved task assignment algorithm is proposed combining the Dubins Path algorithm with improved SOM neural network algorithm. At first, the aimed tasks are assigned to the AUVs by the improved SOM neural network method based on workload balance and neighborhood function. When there exists kinematic constraints or obstacles which may cause failure of trajectory planning, task re-assignment will be implemented by changing the weights of SOM neurals, until the AUVs can have paths to reach all the targets. Then, the Dubins paths are generated in several limited cases. The AUV’s yaw angle is limited, which results in new assignments to the targets. Computation flow is designed so that the algorithm in MATLAB and Python can realize the path planning to multiple targets. Finally, simulation results prove that the proposed algorithm can effectively accomplish the task assignment task for a multi-AUV system.
Performance Evaluation of an Object Detection Model Using Drone Imagery in Urban Areas for Semi-Automatic Artificial Intelligence Dataset Construction
Authors: Kim Phillip , Youn Junhee
Published: September 2024

Modern image processing technologies, such as deep learning techniques, are increasingly used to detect changes in various image media (e.g., CCTV and satellite) and understand their social and scientific significance. Drone-based traffic monitoring involves the detection and classification of moving objects within a city using deep learning-based models, which requires extensive training data. Therefore, the creation of training data consumes a significant portion of the resources required to develop these models, which is a major obstacle in artificial intelligence (AI)-based urban environment management. In this study, a performance evaluation method for semi-moving object detection is proposed using an existing AI-based object detection model, which is used to construct AI training datasets. The tasks to refine the results of AI-model-based object detection are analyzed, and an efficient evaluation method is proposed for the semi-automatic construction of AI training data. Different FBeta scores are tested as metrics for performance evaluation, and it is found that the F2 score could improve the completeness of the dataset with 26.5% less effort compared to the F0.5 score and 7.1% less effort compared to the F1 score. Resource requirements for future AI model development can be reduced, enabling the efficient creation of AI training data.
Attention-Guided Sample-Based Feature Enhancement Network for Crowded Pedestrian Detection Using Vision Sensors
Authors: Tang Shuyuan , Zhou Yiqing , Li Jintao , Liu Chang , Shi Jinglin
Published: September 2024

Occlusion presents a major obstacle in the development of pedestrian detection technologies utilizing computer vision. This challenge includes both inter-class occlusion caused by environmental objects obscuring pedestrians, and intra-class occlusion resulting from interactions between pedestrians. In complex and variable urban settings, these compounded occlusion patterns critically limit the efficacy of both one-stage and two-stage pedestrian detectors, leading to suboptimal detection performance. To address this, we introduce a novel architecture termed the Attention-Guided Feature Enhancement Network (AGFEN), designed within the deep convolutional neural network framework. AGFEN improves the semantic information of high-level features by mapping it onto low-level feature details through sampling, creating an effect comparable to mask modulation. This technique enhances both channel-level and spatial-level features concurrently without incurring additional annotation costs. Furthermore, we transition from a traditional one-to-one correspondence between proposals and predictions to a one-to-multiple paradigm, facilitating non-maximum suppression using the prediction set as the fundamental unit. Additionally, we integrate these methodologies by aggregating local features between regions of interest (RoI) through the reuse of classification weights, effectively mitigating false positives. Our experimental evaluations on three widely used datasets demonstrate that AGFEN achieves a 2.38% improvement over the baseline detector on the CrowdHuman dataset, underscoring its effectiveness and potential for advancing pedestrian detection technologies.
Kinect-Based Gait Analysis System Design and Concurrent Validity in Persons with Anterolateral Shoulder Pain Syndrome, Results from a Pilot Study
Authors: Bernal Fredy , Feipel Veronique , Plaza Mauricio
Published: September 2024

As part of an investigation to detect asymmetries in gait patterns in persons with shoulder injuries, the goal of the present study was to design and validate a Kinect-based motion capture system that would enable the extraction of joint kinematics curves during gait and to compare them with the data obtained through a commercial motion capture system. The study included eight male and two female participants, all diagnosed with anterolateral shoulder pain syndrome in their right upper extremity with a minimum 18 months of disorder evolution. The participants had an average age of 31.8 ± 9.8 years, a height of 173 ± 18 cm, and a weight of 81 ± 15 kg. The gait kinematics were sampled simultaneously with the new system and the Clinical 3DMA system. Shoulder, elbow, hip, and knee kinematics were compared between systems for the pathological and non-pathological sides using repeated measures ANOVA and 1D statistical parametric mapping. For most variables, no significant difference was found between systems. Evidence of a significant difference between the newly developed system and the commercial system was found for knee flexion–extension (p < 0.004, between 60 and 80% of the gait cycle), and for shoulder abduction–adduction. The good concurrent validity of the new Kinect-based motion analysis system found in this study opens promising perspectives for clinical motion tracking using an affordable and simple system.
A Review of Recent Advances in Cognitive-Motor Dual-Tasking for Parkinson’s Disease Rehabilitation
Authors: Tan Xiaohui , Wang Kai , Sun Wei , Li Xinjin , Wang Wenjie , Tian Feng
Published: September 2024

Background: Parkinson’s disease is primarily characterized by the degeneration of motor neurons, leading to significant impairments in movement. Initially, physical therapy was predominantly employed to address these motor issues through targeted rehabilitation exercises. However, recent research has indicated that cognitive training can enhance the quality of life for patients with Parkinson’s. Consequently, some researchers have posited that the simultaneous engagement in computer-assisted motor and cognitive dual-task (CADT) may yield superior therapeutic outcomes. Methods: A comprehensive literature search was performed across various databases, and studies were selected following PRISMA guidelines, focusing on CADT rehabilitation interventions. Results: Dual-task training enhances Parkinson’s disease (PD) rehabilitation by automating movements and minimizing secondary task interference. The inclusion of a sensor system provides real-time feedback to help patients make immediate adjustments during training. Furthermore, CADT promotes more vigorous participation and commitment to training exercises, especially those that are repetitive and can lead to patient boredom and demotivation. Virtual reality-tailored tasks, closely mirroring everyday challenges, facilitate more efficient patient adaptation post-rehabilitation. Conclusions: Although the current studies are limited by small sample sizes and low levels, CADT rehabilitation presents as a significant, effective, and potential strategy for PD.
Optimal Design of a Sensor Network for Guided Wave-Based Structural Health Monitoring Using Acoustically Coupled Optical Fibers
Authors: Soman Rohan , Kim Jee , Boyer Alex , Peters Kara
Published: September 2024

Guided waves (GW) allow fast inspection of a large area and hence have received great interest from the structural health monitoring (SHM) community. Fiber Bragg grating (FBG) sensors offer several advantages but their use has been limited for the GW sensing due to its limited sensitivity. FBG sensors in the edge-filtering configuration have overcome this issue with sensitivity and there is a renewed interest in their use. Unfortunately, the FBG sensors and the equipment needed for interrogation is quite expensive, and hence their number is restricted. In the previous work by the authors, the number and location of the actuators was optimized for developing a SHM system with a single sensor and multiple actuators. But through the use of the phenomenon of acoustic coupling, multiple locations on the structure may be interrogated with a single FBG sensor. As a result, a sensor network with multiple sensing locations and a few actuators is feasible and cost effective. This paper develops a two-step methodology for the optimization of an actuator–sensor network harnessing the acoustic coupling ability of FBG sensors. In the first stage, the actuator–sensor network is optimized based on the application demands (coverage with at least three actuator–sensor pairs) and the cost of the instrumentation. In the second stage, an acoustic coupler network is designed to ensure high-fidelity measurements with minimal interference from other bond locations (overlap of measurements) as well as interference from features in the acoustically coupled circuit (fiber end, coupler, etc.). The non-sorting genetic algorithm (NSGA-II) is implemented for finding the optimal solution for both problems. The analytical implementation of the cost function is validated experimentally. The results show that the optimization does indeed have the potential to improve the quality of SHM while reducing the instrumentation costs significantly.
A Review of Microstrip Patch Antenna-Based Passive Sensors
Authors: Islam Zain Ul , Bermak Amine , Wang Bo
Published: September 2024

This paper briefly overviews and discusses the existing techniques using antennas for passive sensing, starting from the antenna operating principle and antenna structural design to different antenna-based sensing mechanisms. The effects of different electrical properties of the material used to design an antenna, such as conductivity, loss tangent, and resistivity, are discussed to illustrate the fundamental sensing mechanisms. Furthermore, the key parameters, such as operating frequency and antenna impedance, along with the factors affecting the sensing performance, are discussed. Overall, passive sensing using an antenna is mainly achieved by altering the reflected wave characteristics in terms of center frequency, return loss, phase, and received/reflected signal strength. The advantages and drawbacks of each technique are also discussed briefly. Given the increasing relevance, millimeter-wave antenna sensors and resonator sensors are also discussed with their applications and recent advancements. This paper primarily focuses on microstrip-based radiating structures and insights for further sensing performance improvement using passive antennas, which are outlined in this study. In addition, suggestions are made for the current scientific and technical challenges, and future directions are discussed.
Microfluidic-Based Electrical Operation and Measurement Methods in Single-Cell Analysis
Authors: Liu Xing , Zheng Xiaolin
Published: September 2024

Cellular heterogeneity plays a significant role in understanding biological processes, such as cell cycle and disease progression. Microfluidics has emerged as a versatile tool for manipulating single cells and analyzing their heterogeneity with the merits of precise fluid control, small sample consumption, easy integration, and high throughput. Specifically, integrating microfluidics with electrical techniques provides a rapid, label-free, and non-invasive way to investigate cellular heterogeneity at the single-cell level. Here, we review the recent development of microfluidic-based electrical strategies for single-cell manipulation and analysis, including dielectrophoresis- and electroporation-based single-cell manipulation, impedance- and AC electrokinetic-based methods, and electrochemical-based single-cell detection methods. Finally, the challenges and future perspectives of the microfluidic-based electrical techniques for single-cell analysis are proposed.
Enhancing Time-of-Flight Diffraction (TOFD) Inspection through an Innovative Curved-Sole Probe Design
Authors: Sanchez Duo Irati , Lanzagorta Jose Luis , Aizpurua Maestre Iratxe , Galdos Lander
Published: September 2024

Time-of-Flight Diffraction (TOFD) is a method of ultrasonic testing (UT) that is widely established as a non-destructive technique (NDT) mainly used for the inspection of welds. In contrast to other established UT techniques, TOFD is capable of identifying discontinuities regardless of their orientation. This paper proposes a redesign of the typical TOFD transducers, featuring an innovative curved sole aimed at enhancing their defect detection capabilities. This design is particularly beneficial for thick-walled samples, as it allows for deeper inspections without compromising the resolution near the surface area. During this research, an evaluation consisting in simulations of the ultrasonic beam distribution and experimental tests on a component with artificially manufactured defects at varying depths has been performed to validate the new design. The results demonstrate a 30 to 50% higher beam distribution area as well as an improvement in the signal-to-noise ratio (SNR) resulting in a 24% enhancement in the capability of defect detection compared to the traditional approach.
Motion Artifacts in Dynamic EEG Recordings: Experimental Observations, Electrical Modelling, and Design Considerations
Authors: Giangrande Alessandra , Botter Alberto , Piitulainen Harri , Cerone Giacinto Luigi
Published: September 2024

Despite the progress in the development of innovative EEG acquisition systems, their use in dynamic applications is still limited by motion artifacts compromising the interpretation of the collected signals. Therefore, extensive research on the genesis of motion artifacts in EEG recordings is still needed to optimize existing technologies, shedding light on possible solutions to overcome the current limitations. We identified three potential sources of motion artifacts occurring at three different levels of a traditional biopotential acquisition chain: the skin-electrode interface, the connecting cables between the detection and the acquisition systems, and the electrode-amplifier system. The identified sources of motion artifacts were modelled starting from experimental observations carried out on EEG signals. Consequently, we designed customized EEG electrode systems aiming at experimentally disentangling the possible causes of motion artifacts. Both analytical and experimental observations indicated two main residual sites responsible for motion artifacts: the connecting cables between the electrodes and the amplifier and the sudden changes in electrode-skin impedance due to electrode movements. We concluded that further advancements in EEG technology should focus on the transduction stage of the biopotentials amplification chain, such as the electrode technology and its interfacing with the acquisition system.
Sexual Satisfaction in a Sample of Italian Women during the COVID-19 Lockdown Period
Authors: Buattini Matilde , Daminato Luca , Riboli Greta , Nese Mattia , Brighetti Gianni , Giunti Daniel , Borlimi Rosita
Published: September 2024

This study explored the relationships among psychological, relational, and sexual factors and their impact on sexual satisfaction among Italian women during the COVID-19 lockdown. Data were collected via an online survey with 3803 participants. The survey assessed mental health, relationship quality, frequency of sexual intercourse, frequency of masturbation, body awareness, and sexual satisfaction. Correlation analyses revealed positive relationships among all the variables and negative relationships with low mental health. A network analysis showed that sexual satisfaction was the most central variable, strongly linked to relationship quality, frequency of sexual intercourse, and body awareness. An exploratory mediation model examined the indirect effects of mental health and frequency of masturbation on sexual satisfaction through the other variables as mediators. The results indicated that mental health negatively influenced sexual satisfaction both directly and indirectly through relationship quality and body awareness, while frequency of masturbation had a positive indirect effect through body awareness. These findings highlight the complex interplay among mental health, body awareness, relational factors, and sexual satisfaction, emphasizing the importance of holistic approaches in enhancing sexual well-being. Future research should focus on longitudinal studies to establish causality and explore interventions that promote positive body awareness and relationship quality.
Queer Experiences of Religion: How Marginalization within a Religion Affects Its Queer Members
Authors: Baird Rebecca , Hutchins Camryn H. , Kosanovich Seth. E. , Dabbs Christopher R.
Published: September 2024

We sought to understand how the mental state of religious queer individuals is affected by religious marginalization and queer identity. Using a multi-method approach, we analyzed data from 626 participants to assess how a queer status affected psychological distress and life satisfaction, the mediating effect of strength of faith on the relationship between the queer status and life satisfaction, and the moderating effect of experiences with marginalization on the relationship between the strength of faith, psychological distress, and life satisfaction. Queer status was found to have a significant impact on queer individuals’ psychological wellness and life satisfaction. Marginalization experiences decreased psychological wellness and life satisfaction. Our qualitative analyses add to these results, describing the weaponization of queer identity in religious settings. These results can be attributed to the strong main effects of queer status and strength of faith on psychological distress and life satisfaction rather than tertiary variables.
Association of Competitive Adolescent Athletes from Lean and Non-Lean Sports Physical, Social and Psychological Characteristics with Adherence to Mediterranean Diet
Authors: Thoma Fani , Koidou Eirini , Dolopikou Christina , Barkoukis Vassilis , Giaginis Constantinos , K. Papadopoulou Sousana
Published: September 2024

Mediterranean diet (MD) is regarded as one of the healthier dietary patterns which is recommended for athletes. This study aims to investigate the adherence to the Mediterranean diet (AMD) and associated beliefs in a large, diverse sample of competitive adolescent athletes from various sports, including both lean and non-lean sports. Additionally, the study examines factors important regarding intention to AMD within the Theory of Planned Behavior (TPB). In the study took part 711 adolescents (357 male, aged = 14.93 ± 1.38, and 354 female, aged = 14.85 ± 1.35) athletes. The level of AMD was not a significant different between lean (mean = 4.98, SD = ±3.90) and non-lean (mean = 4.77, SD = ±3.68) sport. According to the sport type the 35% of lean sport athletes demonstrated low AMD, 34.1% moderate adherence and 30.9% a high degree. The non lean athletes demonstrated low AMD in 36.3%, moderate adherence 33% and high degree in 30.7%. The results of the mediation analysis indicated a significant mediation effect of intention in the relationship between TPB variables and MD. Based on the results of the study the type of sport does not play a role in the AMD, which, as in the general population, is low. Also validate the TPB and underscore the significance of targeting individuals’ intentions to promote positive dietary behaviors.
Sustainable Strategies for Improving Humanitarian Supply Chain Management in the United Nations Using Dynamic Capability Theory
Authors: Mazar Mirjana , Gossett Kenneth , Shashi Manish
Published: September 2024

The purpose of the qualitative multiple case study was to explore the strategies some supply chain managers of the United Nations (UN) use to leverage operational efficiencies in the UN humanitarian programs. As a result, communities supporting sustainability and peace or those fostering economic development will be able to respond effectively to humanitarian crises. Moreover, the UN can remain operational and engage in political and conflict-reduction interventions integral to economic and social recovery and sustainability. This study applied the qualitative multiple case study through semi-structured interviews with nine supply chain managers in the United Nations, direct observations, document analysis, and artifacts. The research is grounded in the dynamic capability theory (DCT). The research revealed several strategies that supply chain managers of the UN use to ensure operational efficiencies grouped around three themes: (a) analytical, innovation, and knowledge management strategies; (b) effective supply chain management leadership strategies; and (c) risk management strategies. This study is one of the first to apply generic findings of humanitarian supply chain studies to the United Nations, the global organization with diverse mandates that continuously strives to achieve efficiencies required by donors providing financial support, thus remaining operational. The study’s results could help leaders in the various humanitarian organizations who operate in vulnerable environments and under strict scrutiny from donors to deliver their aid programs most efficiently by understanding dynamic capabilities. Previous studies indicate the lack of strategic frameworks applicable to the United Nations that could improve decision-making at the strategic, tactical, and operational levels, facilitate collaboration among supply chain stakeholders, and reduce the costs of the operational performance of the supply chain system in the UN.
Effects of LED Light on Aromatic Medicinal Plants from Lavandula, Salvia, and Thymus Genera: A Systematic Review
Authors: Cáceres-Cevallos Gustavo J. , Jordán María J.
Published: September 2024

LED light technology has been used in recent years in plant breeding due to its proven energy efficiency, low cost, and high quality for the enhancement of crops, including some aromatic medicinal plants (AMPs). Nonetheless, although several studies have shown that specific wavelengths can increase the content of bioactive compounds used by pharmaceutical, medical, and perfumery industries, there is limited information on this topic and the possible implications for plant stress in AMPs. The current systematic review focused on the effects of LED light on the physiological response, metabolite synthesis, and flowering induction in three important AMP genera: Lavandula, Salvia, and Thymus, belonging to the Lamiaceae family. A literature search was performed in the Web of Science and Scopus databases. This review is reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The bibliographic analysis highlights the significant variation in physiological responses to different light spectra between species, even within the same genera, implying a need to optimize light conditions in each species to achieve the best results. Finally, this review provides essential information for laying the groundwork for future research focused on enhancing AMPs using LED light to overcome various types of stress.
Techno-Economic Assessment of Amine-Based Carbon Capture in Waste-to-Energy Incineration Plant Retrofit
Authors: Bertone Michele , Stabile Luca , Cortellessa Gino , Arpino Fausto , Buonanno Giorgio
Published: September 2024

This study offers a detailed techno-economic assessment of Carbon Capture (CC) integration in an existing Waste-to-Energy (WtE) incineration plant, focusing on retrofit application. Post-combustion carbon capture using monoethanolamine (MEA) was modeled for various low-scale plant sizes (3000, 6000, and 12,000 t of CO2 per year), using a process simulator, highlighting the feasibility and implications of retrofitting a WtE incineration plant with CC technology. The comprehensive analysis covers the design of the CC plant and a detailed cost evaluation. Capture costs range from 156 EUR/t to 90 EUR/t of CO2. Additionally, integrating the CO2 capture system reduces the overall plant absolute efficiency from 22.7% (without carbon capture) to 22.4%, 22.1%, and 21.5% for the different capture capacities. This research fills a gap in studying small-scale CC applications for the WtE incineration plants, providing critical insights for similar retrofit projects.
Navigating Health-Related Crises: Unraveling the Role of Confidence in Tourism Recovery in Shaping Sustainable Strategies for Tourists’ Intentions across Pandemic Phases
Authors: Sun Li , Chen Jin , Huang Xiankai
Published: September 2024

The COVID-19 pandemic has severely impacted global tourism, raising the need for sustainable recovery strategies. This study examines how tourists’ confidence in tourism recovery (CTR) influences travel intentions across different pandemic phases—outbreak, stabilization, and conclusion. Positioned within the Theory of Planned Behavior (TPB), the research explores the mediating role of CTR in the relationships between attitude, subjective norms, perceived behavioral control, and travel intention. Using structural equation modeling (SEM), multi-group analysis (MGA), and Importance–Performance Matrix Analysis (IPMA), this study assesses changes in travel behavior during each pandemic phase. Data were collected through three surveys conducted in major Chinese cities between late 2022 and early 2023. The findings reveal that CTR significantly mediates the influence of behavioral factors on travel intention, especially during the stabilization and conclusion phases. The IPMA results highlight key areas for intervention, with CTR, perceived behavioral control, and subjective norms varying in importance across phases. This research underscores the need for adaptive and sustainable strategies to strengthen traveler confidence, offering practical insights for supporting long-term resilience and growth in the tourism industry during and beyond health-related crises.
CSR and Sustainable Environmental Performance: An Exploration of Mediating and Moderating Factors
Authors: Gazi Md. Abu Issa , Hossain Md. Motaher , Islam Shanta , Masud Abdullah Al , Amin Mohammad Bin , Senathirajah Abdul Rahman bin S , Abdullah Masuk
Published: September 2024

Taking into consideration the moderating role of perceived organizational support within the framework of the natural resource-based view (RBV) theory, the purpose of this study is to investigate the connection between corporate social responsibility (CSR) and sustainable environmental performance. Specifically, this study focuses on the roles that green capability and green transformational leadership play as mediators in this relationship. Through the use of a survey questionnaire, information was collected from 420 employees working for small- and medium-sized enterprises (SMEs) in Bangladesh. The data were analyzed with the help of AMOS and SPSS. The findings indicate that the level of CSR has a significant impact on the performance of sustainable environmental practices. To a large extent, green capability and green transformational leadership serve as mediators in the relationship between CSR and sustainable environmental performance. Furthermore, perceived organizational support plays a significant role in moderating the relationship between CSR and sustainable environmental performance. The relationship between green transformational leadership and sustainable environmental performance is also significantly moderated by perceived organizational support. This is a significant contributor to the connection. This multidimensional corporate social responsibility model can be used to assess sustainable environmental performance in both industrialized and developing countries, and it can also be extended to other service sectors, according to the theoretical conclusion that can be drawn from the research. This research demonstrates that there is a direct connection between corporate social responsibility and sustainable environmental performance. As a result, practitioners are able to develop strategies that are effective in terms of corporate social responsibility. These findings should be taken into consideration by policymakers and managers who are dedicated to promoting equitable development of the country.
Review of Root-Stubble Characteristics and Root-Stubble Crushing and Clearing Technologies for Conservation Tillage
Authors: Feng Xin , Yao Jiayue , Gao Yunpeng , Zeng Longchi , Wang Lijun , Wang Bo , Yang Zhilei
Published: September 2024

Conservation tillage (CT) is an agricultural technology for sustainable soil use, and clearing the root-stubble remaining in the seedbed and nursery bed is a core aspect of CT. In this paper, the characteristics and the testing methods of root-stubble and their growth environments were reviewed, which could provide a reference for the setting of parameters in numerical simulations and the design of stubble treatment devices. The methods for the restoration of the environment in CT are described. Moreover, the root-stubble crushing and clearing technologies and the methods for the evaluation of device performance are summarized. Furthermore, the prospects for the physical properties of the root-stubble soil, the reduction of soil adhesion when a cutter crushes the roots, the tracking of the long-term effects of different methods on soil, intelligent devices for the crushing of root-stubble, and challenges and strategies regarding the application of new root-stubble crushing and clearing technologies are discussed. This paper provides a reference for the development of devices for the crushing of root-stubble from the perspective of CT.