bluuebunny/crossref_metadata_2025 huggingface dataset:
Drought Responses in Poaceae: Exploring the Core Components of the ABA Signaling Pathway in Setaria italica and Setaria viridis
Authors: de Oliveira Isabella Peres , Schaaf Camila , de Setta Nathalia
Published: May 2024

Drought severely impacts plant development and reproduction, reducing biomass and seed number, and altering flowering patterns. Drought-tolerant Setaria italica and Setaria viridis species have emerged as prominent model species for investigating water deficit responses in the Poaceae family, the most important source of food and biofuel biomass worldwide. In higher plants, abscisic acid (ABA) regulates environmental stress responses, and its signaling entails interactions between PYR/PYL/RCAR receptors and clade A PP2C phosphatases, which in turn modulate SnRK2 kinases via reversible phosphorylation to activate ABA-responsive genes. To compare the diversity of PYR/PYL/RCAR, PP2C, and SnRK2 between S. italica and S. viridis, and their involvement in water deficit responses, we examined gene and regulatory region structures, investigated orthology relationships, and analyzed their gene expression patterns under water stress via a meta-analysis approach. Results showed that coding and regulatory sequences of PYR/PYL/RCARs, PP2Cs, and SnRK2s are highly conserved between Setaria spp., allowing us to propose pairs of orthologous genes for all the loci identified. Phylogenetic relationships indicate which clades of Setaria spp. sequences are homologous to the functionally well-characterized Arabidopsis thaliana PYR/PYL/RCAR, PP2C, and SnRK2 genes. Gene expression analysis showed a general downregulation of PYL genes, contrasting with upregulation of PP2C genes, and variable expression modulation of SnRK2 genes under drought stress. This complex network implies that ABA core signaling is a diverse and multifaceted process. Through our analysis, we identified promising candidate genes for further functional characterization, with great potential as targets for drought resistance studies, ultimately leading to advances in Poaceae biology and crop-breeding strategies.
A Simple and Efficient Magnesium Hydroxide Modification Strategy for Flame-Retardancy Epoxy Resin
Authors: Dun Linan , Ouyang Zeen , Sun Qihao , Yue Xiaoju , Wu Guodong , Li Bohan , Kang Weidong , Wang Yuanhao
Published: May 2024

Magnesium hydroxide, as a green inorganic flame-retardancy additive, has been widely used in polymer flame retardancy. However, magnesium hydroxide is difficult to disperse with epoxy resin (EP), and its flame-retardancy performance is poor, so it is difficult to use in flame-retardant epoxy resin. In this study, an efficient magnesium hydroxide-based flame retardant (MH@PPAC) was prepared by surface modification of 2-(diphenyl phosphine) benzoic acid (PPAC) using a simple method. The effect of MH@PPAC on the flame-retardancy properties for epoxy resins was investigated, and the flame-retardancy mechanism was studied. The results show that 5 wt% MH@PPAC can increase the limiting oxygen index for EP from 24.1% to 38.9%, achieving a V-0 rating. At the same time, compared to EP, the peak heat release rate, peak smoke production rate, total smoke production rate, and peak CO generation rate for EP/5 wt% MH@PPAC composite material decreased by 53%, 45%, 51.85%, and 53.13% respectively. The cooperative effect for PPAC and MH promotes the formation of a continuous and dense char layer during the combustion process for the EP-blend material, significantly reducing the exchange for heat and combustible gases, and effectively hindering the combustion process. Additionally, the surface modification of PPAC enhances the dispersion of MH in the EP matrix, endowing EP with superior mechanical properties that meet practical application requirements, thereby expanding the application scope for flame-retardant EP-blend materials.
Waterless Dyeing of Polyamide 6.6
Authors: Eren Semiha , Özyurt İrem
Published: May 2024

Waterless dyeing of polyamide 6.6 using scCO2 (supercritical carbon dioxide) was investigated. PA (polyamide) fibers can be dyed with various dyes, including disperse dyes. The conventional aqueous dyeing process uses large amounts of water and produces polluted water. Considering these environmental issues, waterless dyeing of fibers is a forefront issue, and utilization of supercritical carbon dioxide (scCO2) is a commercially viable technology for waterless dyeing. This study tested PA6.6 (polyamide 6.6) dyeing in scCO2 at 100 °C 220 bar pressure for 45 min. Color measurements and color fastness tests were performed, as well as tensile strength, scanning electron microscope (SEM) analysis, and Fourier transform infrared spectroscopy (FTIR) analysis. PA6.6 fabrics yielded higher K/S (color strength, the Kubelka–Munk equation) values with larger molecular weight dye and almost the same color strength with medium and small-sized dyes, demonstrating the ability of dyeing in a supercritical environment without water as a more environmentally friendly dyeing option compared to conventional dyeing.
Experimental and Numerical Investigation of the Fracture Behavior of Extruded Wood–Plastic Composites under Bending
Authors: Vilutis Almontas , Jankauskas Vytenis
Published: May 2024

The ability of wood–plastic composites (WPCs) to withstand various loads and resist plastic failure is attracting more and more interest due to the global increase in demand for WPCs by over 6 million tons per year. Among the most important and innovative research methods are those based on fracture mechanics—their results enable material designers to optimize the structures of these hybrid polymer composites at the nano, micro and macro levels, and they allow engineers to more accurately evaluate and select functional, sustainable, long-lasting and safe product designs. In this study, standard single-edge notched bending (SENB) tests were used to analyze the fracture toughness of two different extruded WPCs along the longitudinal (L) and transverse (T) directions of extrusion. In addition to their resistance to crack propagation, critical fracture criteria, initial contact stiffness, fracture parameters and fracture surfaces, the mechanical properties of these composites were also investigated. The results showed that WPC-A coded composites withstood higher loads until failure in both directions compared to WPC-B. Despite the larger data spread, both types of composites were more resistant to crack propagation in the T direction. Mode II of crack propagation was clearly visible, while mode III was not as pronounced. The experimental results and the numerical finite element (FE) model developed up to 58% of the maximum load correlated well, and the obtained deformation curves were best approximated using cubic equations (R2 > 0.99). The shear stress zone and its location, as well as the distribution of the equivalent stresses, had a major influence on crack propagation in the fracture process zone (FZP).
An Application of Lean Techniques to Construct an Integrated Management Systems Preventive Action Model and Evaluation: Kaizen Projects
Authors: Moso Matshidiso , Olanrewaju Oludolapo Akanni
Published: May 2024

The Occupational Health and Safety system enforces the continual improvement culture in industries for much safer processes and zero injuries. The Quality Management System also enforces the same philosophy of continual improvement within the processing system for zero defects, hence a high productivity rate. Good quality products always result from good Overall Equipment Effectiveness; hence, Process Re-Engineering is essential for the good functioning of machinery. This research is based on Integrated Management System requirements in terms of problem-solving, especially the opportunities that arise within Quality nonconformances, Safety Incidents, as well as Process Engineering related breakdowns. This study aims to develop a troubleshooting system that evaluates continual improvement projects. The method used to develop the troubleshooting system is based on Total Quality Management, where lean principles are combined with kaizen concepts and quality standards. The proposed troubleshooting system is separated into three development phases: the first phase is for recording the details of the fault that has been raised, where one will record full details of the nonconformance, the time and date, validation of the nonconformance by the lab test or any other form of validation depending on the nature of the problem as well as the details of the location of the problem. The second phase is for problem classification, whether it is a quality nonconformance, Safety incident, or engineering-related breakdown. The deeper root cause analysis is performed by an application of lean techniques, which are the eight types of waste, Five Whys and Ishikawa analysis. The eight types of waste identify the type of waste contributed by the problem, the Five Whys analysis assists in finding the reason for the problem occurrence, and the Ishikawa analysis classifies the problem accordingly, which assists the analyst in identifying the area to focus on for problem-solving. The third phase is for a database system and an application of the kaizen philosophy by evaluating continual improvement projects as well as status reports on the permanent solutions to the faults. The proposed troubleshooting model was applied in a case study company to upgrade the problem-solving model that the company was using which was assisting for corrective and preventive action. The study resulted in drastic improvements; hence, continual improvement projects were evaluated within the problem occurrences.
CODAS–Hamming–Mahalanobis Method for Hierarchizing Green Energy Indicators and a Linearity Factor for Relevant Factors’ Prediction through Enterprises’ Opinions
Authors: Riosvelasco-Monroy Georgina Elizabeth , Pérez-Olguín Iván Juan Carlos , Noriega-Morales Salvador , Pérez-Domínguez Luis Asunción , Méndez-González Luis Carlos , Rodríguez-Picón Luis Alberto
Published: May 2024

As enterprises look forward to new market share and supply chain opportunities, innovative strategies and sustainable manufacturing play important roles for micro-, small, and mid-sized enterprises worldwide. Sustainable manufacturing is one of the practices aimed towards deploying green energy initiatives to ease climate change, presenting three main pillars—economic, social, and environmental. The issue of how to reach sustainability goals within the sustainable manufacturing of pillars is a less-researched area. This paper’s main purpose and novelty is two-fold. First, it aims to provide a hierarchy of the green energy indicators and their measurements through a multi-criteria decision-making point of view to implement them as an alliance strategy towards sustainable manufacturing. Moreover, we aim to provide researchers and practitioners with a forecasting method to re-prioritize green energy indicators through a linearity factor model. The CODAS–Hamming–Mahalanobis method is used to obtain preference scores and rankings from a 50-item list. The resulting top 10 list shows that enterprises defined nine items within the economic pillar as more important and one item on the environmental pillar; items from the social pillar were less important. The implication for MSMEs within the manufacturing sector represents an opportunity to work with decision makers to deploy specific initiatives towards sustainable manufacturing, focused on profit and welfare while taking care of natural resources. In addition, we propose a continuous predictive analysis method, the linearity factor model, as a tool for new enterprises to seek a green energy hierarchy according to their individual needs. The resulting hierarchy using the predictive analysis model presented changes in the items’ order, but it remained within the same two sustainable manufacturing pillars: economic and environmental.
Drug–Drug Interactions of Hydroxychloroquine and Chloroquine in Older Patients with COVID-19 during the First Pandemic Waves: The GeroCovid Observational Study
Authors: Trevisan Caterina , Cignarella Andrea , Grandieri Andrea , Sergi Giuseppe , Fumagalli Stefano , Monzani Fabio , Okoye Chukwuma , Bellelli Giuseppe , Malara Alba , Gareri Pietro , Volpato Stefano , Antonelli Incalzi Raffaele ,   The GeroCovid Acute Ward Working Group
Published: May 2024

Objective: Chloroquine (CQ) and hydroxychloroquine (HCQ) were used as off-label treatments for SARS-CoV-2 infection during the first pandemic waves. The urgency of combatting COVID-19 led to the dissemination of medical recommendations with a scarce awareness of possible drug–drug interactions. This issue primarily concerned people already taking multiple medications, such as older individuals. We estimated the prevalence of drug interactions with CQ or HCQ in COVID-19 inpatients during the first pandemic waves and their possible association with hospitalization-related outcomes. Methods: This study considers 487 patients aged ≥60, hospitalized for COVID-19 from March to December 2020, and treated with CQ or HCQ. Data on acute and chronic therapies and hospitalization length and outcomes were derived from medical records. The presence of drugs potentially interacting with CQ and HCQ was identified based on published literature and drug databases. Results: In our sample (mean age 77.1 years, 47.8% females), 255 (52.4%) patients presented with one drug interaction with CQ or HCQ, and 114 (23.4%) had more than two interactions. The most frequent drugs potentially interacting with CQ or HCQ were lopinavir/ritonavir (50.4%), azithromycin (47.2%), tocilizumab (15.4%), levofloxacin (8.7%), clarithromycin (6.0%), amlodipine (3.3%), and trazodone (2.4%). No substantial differences in the duration and outcomes of the hospitalization emerged as a function of the presence of drug–drug interactions. Conclusions: Many older patients prescribed with CQ or HCQ, which have lately proved ineffective against COVID-19, were exposed to the risk of drug–drug interaction. This underlines that medical recommendations should undergo careful peer review before being widely disseminated, even in emergencies like a pandemic.
Autonomous Full 3D Coverage Using an Aerial Vehicle, Performing Localization, Path Planning, and Navigation towards Indoors Inventorying for the Logistics Domain
Authors: Tsiakas Kosmas , Tsardoulias Emmanouil , Symeonidis Andreas L.
Published: May 2024

Over the last years, a rapid evolution of unmanned aerial vehicle (UAV) usage in various applications has been observed. Their use in indoor environments requires a precise perception of the surrounding area, immediate response to its changes, and, consequently, a robust position estimation. This paper provides an implementation of navigation algorithms for solving the problem of fast, reliable, and low-cost inventorying in the logistics industry. The drone localization is achieved with a particle filter algorithm that uses an array of distance sensors and an inertial measurement unit (IMU) sensor. Navigation is based on a proportional–integral–derivative (PID) position controller that ensures an obstacle-free path within the known 3D map. As for the full 3D coverage, an extraction of the targets and then their final succession towards optimal coverage is performed. Finally, a series of experiments are carried out to examine the robustness of the positioning system using different motion patterns and velocities. At the same time, various ways of traversing the environment are examined by using different configurations of the sensor that is used to perform the area coverage.
Inner Niger Delta Inundation Extent (2010–2022) Based on Landsat Imagery and the Google Earth Engine
Authors: Bonkoungou Benjamin , Bossa Aymar Yaovi , van der Kwast Johannes , Mul Marloes , Sintondji Luc Ollivier
Published: May 2024

The Inner Niger Delta (IND), one of the largest floodplain systems in Africa, sustains the livelihoods of more than three million people and is a driver of the rural economy of Mali as far as agriculture, fish production, and livestock are concerned. Because the IND ecosystem and economy are flood-dependent, it is important to monitor seasonal flooding variations. Many attempts to accomplish this task have relied on detailed datasets, such as daily discharge, daily rainfall, and evapotranspiration, which are not easily accessible for data-sparse areas. Additionally, because the area is large, this remains a challenging task. In this study, the interannual variability of seasonal inundation in the IND was investigated by leveraging the computing power of the Google Earth Engine and its large catalogue of open datasets. The main objective was to analyse the temporal and spatial distributions of the inundation extent during the last 13 years. A collection of Landsat 5, 7, 8, and 9 images were composited and different bands were used with various water and vegetation indices in a pixel-based supervised classification to detect the flood extent between 2010 and 2022. A significant improvement in classification accuracy was observed thanks to the different indices. The results suggest a general increasing trend in the maximum annual inundation extent. Throughout the study period, the maximum inundated area varied between 15,209 km2 in autumn 2011 and 21,536 km2 in autumn 2022. The upstream water intake led to a decrease of about 6–10% of the inundated area. Similar fluctuations in the inundated area, precipitation, and river discharge were observed. The proposed approach demonstrates a great potential for monitoring annual inundation, especially for large areas such as the IND, where in situ measurements are sparse.
Effect of Argo Salinity Drift since 2016 on the Estimation of Regional Steric Sea Level Change Rates
Authors: Tang Lu , Zhou Hao , Li Jin , Wang Penghui , Su Xiaoli , Luo Zhicai
Published: May 2024

Since 2016, the Argo (Array for Real-Time Geostrophic Oceanography) ocean salinity data has exhibited significant drift, directly affecting the accurate quantification of the global steric sea level (SSL) rates. To further investigate how salinity drift affects the estimation of SSL rates in different depths and regions, we divide the 0–2000 m into three layers (0–300 m, 300–1000 m and 1000–2000 m) and select five open oceans (the South and North Pacific, the South and North Atlantic, and the Indian Ocean) for discussion. By comparing the SSL rates between the periods of 2005–2015 and 2005–2019, we can evaluate the impact of salinity drift. Taking the estimated results from the IPRC (provided by the International Pacific Research Center at the University of Hawaii) and BOA (provided by the Second Institute of Oceanography, China) data as examples, we find that the effect of salinity drift is the largest at the depth of 1000–2000 m, about 29% for IPRC data and about 18% for BOA data. Moreover, the South Atlantic is susceptible to the effects of salinity drift, with an approximately 13% impact for IPRC data and 21% for BOA data.
Estimating Forest Aboveground Biomass Using a Combination of Geographical Random Forest and Empirical Bayesian Kriging Models
Authors: Wu Zhenjiang , Yao Fengmei , Zhang Jiahua , Liu Haoyu
Published: May 2024

Accurately estimating forest aboveground biomass (AGB) is imperative for comprehending carbon cycling, calculating carbon budgets, and formulating sustainable forest management plans. Currently, random forest (RF) and other machine learning models are widely used to estimate forest AGB, as they can effectively handle nonlinear relationships. However, by constructing a global model using all the samples collected from a study area, these models fail to account for the spatial heterogeneity in the AGB and cannot correct the prediction biases, thereby constraining the estimation accuracy. To overcome these limitations, we proposed a novel approach termed geographical random forest and empirical Bayesian kriging (GRFEBK). This hybrid model combines the localized modeling capability of geographical random forest (GRF) with the bias correction strength of empirical Bayesian kriging (EBK). GRF adapts RF to account for the spatial heterogeneity of the AGB, while EBK utilizes the spatial autocorrelation of residuals to correct the prediction deviations. This study was conducted in Hainan Island, utilizing spectral bands, vegetation indices, tasseled cap components derived from Landsat-8 imagery, backscattering coefficients from ALOS-2 synthetic aperture radar, topographic features, and the forest canopy height as the explanatory variables. A total of 195 forest aboveground biomass (AGB) samples were collected for modeling and assessing the predictive accuracy. The results demonstrate that, among the tested models, including GRFEBK, RF, support vector machine (SVM), k-nearest neighbor (KNN), geographically weighted regression (GWR), GRF, and EBK, GRFEBK attains the highest R2 (0.78) and the lowest RMSE (36.04 Mg/ha) and RRMSE (22.87%), significantly outperforming the conventional models and using GRF or EBK alone. These results demonstrate that by accounting for local non-stationarity in AGB and correcting prediction biases, GRFEBK achieves significantly higher accuracy than conventional RF and other models. While the results are promising, the computational cost of GRFEBK and its performance under varying geographical conditions warrant further investigation at larger scales to assess its broader applicability. Nevertheless, GRFEBK provides an innovative and more reliable approach for accurate forest AGB estimation with great potential to support global forest resource monitoring.
Mesoscale Eddy Effects on Vertical Correlation of Sound Field and Array Gain Performance
Authors: Wu Yushen , Qin Jixing , Wu Shuanglin , Li Zhenglin , Wang Mengyuan , Gu Yiming , Wang Yang
Published: May 2024

To solve the problem of array detection performance in environments with mesoscale eddies, this study utilizes the Gaussian eddy model to describe the sound speed structure disturbed by eddies. Through numerical simulations, the corresponding sound field is obtained, and the transmission loss influenced by the eddy is analyzed. Furthermore, to investigate the relation between the array gain and spatial correlation in the eddy environments, the differences in vertical correlation at different positions and their effects on the vertical array gain of conventional beamforming (CBF) are studied. When the source is around the eddy center, the conclusions drawn are as follows: (1) The presence of an eddy changes the turning-point depth and the sound field distribution, significantly affecting the direct sound region and the first convergence zone, while having a minor impact on the first shadow zone. (2) In different eddy-induced environments, the first convergence zone maintains a high vertical correlation, but the vertical correlation of the direct sound region is greatly influenced by the eddy. (3) The array gain of CBF is consistent with the vertical correlation. When the correlation between each element of the sound field is great, the array gain increases with the number of array elements.
Reducing Driving Risk Factors in Adolescents with Attention Deficit Hyperactivity Disorder (ADHD): Insights from EEG and Eye-Tracking Analysis
Authors: Keren Anat , Fisher Orit , Hamde Anwar , Tsafrir Shlomit , Ratzon Navah Z.
Published: May 2024

Adolescents with attention deficit hyperactivity disorder (ADHD) face significant driving challenges due to deficits in attention and executive functioning, elevating their road risks. Previous interventions targeting driving safety among this cohort have typically addressed isolated aspects (e.g., cognitive or behavioral factors) or relied on uniform solutions. However, these approaches often overlook this population’s diverse needs. This study introduces the “Drive-Fun” innovative intervention (DFI), aimed at enhancing driving skills among this vulnerable population. The intervention was tested in a pilot study including 30 adolescents aged 15–18, comparing three groups: DFI, an educational intervention, and a control group with no treatment. Assessments included a driving simulator, EEG, and Tobii Pro Glasses 2. Evaluation was conducted pre- and post-intervention and at a 3-month follow-up. Results indicated that the DFI group significantly improved in the simulated driving performance, attentional effort, and focused gaze time. The findings underscore that holistic strategies with personalized, comprehensive approaches for adolescents with ADHD are particularly effective in improving driving performance. These outcomes not only affirm the feasibility of the DFI but also highlight the critical role of sensor technologies in accurately measuring and enhancing simulator driving performance in adolescents with ADHD. Outcomes suggest a promising direction for future research and application.
A Reinforcement Ensemble Learning Method for Rolling Bearing Fault Diagnosis under Variable Work Conditions
Authors: Li Yanning , Zhang Yi , Wang Ruixin , Fu Jiangfeng
Published: May 2024

Ensuring the smooth operation of rolling bearings requires a precise fault diagnosis. Particularly, identifying fault types under varying working conditions holds significant importance in practical engineering. Thus, we propose a reinforcement ensemble method for diagnosing rolling bearing faults under varying working conditions. Firstly, a reinforcement model was designed to select the optimal base learner. Stratified random sampling was used to extract four datasets from raw training data. The reinforcement model was trained by these four datasets, respectively, and we obtained four optimal base learners. Then, a sparse ANN was designed as the ensemble model and the reinforcement learning model that can successfully identify the fault type under variable work conditions was constructed. Extensive experiments were conducted, and the results demonstrate the superiority of the proposed method over other intelligent approaches, with significant practical engineering benefits.
Validation of Inertial-Measurement-Unit-Based Ex Vivo Knee Kinematics during a Loaded Squat before and after Reference-Frame-Orientation Optimisation
Authors: Sagasser Svenja , Sauer Adrian , Thorwächter Christoph , Weber Jana G. , Maas Allan , Woiczinski Matthias , Grupp Thomas M. , Ortigas-Vásquez Ariana
Published: May 2024

Recently, inertial measurement units have been gaining popularity as a potential alternative to optical motion capture systems in the analysis of joint kinematics. In a previous study, the accuracy of knee joint angles calculated from inertial data and an extended Kalman filter and smoother algorithm was tested using ground truth data originating from a joint simulator guided by fluoroscopy-based signals. Although high levels of accuracy were achieved, the experimental setup leveraged multiple iterations of the same movement pattern and an absence of soft tissue artefacts. Here, the algorithm is tested against an optical marker-based system in a more challenging setting, with single iterations of a loaded squat cycle simulated on seven cadaveric specimens on a force-controlled knee rig. Prior to the optimisation of local coordinate systems using the REference FRame Alignment MEthod (REFRAME) to account for the effect of differences in local reference frame orientation, root-mean-square errors between the kinematic signals of the inertial and optical systems were as high as 3.8° ± 3.5° for flexion/extension, 20.4° ± 10.0° for abduction/adduction and 8.6° ± 5.7° for external/internal rotation. After REFRAME implementation, however, average root-mean-square errors decreased to 0.9° ± 0.4° and to 1.5° ± 0.7° for abduction/adduction and for external/internal rotation, respectively, with a slight increase to 4.2° ± 3.6° for flexion/extension. While these results demonstrate promising potential in the approach’s ability to estimate knee joint angles during a single loaded squat cycle, they highlight the limiting effects that a reduced number of iterations and the lack of a reliable consistent reference pose inflicts on the sensor fusion algorithm’s performance. They similarly stress the importance of adapting underlying assumptions and correctly tuning filter parameters to ensure satisfactory performance. More importantly, our findings emphasise the notable impact that properly aligning reference-frame orientations before comparing joint kinematics can have on results and the conclusions derived from them.
Automated Monitoring of Human–Computer Interaction for Assessing Teachers’ Digital Competence Based on LMS Data Extraction
Authors: de-Torres Eduard , Canaleta Xavi , Fonseca David , Alsina Maria
Published: May 2024

The fast-paced evolution of technology has compelled the digitalization of education, requiring educators to interact with computers and develop digital competencies relevant to the teaching–learning process. This need has prompted various organizations to define frameworks for assessing digital competency emphasizing teachers’ interaction with computer technologies in education. Different authors have presented assessment methods for teachers’ digital competence based on the video analysis of recorded classes using sensors such as cameras, microphones, or electroencephalograms. The main limitation of these solutions is the large number of resources they require, making it difficult to assess large numbers of teachers in resource-constrained environments. This article proposes the automation of teachers’ digital competence evaluation process based on monitoring metrics obtained from teachers’ interaction with a Learning Management System (LMS). Based on the Digital Competence Framework for Educators (DigCompEdu), indicators were defined and extracted that allow automatic measurement of a teacher’s competency level. A tool was designed and implemented to conduct a successful proof of concept capable of automating the evaluation process of all university faculty, including 987 lecturers from different fields of knowledge. Results obtained allow for drawing conclusions on technological adoption according to the teacher’s profile and planning educational actions to improve these competencies.
Wearable-Based Integrated System for In-Home Monitoring and Analysis of Nocturnal Enuresis
Authors: Lee Sangyeop , Moon Junhyung , Lee Yong Seung , Shin Seung-chul , Lee Kyoungwoo
Published: May 2024

Nocturnal enuresis (NE) is involuntary bedwetting during sleep, typically appearing in young children. Despite the potential benefits of the long-term home monitoring of NE patients for research and treatment enhancement, this area remains underexplored. To address this, we propose NEcare, an in-home monitoring system that utilizes wearable devices and machine learning techniques. NEcare collects sensor data from an electrocardiogram, body impedance (BI), a three-axis accelerometer, and a three-axis gyroscope to examine bladder volume (BV), heart rate (HR), and periodic limb movements in sleep (PLMS). Additionally, it analyzes the collected NE patient data and supports NE moment estimation using heuristic rules and deep learning techniques. To demonstrate the feasibility of in-home monitoring for NE patients using our wearable system, we used our datasets from 30 in-hospital patients and 4 in-home patients. The results show that NEcare captures expected trends associated with NE occurrences, including BV increase, HR increase, and PLMS appearance. In addition, we studied the machine learning-based NE moment estimation, which could help relieve the burdens of NE patients and their families. Finally, we address the limitations and outline future research directions for the development of wearable systems for NE patients
Mitigating Trunk Compensatory Movements in Post-Stroke Survivors through Visual Feedback during Robotic-Assisted Arm Reaching Exercises
Authors: Lee Seong-Hoon , Song Won-Kyung
Published: May 2024

Trunk compensatory movements frequently manifest during robotic-assisted arm reaching exercises for upper limb rehabilitation following a stroke, potentially impeding functional recovery. These aberrant movements are prevalent among stroke survivors and can hinder their progress in rehabilitation, making it crucial to address this issue. This study evaluated the efficacy of visual feedback, facilitated by an RGB-D camera, in reducing trunk compensation. In total, 17 able-bodied individuals and 18 stroke survivors performed reaching tasks under unrestricted trunk conditions and visual feedback conditions. In the visual feedback modalities, the target position was synchronized with trunk movement at ratios where the target moved at the same speed, double, and triple the trunk’s motion speed, providing real-time feedback to the participants. Notably, trunk compensatory movements were significantly diminished when the target moved at the same speed and double the trunk’s motion speed. Furthermore, these conditions exhibited an increase in the task completion time and perceived exertion among stroke survivors. This outcome suggests that visual feedback effectively heightened the task difficulty, thereby discouraging unnecessary trunk motion. The findings underscore the pivotal role of customized visual feedback in correcting aberrant upper limb movements among stroke survivors, potentially contributing to the advancement of robotic-assisted rehabilitation strategies. These insights advocate for the integration of visual feedback into rehabilitation exercises, highlighting its potential to foster more effective recovery pathways for post-stroke individuals by minimizing undesired compensatory motions.
A Review on the State of the Art in Copter Drones and Flight Control Systems
Authors: Peksa Janis , Mamchur Dmytro
Published: May 2024

This paper presents an overview on the state of the art in copter drones and their components. It starts by providing an introduction to unmanned aerial vehicles in general, describing their main types, and then shifts its focus mostly to multirotor drones as the most attractive for individual and research use. This paper analyzes various multirotor drone types, their construction, typical areas of implementation, and technology used underneath their construction. Finally, it looks at current challenges and future directions in drone system development, emerging technologies, and future research topics in the area. This paper concludes by highlighting some key challenges that need to be addressed before widespread adoption of drone technologies in everyday life can occur. By summarizing an up-to-date survey on the state of the art in copter drone technology, this paper will provide valuable insights into where this field is heading in terms of progress and innovation.
Social Factors Associated with Insecurity in Nigerian Society
Authors: Omodero Cordelia Onyinyechi
Published: May 2024

The study examines the societal issues that contribute to the level of insecurity in Nigerian society using the Autoregressive Distributed Lag (ARDL) co-integration technique with data from 1991 to 2022. The variables used as independent and societal factors generating insecurity include the unemployment rate; the high cost of living in society; insufficient funds for education, community social services, healthcare services, and infrastructure; and agricultural development. Among the criteria evaluated, inadequate school financing and the unemployment rate appear to be the most significant sources of insecurity in society as a result of young people’s involvement in abduction, violence, and other societal issues. The study recognizes that government measures aimed at reorienting society towards a peaceful environment for habitation are critical. It is of the utmost importance to provide employment for young people and adequately support schools in order to lower the number of youngsters who are not in school. These out-of-school children are readily persuaded to join groups that make society untenable. Thus, free education is strongly suggested to engage and develop their thoughts in a constructive manner so that they can be valuable to society.
Insights into Key Biometric, Physiological and Biochemical Markers of Magnesium (Mg) Deficiency Stress in the Halophyte Cakile maritima
Authors: Houmani Hayet , Hidri Rabaa , Farhat Nèjia , Debez Ahmed
Published: May 2024

Magnesium is a key element for plant growth and development. Plant responses to Mg deficiency were well investigated, especially in glycophytes. Such responses include a reduction in plant growth and biomass allocation between shoots and roots, photosynthates partitioning from source to sink organs, the accumulation of carbohydrates, and an induction of several Mg transporters. Some physiological and biochemical parameters are good markers of Mg deficiency stress even though they are not well investigated. In the present study, the halophyte Cakile maritima was subjected to Mg shortage, and several Mg stress indices were analyzed. Our data showed that Mg starvation affected shoot and plant length, leaf number, and plant organ growth. A significant decrease in chlorophyll synthesis and photosynthetic activity was also recorded. Mg deficiency triggered oxidative damage as electrolyte leakage and lipid peroxidation were increased by Mg deficiency while the membrane stability index decreased. For a deeper understanding of the effect of Mg starvation on C. maritima, several tolerance stress indices were evaluated, demonstrating a negative impact of Mg stress on almost all those parameters. This study provided important insights on several markers of Mg deficiency stress, which were informative by themselves as unique and early signals of Mg deficiency stress in this halophyte.
Persistent Vulnerability after Disaster Risk Reduction (DRR) Response: The Case of Salgar, Colombia
Authors: Valencia Londoño Paula Andrea , Valencia Londoño Diana
Published: May 2024

Community-based disaster management (CBDM) has replaced traditional models of disaster risk reduction (DRR), giving the community a more participatory role in the planning and implementation of risk mitigation and preparedness strategies, disaster response, and post-disaster recovery measures. This shift in disaster response approaches has impacted understandings of vulnerability and resilience, leading scholars and policy makers to move away from a physical definition of vulnerability and to incorporate social variables. However, in Colombia, a traditional DRR approach still prevails. The National Risk Management Policy employs a top-down approach to risk reduction and disaster management, relying on the action of governmental authorities without community participation in the design or implementation of risk management planning and strategy. This article reveals the deficiencies of traditional DRR approaches. The Colombian government’s post-disaster resettlement project after a 2015 landslide in Salgar, Antioquia that resulted in 98 people dead or missing did not contribute to the reduction of vulnerability for the resettled community. To accurately measure post-disaster vulnerability and resilience, a new holistic model of indicators that includes both social and biophysical variables that illustrate and measure the relevance of preexisting vulnerabilities was developed. Local data was collected through 178 surveys administered to the inhabitants of Salgar’s three post-disaster resettlement sectors—La Habana, La Florida, and Las Margaritas—to construct an accurate picture of the populations affected by the disaster. Our results show that in the case of Salgar, social vulnerabilities persist even in the physical components of the resettlement sites where new infrastructure would be expected to reduce hazardous conditions and exposure to risk.
Spatial Heterogeneity Analysis of Factors on Commuting Carbon Emissions: Evidence from the Shenzhen Metropolitan Area in China
Authors: Li Xin , Zhang Jiayue , Zhang Wenna , Tan Yue
Published: May 2024

As an essential part of daily life, commuting produces considerable carbon emissions and is currently receiving increased amounts of attention. Comprehensive explorations of carbon emissions and the spatial distribution of their effects based on previous studies are lacking. First, we adopt stepwise regression and geographically weighted regression (GWR) to explore the diverse impacts of carbon emissions on the different layers of metropolitan areas, employing factors from the perspectives of socioeconomics, transportation services, and road networks. Our findings show that optimizing the road network structure could be an effective approach to reducing carbon emissions from commuting, especially in the periphery of metropolitan areas. In addition, the mixed use of land contributes to reducing carbon emissions from commuting, especially in the central areas. Thus, the coverage of public transport should be improved, especially in peripheral regions. Policymakers should monitor the spatial heterogeneity of variables and develop suitable policies to adapt to the conditions of the different layers of metropolitan areas.
Disruptive Factors in Product Portfolio Management: An Exploratory Study in B2B Manufacturing for Sustainable Transition
Authors: Gramberg Till , Bauernhansl Thomas , Eggert Andreas
Published: May 2024

Business-to-business (B2B) manufacturing companies are increasingly confronted with transformative trends such as sustainability, digitalization, and servitization. These trends are changing how product portfolios are developed, and how value contributions are assessed, and therefore have disruptive potential. Dealing with these disruptive factors in Product Portfolio Management (PPM) is a largely unexplored topic. This study presents an empirical-qualitative exploration that contributes significantly to the field. The aim is to clarify the extent to which disruptive factors influence the evaluation and shaping of the product portfolio in B2B manufacturing companies. The Gioia method was used to evaluate 21 semi-structured interviews with experts from leading B2B manufacturing companies. Eight overarching challenges in PPM resulting from disruptive factors were identified. Based on the eight overarching challenges and their associated causal relationships, two aggregated dimensions of action were derived: (1) increasing speed and flexibility by using generative artificial intelligence (AI) in a defined PPM process and (2) adjusting the product portfolio evaluation to consider various strategic drivers. These two dimensions of action call for future research to overcome the disruptive factors in PPM.
Ecologically Oriented Freeway Control Methods Integrated Speed Limits and Ramp Toll Booths Layout
Authors: Yang Pengsen , Ma Minghui , Wu Chaoteng
Published: May 2024

Traffic exhaust pollution, especially in congested areas of freeways, is one of the main causes of air pollution. With the increase in the number of vehicles, traffic and environmental issues have become more prominent. In addition, traffic congestion leads to frequent starting and stopping of vehicles, further exacerbating environmental pollution. This article focuses on the problem of frequent starting and stopping of vehicles, using variable speed limit control to smooth traffic flow, reduce vehicle speed, and alleviate exhaust emissions caused by traffic congestion. At the same time, considering the traffic and environmental benefits of bottleneck areas on freeways, the VT-Micro model is used to calculate exhaust emissions, and a coordinated control method for the mainline and ramp of freeways is proposed. The simulation experiment results show that the total driving time of the mainline and ramp collaborative control method considering environmental benefits has been reduced by 24.69%, CO emissions have been reduced by 4.79%, HC emissions have been reduced by 7.65%, NOx emissions have been reduced by 2.48%, and fuel consumption has been reduced by 4.98%.
Does Environmental Regulation Affect Circular Economy Performance? Evidence from China
Authors: Peng Baoting , Shen Xin
Published: May 2024

Environmental regulation is an important means to solve the environmental pollution challenges faced by economic development. Under the predicament of economic growth facing enormous environmental challenges, this paper aims to explore whether environmental regulation has a promoting effect on the circular economy performance, construct the mechanism through which environmental regulation impacts circular economy performance, as well as test regional heterogeneity on the impact of environmental regulation on circular economy performance under different levels of economic development. Exploring these factors is of great significance for meeting people’s demand for better living environments and promoting green and sustainable economic development. This paper takes interprovincial panel data of China from 2011 to 2022 as the sample and adopts structural equation modeling to explore the influence mechanism of environmental regulation on circular economy performance by constructing a measurement system of environmental regulation and circular economy performance. The results indicate the following: environmental regulation has a significant role in promoting circular economy performance, and there is a partial mediating effect of industrial structure upgrading within it; heterogeneity analysis shows that the promoting effect of environmental regulation on circular economy performance varies according to the different levels of regional economic development. Therefore, environmental regulation policies should be moderately adjusted to promote balanced economic and environmental development; environmental regulation policies should be enriched to promote industrial structure transformation and upgrading; regional synergistic governance should be improved to facilitate the promotion of the circular economy.
Landslide Susceptibility Assessment and Future Prediction with Land Use Change and Urbanization towards Sustainable Development: The Case of the Li River Valley in Yongding, China
Authors: Yang Chi , Wang Jinghan , Li Shuyi , Xiong Ruihan , Li Xiaobo , Gao Lin , Guo Xu , Ma Chuanming , Xiong Hanxiang , Qiu Yang
Published: May 2024

The land use change (LUC) and urbanization caused by human activities have markedly increased the occurrence of landslides, presenting significant challenges in accurately predicting landslide susceptibility despite decades of model advancements. This study, focusing on the Li River Valley (LRV) within the Yongding District, China, employs two common models, namely an analytic hierarchy process–comprehensive index (AHP-CI) model and a logistic regression (LR) model to assess landslide susceptibility (LS). The AHP-CI model is empirically based, with the advantage of being constructible and applicable at various scales without a dataset, though it remains highly subjective. The LR model is a statistical model that requires a training set. The two models represent heuristic and statistical approaches, respectively, to assessing LS. Meanwhile, the basic geological and environmental conditions are considered in the AHP-CI model, while the LR model accounts for the conditions of LUC and urbanization. The results of the multicollinearity diagnostics reflect the rationality of the predisposing factor selection (1.131 < VIF < 4.441). The findings reveal that the AHP-CI model underperforms in LUC and urbanization conditions (AUROC = 0.645, 0.628, and 0.667 for different validation datasets). However, when all the time-varying human activity predisposing factors are considered, the LR model (AUROC = 0.852) performs significantly better under the conditions of solely considering 2010 (AUROC = 0.744) and 2020 (AUROC = 0.810). The CA–Markov model was employed to project the future land use for the short-term (2025), mid-term (2030), and long-term (2040) planning periods. Based on these projections, maps of future LS were created. Importantly, this paper discussed the relationships between landslide management and regional sustainable development under the framework of the UN SDGs, which are relevant to Goal 1, Goal 11, Goal 13, and Goal 15. Finally, this study highlights the importance of integrating strategic land planning, reforestation efforts, and a thorough assessment of human impact predisposing factors with SDG-aligned LS predictions, advocating for a comprehensive, multi-stakeholder strategy to promote sustainable landslide mitigation.