bluuebunny/crossref_metadata_2025 huggingface dataset:
Vegetation Trends Due to Land Cover Changes on the Tibetan Plateau for 2015–2100 Largely Explained by Forest
Authors: Wang Fangfang , Ma Yaoming
Published: December 2024

Vegetation changes on the Tibetan Plateau are indicative of the dual impacts of climate change and human activities, with satellite data offering a potent tool for monitoring these alterations. However, the impacts of future land cover change on vegetation changes on the Tibetan Plateau under different climate scenarios remain unclear. This study systematically investigates vegetation trends and their contributions driven by land cover changes under eight future climate scenarios from 2015 to 2100 using remotely sensed land cover and NDVI data. We estimated consistent NDVI data for land cover changes under the climate scenarios and quantified the vegetation trends and the relative contributions of each land cover type using a relative importance matrix. The study found that (1) Grasslands will remain the dominant land cover, increasing by 4.13% from 2015 to 2100, while Forests, particularly Woody Savannas and Mixed Forests, will significantly influence vegetation trends, with maximum contributions of 48–55% across seasons. (2) Vegetation trends under climate scenarios exhibit greening, browning followed by greening, fluctuation, and browning patterns, with greening being predominant. (3) Forests dominate vegetation trends in most scenarios, especially under pathways of sustainability (SSP1) and fossil-fueled development (SSP5). (4) The seasonal patterns of vegetation changes due to land cover changes are generally similar to the annual one; variations in land cover changes under different scenarios lead to differences in vegetation seasonal patterns. Our research promotes the understanding of the interaction between future land cover changes and vegetation changes on the Tibetan Plateau.
Si APD-Based High Speed Infrared Radiation Thermometry for Analysing the Temperature Instability of a Combustion Chamber
Authors: Karapateas Louis , Lai Yufeng , Meng Xiangfei , Zhang Yang , Willmott Jon R. , Hobbs Matthew J.
Published: December 2024

This study introduces a novel approach to analysing the combustion process using a high-speed, non-contact, optical fibre-coupled Si avalanche photodiode (APD)-based infrared radiation thermometer (IRT). The Si APD-IRT, combined with an optimised field-programmable gate array (FPGA)-based digital design, achieves a response time of 1 µs, faster than commercially available instruments. Our instrument captures the entire ignition and reignition cycle of a Jet A kerosene droplet with high temporal precision within a combustion chamber, a feat impossible with traditional thermocouples. The FPGA module was validated with a 1 µs data acquisition time, using a 40 MHz onboard clock, achieving throughput of 0.64 Gbps with efficiencies of 0.062 Mbps/slice in lookup tables (LUTs), confirming a low-area design compared to conventional FPGAs. The IRT achieves a root mean square (RMS) noise specification of 0.5 °C at a 1 µs acquisition time and a target temperature of approximately 1000 °C. A measurement uncertainty of within ±0.25% °C + 2 °C confirms that it lies within the bounds of commercial instrumentations. Our instrument was demonstrated to capture transient temperature fluctuations during combustion and characterises Jet A kerosene fuel droplets, laying the foundation for understanding sustainable aviation fuels (SAFs) and their role in transitioning from aviation fossil fuels, enabling effective research and development.
SiCRNN: A Siamese Approach for Sleep Apnea Identification via Tracheal Microphone Signals
Authors: Lillini Davide , Aironi Carlo , Migliorelli Lucia , Gabrielli Leonardo , Squartini Stefano
Published: December 2024

Sleep apnea syndrome (SAS) affects about 3–7% of the global population, but is often undiagnosed. It involves pauses in breathing during sleep, for at least 10 s, due to partial or total airway blockage. The current gold standard for diagnosing SAS is polysomnography (PSG), an intrusive procedure that depends on subjective assessment by expert clinicians. To address the limitations of PSG, we propose a decision support system, which uses a tracheal microphone for data collection and a deep learning (DL) approach—namely SiCRNN—to detect apnea events during overnight sleep recordings. Our proposed SiCRNN processes Mel spectrograms using a Siamese approach, integrating a convolutional neural network (CNN) backbone and a bidirectional gated recurrent unit (GRU). The final detection of apnea events is performed using an unsupervised clustering algorithm, specifically k-means. Multiple experimental runs were carried out to determine the optimal network configuration and the most suitable type and frequency range for the input data. Tests with data from eight patients showed that our method can achieve a Recall score of up to 95% for apnea events. We also compared the proposed approach to a fully convolutional baseline, recently introduced in the literature, highlighting the effectiveness of the Siamese training paradigm in improving the identification of SAS.
Digitalization of an Industrial Process for Bearing Production
Authors: Rodriguez-Fortun Jose-Manuel , Alvarez Jorge , Monzon Luis , Salillas Ricardo , Noriega Sergio , Escuin David , Abadia David , Barrutia Aitor , Gaspar Victor , Romeo Jose Antonio , Cebrian Fernando , del-Hoyo-Alonso Rafael
Published: December 2024

The developments in sensing, actuation, and algorithms, both in terms of Artificial Intelligence (AI) and data treatment, have open up a wide range of possibilities for improving the quality of the production systems in diverse industrial fields. The present paper describes the automatizing process performed in a production line for high-quality bearings. The actuation considered new sensing elements at the machine level and the treatment of the information, fusing the different sources in order to detect quality defects in the grinding process (waviness, burns) and monitoring the state of the tool. At a supervision level, an AI model has been developed for monitoring the complete line and compensating deviations in the dimension of the final assembly. The project also contemplated the hardware architecture for improving the data acquisition and communication among the machines and databases, the data treatment units, and the human interfaces. The resulting system gives feedback to the operator when deviations or potential errors are detected so that the quality issues are recognized and can be amended in advance, thereby reducing the quality cost.
Green ILC: A Novel Energy-Efficient Iterative Learning Control Approach
Authors: Dou Yu , Prempain Emmanuel
Published: December 2024

In this paper, we introduce Green Iterative Learning Control (Green ILC), an innovative hybrid control method that addresses the critical need for energy-efficient control in dynamic, repetitive-task environments. By integrating the iterative refinement capabilities of traditional Iterative Learning Control (ILC) with the optimization strengths of gradient descent, Green ILC achieves a balanced trade-off between tracking accuracy and energy consumption. This novel approach introduces a cost function that minimizes both tracking errors and control effort, enabling the system to adaptively optimize performance over iterations. Theoretical analysis and simulation results demonstrate that Green ILC not only achieves faster convergence but also provides significant energy savings compared with traditional ILC methods. Notably, Green ILC reduces energy consumption by prioritizing efficiency, making it particularly suitable for energy-intensive applications such as robotics, manufacturing, and process control. While a slight decrease in tracking accuracy is observed, this trade-off is acceptable for scenarios where energy efficiency is paramount. This work establishes Green ILC as a promising solution for modern industrial systems requiring robust and sustainable control strategies.
The Effects of Cognition and Vision While Walking in Younger and Older Adults
Authors: Pham Trong , Suen Meagan , Cho Young-Hee , Krishnan Vennila
Published: December 2024

This study investigated how various cognitive tasks and visual challenges affect dual-task walking costs (DTWC) in younger and older adults. Twenty younger adults (Meanage = 22.25, SD = 3.04, 4 males) and eighteen older adults (Meanage = 71.75, SD = 5.17, 7 males) completed single-task walking and dual-task walking. The dual tasks involved walking while performing either (a) serial-subtraction by 3s or (b) a Stroop task. Both single tasks and dual tasks were performed under both normal vision and peripheral-vision-loss conditions. Results showed no significant three-way interaction but two significant two-way interactions: DTWC for step-length was greater during Stroop compared to serial-subtraction, (a) more in older adults regardless of vision (p = 0.022) and (b) more under peripheral-vision-loss regardless of age (p = 0.033). In addition, DTWC for various gait parameters was greater under (a) Stroop compared to serial-subtraction, (b) peripheral-vision-loss compared to normal vision, and (c) older adults compared to younger adults. These findings suggest that, when engaging in a cognitively demanding task, older adults place greater emphasis on maintaining gait compared to younger adults, likely to offset the negative impacts of additional cognitive load and deteriorated vision. Future research should further examine how different cognitive tasks and visual challenges interact across age groups.
Sleeping and Eating Behavior Recognition of Horses Based on an Improved SlowFast Network
Authors: Liu Yanhong , Zhou Fang , Zheng Wenxin , Bai Tao , Chen Xinwen , Guo Leifeng
Published: December 2024

The sleeping and eating behaviors of horses are important indicators of their health. With the development of the modern equine industry, timely monitoring and analysis of these behaviors can provide valuable data for assessing the physiological state of horses. To recognize horse behaviors in stalls, this study builds on the SlowFast algorithm, introducing a novel loss function to address data imbalance and integrating an SE attention module in the SlowFast algorithm’s slow pathway to enhance behavior recognition accuracy. Additionally, YOLOX is employed to replace the original target detection algorithm in the SlowFast network, reducing recognition time during the video analysis phase and improving detection efficiency. The improved SlowFast algorithm achieves automatic recognition of horse behaviors in stalls. The accuracy in identifying three postures—standing, sternal recumbency, and lateral recumbency—is 92.73%, 91.87%, and 92.58%, respectively. It also shows high accuracy in recognizing two behaviors—sleeping and eating—achieving 93.56% and 98.77%. The model’s best overall accuracy reaches 93.90%. Experiments show that the horse behavior recognition method based on the improved SlowFast algorithm proposed in this study is capable of accurately identifying horse behaviors in video data sequences, achieving recognition of multiple horses’ sleeping and eating behaviors. Additionally, this research provides data support for livestock managers in evaluating horse health conditions, contributing to advancements in modern intelligent horse breeding practices.
Improving Multiscale Fuzzy Entropy Robustness in EEG-Based Alzheimer’s Disease Detection via Amplitude Transformation
Authors: Arpaia Pasquale , Cacciapuoti Maria , Cataldo Andrea , Criscuolo Sabatina , De Benedetto Egidio , Masciullo Antonio , Pesola Marisa , Schiavoni Raissa
Published: December 2024

This study investigates the effectiveness of amplitude transformation in enhancing the performance and robustness of Multiscale Fuzzy Entropy for Alzheimer’s disease detection using electroencephalography signals. Multiscale Fuzzy Entropy is a complexity measure particularly sensitive to intra- and inter-subject variations in signal amplitude, as well as the selection of key parameters such as embedding dimension (m) and similarity criterion (r), which often result in inconsistent outcomes when applied to multivariate data, such as electroencephalography signals. To address these challenges and to generalize the possibility of adopting Multiscale Fuzzy Entropy as a diagnostic tool for Alzheimer’s disease, this research explores amplitude transformation preprocessing on electroencephalography signals in Multiscale Fuzzy Entropy calculation across varying parameters. The statistical analysis of the obtained results demonstrates that amplitude transformation preprocessing significantly enhances Multiscale Fuzzy Entropy’s ability to detect Alzheimer’s disease, achieving higher and more consistent significant comparison percentages, with an average of 73.2% across all parameter combinations, compared with only one raw data combination exceeding 65%. Clustering analysis corroborates these findings, showing that amplitude transformation improves the differentiation between Alzheimer’s disease patients and healthy subjects. These results highlight the potential of amplitude transformation to stabilize Multiscale Fuzzy Entropy performance, making it a more reliable tool for early Alzheimer’s disease detection.
Lightweight Two-Layer Control Architecture for Human-Following Robot
Authors: Acosta-Amaya Gustavo A. , Miranda-Montoya Deimer A. , Jimenez-Builes Jovani A.
Published: December 2024

(1) Background: Human detection and tracking are critical tasks for assistive autonomous robots, particularly in ensuring safe and efficient human–robot interaction in indoor environments. The increasing need for personal assistance among the elderly and people with disabilities has led to the development of innovative robotic systems. (2) Methods: This research presents a lightweight two-layer control architecture for a human-following robot, integrating a fuzzy behavior-based control system with low-level embedded controllers. The system uses an RGB-D sensor to capture distance and angular data, processed by a fuzzy controller to generate speed set-points for the robot’s motors. The low-level control layer was developed using pole placement and internal model control (IMC) methods. (3) Results: Experimental validation demonstrated that the proposed architecture enables the robot to follow a person in real time, maintaining the predefined following distance of 1.3 m in each of the five conducted trials. The IMC-based controller demonstrated superior performance compared to the pole placement controller across all evaluated metrics. (4) Conclusions: The proposed control architecture effectively addresses the challenges of human-following in indoor environments, offering a robust, real-time solution suitable for assistive robotics with limited computational resources. The system’s modularity and scalability make it a promising approach for future developments in personal assistance robotics.
Low-Quality Sensor Data-Based Semi-Supervised Learning for Medical Image Segmentation
Authors: Li Hengfan , Xu Xuanbo , Liu Ziheng , Xia Qingfeng , Xia Min
Published: December 2024

Traditional medical image sensors face multiple challenges. First, these sensors typically rely on large amounts of labeled data, which are time-consuming and costly to obtain. Second, when the data volume and image size are large, traditional sensors have limited computational power, making it difficult to effectively train and infer models. Additionally, traditional sensors have poor generalization ability and struggle to adapt to datasets with different modalities. This paper devises a novel framework, named LSDSL, and deploys it in the sensor. LSDSL utilizes low-quality sensor data for semi-supervised learning in medical image segmentation. in supervised learning, we devise the hard region exploration (hre) module to enhance the model’s comprehension of low-quality pixels in hard regions. in unsupervised learning, we introduce a pseudo-label sharing (ps) module, which allows low-quality pixels in one network to learn from the high-quality pixels in the other networks. our model outperforms other semi-supervised methods on the datasets of two different modalities (CT and MRI) in medical image sensors, achieving superior inference speed and segmentation accuracy.
An Investigation of Extended-Dimension Embedded CKF-SLAM Based on the Akaike Information Criterion
Authors: Xu Hanghang , Chen Yijin , Song Wenhui , Wang Lianchao
Published: December 2024

Simultaneous localization and mapping (SLAM) faces significant challenges due to high computational costs, low accuracy, and instability, which are particularly problematic because SLAM systems often operate in real-time environments where timely and precise state estimation is crucial. High computational costs can lead to delays, low accuracy can result in incorrect mapping and localization, and instability can make the entire system unreliable, especially in dynamic or complex environments. As the state-space dimension increases, the filtering error of the standard cubature Kalman filter (CKF) grows, leading to difficulties in multiplicative noise propagation and instability in state estimation results. To address these issues, this paper proposes an extended-dimensional embedded CKF based on truncated singular-value decomposition (TSVD-AECKF). Firstly, singular-value decomposition (SVD) is employed instead of the Cholesky decomposition in the standard CKF to mitigate the non-positive definiteness of the state covariance matrix. Considering the effect of small singular values on the stability of state estimation, a method is provided to truncate singular values by determining the truncation threshold using the Akaike information criterion (AIC). Furthermore, the system noise is embedded into the state variables, and an embedding volume criterion is used to improve the conventional CKF while extending the dimensionality. Finally, the proposed algorithm was validated and analyzed through both simulations and real-world experiments. The results indicate that the proposed method effectively mitigates the increase in localization error as the state-space dimension grows, enhancing time efficiency by 55.54%, and improving accuracy by 35.13% compared to the standard CKF algorithm, thereby enhancing the robustness and stability of mapping.
Development of Robust Lane-Keeping Algorithm Using Snow Tire Track Recognition in Snowfall Situations
Authors: Kim Donghyun , Jeong Yonghwan
Published: December 2024

This study proposed a robust lane-keeping algorithm designed for snowy road conditions, utilizing a snow tire track detection model based on machine learning. The proposed algorithm is structured into two primary modules: a snow tire track detector and a lane center estimator. The snow tire track detector utilizes YOLOv5, trained on custom datasets generated from public videos captured on snowy roads. Video frames are annotated with the Computer Vision Annotation Tool (CVAT) to identify pixels containing snow tire tracks. To mitigate overfitting, the detector is trained on a combined dataset that incorporates both snow tire track images and road scenes from the Udacity dataset. The lane center estimator uses the detected tire tracks to estimate a reference line for lane keeping. Detected tracks are binarized and transformed into a bird’s-eye view image. Then, skeletonization and Hough transformation techniques are applied to extract tire track lines from the classified pixels. Finally, the Kalman filter estimates the lane center based on tire track lines. Evaluations conducted on unseen images demonstrate that the proposed algorithm provides a reliable lane reference, even under heavy snowfall conditions.
Antidiabetic Effect of Dihydrobetulonic Acid Derivatives as Pparα/γ Agonists
Authors: Khvostov Mikhail V. , Blokhin Mikhail E. , Borisov Sergey A. , Fomenko Vladislav V. , Meshkova Yulia V. , Zhukova Natalia A. , Nikonova Sophia V. , Pavlova Sophia V. , Pogosova Maria A. , Medvedev Sergey P. , Luzina Olga A. , Salakhutdinov Nariman F.
Published: December 2024

Dual PPARα/γ agonists can normalize both glucose and lipid metabolism in patients with type 2 diabetes mellitus. The development of such drugs faced the detection of various toxic effects in phase III clinical trials. However, two drugs of this class managed to pass all stages of clinical trials, which makes the search for new dual PPARα/γ agonists promising. In the present study, a series of dihydrobetulonic acid amides differing in the length of the amino-alcohol linker and incorporating a pharmacophore fragment of (S)-2-ethoxy-3-phenylpropanoic acid were synthesized. The in vitro study showed that the length of the aminoalcohol linker dramatically affects the level of activation of PPAR-α and γ receptors. The synthesized compounds were tested for their ability to improve glycemic control and to counter lipid abnormalities in C57Bl/6 Ay/a mice at a dose of 30 mg/kg. Of all the compounds tested, the dihydrobetulonic acid derivative with an aminoethanol linker (15a) had the most pronounced effect in improving insulin sensitivity and glucose tolerance, and in reducing blood triglyceride levels. In addition, 15a dramatically counteracted the pathological changes in the liver, pancreas, kidney, and brown fat tissue that are characteristic of type 2 diabetes.
RIP Sensing Matrices Construction for Sparsifying Dictionaries with Application to MRI Imaging
Authors: Ho Jinn , Hwang Wen-Liang , Heinecke Andreas
Published: December 2024

Practical applications of compressed sensing often restrict the choice of its two main ingredients. They may (i) prescribe the use of particular redundant dictionaries for certain classes of signals to become sparsely represented or (ii) dictate specific measurement mechanisms which exploit certain physical principles. On the problem of RIP measurement matrix design in compressed sensing with redundant dictionaries, we give a simple construction to derive sensing matrices whose compositions with a prescribed dictionary have with high probability the RIP in the klog(n/k) regime. Our construction thus provides recovery guarantees usually only attainable for sensing matrices from random ensembles with sparsifying orthonormal bases. Moreover, we use the dictionary factorization idea that our construction rests on in the application of magnetic resonance imaging, in which also the sensing matrix is prescribed by quantum mechanical principles. We propose a recovery algorithm based on transforming the acquired measurements such that the compressed sensing theory for RIP embeddings can be utilized to recover wavelet coefficients of the target image, and show its performance on examples from the fastMRI dataset.
Signal Processing for Transient Flow Rate Determination: An Analytical Soft Sensor Using Two Pressure Signals
Authors: Brumand-Poor Faras , Kotte Tim , Pasquini Enrico Gaspare , Schmitz Katharina
Published: December 2024

Accurate knowledge of the flow rate is essential for hydraulic systems, enabling the calculation of hydraulic power when combined with pressure measurements. These data are crucial for applications such as predictive maintenance. However, most flow rate sensors in fluid power systems operate invasively, disrupting the flow and producing inaccurate results, especially under transient conditions. Utilizing pressure transducers represents a non-invasive soft sensor approach since no physical flow rate sensor is used to determine the flow rate. Usually, this approach relies on the Hagen–Poiseuille (HP) law, which is limited to steady and incompressible flow. This paper introduces a novel soft sensor with an analytical model for transient, compressible pipe flow based on two pressure signals. The model is derived by solving fundamental fluid equations in the Laplace domain and converting them back to the time domain. Using the four-pole theorem, this model contains a relationship between the pressure difference and the flow rate. Several unsteady test cases are investigated and compared to a steady soft sensor based on the HP law, highlighting our soft sensor’s promising capability. It exhibits an overall error of less than 0.15% for the investigated test cases in a distributed-parameter simulation, whereas the HP-based sensor shows errors in the double-digit range.
Emergency Urological Surgery Performed by General Surgeons: A Systematic Review
Authors: Miao Patrick , Paynter Jessica A. , Qin Kirby R. , Brennan Janelle
Published: December 2024

Background: Across the world, many populations have limited access to urology care resulting in local general surgeons performing emergency urology procedures. This systematic review aims to evaluate the nature and outcomes of emergency urological surgeries performed by general surgeons. Methods: A systematic review was undertaken according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines with MEDLINE, Embase, Web of Science, and Google Scholar searched from inception to June 2024 for studies where general surgeons performed emergent urological surgery. We excluded studies on paediatric urology, acute scrotum, and Fournier’s gangrene. The Downs and Black checklist was used to assess risk of bias. Results: From 2093 initial results, six studies were included after screening. Two studies were from Australia, two from the United States of America, and one each from India and South Africa. For 977 emergency urological interventions, general surgeons (n = 486) and urologists (n = 491) each performed approximately half of the surgeries. Mortality rates for general surgeon-performed urology ranged from 0 to 8.0%. One study compared outcomes between the two surgical specialties, showing no significant difference for mortality or complication rates. No follow-up data was reported. The most performed procedures by general surgeons were traumatic bladder repair, ureteric stent insertion, and percutaneous nephrostomy. Conclusions: General surgeons perform a wide spectrum of emergency urological surgery when needed. Targeted training and utilisation of general surgeons could improve patient access to emergency urological surgery.
Spontaneous Calyceal Rupture from an Unusual Source
Authors: Razi Basil , Cole-Clark Dane , Self Duncan , Latif Edward
Published: December 2024

A computed tomography intravenous pyelogram (CT IVP) is a very common investigation performed for a wide range of urological presentations such as abdominal pain and haematuria. We report a rare case of spontaneous calyceal rupture and associated haemorrhage during a CT.
It’s Getting Hot in There: In Vitro Study on Ureteral Tissue Thermal Profiles During Laser Ureteral Lithotripsy
Authors: Tee Zhou Yin , Yong Chun Hou , Goh Yue Keng , Lim Meng Shi
Published: December 2024

Introduction: The integration of laser technology in urologic interventions, especially ureteral lithotripsy, has greatly advanced the field, with laser lithotripsy becoming the preferred method for treating ureteric stones via ureteroscopy. Recent advancements focus on enhancing power settings and reducing operating times, introducing high-power laser equipment capable of frequencies up to 120 Hz. However, concerns arise regarding thermal injuries to adjacent tissues due to increased energy delivery, potentially causing ureteric strictures. Objective: To explore temperature dynamics during ureteroscopic laser lithotripsy, considering factors like laser power settings and ureteroscope size, to optimize outcomes and mitigate risks for patients. Methods: A simulated in vitro model for ureteroscopic laser lithotripsy was designed with a holmium laser. Measurements of the temperature were recorded using a thermocouple placed at the laser tip at different sizes of ureteroscope (URS 6.0 Fr and URS 7.0 Fr), holmium laser (272 µm and 365 µm), various power settings (5 to 25 Hz; 0.2 to 3.0 J) and activation durations (3 to 30 s). Analysis of the variables associated with temperature change was performed. Results: All of the variables showed rising temperature trends as the laser activation time was prolonged, while ureteroscope size had no significant impact. Smaller laser fibers exhibited lower overall temperature profiles, around 34–35 °C. Notably, power settings significantly influenced temperature, with a substantial rise at 20 W (42.62 °C) and 30 W (40.02 °C). There was a significant rise in temperature as power (J × Hz) increased, where frequency carries a higher effect than energy at the same power setting. Conclusions: The recommendation includes exercising caution with higher power levels, shorter activation times, and preferably using small-caliber laser fibers to maintain lower temperatures.
Robust Reinforcement Learning Strategies with Evolving Curriculum for Efficient Bus Operations in Smart Cities
Authors: Tang Yuhan , Qu Ao , Jiang Xuan , Mo Baichuan , Cao Shangqing , Rodriguez Joseph , Koutsopoulos Haris N , Wu Cathy , Zhao Jinhua
Published: December 2024

Public transit systems are critical to the quality of urban life, and enhancing their efficiency is essential for building cost-effective and sustainable smart cities. Historically, researchers sought reinforcement learning (RL) applications to mitigate bus bunching issues with holding strategies. Nonetheless, these attempts often led to oversimplifications and misalignment with the goal of reducing the total time passengers spent in the system, resulting in less robust or non-optimal solutions. In this study, we introduce a novel setting where each bus, supervised by an RL agent, can appropriately form aggregated policies from three strategies (holding, skipping station, and turning around to serve the opposite direction). It’s difficult to learn them all together, due to learning complexity, we employ domain knowledge and develop a gradually expanding action space curriculum, enabling agents to learn these strategies incrementally. We incorporate Long Short-Term Memory (LSTM) in our model considering the temporal interrelation among these actions. To address the inherent uncertainties of real-world traffic systems, we impose Domain Randomization (DR) on variables such as passenger demand and bus schedules. We conduct extensive numerical experiments with the integration of synthetic and real-world data to evaluate our model. Our methodology proves effective, enhancing bus schedule reliability and reducing total passenger waiting time by over 15%, thereby improving bus operation efficiency and smoothering operations of buses that align with sustainable goals. This work highlights the potential of robust RL combined with curriculum learning for optimizing public transport in smart cities, offering a scalable solution for real-world multi-agent systems.
Enhancing Urban Electric Vehicle (EV) Fleet Management Efficiency in Smart Cities: A Predictive Hybrid Deep Learning Framework
Authors: Aldossary Mohammad
Published: December 2024

Rapid technology advances have made managing charging loads and optimizing routes for electric vehicle (EV) fleets, especially in cities, increasingly important. IoT sensors in EV charging stations and cars enhance prediction and optimization algorithms with real-time data on charging behaviors, traffic, vehicle locations, and environmental factors. These IoT data enable the GNN-ViGNet hybrid deep learning model to anticipate electric vehicle charging needs. Data from 400,000 IoT sensors at charging stations and vehicles in Texas were analyzed to identify EV charging patterns. These IoT sensors capture crucial parameters, including charging habits, traffic conditions, and other environmental elements. Frequency-Aware Dynamic Range Scaling and advanced preparation methods, such as Categorical Encoding, were employed to improve data quality. The GNN-ViGNet model achieved 98.9% accuracy. The Forecast Accuracy Rate (FAR) and Charging Load Variation Index (CLVI) were introduced alongside Root-Mean-Square Error (RMSE) and Mean Square Error (MSE) to assess the model’s predictive power further. This study presents a prediction model and a hybrid Coati–Northern Goshawk Optimization (Coati–NGO) route optimization method. Routes can be real-time adjusted using IoT data, including traffic, vehicle locations, and battery life. The suggested Coati–NGO approach combines the exploratory capabilities of Coati Optimization (COA) with the benefits of Northern Goshawk Optimization (NGO). It was more efficient than Particle Swarm Optimization (919 km) and the Firefly Algorithm (914 km), reducing the journey distance to 511 km. The hybrid strategy converged more quickly and reached optimal results in 100 rounds. This comprehensive EV fleet management solution enhances charging infrastructure efficiency, reduces operational costs, and improves fleet performance using real-time IoT data, offering a scalable and practical solution for urban EV transportation.
Integrated Energy Management in Small-Scale Smart Grids Considering the Emergency Load Conditions: A Combined Battery Energy Storage, Solar PV, and Power-to-Hydrogen System
Authors: Jokar Hossein , Niknam Taher , Dehghani Moslem , Siano Pierluigi , Ouahada Khmaies , Aly Mokhtar
Published: December 2024

This study introduces an advanced Mixed-Integer Linear Programming model tailored for comprehensive electrical and thermal energy management in small-scale smart grids, addressing emergency load shedding and overload situations. The model integrates combined heat and power sources, capable of simultaneous electricity and heat generation, alongside a mobile photovoltaic battery storage system, a wind resource, a thermal storage tank, and demand response programs (DRPs) for both electrical and thermal demands. Power-to-hydrogen systems are also incorporated to efficiently convert electrical energy into heat, enhancing network synergies. Utilizing the robust Gurobi solver, the model aims to minimize operating, fuel, and maintenance costs while mitigating environmental impact. Simulation results under various scenarios demonstrate the model’s superior performance. Compared to conventional evolutionary methods like particle swarm optimization, non-dominated sorting genetic algorithm III, and biogeography-based optimization, the proposed model exhibits remarkable improvements, outperforming them by 11.4%, 5.6%, and 11.6%, respectively. This study emphasizes the advantages of employing DRP and heat tank equations to balance electrical and thermal energy relationships, reduce heat losses, and enable the integration of larger photovoltaic systems to meet thermal constraints, thus broadening the problem’s feasible solution space.
Diversity, Local Wisdom, and Unique Characteristics of Millennials as Capital for Innovative Learning Models: Evidence from North Sumatra, Indonesia
Authors: Amin Muryanto , Ritonga Alwi Dahlan
Published: December 2024

This study examines the intersection of identity politics, local wisdom, and millennial engagement in fostering social cohesion in North Sumatra, Indonesia. Against the backdrop of ethnic and religious diversity, identity politics has often served as a tool for polarization, particularly during elections, targeting vulnerable millennial demographics. Utilizing a qualitative approach with in-depth interviews and focus group discussions, this study highlights how local wisdom values, including gotong royong (mutual cooperation) and Dalihan Na Tolu (Batak cultural values), support social harmony and mitigate divisive identity-based narratives. These findings underscore the unique potential of millennials to bridge social divides if they engage in community programs that incorporate these values. Additionally, this study proposes an innovative education model that integrates local wisdom and digital technology to enhance millennials’ awareness and resilience against identity politics. This approach enhances social cohesion and positions millennials as agents of positive social change. This study contributes to the broader discourse on multiculturalism, identity politics, and generational roles in national unity within Indonesia’s socio-political landscape.
Child Labor in Sindh, Pakistan: Patterns and Areas in Need of Intervention
Authors: Maqbool Nadia , Newton Paul , Shah Tayyab
Published: December 2024

Child labor remains a predominant issue in Pakistan despite the country’s existing policies and frameworks aimed at abolishing it. Through this study, we investigated the child labor distribution across Sindh and examined the factors that shape the regional patterns. We analyzed the data available through the 2018–19 Sindh Multiple Indicator Cluster Surveys, MICS 6, from 20,030 households with 40,633 children in the 5–17 age bracket. By applying prevalence statistics, chi-square tests, and regression modeling to these data, we investigated the trends in child labor prevalence, identified the correlation between child labor and various socioeconomic and geodemographic variables, and finally mapped the geospatial patterns of child labor across districts in Sindh, enabling us to identify and prioritize the districts in need of immediate intervention. The findings revealed that about 20 percent of the children in Sindh are engaged in child labor, with a high prevalence among males and in the 15–17 age bracket. Moreover, poverty and rural dwellings raise this issue. Other socioeconomic and geographic factors reinforcing this issue are a lack of education among children, mothers, or caretakers and mothers’ or caretakers’ functional difficulties. However, children’s functional difficulties lower their prevalence in labor. Among the 29 districts across Sindh, Kambar Shahdadkot has the highest prevalence of child labor.
Oxidative Stress in the Pathogenesis of Neurodegenerative Diseases
Authors: Jurcau Maria-Carolina , Jurcau Anamaria , Diaconu Razvan-Gabriel
Published: December 2024

Neurodegenerative diseases are devastating conditions with a rising incidence and prevalence due to the aging of the population for which we currently do not have efficient therapies. Despite compelling evidence provided by basic research on the involvement of oxidative stress in their pathogenesis, most trials with antioxidants have failed. The reasons may relate to the low bioavailability of the used compounds or to starting therapy late, when the pathogenic cascades have already induced irreversible damage. The current review discusses the sources of oxidative stress in the central nervous system, the involvement of reactive oxygen species in the pathogenesis of Alzheimer’s disease, Parkinson’s disease, and amyotrophic lateral sclerosis, and the importance of further research on improved delivery methods of antioxidants as well as the search for biomarkers that could help in early diagnosis in the hope of finding more efficient therapies for these diseases.
Silicon Induces Salt Stress Amelioration in Sunflower Plants by Improving Photosynthetic Pigments and Mineral Status
Authors: Calero Hurtado Alexander , Aparecida Chiconato Denise , Sousa Junior Gilmar da Silveira , Prado Renato de Mello , Peña Calzada Kolima , Olivera Viciedo Dilier
Published: December 2024

Silicon (Si) has been extensively studied for its ability to decrease sodium (NaCl) toxicity in various plant species. Nonetheless, the processes that drive these responses are still not well understood. In this study, we investigate the effects of silicon (Si) on the modification of hydrogen peroxide concentration [H2O2], photosynthetic pigment content, nutrient accumulation, and the production of root and shoot dry biomass in sunflower (Helianthus annuus L.) plants hydroponically grown with NaCl (0 and 100 mM) in combination with Si (0 and 2.0 mM). Salt stress induced a significant decrease in plant growth due to high [H2O2] and a decrease in photosynthetic pigment content and nutritional status, denoting that there is oxidative and ionic stress. Nevertheless, Si addition to the growth medium consistently decreased the [H2O2] in sunflower and photosynthetic pigment content, and macro- and micronutrient accumulation, which was associated with an increase in root and shoot dry matter production. These findings indicate that adding Si to the growth medium is crucial for enhancing plant resistance to salt-induced ionic and osmotic stress, making it a promising strategy for improving crop growth and management under salinity conditions.
Bridging Nutritional and Environmental Sustainability Within Planetary Boundaries in Food Life Cycle Assessments: SWOT Review and Development of the Planet Health Conformity Index
Authors: Meier Toni , Schade Susann , Forner Frank , Eberle Ulrike
Published: December 2024

To promote sustainable food choices, it is essential to provide easily understandable information that integrates health, environmental impacts and planetary boundaries. For this purpose, the Planet Health Conformity Index (PHC) was developed and tested. Current labels, such as the Nutri-Score for health and the Eco-Score for environmental impacts, provide separate information, which may result in consumers receiving conflicting messages. The PHC combines these dimensions into a single label, aligning with consumer demand for clearer guidance and fostering sustainable food consumption and development. Methods: The PHC assesses 18 nutrients and five environmental impacts—Global Warming Potential (GWP), cropland use, freshwater use, nitrogen application (N-min) and phosphorus application (P-min)—within the framework of planetary boundaries. Six different algorithm designs, varying in capping and weighting, were tested on 125 food products from the German market. The analysis compared mass-, energy- and multi-nutrient-based functional units. Results: Under mass- and energy-based units, many products meet planetary boundaries. However, incorporating nutrient profiles often leads to exceeding these boundaries (exceedance rate PHC: GWP: 38% of products transgressed the boundary, cropland use: 41%, freshwater use: 27%, N-min: 34%, P-min: 71%). Accordingly, the PHC contextualizes nutritional strengths and weaknesses environmentally. Moreover, it disaggregates the Planetary Health Diet (PHD) at the nutrient level, facilitating adaptation to individual nutritional needs. Conclusions: Traditional food Life Cycle Assessments should include nutrients in the functional unit and consider planetary boundaries to enable more accurate food comparisons. The PHC presented here takes these aspects into account. In addition, its dual-factor approach, integrating health and environmental metrics, ensures broad applicability. Thus, the PHC Index can be applied not only to single food items but also to recipes, dishes, menus and entire diets.
Responses of Changes in Green Space Patterns to Carbon Sequestration in Municipal Areas of the Low-Latitude Plateau in Southwestern China: A Case Study of the Kunming Municipal Area
Authors: Feng Yali , Wang Jin , Pan Yue , Li Chunhua
Published: December 2024

This study focuses on the Kunming municipal area, located in the low-latitude plateau of southwestern China, utilizing remote sensing image data from four distinct periods between 2005 and 2020 to analyze changes in its green landscape patterns. Net primary productivity (NPP) was employed as a metric for carbon sequestration analysis to assess variations in NPP within the Kunming municipal area. Based on Pearson correlation analysis and the XGBoost-SHAP model, the correlations, important indicators, and responses of changes in the green space patterns of the Kunming municipal area to changes in carbon sequestration were analyzed and combined with policy and human factors. The findings indicate the following: (1) From 2005 to 2020, the area proportions of various green space types within the Kunming municipal area were ranked as follows: forest land > grassland > cultivated land > water bodies. (2) Between 2005 and 2015, the patch shapes of green spaces became increasingly complex, with heightened fragmentation among patches. After 2015, this complexity was reduced while connectivity continued to decline alongside an increase in the landscape heterogeneity and richness. (3) Over the period from 2005 to 2020, NPP values for cultivated land, forest land, and grassland exhibited a trend of decreasing and then increasing, reaching their lowest point in 2010. High NPP areas were predominantly found in regions characterized by a hilly topography, elevated altitudes, and substantial natural vegetation cover. (4) There was a significant correlation between green space pattern indices and NPP (p < 0.01), with SHDI, CONTAG, and DIVISION identified as three critical indices influencing NPP. The relationship between landscape patterns and carbon sequestration was most pronounced during the period from 2015 to 2020, followed by that from 2005 to 2010.
Carbon Border Adjustment Mechanism (CBAM) to Tackle Carbon Leakage in the International Fertilizer Trade
Authors: Bux Christian , Rana Roberto Leonardo , Tricase Caterina , Geatti Paola , Lombardi Mariarosaria
Published: December 2024

Carbon emissions have negative effects on the climate regardless of the location they are generated, and several strategies were introduced to meet the Sustainable Development Goals, precisely, Goal 13 “Take urgent action to combat climate change and its impacts”. Recently, to tackle the so-called carbon leakage, the European Union (EU) introduced the Carbon Border Adjustment Mechanism (CBAM), which is a crucial instrument to establish a fair price for the carbon emissions during the production of certain carbon-intensive goods, including fertilizers. The objective of this study is to assess the efficacy of the CBAM in addressing carbon leakage within the EU by evaluating the virtual carbon emission flows to the EU in the timespan 2019–2023, focusing on the top ten primary exporters of fertilizers. The assessment is based on the comparison of the world weighted average (WWA) emission factor and the country-specific one, to identify a more suitable method for measuring carbon emission flows. Results highlighted the opportunity of treating countries individually, rather than employing WWA emission factors. Emissions could be minimized by reducing production levels in countries with lax environmental policies, but this could penalize third-party economies. Sustainable development can be achieved by introducing fair environmental policies, maintaining constant production levels, economically compensating production economies, and exporting skills and know-how.