The relative supply chain environment is rapidly changing due to technological advancement, increasing complexity, and pressure to improve sustainability and supply chain risk. Sourcing and procurement years were recognized as cost-focused processes. However, digital transformation is revolutionizing them. AI, blockchain, IoT, RPA, and cloud options are the technologies that can help organizations move procurement into the realm of a strategic value-creating process. Thus, concerning the focus of this article, it is possible to identify several major digital transformation trends for procurement: Increasing operational efficiency, Enhancing supplier relationships, Managing risks, and Achieving sustainable procurement. Using examples of case studies and a comparative analysis of traditional and digital procurement approaches, including observations, this paper illustrates the change inhered by digital tools. It also covers issues like organizational resistance, integration issues, and cybersecurity, which are further explained to reduce such problems. At last, this article underscores the management decision-makers focus and the value of digital procurement and sustainable competitive advantage in modern procurement and supply chain management.

This paper presents novel observations on the impact of ground-bounce noise (GBN) on logic-HIGH levels of the output response of Complementary Metal Oxide Semiconductor (CMOS) inverter circuits. While the impact of power supply noise on logic-HIGH is undeniable and comprehensively studied in the literature, the impact of ground bounce is perceived, and the analysis is majorly focused on logic-LOW. This work demonstrates as well as analyses the significant impact of GBN on logic-HIGH and the observations are supported by a theoretical investigation using both the analytical and small-signal approaches. Several examples are considered to validate the novel observations considering both the simulation and measurement-based case studies. The simulation-based case studies are performed using several CMOS technologies, and measurement-based case studies are performed using standard HEX inverter Integrated Circuits (ICs). There is a close correlation observed between the practical observations and the corresponding theoretical foundation.

Accurate locating of small-diameter underground pipelines (typically 20-65 mm) is crucial for avoiding or minimizing the damage during urban construction and improving maintenance efficiency. However, conventional pipeline inspection gauges (PIGs) equipped with high-grade inertial measurement units (IMUs), which are primarily designed for normal size pipeline, cannot access small-diameter or tightly curved pipelines due to their large overall size, due to the large-sized high-precision IMUs they rely on. This limitation leaves a critical gap in accurately locating such pipelines, as no effective method currently exists for their precise positioning. To address this challenge, we propose a novel method using an air-propelled inertial positioning ball (IPB) integrated with a chip-level micro-electro-mechanical system (MEMS) IMU, so as to make the device compact and lightweight enough to traverse the small-diameter pipelines. But it comes with a serious problem that the MEMS IMU chip has large sensor errors leading to fast position drift and therefore can only keep positioning accuracy for several seconds. Unlike the conventional PIGs, which are typically pulled through pipelines by steel cables at a low speed (around 1 m/s), the lightweight IPB is designed to be propelled by airflow, allowing it to fly rapidly (around 10 m/s) through the pipeline. This significantly reduces the integration time of the MEMS IMU, thereby mitigating its cumulative errors effectively. Field tests conducted in a 48-meter-long small-diameter pipe demonstrate the effectiveness of the IPB, with single-run positioning errors of 0.77 meters in the transverse direction and 1.13 meters in the height direction. By averaging the results from 4 independent runs, the maximum positioning errors were reduced to 0.36 meters (0.75% of pipe length) in the transverse direction and 0.36 meters (0.75% of pipe length) in the height direction. The proposed new approach provides a practical, efficient, and accurate solution for locating small-diameter and tightly curved underground pipelines, addressing a critical gap in underground pipeline surveying.

For educational institutions in STEM areas, the provision of practical learning scenarios is, traditionally, a major concern. In the 21st century, the explosion of ICTs, as well as the universalization of low-cost hardware, have allowed the proliferation of technical solutions for any field; in the case of experimentation, encouraging the emergence and proliferation of non-traditional experimentation platforms. This movement has resulted in enriched practical environments, with wider adaptability for both students and teachers. In this paper, the evolution of scholar production has been analyzed at the global level from 2000 to 2020. Current and emerging experimentation scenarios have been identified, specifying the scope and boundaries between them.

In this paper we continue our previous investigation about the use of stress function in the flow of generalized Newtonian fluids through conduits of circular and noncircular (or/and multiply connected) cross sections where we inspect the flow of power law fluids in tubes of elliptical cross sections. We derive analytical expressions for the flow velocity profile and for the volumetric flow rate. We also develop numerical algorithms for computing the velocity profile and the volumetric flow rate for this flow where these algorithms produce virtually identical results to the results obtained from the aforementioned analytical expressions. The obtained analytical expressions were tested against the available analytical expressions for the special cases of Newtonian flow in circular tubes, Newtonian flow in elliptical tubes and power law flow in circular tubes and the results were identical. The obtained analytical expressions were also tested for sensible trends, tendencies and correlations and they passed all these tests. [1]

In this paper, we present a novel algorithm for multipath channel estimation for a known preamble sequence in the presence of significant phase noise (PHN) that affects both transmitter and receiver oscillators. The PHN exists in both the receiver and the transmitter and is assumed to vary significantly over the channel length, so it is not possible to approximate the PHN to appear only in one of them. The nonlinearity induced by PHN presents obstacles for conventional linear channel estimation algorithms such as least squares, linear MMSE, or Kalman filters. To address this challenge, we introduce an algorithm based on sequential Monte Carlo methods, also known as Particle Filter. The algorithm’s initial condition is derived using a novel low-complexity windowing method. In addition, we suggest a specially designed preamble sequence that improve further the performance.

Cyber-physical systems (CPS) are challenging to control due to the complex uncertainties arising from physical and virtual sources. Enhancing CPS with self-adaptation is beneficial in addressing these uncertainties. While reactive adaptation often struggles with reliability, proactive adaptation could be more advantageous by preparing systems to make informed decisions considering the consequences of changes before they occur. CPS and their execution environment usually exhibit time-varying or nonlinear dynamics, which are more complex to predict than linear systems, while recent proposals of proactive self-adaptation methods have focused on linear systems. This work bridges this gap by proposing a method for Proactive self-adaptation for Nonlinear Cyber-physical Systems (PANCS). PANCS is developed and evaluated through a robotic running example. The results demonstrate its applicability and effectiveness in mitigating uncertainties and maintaining safety, performance, and accuracy during robot operation, compared to a baseline method.

The advent of 6G networks necessitates innovative solutions to address the limitations of traditional terrestrial wireless communications, particularly in remote and challenging terrains. The space-air-ground integrated network (SAGIN) combining satellite, aerial, and terrestrial segments, is a promising architecture to provide seamless, high-speed, and reliable connectivity. However, ensuring secure communication across these heterogeneous layers remains a challenge. This paper explores the integration of reconfigurable intelligent surfaces (RISs) in SAGIN, presenting a comprehensive strategy to enhance physical layer security (PLS) across the network. The paper first analyzes existing PLS challenges in SAGIN and explores how RISs integration and enabling technologies can address these security concerns. A practical case study demonstrates the application of RISs in the aerial layer to enhance SAGIN security. The findings demonstrate RISs' potential to mitigate eavesdropping risks, ensuring reliable communication systems. Finally, the paper identifies open research directions and challenges for future investigations.

We consider the best arm identification (BAI) problem in the K−armed bandit framework with a modification-the agent is allowed to play a subset of arms at each time slot instead of one arm. Consequently, the agent observes the sample average of the rewards of the arms that constitute the probed subset. Several trade-offs arise here-e.g., sampling a larger number of arms together results in a wider view of the environment, while sampling fewer arms enhances the information about individual reward distributions. Furthermore, grouping a large number of suboptimal arms together albeit reduces the variance of the reward of the group, it may enhance the group mean to make it close to that containing the optimal arm. To solve this problem, we propose an algorithm that constructs log 2 K groups and performs a likelihood ratio test to detect the presence of the best arm in each of these groups. Then a Hamming decoding procedure determines the unique best arm. We derive an upper bound for the error probability of the proposed algorithm based on a new hardness parameter H4. Finally, we demonstrate cases under which it outperforms the state-of-the-art algorithms for the single play case.

This study examines the impact of adding dimensions (objective functions) to the optimization process and explores how prioritizing certain metrics within Pareto front calculations affects model quality in Marketing-Mix Modelling (MMM). Using Meta's Robyn library and Nevergrad for optimization, we evaluated models across NRMSE, Decomp.RSSD, KL Divergence, and MAPE. Results indicate that, as anticipated, augmenting the number of optimization dimensions leads to a deterioration in model performance, specifically in terms of metric optimality, which in turn results in less reliable insights. It also highlights the phenomenon of indexing, where prioritizing specific metrics, in the sense of choosing them as the organizing metric for the Pareto fronts (e.g., by NRMSE, Decomp.RSSD, KL Divergence, or MAPE) impacts outcomes, demonstrating the complexity of multidimensional optimization. These findings have far-reaching implications not only in marketing but also in other domains that leverage multi-objective optimization through Pareto fronts. The results show that models from two-way optimization performed as good if not better than those from three-way and four-way optimization in balancing different objectives. However, when evaluated using Decomp.RSSD, the difference was less clear. This suggests that as the number of objectives increases, the trade-offs between them lead to a decrease in overall model quality. Hence, we have the title of the paper: "Only Two Can Tango at the Pareto Front".

Solar energy is a ubiquitous and free-of-charge source of renewable energy. When combined with other sources and applied to drones, this source of energy can significantly enhance drone endurance. In this paper, a novel approach is used to model photovoltaic solar cells under various environmental conditions. Photovoltaic (PV) cells are modeled and simulated using double and three-diode models with the ant colony optimization (ACO) algorithm to study the behavior of PV cells at different temperatures. This research uses a CdTe thin-film cell whose experimental data are extracted from the SCAPS simulation program at temperatures of 250 – 310 K and irradiance of 1000 W/m2. In 30 runs and 200 iteration steps with a population of 30 ants, the best root-mean-square error (RMSE) value is 5.2936 × 10-5 and 1.711 × 10-5 in double and three-diode cases, respectively. This shows that efficiency and performance of photovoltaic solar cell embedded in drones has improved drastically compared to other similar researches, which indicates the considerable superiority of the proposed method over many other algorithms developed so far. In each iteration step, the average time is 2.304 and 2.612 s in double and three-diode models, respectively. The number of investigated points is between 83 – 93 points depending on the temperature of the experiment in descending order. Moreover, the corresponding optimization has been performed on a 30-core server with 32GB RAM.

This paper proposes a stacked ensemble framework for path loss prediction in complex urban wireless environments. The approach integrates five base learners-Linear Regression, Random Forest, Decision Tree, Gradient Boosting, and Support Vector Regression-combined using a neural network meta-learner to enhance predictive accuracy. Drive test data from two Nigerian cities were preprocessed and used for model training, with k-fold cross-validation and random search applied for hyperparameter tuning. Evaluation metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R²), confirm that the stacked model improves prediction accuracy by 9.5% over the best-performing base model. The model's generalization was further validated on an independent dataset. These findings demonstrate the advantage of ensemble learning in improving path loss prediction accuracy for 5G network planning.

We explored Naïve Bayes classification methods on the Page Blocks Classification Dataset, which categorizes page blocks into text, horizontal line, graphic, vertical line, and picture. Utilizing both Gaussian Naïve Bayes and Categorical Naïve Bayes classifiers, we evaluated their performance on this multi-class classification problem. The dataset, with 5473 instances and 10 attributes, presents a practical challenge for distinguishing various page block types in document images. We compared the accuracy, precision, recall, and F1-score of the classifiers across various scenarios. Results show that the Gaussian Naïve Bayes classifier slightly outperforms the Categorical Naïve Bayes in accuracy and F1-score, likely due to the continuous nature of the dataset's attributes. Additionally, the Gaussian classifier demonstrates faster processing times. Our findings suggest that while both models are viable, the Gaussian Naïve Bayes classifier may be more suitable for similar datasets. Future research directions include integrating feature selection techniques and exploring hybrid models.

This study presents a novel partial differential equation (PDE)-based model for neural stem cell (NSC) therapy in spinal cord injury (SCI) treatment. Existing models often fail to accurately represent NSC migration, differentiation, and interaction with biomaterials and immune responses. By integrating scaffoldmediated diffusion and immune response dynamics, the proposed model offers a more realistic framework for regenerative therapy optimization. The model improves upon previous approaches by providing precise control over growth factor distribution, ensuring sustained bioavailability for enhanced NSC survival and functional integration. Applications of this framework extend beyond SCI treatment, with potential implications for neurodegenerative diseases, brain injury repair, and personalized medicine. Future research will focus on experimental validation and machine learning-driven optimization to refine the predictive capabilities of the model.

Object identification is a fundamental aspect for efficient communication between two agents (humans or software) operating in the same context; requiring agents to have a shared understanding of the objects' characteristics and of their current context. In case these agents do not have the same observing capabilities, establishing a shared understanding can become significantly challenging, and could lead to communication failure. In this article, we explore the design space of automatic identification systems, highlighting the absence of a formal model of (automatic) object identification that help agents choose optimal discriminative characteristics of an object to differentiate it from other objects in a given context. Our objective is to deepen the understanding of automatic identification approaches and systems. Hence, we propose a model that supports designers of (automatic) identification systems in pinpointing discriminative characteristics of an object that are pertinent to consider in a given use case. This model enables structured analysis of the compatibility of diverse (multimodal) identification systems, promoting effective communication among agents, and opens a path for systems to automatically select discriminative characteristics at run time. Our proposed model considers endogenous and exogenous object characteristics and their variations over time. It also includes the perceptual subjectivity of these characteristics in identification systems. To validate our model, we implement scene understanding systems that showcase, in various scenarios, the discriminatory power of object characteristics, and demonstrate the role of our proposed model in enabling effective communication between two systems (agents) with differing capabilities.

L3 autonomy represents a significant advancement in self-driving technology, paving the way for fully autonomous vehicles. The significance of this research is rooted in its identification of substantial opportunities for further exploration in the domain of autonomous vehicle technology. L3 autonomous driving requires a 'handover' of control from the vehicle to the driver under certain circumstances, which is critical for ensuring safety and reliability. The foremost challenge for L3 autonomous vehicles is ensuring a seamless transition of control between the human driver and the autonomous system. This requires developing a robust and reliable takeover request strategy that accurately accounts for driver performance and experience during all takeover phases. Enhancing safety and improving the driving experience would build trust in technology. This paper addresses current challenges and sets the foundation for future research by proposing an advanced personalized framework for driver takeovers. This framework aims to improve the reliability and safety of control transitions between human drivers and autonomous systems. Additionally, it provides a structured methodology for future studies to build upon, ultimately contributing to the broader adoption and enhancement of autonomous driving technologies.

Self-supervised monocular depth estimation (MDE) relies solely on sequential image frames for supervision. However, its performance in dynamic scenes is suboptimal, as the model's depth estimation of dynamic objects deteriorates over the course of training. This paper proposes an innovative Depth Self-Inhibition (DSI) training framework to suppress this process, allowing the MDE model to maintain strong performance in dynamic scenes. This method consists of two stages: the raw stage and the self-inhibition stage. In the raw stage, we train the MDE model without considering dynamic objects, aiming to identify unreliable depth regions where the depth values change significantly during training. In the self-inhibition stage, we retrain the MDE model and use the novel Disparity Difference Mask (DD-mask) method to exclude these unreliable regions from the loss. Additionally, the Ground-contact-prior Disparity Smoothness Loss (GDS-Loss) is employed to supervise depth learning in these areas. In this stage, we use a novel Normal Distribution-based Loss (ND-Loss), which outperforms L1 loss in dynamic scenes. During the evaluation, we introduce the Ratio Median Scaling (RM-scaling) method to address scale ambiguity in the estimated depth, providing a more reliable performance evaluation than the existing median scaling method. Experimental results show that the DSI training framework can be conveniently applied into existing MDE models, significantly improving their performance on the Cityscapes and KITTI datasets, particularly in moving object regions. The code will be released.

Precise and efficient control of permanent magnet synchronous motors (PMSMs) is critical for many industrial and transportation applications, particularly in electric vehicle drives. Recent advances in data-driven control using deep reinforcement learning (RL) techniques have enabled automated control designs within minutes without requiring plant-specific a priori knowledge. However, these RL techniques typically lack generalizability across motor instances with differing parameters, necessitating retraining for each new system. Meta-reinforcement learning (MRL) extends the capabilities of standard RL by enabling controllers to adapt to new tasks, i.e. different motors, with little to no retraining. This work introduces a universal control algorithm based on deep q-learning direct torque control (DQDTC), extended with MRL techniques, termed meta-DQDTC. Applied to a diverse set of differently parameterized PMSMs, and equipped with online system identification capability, meta-DQDTC performs on par with individually trained RL controllers that are specialized for a single drive. This effectively synthesizes a universal PMSM controller that nearly instantly adapts to different motors without additional training. A key advantage is the significant reduction in controller synthesis time: traditional model-based methods might require days of manual effort, whereas trained only once the meta-DQDTC can be deployed within seconds. Experimental results demonstrate that meta-DQDTC can be successfully transferred to a realworld system, despite being trained exclusively on simulated motors with idealized assumptions. Notably, the specific real-world experiment has herein not been simulated during training, validating the generalization and transfer capability of the meta-DQDTC.