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20 pages, 1457 KiB  
Article
A Semi-Random Elliptical Movement Model for Relay Nodes in Flying Ad Hoc Networks
by Hyeon Choe and Dongsu Kang
Telecom 2025, 6(3), 56; https://doi.org/10.3390/telecom6030056 - 1 Aug 2025
Viewed by 132
Abstract
This study presents a semi-random mobility model called Semi-Random Elliptical Movement (SREM), developed for relay-oriented Flying Ad Hoc Networks (FANETs). In FANETs, node distribution has a major impact on network performance, making the mobility model a critical design element. While random models offer [...] Read more.
This study presents a semi-random mobility model called Semi-Random Elliptical Movement (SREM), developed for relay-oriented Flying Ad Hoc Networks (FANETs). In FANETs, node distribution has a major impact on network performance, making the mobility model a critical design element. While random models offer simplicity and path diversity, they often result in unstable relay paths due to inconsistent node placement. In contrast, planned path models provide alignment but lack the flexibility needed in dynamic environments. SREM addresses these challenges by enabling nodes to move along elliptical trajectories, combining autonomous movement with alignment to the relay path. This approach encourages natural node concentration along the relay path while maintaining distributed mobility. The spatial characteristics of SREM have been analytically defined and validated through the Monte Carlo method, confirming stable node distributions that support effective relaying. Computer simulation results show that SREM performs better than general mobility models that do not account for relaying, offering more suitable performance in relay-focused scenarios. These findings suggest that SREM provides both structural consistency and practical effectiveness, making it a strong candidate for improving the realism and reliability of FANET simulations involving relay-based communication. Full article
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12 pages, 5079 KiB  
Article
Enhancing QoS in Opportunistic Networks Through Direct Communication for Dynamic Routing Challenges
by Ambreen Memon, Aqsa Iftikhar, Muhammad Nadeem Ali and Byung-Seo Kim
Telecom 2025, 6(3), 55; https://doi.org/10.3390/telecom6030055 - 1 Aug 2025
Viewed by 123
Abstract
Opportunistic Networks (OppNets) lack the capability to maintain consistent end-to-end paths between source and destination nodes, unlike Mobile Ad Hoc Networks (MANETs). This absence of stable routing presents substantial challenges for data transmission in OppNets. Due to node mobility, routing paths are inherently [...] Read more.
Opportunistic Networks (OppNets) lack the capability to maintain consistent end-to-end paths between source and destination nodes, unlike Mobile Ad Hoc Networks (MANETs). This absence of stable routing presents substantial challenges for data transmission in OppNets. Due to node mobility, routing paths are inherently dynamic, requiring the selection of neighboring nodes as intermediate hops to forward data toward the destination. However, frequent node movement can cause considerable delays for senders attempting to identify appropriate next hops, consequently degrading the quality of service (QoS) in OppNets. To mitigate this challenge, this paper proposes an alternative approach for scenarios where senders cannot locate suitable next hops. Specifically, we propose utilizing direct communication via line of sight (LoS) between sender and receiver nodes to satisfy QoS requirements. The proposed scheme is experimented with using the ONE simulator, which is widely used for OppNet experiments and study, and compared against existing schemes such as the history-based routing protocol (HBRP) and AEProphet routing protocol. Full article
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21 pages, 1127 KiB  
Article
Quality of Life, Perceived Social Support, and Treatment Adherence Among Methadone Maintenance Program Users: An Observational Cross-Sectional Study
by Pedro López-Paterna, Ismail Erahmouni-Bensliman, Raquel Sánchez-Ruano, Ricardo Rodríguez-Barrientos and Milagros Rico-Blázquez
Healthcare 2025, 13(15), 1849; https://doi.org/10.3390/healthcare13151849 - 29 Jul 2025
Viewed by 263
Abstract
Background/Objectives: The consumption of opioids is a public health problem that significantly affects quality of life. In Spain, 7585 people are enrolled in the Methadone Maintenance Programme (MMP), which is an effective intervention with a low adherence rate. In this study, factors associated [...] Read more.
Background/Objectives: The consumption of opioids is a public health problem that significantly affects quality of life. In Spain, 7585 people are enrolled in the Methadone Maintenance Programme (MMP), which is an effective intervention with a low adherence rate. In this study, factors associated with the quality of life of MMP users, especially perceived social support and treatment adherence, were analysed. We hypothesised that low levels of adherence and social support would be associated with poorer quality of life. Methods: This was a cross-sectional observational study with an analytical approach. Quality of life (WHOQoL-BREF), perceived social support (DUKE-UNC-11), and treatment adherence (MMAS-8) among MMP users were studied, and data on sociodemographic and clinical characteristics were collected through ad hoc questionnaires and a review of electronic medical records. Linear and logistic regression models were used. Results: A total of 70 individuals were included in this study. The mean age was 56.9 years, and 83% of the participants were male. The perceived quality of life was low in the four domains evaluated (range of 47.4–48.2). A total of 38.57% of the participants had low perceived social support. Treatment adherence was low or moderate in 77.1% of the participants. Greater perceived social support was associated with better quality of life in all domains (p < 0.05). Quality of social life was negatively associated with the use of nonbenzodiazepine neuroleptics and HIV status. Treatment adherence was lower in insulin therapy users. Conclusions: Social support is a key determinant of the quality of life of MMP users. Health policies should promote social support networks as a strategy to improve the well-being of this population. Full article
(This article belongs to the Special Issue Advances in Primary Health Care and Community Health)
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16 pages, 1145 KiB  
Review
Beyond Global Metrics: The U-Smile Method for Explainable, Interpretable, and Transparent Variable Selection in Risk Prediction Models
by Katarzyna B. Kubiak, Agata Konieczna, Anna Tyranska-Fobke and Barbara Więckowska
Appl. Sci. 2025, 15(15), 8303; https://doi.org/10.3390/app15158303 - 25 Jul 2025
Viewed by 126
Abstract
Variable selection (VS) is a critical step in developing predictive binary classification (BC) models. Many traditional methods for assessing the added value of a candidate variable provide global performance summaries and lack an interpretable graphical summary of results. To address this limitation, we [...] Read more.
Variable selection (VS) is a critical step in developing predictive binary classification (BC) models. Many traditional methods for assessing the added value of a candidate variable provide global performance summaries and lack an interpretable graphical summary of results. To address this limitation, we developed the U-smile method, a residual-based, post hoc evaluation approach for assessing prediction improvements and worsening separately for events and non-events. The U-smile method produces three families of interpretable BA-RB-I coefficients at three levels of generality and a standardized graphical summary through U-smile and prediction improvement–worsening (PIW) plots, enabling transparent, interpretable, and explainable VS. Validated in balanced and imbalanced BC scenarios, the method proved robust to class imbalance and collinearity, and more sensitive than traditional metrics in detecting subtle but meaningful effects. Moreover, the method’s intuitive visual output (U-smile plot) facilitates the rapid communication of results to non-technical stakeholders, bridging the gap between data science and applied decision-making. The U-smile method supports both local and global evaluations and complements existing explainable machine learning (XML) and artificial intelligence (XAI) tools without overlapping in their functions. The U-smile method offers a transparency-enhancing and human-oriented approach for ethical and fair VS, making it highly suited for high-stakes domains, e.g., healthcare and public health. Full article
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28 pages, 4562 KiB  
Article
A Capacity-Constrained Weighted Clustering Algorithm for UAV Self-Organizing Networks Under Interference
by Siqi Li, Peng Gong, Weidong Wang, Jinyue Liu, Zhixuan Feng and Xiang Gao
Drones 2025, 9(8), 527; https://doi.org/10.3390/drones9080527 - 25 Jul 2025
Viewed by 198
Abstract
Compared to traditional ad hoc networks, self-organizing networks of unmanned aerial vehicle (UAV) are characterized by high node mobility, vulnerability to interference, wide distribution range, and large network scale, which make network management and routing protocol operation more challenging. Cluster structures can be [...] Read more.
Compared to traditional ad hoc networks, self-organizing networks of unmanned aerial vehicle (UAV) are characterized by high node mobility, vulnerability to interference, wide distribution range, and large network scale, which make network management and routing protocol operation more challenging. Cluster structures can be used to optimize network management and mitigate the impact of local topology changes on the entire network during collaborative task execution. To address the issue of cluster structure instability caused by the high mobility and vulnerability to interference in UAV networks, we propose a capacity-constrained weighted clustering algorithm for UAV self-organizing networks under interference. Specifically, a capacity-constrained partitioning algorithm based on K-means++ is developed to establish the initial node partitions. Then, a weighted cluster head (CH) and backup cluster head (BCH) selection algorithm is proposed, incorporating interference factors into the selection process. Additionally, a dynamic maintenance mechanism for the clustering network is introduced to enhance the stability and robustness of the network. Simulation results show that the algorithm achieves efficient node clustering under interference conditions, improving cluster load balancing, average cluster head maintenance time, and cluster head failure reconstruction time. Furthermore, the method demonstrates fast recovery capabilities in the event of node failures, making it more suitable for deployment in complex emergency rescue environments. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicles for Enhanced Emergency Response)
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18 pages, 3870 KiB  
Article
Universal Vector Calibration for Orientation-Invariant 3D Sensor Data
by Wonjoon Son and Lynn Choi
Sensors 2025, 25(15), 4609; https://doi.org/10.3390/s25154609 - 25 Jul 2025
Viewed by 232
Abstract
Modern electronic devices such as smartphones, wearable devices, and robots typically integrate three-dimensional sensors to track the device’s movement in the 3D space. However, sensor measurements in three-dimensional vectors are highly sensitive to device orientation since a slight change in the device’s tilt [...] Read more.
Modern electronic devices such as smartphones, wearable devices, and robots typically integrate three-dimensional sensors to track the device’s movement in the 3D space. However, sensor measurements in three-dimensional vectors are highly sensitive to device orientation since a slight change in the device’s tilt or heading can change the vector values. To avoid complications, applications using these sensors often use only the magnitude of the vector, as in geomagnetic-based indoor positioning, or assume fixed device holding postures such as holding a smartphone in portrait mode only. However, using only the magnitude of the vector loses the directional information, while ad hoc posture assumptions work under controlled laboratory conditions but often fail in real-world scenarios. To resolve these problems, we propose a universal vector calibration algorithm that enables consistent three-dimensional vector measurements for the same physical activity, regardless of device orientation. The algorithm works in two stages. First, it transforms vector values in local coordinates to those in global coordinates by calibrating device tilting using pitch and roll angles computed from the initial vector values. Second, it additionally transforms vector values from the global coordinate to a reference coordinate when the target coordinate is different from the global coordinate by correcting yaw rotation to align with application-specific reference coordinate systems. We evaluated our algorithm on geomagnetic field-based indoor positioning and bidirectional step detection. For indoor positioning, our vector calibration achieved an 83.6% reduction in mismatches between sampled magnetic vectors and magnetic field map vectors and reduced the LSTM-based positioning error from 31.14 m to 0.66 m. For bidirectional step detection, the proposed algorithm with vector calibration improved step detection accuracy from 67.63% to 99.25% and forward/backward classification from 65.54% to 100% across various device orientations. Full article
(This article belongs to the Section Intelligent Sensors)
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23 pages, 1885 KiB  
Article
Applying Machine Learning to DEEC Protocol: Improved Cluster Formation in Wireless Sensor Networks
by Abdulla Juwaied and Lidia Jackowska-Strumillo
Network 2025, 5(3), 26; https://doi.org/10.3390/network5030026 - 24 Jul 2025
Viewed by 190
Abstract
Wireless Sensor Networks (WSNs) are specialised ad hoc networks composed of small, low-power, and often battery-operated sensor nodes with various sensors and wireless communication capabilities. These nodes collaborate to monitor and collect data from the physical environment, transmitting it to a central location [...] Read more.
Wireless Sensor Networks (WSNs) are specialised ad hoc networks composed of small, low-power, and often battery-operated sensor nodes with various sensors and wireless communication capabilities. These nodes collaborate to monitor and collect data from the physical environment, transmitting it to a central location or sink node for further processing and analysis. This study proposes two machine learning-based enhancements to the DEEC protocol for Wireless Sensor Networks (WSNs) by integrating the K-Nearest Neighbours (K-NN) and K-Means (K-M) machine learning (ML) algorithms. The Distributed Energy-Efficient Clustering with K-NN (DEEC-KNN) and with K-Means (DEEC-KM) approaches dynamically optimize cluster head selection to improve energy efficiency and network lifetime. These methods are validated through extensive simulations, demonstrating up to 110% improvement in packet delivery and significant gains in network stability compared with the original DEEC protocol. The adaptive clustering enabled by K-NN and K-Means is particularly effective for large-scale and dynamic WSN deployments where node failures and topology changes are frequent. These findings suggest that integrating ML with clustering protocols is a promising direction for future WSN design. Full article
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12 pages, 479 KiB  
Article
Assessing the Potential of Fecal NIRS for External Marker and Digestibility Predictions in Broilers
by Oussama Tej, Elena Albanell, Ibtissam Kaikat and Carmen L. Manuelian
Animals 2025, 15(15), 2181; https://doi.org/10.3390/ani15152181 - 24 Jul 2025
Viewed by 273
Abstract
This study evaluated fecal near-infrared spectroscopy (fNIRS) potential to predict three external markers (Yb, Ti, and polyethylene glycol (PEG)) and dry matter digestibility (DMD) calculated from these markers and fiber fractions. A total of 192 fecal samples were collected from 576 Ross 308 [...] Read more.
This study evaluated fecal near-infrared spectroscopy (fNIRS) potential to predict three external markers (Yb, Ti, and polyethylene glycol (PEG)) and dry matter digestibility (DMD) calculated from these markers and fiber fractions. A total of 192 fecal samples were collected from 576 Ross 308 male chicks supplemented with TiO2 (2 g/kg), Yb2O3 (50 mg/kg), and PEG (5 g/kg) for 8 d. Reference values for Ti and Yb were obtained using an inductively coupled plasma–optical emission spectrometer, for fiber fractions via ANKOM, and for PEG content using an ad hoc fNIRS model. Prediction models were developed in external validation with 25% of the samples. Good and fair prediction models were built for Ti and Yb, respectively, and considered adequate for rough screening. The DMD models based on Yb and ADF were unreliable, whereas the model based on Ti was suitable for rough screening. The PEG prediction model built during the adaptation period performed exceptionally well; however, the DMD prediction based on PEG highlighted limitations due to diet differences during both the adaptation and experimental periods. In conclusion, fNIRS shows promise for screening Ti and Yb fecal content and DMD using Ti. However, tailored PEG prediction equations need to be developed for each specific diet. Full article
(This article belongs to the Section Poultry)
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21 pages, 354 KiB  
Article
Adaptive Broadcast Scheme with Fuzzy Logic and Reinforcement Learning Dynamic Membership Functions in Mobile Ad Hoc Networks
by Akobir Ismatov, BeomKyu Suh, Jian Kim, YongBeom Park and Ki-Il Kim
Mathematics 2025, 13(15), 2367; https://doi.org/10.3390/math13152367 - 23 Jul 2025
Viewed by 228
Abstract
Broadcasting in Mobile Ad Hoc Networks (MANETs) is significantly challenged by dynamic network topologies. Traditional fuzzy logic-based schemes that often rely on static fuzzy tables and fixed membership functions are limiting their ability to adapt to evolving network conditions. To address these limitations, [...] Read more.
Broadcasting in Mobile Ad Hoc Networks (MANETs) is significantly challenged by dynamic network topologies. Traditional fuzzy logic-based schemes that often rely on static fuzzy tables and fixed membership functions are limiting their ability to adapt to evolving network conditions. To address these limitations, in this paper, we conduct a comparative study of two innovative broadcasting schemes that enhance adaptability through dynamic fuzzy logic membership functions for the broadcasting problem. The first approach (Model A) dynamically adjusts membership functions based on changing network parameters and fine-tunes the broadcast (BC) versus do-not-broadcast (DNB) ratio. Model B, on the other hand, introduces a multi-profile switching mechanism that selects among distinct fuzzy parameter sets optimized for various macro-level scenarios, such as energy constraints or node density, without altering the broadcasting ratio. Reinforcement learning (RL) is employed in both models: in Model A for BC/DNB ratio optimization, and in Model B for action decisions within selected profiles. Unlike prior fuzzy logic or reinforcement learning approaches that rely on fixed profiles or static parameter sets, our work introduces adaptability at both the membership function and profile selection levels, significantly improving broadcasting efficiency and flexibility across diverse MANET conditions. Comprehensive simulations demonstrate that both proposed schemes significantly reduce redundant broadcasts and collisions, leading to lower network overhead and improved message delivery reliability compared to traditional static methods. Specifically, our models achieve consistent packet delivery ratios (PDRs), reduce end-to-end Delay by approximately 23–27%, and lower Redundancy and Overhead by 40–60% and 40–50%, respectively, in high-density and high-mobility scenarios. Furthermore, this comparative analysis highlights the strengths and trade-offs between reinforcement learning-driven broadcasting ratio optimization (Model A) and parameter-based dynamic membership function adaptation (Model B), providing valuable insights for optimizing broadcasting strategies. Full article
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21 pages, 4341 KiB  
Article
Structural Monitoring Without a Budget—Laboratory Results and Field Report on the Use of Low-Cost Acceleration Sensors
by Sven Giermann, Thomas Willemsen and Jörg Blankenbach
Sensors 2025, 25(15), 4543; https://doi.org/10.3390/s25154543 - 22 Jul 2025
Viewed by 285
Abstract
Authorities responsible for critical infrastructure, particularly bridges, face significant challenges. Many bridges, constructed in the 1960s and 1970s, are now approaching or have surpassed their intended service life. A report from the German Federal Ministry for Digital and Transport (BMVI) indicates that about [...] Read more.
Authorities responsible for critical infrastructure, particularly bridges, face significant challenges. Many bridges, constructed in the 1960s and 1970s, are now approaching or have surpassed their intended service life. A report from the German Federal Ministry for Digital and Transport (BMVI) indicates that about 12% of the 40,000 federal trunk road bridges in Germany are in “inadequate or unsatisfactory” condition. Similar issues are observed in other countries worldwide. Economic constraints prevent ad hoc replacements, necessitating continued operation with frequent and costly inspections. This situation creates an urgent need for cost-effective, permanent monitoring solutions. This study explores the potential use of low-cost acceleration sensors for monitoring infrastructure structures. Inclination is calculated from the acceleration data of the sensor, using gravitational acceleration as a reference point. Long-term changes in inclination may indicate a change in the geometry of the structure, thereby triggering alarm thresholds. It is particularly important to consider specific challenges associated with low measurement accuracy and the susceptibility of sensors to environmental influences in a low-cost setting. The results of laboratory tests allow for an estimation of measurement accuracy and an analysis of the various error characteristics of the sensors. The article outlines the methodology for developing low-cost inclination sensor systems, the laboratory tests conducted, and the evaluation of different measures to enhance sensor accuracy. Full article
(This article belongs to the Section Intelligent Sensors)
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32 pages, 1575 KiB  
Article
Entropy Accumulation Under Post-Quantum Cryptographic Assumptions
by Ilya Merkulov and Rotem Arnon
Entropy 2025, 27(8), 772; https://doi.org/10.3390/e27080772 - 22 Jul 2025
Viewed by 258
Abstract
In device-independent (DI) quantum protocols, security statements are agnostic to the internal workings of the quantum devices—they rely solely on classical interactions with the devices and specific assumptions. Traditionally, such protocols are set in a non-local scenario, where two non-communicating devices exhibit Bell [...] Read more.
In device-independent (DI) quantum protocols, security statements are agnostic to the internal workings of the quantum devices—they rely solely on classical interactions with the devices and specific assumptions. Traditionally, such protocols are set in a non-local scenario, where two non-communicating devices exhibit Bell inequality violations. Recently, a new class of DI protocols has emerged that requires only a single device. In this setting, the assumption of no communication is replaced by a computational one: the device cannot solve certain post-quantum cryptographic problems. Protocols developed in this single-device computational setting—such as for randomness certification—have relied on ad hoc techniques, making their guarantees difficult to compare and generalize. In this work, we introduce a modular proof framework inspired by techniques from the non-local DI literature. Our approach combines tools from quantum information theory, including entropic uncertainty relations and the entropy accumulation theorem, to yield both conceptual clarity and quantitative security guarantees. This framework provides a foundation for systematically analyzing DI protocols in the single-device setting under computational assumptions. It enables the design and security proof of future protocols for DI randomness generation, expansion, amplification, and key distribution, grounded in post-quantum cryptographic hardness. Full article
(This article belongs to the Section Quantum Information)
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42 pages, 2129 KiB  
Review
Ensemble Learning Approaches for Multi-Class Intrusion Detection Systems for the Internet of Vehicles (IoV): A Comprehensive Survey
by Manal Alharthi, Faiza Medjek and Djamel Djenouri
Future Internet 2025, 17(7), 317; https://doi.org/10.3390/fi17070317 - 19 Jul 2025
Viewed by 436
Abstract
The emergence of the Internet of Vehicles (IoV) has revolutionized intelligent transportation and communication systems. However, IoV presents many complex and ever-changing security challenges and thus requires robust cybersecurity protocols. This paper comprehensively describes and evaluates ensemble learning approaches for multi-class intrusion detection [...] Read more.
The emergence of the Internet of Vehicles (IoV) has revolutionized intelligent transportation and communication systems. However, IoV presents many complex and ever-changing security challenges and thus requires robust cybersecurity protocols. This paper comprehensively describes and evaluates ensemble learning approaches for multi-class intrusion detection systems in the IoV environment. The study evaluates several approaches, such as stacking, voting, boosting, and bagging. A comprehensive review of the literature spanning 2020 to 2025 reveals important trends and topics that require further investigation and the relative merits of different ensemble approaches. The NSL-KDD, CICIDS2017, and UNSW-NB15 datasets are widely used to evaluate the performance of Ensemble Learning-Based Intrusion Detection Systems (ELIDS). ELIDS evaluation is usually carried out using some popular performance metrics, including Precision, Accuracy, Recall, F1-score, and Area Under Receiver Operating Characteristic Curve (AUC-ROC), which were used to evaluate and measure the effectiveness of different ensemble learning methods. Given the increasing complexity and frequency of cyber threats in IoV environments, ensemble learning methods such as bagging, boosting, and stacking enhance adaptability and robustness. These methods aggregate multiple learners to improve detection rates, reduce false positives, and ensure more resilient intrusion detection models that can evolve alongside emerging attack patterns. Full article
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60 pages, 9590 KiB  
Article
Dealing with High-Risk Police Activities and Enhancing Safety and Resilience: Qualitative Insights into Austrian Police Operations from a Risk and Group Dynamic Perspective
by Renate Renner, Vladimir M. Cvetković and Nicola Lieftenegger
Safety 2025, 11(3), 68; https://doi.org/10.3390/safety11030068 - 18 Jul 2025
Viewed by 690
Abstract
Special police units like Austria’s EKO Cobra are uniquely trained to manage high-risk operations, including terrorism, amok situations, and hostage crises. This study explores how group dynamics contribute to operational safety and resilience, emphasising the interconnection between risk perception, training, and operational practices. [...] Read more.
Special police units like Austria’s EKO Cobra are uniquely trained to manage high-risk operations, including terrorism, amok situations, and hostage crises. This study explores how group dynamics contribute to operational safety and resilience, emphasising the interconnection between risk perception, training, and operational practices. Interviews with current and former EKO Cobra members reveal key risk factors, including overconfidence, insufficient training, inadequate equipment, and the challenges of high-stakes scenarios. Using a structured yet thematically flexible interview analysis approach, the study adopts group dynamics theory as its framework and applies a semi-inductive, semi-deductive qualitative methodology. It examines risk categorisation in ad hoc operations, as well as the interplay between risk perception and training, proposing actionable strategies to enhance safety and preparedness through tailored training programmes. The findings underscore the transformative impact of intensive scenario-based and high-stress training, which enhances situational awareness and reinforces team-based responses through cohesion and effective communication. Group dynamics, including cohesion and effective communication, play a pivotal role in mitigating risks and ensuring operational success. Importantly, this research advocates for continuous, adaptive, and specialised training to address evolving challenges. By linking theoretical frameworks with practical and actionable insights, this study proposes a holistic training approach that promotes both resilience and long-term sustainability in police operations. These findings offer valuable guidance to elite units like EKO Cobra for broader policy frameworks by providing insights that make police operations safer and more effective and resilient. Full article
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20 pages, 774 KiB  
Article
Robust Variable Selection via Bayesian LASSO-Composite Quantile Regression with Empirical Likelihood: A Hybrid Sampling Approach
by Ruisi Nan, Jingwei Wang, Hanfang Li and Youxi Luo
Mathematics 2025, 13(14), 2287; https://doi.org/10.3390/math13142287 - 16 Jul 2025
Viewed by 312
Abstract
Since the advent of composite quantile regression (CQR), its inherent robustness has established it as a pivotal methodology for high-dimensional data analysis. High-dimensional outlier contamination refers to data scenarios where the number of observed dimensions (p) is much greater than the [...] Read more.
Since the advent of composite quantile regression (CQR), its inherent robustness has established it as a pivotal methodology for high-dimensional data analysis. High-dimensional outlier contamination refers to data scenarios where the number of observed dimensions (p) is much greater than the sample size (n) and there are extreme outliers in the response variables or covariates (e.g., p/n > 0.1). Traditional penalized regression techniques, however, exhibit notable vulnerability to data outliers during high-dimensional variable selection, often leading to biased parameter estimates and compromised resilience. To address this critical limitation, we propose a novel empirical likelihood (EL)-based variable selection framework that integrates a Bayesian LASSO penalty within the composite quantile regression framework. By constructing a hybrid sampling mechanism that incorporates the Expectation–Maximization (EM) algorithm and Metropolis–Hastings (M-H) algorithm within the Gibbs sampling scheme, this approach effectively tackles variable selection in high-dimensional settings with outlier contamination. This innovative design enables simultaneous optimization of regression coefficients and penalty parameters, circumventing the need for ad hoc selection of optimal penalty parameters—a long-standing challenge in conventional LASSO estimation. Moreover, the proposed method imposes no restrictive assumptions on the distribution of random errors in the model. Through Monte Carlo simulations under outlier interference and empirical analysis of two U.S. house price datasets, we demonstrate that the new approach significantly enhances variable selection accuracy, reduces estimation bias for key regression coefficients, and exhibits robust resistance to data outlier contamination. Full article
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11 pages, 239 KiB  
Article
Patient Satisfaction and Outcomes of Penile Prosthesis Implantation in Psychogenic and Organic Erectile Dysfunction: A Comparative Study
by Maurizio De Rocco Ponce, Alejandro Silva Garretón, Ángela Sousa Iglesias, Sebastian Dumas Castro, Ricardo Contreras Garcia, Luis Malca Caballero, Josvany Rene Sanchez Curbelo, Doron Vantman Luft, Eduard Ruiz Castañé and Osvaldo Rajmil
J. Clin. Med. 2025, 14(14), 5032; https://doi.org/10.3390/jcm14145032 - 16 Jul 2025
Viewed by 450
Abstract
Background: Penile prosthesis implantation (PPI) is an established treatment for erectile dysfunction (ED). Nevertheless, the effectiveness of and satisfaction with PPI in mainly psychogenic ED compared to mainly organic ED patients remain underexplored. Aim: To evaluate patient satisfaction outcomes following PPI [...] Read more.
Background: Penile prosthesis implantation (PPI) is an established treatment for erectile dysfunction (ED). Nevertheless, the effectiveness of and satisfaction with PPI in mainly psychogenic ED compared to mainly organic ED patients remain underexplored. Aim: To evaluate patient satisfaction outcomes following PPI in individuals diagnosed as mainly psychogenic ED vs. mainly organic ED. Methods: Twenty-five patients with psychogenic ED who underwent PPI were included. Data were collected from medical records and a follow-up assessment was done using the Quality of Life and Sexuality with Penile Prosthesis (QolSPP) questionnaire. Additionally, the patients filled out an ad hoc questionnaire including self-reported satisfaction rated on a 1-to-10 scale, the Global Assessment Questionnaire-Questions 1 and 2 (GAQ-1, 2), and the Sexual Encounter Profile Questions 2 and 5 (SEP-2, 5). Results were compared with those of 36 patients with mainly organic ED (control) for comparative analysis. Results: In the psychogenic ED group, 96% reported improved erections, 92% felt more confident initiating sex, 92% achieved penetration and 95% had satisfactory sexual encounters. The overall satisfaction score was 8.71 on a 10-point scale. Comparative analysis using the QolSPP questionnaire revealed statistically significant differences favouring the psychogenic group in 8 of 16 questions, regarding prosthesis satisfaction and overall well-being. Surgical complications were noted in 16% of the psychogenic group, compared to a 2.8% complication rate in the organic ED control group. Conclusions: The findings indicate high levels of satisfaction with PPI among patients with psychogenic ED, comparable to those with organic ED. However, an increase in complications in the psychogenic cohort highlights the need for careful consideration of surgical risks in this population. Full article
(This article belongs to the Section Reproductive Medicine & Andrology)
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