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18 pages, 595 KiB  
Article
Diazepam Photocatalytic Degradation in Laboratory- vs. Pilot-Scale Systems: Differences in Degradation Products and Reaction Kinetics
by Kristina Tolić Čop, Mia Gotovuša, Dragana Mutavdžić Pavlović, Dario Dabić and Ivana Grčić
Nanomaterials 2025, 15(11), 827; https://doi.org/10.3390/nano15110827 (registering DOI) - 29 May 2025
Abstract
Industrial growth led to the expansion of existing environmental problems, where different kinds of pollutants can enter the environment by many known routes, particularly through wastewater. Among other contaminants, pharmaceuticals, such as diazepam, once released, pose a significant challenge related to their removal [...] Read more.
Industrial growth led to the expansion of existing environmental problems, where different kinds of pollutants can enter the environment by many known routes, particularly through wastewater. Among other contaminants, pharmaceuticals, such as diazepam, once released, pose a significant challenge related to their removal from complex environmental matrices due to their persistence and potential toxicity. For this reason, it is a great challenge to find suitable methods for the treatment of wastewater. The aim of this paper was to investigate the stability of diazepam, subjecting it to various degradation processes (hydrolysis and photolysis), focusing on photocatalysis, an advanced oxidation process commonly used for the purification of industrial wastewater. The photocatalytic system consisted of UV-A and simulated solar irradiation with titanium dioxide (TiO2) immobilized on a glass mesh as a photocatalyst, with an additional reaction performed in the presence of an oxidizing agent, i.e., hydrogen peroxide, to improve diazepam removal from water matrices. The kinetic rate of diazepam degradation was monitored with a high-performance liquid chromatograph coupled with a photodiode array detector (HPLC-PDA). The target compound was characterized as a hydrolytically and photolytically stable compound with t1/2 = 25 h. The presence of an immobilized TiO2 catalyst contributed significantly to the degradation of diazepam under the influence of UV-A and simulated solar radiation, with t1/2 in the range of 1.61–2.56 h. Five degradation products of diazepam were identified at the laboratory scale by MS analysis (m/z = 267, m/z = 273, m/z = 301, m/z = 271, and m/z = 303), while the toxicity assessment revealed that diazepam exhibited developmental toxicity and a low bioaccumulation factor. The pilot-scale process resulted in significant improvements in diazepam degradation with the fastest degradation kinetics (0.6888 h−1). These results obtained at the pilot scale highlight the potential for industrial-scale implementation, offering a promising and innovative solution for pharmaceutical removal from wastewater. Full article
34 pages, 4080 KiB  
Article
Comprehensive Assessment of Potentially Toxic Element (PTE) Contamination in Honey from a Historically Polluted Agro-Industrial Landscape: Implications for Agricultural Sustainability and Food Safety
by Ioana Andra Vlad, Szilárd Bartha, Győző Goji, Ioan Tăut, Florin Alexandru Rebrean, Laviniu Ioan Nuțu Burescu, Călin Gheorghe Pășcuț, Petrică Tudor Moțiu, Adrian Tunduc, Claudiu Ion Bunea and Florin-Dumitru Bora
Agriculture 2025, 15(11), 1176; https://doi.org/10.3390/agriculture15111176 (registering DOI) - 29 May 2025
Abstract
Honey is increasingly recognized not only as a functional food but also as a potential bioindicator of environmental pollution. This study assessed the concentrations of four potentially toxic elements (PTEs)—lead (Pb), cadmium (Cd), copper (Cu), and zinc (Zn)—in 48 multifloral honey samples collected [...] Read more.
Honey is increasingly recognized not only as a functional food but also as a potential bioindicator of environmental pollution. This study assessed the concentrations of four potentially toxic elements (PTEs)—lead (Pb), cadmium (Cd), copper (Cu), and zinc (Zn)—in 48 multifloral honey samples collected in 2023 from seven locations across a historically polluted agro-industrial region in Romania. Samples were analyzed using Flame Atomic Absorption Spectrometry (FAAS) and Graphite Furnace AAS (GFAAS), with quality control ensured through certified reference materials. Results revealed that Pb (0.72–1.69 mg/kg) and Cd (0.02–0.37 mg/kg) levels consistently exceeded international safety thresholds, while Cu (0.62–2.22 mg/kg) and Zn (0.91–1.93 mg/kg), although essential nutrients, were found in elevated concentrations. Spatial analysis indicated a general trend of higher contamination in sites located closer to former industrial facilities, influenced by factors such as altitude and atmospheric transport. These findings confirm the persistent environmental burden in post-industrial landscapes and support the use of honey as a cost-effective tool for pollution monitoring. The study underscores the need for targeted environmental policies, sustainable apicultural practices, and continued surveillance to protect ecosystem health and food safety. Full article
(This article belongs to the Section Agricultural Product Quality and Safety)
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16 pages, 819 KiB  
Review
Understanding Refeeding Syndrome in Critically Ill Patients: A Narrative Review
by Raffaele Borriello, Giorgio Esposto, Maria Elena Ainora, Giorgio Podagrosi, Giuliano Ferrone, Irene Mignini, Linda Galasso, Antonio Gasbarrini and Maria Assunta Zocco
Nutrients 2025, 17(11), 1866; https://doi.org/10.3390/nu17111866 (registering DOI) - 29 May 2025
Abstract
Refeeding syndrome (RS) is defined as the spectrum of metabolic and biochemical disorders related to rapid nutritional replenishment after a prolonged period of fasting. It is caused by an abrupt shift in electrolytes and fluid among intra- and extracellular compartments, leading to metabolic [...] Read more.
Refeeding syndrome (RS) is defined as the spectrum of metabolic and biochemical disorders related to rapid nutritional replenishment after a prolonged period of fasting. It is caused by an abrupt shift in electrolytes and fluid among intra- and extracellular compartments, leading to metabolic disturbances like hypophosphatemia, vitamin deficiency, and fluid overload. RS often remains underdiagnosed due to variability in definition and diagnostic criteria adopted, overlapping clinical features with other complications and low awareness among clinicians. Critically ill individuals, particularly those admitted to intensive care units (ICUs), represent a cohort with peculiar features that may heighten RS risk due to their baseline frailty, frequent undernutrition, and the metabolic stress of acute illness. However, studies specifically conducted in ICU settings have yielded conflicting results regarding incidence rates, prognostic impact, and specific risk factors. Despite these differences, all evidence consistently highlights RS as a frequent and serious complication in critically ill patients. Early detection and prevention are essential, relying on prompt nutritional assessment at ICU admission, careful monitoring of serum electrolytes before and during refeeding, and a conservative caloric approach to nutrient reintroduction, alongside supportive therapy and electrolyte supplementation if RS manifestations occur. Clinicians should be aware of the significant prevalence and potential severity of RS in critically ill patients, along with the ongoing challenges related to its early recognition, prevention, and optimal nutritional management. This review aims to provide a comprehensive overview of the current knowledge on the incidence, prognostic impact, risk factors, clinical manifestations, and nutritional management of RS in critically ill patients while highlighting existing evidence gaps and key areas requiring clinical attention. Full article
(This article belongs to the Special Issue Nutritional Management in Intensive Care)
20 pages, 3282 KiB  
Article
IPM Adoption in Common Beans in Brazil
by Amanda Lopes Ferreira, Alcido Elenor Wander and Patricia Valle Pinheiro
Horticulturae 2025, 11(6), 611; https://doi.org/10.3390/horticulturae11060611 (registering DOI) - 29 May 2025
Abstract
Common beans (Phaseolus vulgaris) are an important source of protein for the Brazilian population. They are cultivated all over the country, in three cropping seasons/year, totaling 2.7 million tons, mostly for domestic consumption. Pest management is a big challenge and is [...] Read more.
Common beans (Phaseolus vulgaris) are an important source of protein for the Brazilian population. They are cultivated all over the country, in three cropping seasons/year, totaling 2.7 million tons, mostly for domestic consumption. Pest management is a big challenge and is mostly carried out with the intensive use of pesticides. Integrated pest management (IPM) is essential for sustainability. This technology is based on applying insecticides only when the pest population reaches the Economic Threshold. For that, it is necessary to monitor the crop for the occurrence of pests and beneficial arthropods. Although the concept of IPM and its benefits have long been known and widespread, it is not clear whether bean producers adopt the technology, since informal reports suggest that preventive insecticide applications are still highly used in the crop. The objective of this study was to survey the level of IPM adoption among bean producers in different regions of Brazil, using a questionnaire, applied to 103 producers/consultants. The results show that the estimated rate of IPM adoption by common bean producers in Brazil is 46.6%. The main causes of the low adoption are a lack of understanding of IPM concepts, high confidence in the efficiency of pesticides, and high costs of crop monitoring. Full article
(This article belongs to the Section Insect Pest Management)
21 pages, 8243 KiB  
Article
High Precision Detection Pipe Bursts Based on Small Sample Diagnostic Method
by Guoxin Shi, Xianpeng Wang, Jingjing Zhang and Xinlei Gao
Sensors 2025, 25(11), 3431; https://doi.org/10.3390/s25113431 (registering DOI) - 29 May 2025
Abstract
In order to improve the accuracy of pipe burst detection in water distribution networks (WDNs), a novel small sample diagnosis method (SSDM) based on the head loss ratio (HLR) method and deep transfer learning (DTL) method has been proposed. In this paper, the [...] Read more.
In order to improve the accuracy of pipe burst detection in water distribution networks (WDNs), a novel small sample diagnosis method (SSDM) based on the head loss ratio (HLR) method and deep transfer learning (DTL) method has been proposed. In this paper, the burst state was quickly detected through the limited data analysis of pressure monitoring points. The HLR method was introduced to enhance data features. DTL was introduced to improve the accuracy of small sample burst detection. The simulated data and real data were enhanced by HLR. Then, the model was trained and obtained through the DTL. The performance of the model was evaluated in both simulated and real scenarios. The results indicate that the leaked features can be improved by 350% by the HLR. The accuracy of SSDM reaches 99.56%. The SSDM has been successfully applied to the detection of real WDNs. The proposed method provides potential application value for detecting pipe bursts. Full article
(This article belongs to the Section Industrial Sensors)
25 pages, 7225 KiB  
Article
Examples of Problems with Estimating the State of Charge of Batteries for Micro Energy Systems
by Marian Kampik, Marcin Fice, Krzysztof Sztymelski, Wojciech Oliwa and Grzegorz Wieczorek
Energies 2025, 18(11), 2850; https://doi.org/10.3390/en18112850 (registering DOI) - 29 May 2025
Abstract
Accurate estimation of the state of charge (SOC) is important for the effective management and utilization of lithium-ion battery packs. While advanced estimation methods present in scientific literature commonly rely on detailed cell parameters and laboratory-controlled conditions, practical engineering applications often require solutions [...] Read more.
Accurate estimation of the state of charge (SOC) is important for the effective management and utilization of lithium-ion battery packs. While advanced estimation methods present in scientific literature commonly rely on detailed cell parameters and laboratory-controlled conditions, practical engineering applications often require solutions applicable to battery packs with unknown or limited internal characteristics. In this context, this study compares three different SOC estimation strategies—voltage-based, coulomb counting, and charge balance methods—implemented in an independent telemetry module (TIO) and their performance against a commercial battery management system (Orion BMS2). Experimental results demonstrate that the voltage-based method provides insufficient accuracy due to its inherent sensitivity to voltage thresholds and internal resistance under load conditions. Conversely, coulomb counting, with periodic recalibration through full charging cycles, showed significantly improved accuracy, closely matching the Orion BMS2 outputs when properly initialized. The results confirm the viability of coulomb counting as a pragmatic approach for battery packs lacking detailed cell data. Future research should address reducing dependency on periodic full-charge resets by incorporating adaptive estimation techniques, such as Kalman filtering or observers, and leveraging open-circuit voltage measurements and temperature compensation to further enhance accuracy while maintaining the simplicity and external applicability of the monitoring system. Full article
(This article belongs to the Special Issue Sustainable Development of Fuel Cells and Hydrogen Technologies)
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27 pages, 2319 KiB  
Article
Longitudinal Symptom Analysis of COVID-19 Survivors and Post-COVID Syndrome Patients
by Eduarda Martins de Faria, Cíntia Moraes de Sá Sousa, Caroline de Oliveira Ribeiro, Márcio Neves Bóia, Agnaldo José Lopes and Pedro Lopes de Melo
Biomedicines 2025, 13(6), 1334; https://doi.org/10.3390/biomedicines13061334 (registering DOI) - 29 May 2025
Abstract
Background/Objectives: The present study aimed to analyze changes in symptom intensity during the recovery period of COVID-19 survivors and patients with post-COVID syndrome. Methods: Initially, we described a new remote patient monitoring system to track the intensity of specific symptoms in individuals’ home [...] Read more.
Background/Objectives: The present study aimed to analyze changes in symptom intensity during the recovery period of COVID-19 survivors and patients with post-COVID syndrome. Methods: Initially, we described a new remote patient monitoring system to track the intensity of specific symptoms in individuals’ home environments. Remote patient monitoring (RPM) was implemented over 15 days in a cohort of 133 individuals aged 20 to 78 years, divided into four groups: mild (MG, n = 40), Hospital Discharge Without Invasive Mechanical Ventilation (WIMV, n = 40), Hospital Discharge With Invasive Mechanical Ventilation (IMV, n = 13), and reinfected (RG, n = 40). Results: The most prevalent symptoms reported across all groups, based on average intensity, were shortness of breath, fatigue, cough, headache, and body pain. The WIMV group exhibited the highest average intensities in six symptoms (p < 0.01), while the IMV group reported the highest averages in four symptoms (p < 0.05). Fatigue was the symptom with the highest overall intensity, followed by memory lapses. The hospitalized groups demonstrated the highest intensities and most persistent symptoms (p < 0.05). Blood pressure was significantly higher in the MG group compared to the RG group (p < 0.0001), although all values remained within the normal range. Conclusions: These results provide novel insights, revealing distinct differences in the symptom profiles among the studied groups. These findings hold significant implications for developing more personalized care strategies and informing future pandemic preparedness and response efforts. Full article
14 pages, 4265 KiB  
Article
Spectrum Fitting Approach for Passive Wireless SAW Sensor Interrogation Using Software-Defined Radio
by Shihao Wang, Qi Wang, Guopeng Zhu, Lei Liu, Xinning Cao, Tingxin Ren, Yue Zhou and Hao Jin
Micromachines 2025, 16(6), 656; https://doi.org/10.3390/mi16060656 (registering DOI) - 29 May 2025
Abstract
Passive wireless surface acoustic wave (SAW) sensors are widely adopted for monitoring the safety status of industrial equipment due to their compact size and maintenance-free operation. Replacing traditional discrete-component interrogators with software-defined radio (SDR) architectures offers lower cost and greater flexibility. However, conventional [...] Read more.
Passive wireless surface acoustic wave (SAW) sensors are widely adopted for monitoring the safety status of industrial equipment due to their compact size and maintenance-free operation. Replacing traditional discrete-component interrogators with software-defined radio (SDR) architectures offers lower cost and greater flexibility. However, conventional frequency estimation methods often rely on iterative algorithms with high computational complexity, limiting their real-time applicability. This paper presents an SAW sensing system based on an SDR platform and a non-iterative spectrum-fitting method for SAW frequency measurement. The feasibility of the proposed method is theoretically analyzed, and its performance under different window functions and length of fast Fourier transform (FFT) configurations is evaluated through simulations and experimental measurements. The results demonstrate a favorable trade-off between time efficiency and SAW frequency measurement accuracy. Compared to traditional approaches, the proposed method reduces complexity while maintaining ± 3kHz peak-to-peak accuracy with only 4096-point FFT length according to experimental results. Full article
16 pages, 4737 KiB  
Article
A New Method for Determining Production Profiles Based on Intelligent Slow-Release Chemical Tracers
by Liang Wang, Lingang Lv and Peng Chen
Processes 2025, 13(6), 1705; https://doi.org/10.3390/pr13061705 (registering DOI) - 29 May 2025
Abstract
With the development of tracer technology and the improvement of fine management in oil fields, chemical tracer monitoring is widely used to analyze the production profiles in commingled wells and horizontal wells. However, most existing tracer technologies can only determine the production profile [...] Read more.
With the development of tracer technology and the improvement of fine management in oil fields, chemical tracer monitoring is widely used to analyze the production profiles in commingled wells and horizontal wells. However, most existing tracer technologies can only determine the production profile and cannot calculate the water cut. This paper proposes an intelligent slow-release chemical tracer monitoring technology and a corresponding interpretation methodology, which can quantify the oil and water production rates and dynamically analyze the water cut of production profiles by simultaneous deployment of oil-soluble and water-soluble tracers. To validate this approach, this method was applied to well A of the Bohai Oilfield. The results showed that the calculation model based on produced tracer concentration can quantitatively determine the production profile and water cut of the monitored well. During the stable production period, Well A exhibited high production rates and a low water cut, and the contribution of oil production varied greatly among different layers. The first and third sections were identified as the main contributors, accounting for 51.8% and 23.2% of production, respectively, while the second and fourth sections showed lower contributions of 15.1% and 9.9%. The water cut of each section was below 30%. This intelligent slow-release tracer monitoring technology allowed for continuous production profiles in the monitored well. The proposed method provides effective guidance for characterizing the production profile and water flooding patterns of each layer. It is helpful for the efficient development of oil and gas reservoirs. Full article
(This article belongs to the Section Chemical Processes and Systems)
16 pages, 1075 KiB  
Article
Passive Indoor People Counting by Bluetooth Signal Deformation Analysis with Deep Learning
by Giancarlo Iannizzotto, Lucia Lo Bello and Andrea Nucita
Appl. Sci. 2025, 15(11), 6142; https://doi.org/10.3390/app15116142 (registering DOI) - 29 May 2025
Abstract
This study presents a novel approach to passive human counting in indoor environments using Bluetooth Low Energy (BLE) signals and deep learning. The motivation behind this research is the need for non-intrusive, privacy-preserving occupancy monitoring in sensitive indoor settings, where traditional camera-based solutions [...] Read more.
This study presents a novel approach to passive human counting in indoor environments using Bluetooth Low Energy (BLE) signals and deep learning. The motivation behind this research is the need for non-intrusive, privacy-preserving occupancy monitoring in sensitive indoor settings, where traditional camera-based solutions may be unsuitable. Our method leverages the deformations that BLE signals undergo when interacting with the human body, enabling occupant detection and counting without requiring wearable devices or visual tracking. We evaluated three deep neural network models—Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and a hybrid CNN+LSTM architecture—under both classification and regression settings. Experimental results indicate that the hybrid CNN+LSTM model outperforms the others in terms of accuracy and mean absolute error. Notably, in the regression setup, the model can generalize to occupancy values not present in the fine-tuning dataset, requiring only a few minutes of calibration data to adapt to a new environment. We believe that this approach offers a valuable solution for real-time people counting in critical environments such as laboratories, clinics, or hospitals, where preserving privacy may limit the use of camera-based systems. Overall, our method demonstrates high adaptability and robustness, making it suitable for practical deployment in diverse indoor scenarios. Full article
(This article belongs to the Special Issue Monitoring of Human Physiological Signals)
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17 pages, 9972 KiB  
Article
Improving Agricultural Efficiency of Dry Farmlands by Integrating Unmanned Aerial Vehicle Monitoring Data and Deep Learning
by Tung-Ching Su, Tsung-Chiang Wu and Hsin-Ju Chen
Land 2025, 14(6), 1179; https://doi.org/10.3390/land14061179 (registering DOI) - 29 May 2025
Abstract
This study aimed to address the challenge of monitoring and managing soil moisture in dryland agriculture with supplemental irrigation under increasingly extreme climate conditions. Using unmanned aerial vehicles (UAVs) equipped with hyperspectral sensors, we collected imagery of wheat fields on Kinmen Island at [...] Read more.
This study aimed to address the challenge of monitoring and managing soil moisture in dryland agriculture with supplemental irrigation under increasingly extreme climate conditions. Using unmanned aerial vehicles (UAVs) equipped with hyperspectral sensors, we collected imagery of wheat fields on Kinmen Island at various growth stages. The Modified Perpendicular Drought Index (MPDI) was calculated to quantify soil drought conditions. Simultaneously, soil samples were collected to measure the actual soil moisture content. These datasets were used to develop a Gradient Boosting Regression (GBR) model to estimate soil moisture across the entire field. The resulting AI-based model can guide decisions on the timing and scale of supplemental irrigation, ensuring water is applied only when needed during crop growth. Furthermore, MPDI values and wheat spike samples were used to construct another GBR model for yield prediction. When applying MPDI values from multispectral imagery collected at a similar stage in the following year, the model achieved a prediction accuracy of over 90%. The proposed approach offers a reliable solution for enhancing the resilience and productivity of dryland crops under climate stress and demonstrates the potential of integrating remote sensing and machine learning in precision water management. Full article
(This article belongs to the Special Issue Challenges and Future Trends in Land Cover/Use Monitoring)
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13 pages, 656 KiB  
Article
Psychological Assessment and Psychosocial Outcomes in Bariatric Surgery Candidates: A Retrospective Study
by Maria Rosaria Magurano, Daniele Napolitano, Mattia Bozzetti, Alessio Lo Cascio, Lorenzo Oppo, Laura Antonella Fernandez Tayupanta, Serena Ferrazzoli, Lucia Lopasso, Emanuela Rellini, Marco Raffaelli and Daniela Pia Rosaria Chieffo
Healthcare 2025, 13(11), 1294; https://doi.org/10.3390/healthcare13111294 (registering DOI) - 29 May 2025
Abstract
Background/Objectives: Psychological vulnerability in individuals with obesity represents a significant concern in the context of bariatric surgery. This study aimed to assess psychosocial functioning and identify the psychological, clinical, and sociodemographic predictors of impairment among patients undergoing preoperative evaluation. Methods: A retrospective [...] Read more.
Background/Objectives: Psychological vulnerability in individuals with obesity represents a significant concern in the context of bariatric surgery. This study aimed to assess psychosocial functioning and identify the psychological, clinical, and sociodemographic predictors of impairment among patients undergoing preoperative evaluation. Methods: A retrospective observational study was conducted on patients referred for bariatric surgery at a single academic medical center. Data were collected through clinical interviews and validated psychometric tools: the Clinical Impairment Assessment (CIA), the Patient Health Questionnaire-9 (PHQ-9), and the Generalized Anxiety Disorder-7 (GAD-7). Robust multiple regression analysis determined associations between CIA scores and psychological and demographic factors. Results: A total of 688 patients were evaluated (median age: 46 years; 70.3% female). Most had a high school education (56.9%) and were employed (69%). Elevated scores on the Clinical Impairment Assessment (CIA) were significantly associated with female gender (β = 1.075, p = 0.029), moderate anxiety (GAD-7 ≥ 10; β = 3.85, p < 0.001), and severe depressive symptoms (PHQ-9 ≥ 15; β = 16.67, p < 0.001). Other significant predictors included prior psychotherapy (β = 1.18, p = 0.044), aesthetic motivation for surgery (β = 0.92, p = 0.120), and expectations that weight loss would improve self-esteem (β = 2.11, p = 0.001) or social relationships (β = 1.98, p = 0.002). Conversely, physical activity was associated with lower CIA scores (β = –1.23, p = 0.050). The regression model showed strong explanatory power (McFadden R2 = 0.529). Conclusions: This study highlights key predictors of psychosocial distress in bariatric candidates, underscoring the importance of comprehensive psychological assessment before surgery. The CIA appears to be a valuable screening and monitoring tool. Future research should explore the longitudinal evolution of psychosocial functioning and support the integration of psychological care into multidisciplinary bariatric programs. Full article
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19 pages, 1343 KiB  
Article
Evaluating Robotic Walker Performance: Stability, Responsiveness, and Accuracy in User Movement Detection
by Larisa Dunai, Isabel Seguí Verdú, Sui Liang and Ismael Lengua Lengua
Sensors 2025, 25(11), 3428; https://doi.org/10.3390/s25113428 (registering DOI) - 29 May 2025
Abstract
This work presents the experimental evaluation of a robotic walker following the full implementation of its sensor and motorization system. The aging population and increasing mobility impairments drive the need for assistive robotic technologies that enhance safe and independent movement. The main objective [...] Read more.
This work presents the experimental evaluation of a robotic walker following the full implementation of its sensor and motorization system. The aging population and increasing mobility impairments drive the need for assistive robotic technologies that enhance safe and independent movement. The main objective was to validate the device’s behavior in real-use scenarios by assessing its stability, responsiveness, and accuracy in detecting user movement. Tests were carried out in straight-line walking and on paths involving directional changes, both with and without motor assistance, using a cohort of five test users. Principal Component Analysis (PCA) and t-SNE dimensionality reduction techniques were applied to analyze the inertial (IMU) and proximity (TOF) sensor data, complemented by motor control monitoring through wheel Hall sensors, to explore gait patterns and system performance. Additionally, synchronized measurements between the user’s and walker’s inertial units and Time-of-Flight sensors allowed the evaluation of spatial alignment and motion correlation. The results provide a foundation for future system adjustment and optimization, ensuring the walker offers effective, safe, and adaptive assistance tailored to the user’s needs. Findings reveal that the walker successfully distinguishes individual gait patterns and adapts its behavior accordingly, demonstrating its potential for personalized mobility support. Full article
(This article belongs to the Section Navigation and Positioning)
15 pages, 7136 KiB  
Article
Source-Free Domain Adaptation for Cross-Modality Abdominal Multi-Organ Segmentation Challenges
by Xiyu Zhang, Xu Chen, Yang Wang, Dongliang Liu and Yifeng Hong
Information 2025, 16(6), 460; https://doi.org/10.3390/info16060460 - 29 May 2025
Abstract
Abdominal organ segmentation in CT images is crucial for accurate diagnosis, treatment planning, and condition monitoring. However, the annotation process is often hindered by challenges such as low contrast, artifacts, and complex organ structures. While unsupervised domain adaptation (UDA) has shown promise in [...] Read more.
Abdominal organ segmentation in CT images is crucial for accurate diagnosis, treatment planning, and condition monitoring. However, the annotation process is often hindered by challenges such as low contrast, artifacts, and complex organ structures. While unsupervised domain adaptation (UDA) has shown promise in addressing these issues by transferring knowledge from a different modality (source domain), its reliance on both source and target data during training presents a practical challenge in many clinical settings due to data privacy concerns. This study aims to develop a cross-modality abdominal multi-organ segmentation model for label-free CT (target domain) data, leveraging knowledge solely from a pre-trained source domain (MRI) model without accessing the source data. To achieve this, we generate source-like images from target-domain images using a one-way image translation approach with the pre-trained model. These synthesized images preserve the anatomical structure of the target, enabling segmentation predictions from the pre-trained model. To further enhance segmentation accuracy, particularly for organ boundaries and small contours, we introduce an auxiliary translation module with an image decoder and multi-level discriminator. The results demonstrate significant improvements across several performance metrics, including the Dice similarity coefficient (DSC) and average symmetric surface distance (ASSD), highlighting the effectiveness of the proposed method. Full article
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19 pages, 2588 KiB  
Article
Research on Tree Point Cloud Enhancement Based on Deep Learning
by Haoran Liu, Hao Zhong, Guangqiang Xie and Ping Zhang
Forests 2025, 16(6), 915; https://doi.org/10.3390/f16060915 - 29 May 2025
Abstract
The acquisition of high-quality tree point cloud datasets facilitates research in various forestry fields, including tree species classification, diversity monitoring, and biomass estimation. However, due to limitations in sensor performance and occlusion between trees, tree point clouds acquired using LiDAR scanners often exhibit [...] Read more.
The acquisition of high-quality tree point cloud datasets facilitates research in various forestry fields, including tree species classification, diversity monitoring, and biomass estimation. However, due to limitations in sensor performance and occlusion between trees, tree point clouds acquired using LiDAR scanners often exhibit missing data. This not only degrades the quality of the point clouds, but also significantly reduces the number of usable samples. Therefore, this study proposed a tree point cloud enhancement system, which included the completion network and the sample augmentation network. The point cloud completion network utilized a transformer-based improved module to predict missing point clouds and combined up-sampling processing to progressively complete the point clouds from coarse to fine. This could improve the subsequent model decisions and performance through data balancing. On the other hand, the sample augmentation network, based on an adversarial learning strategy, separately constructed the generator and the classifier. By applying shape transformations, point displacements, and point drop to complete point cloud samples, the learnable parameters in the generator and the classifier were alternately optimized. This process enhanced both the quality and the quantity of the tree point cloud dataset. In addition, this study introduced a multi-head attention pooling layer, which further enhanced the joint network’s ability to learn and extract tree structural features. The experimental results showed that the completion network successfully restored missing tree point clouds of various types, achieving an average Chamfer Distance of 4.84 and an average F-score of 0.90. The experiments also demonstrated the effectiveness and robustness of the sample augmentation network, which improved classification accuracy by approximately 2.9% compared to the original dataset. Full article
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