Search Results (104)

Introduction/Objective: Post-bariatric surgery patients require long-term, coordinated care to address complex nutritional, physiological, and behavioral challenges. Personalized smart nutrition, combining individualized dietary strategies with targeted monitoring, has emerged as a valuable direction for optimizing recovery and long-term outcomes. This article examines how traditional medical protocols can be enhanced by digital solutions in a multidisciplinary framework. Methods: The study analyzes current clinical practices, including personalized meal planning, physical rehabilitation, biochemical marker monitoring, and psychological counseling, as applied in post-bariatric care. These established approaches are then analyzed in relation to the NutriMonitCare system, a digital health system developed and tested in a laboratory environment. Used here as an illustrative example, the NutriMonitCare system demonstrates the potential of digital tools to support clinicians through real-time monitoring of dietary intake, activity levels, and physiological parameters. Results: Findings emphasize that medical protocols remain the cornerstone of post-surgical management, while digital tools may provide added value by enhancing data availability, supporting individualized decision making, and reinforcing patient adherence. Systems like the NutriMonitCare system could be integrated into interdisciplinary care models to refine nutrition-focused interventions and improve communication across care teams. However, their clinical utility remains theoretical at this stage and requires further validation. Conclusions: In conclusion, the integration of digital health tools with conventional post-operative care has the potential to advance personalized smart nutrition. Future research should focus on clinical evaluation, real-world testing, and ethical implementation of such technologies into established medical workflows to ensure both efficacy and patient safety. Full article

Pelvic organ prolapse (POP) is a common pelvic floor disorder with substantial impact on women’s quality of life, necessitating accurate and reproducible diagnostic methods. This study investigates the use of three-dimensional (3D) transperineal ultrasound, integrated with artificial intelligence (AI), to evaluate pelvic floor biomechanics and identify correlations between biometric parameters and prolapse severity. Thirty-seven female patients diagnosed with genital prolapse (mean age: 65.3 ± 10.6 years; mean BMI: 29.5 ± 3.8) were enrolled. All participants underwent standardized 3D transperineal ultrasound using the Mindray Smart Pelvic system, an AI-assisted imaging platform. Key biometric parameters—anteroposterior diameter, laterolateral diameter, and genital hiatus area—were measured under three functional states: rest, maximal Valsalva maneuver, and voluntary pelvic floor contraction. Additionally, two functional indices were derived: the distensibility index (ratio of Valsalva to rest) and the contractility index (ratio of contraction to rest), reflecting pelvic floor elasticity and muscular function, respectively. Statistical analysis included descriptive statistics and univariate correlation analysis using Pelvic Organ Prolapse Quantification (POP-Q) system scores. Results revealed a significant correlation between laterolateral diameter and prolapse severity across multiple compartments and functional states. In apical prolapse, the laterolateral diameter measured at rest and during both Valsalva and contraction showed positive correlations with POP-Q point C, indicating increasing transverse pelvic dimensions with more advanced prolapse (e.g., r = 0.42 to 0.58; p < 0.05). In anterior compartment prolapse, the same parameter measured during Valsalva and contraction correlated significantly with POP-Q point AA (e.g., r = 0.45 to 0.61; p < 0.05). Anteroposterior diameters and genital hiatus area were also analyzed but showed weaker or inconsistent correlations. AI integration facilitated real-time image segmentation and automated measurement, reducing operator dependency and increasing reproducibility. These findings highlight the laterolateral diameter as a strong, reproducible anatomical marker for POP severity, particularly when assessed dynamically. The combined use of AI-enhanced imaging and functional indices provides a novel, standardized, and objective approach for assessing pelvic floor dysfunction. This methodology supports more accurate diagnosis, individualized management planning, and long-term monitoring of pelvic floor disorders. Full article

Current tomato harvesters rely primarily on external color as the sole indicator of ripeness. However, this approach often results in premature harvesting, leading to insufficient lycopene accumulation and a suboptimal nutritional content for human consumption. Such limitations are especially critical in controlled-environment agriculture (CEA) systems, where maximizing fruit quality and nutrient density is essential for both the yield and consumer health. To address that challenge, this study introduces a novel, multimodal harvest readiness framework tailored to nutrient film technology (NFT)-based smart farms. The proposed approach integrates plant-level stress diagnostics and fruit-level phenotyping using wearable biosensors, AI-assisted computer vision, and non-invasive physiological sensing. Key physiological markers—including the volatile organic compound (VOC) methanol, phytohormones salicylic acid (SA) and indole-3-acetic acid (IAA), and nutrients nitrate and ammonium concentrations—are combined with phenomic traits such as fruit color (a*), size, chlorophyll index (rGb), and water status. The innovation lies in a four-stage decision-making pipeline that filters physiologically stressed plants before selecting ripened fruits based on internal and external quality indicators. Experimental validation across four plant conditions (control, water-stressed, light-stressed, and wounded) demonstrated the efficacy of VOC and hormone sensors in identifying optimal harvest candidates. Additionally, the integration of low-cost electrochemical ion sensors provides scalable nutrient monitoring within NFT systems. This research delivers a robust, sensor-driven framework for autonomous, data-informed harvesting decisions in smart indoor agriculture. By fusing real-time physiological feedback with AI-enhanced phenotyping, the system advances precision harvest timing, improves fruit nutritional quality, and sets the foundation for resilient, feedback-controlled farming platforms suited to meeting global food security and sustainability demands. Full article

Background/Objectives: In the context of accelerating climate change and growing food insecurity, improving crop resilience to abiotic stresses such as drought, salinity, heat, and cold is a critical agricultural and scientific challenge. Understanding the biochemical mechanisms that underlie plant stress responses is essential for developing resilient crop varieties This review aims to provide an integrative overview of how metabolomics can elucidate biochemical mechanisms underlying stress tolerance and guide the development of stress-resilient crops. Methods: We reviewed the recent literature on metabolomic studies addressing abiotic stress responses in various crop species, focusing on both targeted and untargeted approaches using platforms such as nuclear magnetic resonance (NMR), liquid chromatography–mass spectrometry (LC-MS), and gas chromatography–mass spectrometry (GC-MS). We also included emerging techniques such as capillary electrophoresis–mass spectrometry (CE-MS), ion mobility spectrometry (IMS-MS), Fourier transform infrared spectroscopy (FT-IR), and data-independent acquisition (DIA). Additionally, we discuss the integration of metabolomics with transcriptomics and physiological data to support system-level insights. Results: The reviewed studies identify common stress-responsive metabolites, including osmoprotectants, antioxidants, and signaling compounds, which are consistently linked to enhanced tolerance. Novel metabolic biomarkers and putative regulatory hubs are highlighted as potential targets for molecular breeding and bioengineering. We also address ongoing challenges related to data standardization and reproducibility across analytical platforms. Conclusions: Metabolomics is a valuable tool for advancing our understanding of plant abiotic stress responses. Its integration with other omics approaches and phenotypic analyses offers promising avenues for improving crop resilience and developing climate-adaptive agricultural strategies. Full article

Traditionally, detecting misinformation (real vs. fake) and authorship (human vs. AI) have been addressed as separate classification tasks, leaving a critical gap in real-world scenarios where these challenges increasingly overlap. Motivated by this need, we introduce a unified framework—the Shared–Private Synergy Model (SPSM)—that tackles both authenticity and authorship classification under one umbrella. Our approach is tested on a novel multi-label dataset and evaluated through an exhaustive suite of methods, including traditional machine learning, stylometric feature analysis, and pretrained large language model-based classifiers. Notably, the proposed SPSM architecture incorporates multitask learning, shared–private layers, and hierarchical dependencies, achieving state-of-the-art results with over 96% accuracy for authenticity (real vs. fake) and 98% for authorship (human vs. AI). Beyond its superior performance, our approach is interpretable: stylometric analyses reveal how factors like sentence complexity and entity usage can differentiate between fake news and AI-generated text. Meanwhile, LLM-based classifiers show moderate success. Comprehensive ablation studies further highlight the impact of task-specific architectural enhancements such as shared layers and balanced task losses on boosting classification performance. Our findings underscore the effectiveness of synergistic PLM architectures for tackling complex classification tasks while offering insights into linguistic and structural markers of authenticity and attribution. This study provides a strong foundation for future research, including multimodal detection, cross-lingual expansion, and the development of lightweight, deployable models to combat misinformation in the evolving digital landscape and smart society. Full article

Although diabetic polyneuropathy (DPN) has a very high prevalence among people with diabetes, gait analysis using cyclograms is very limited, and cyclogram research, in general, is limited to standard measures available in software packages. In this study, cyclograms (movements of the centre of pressure, COP, on and between the plantar surfaces) of diabetics and healthy individuals recorded with a smart insole were compared in terms of geometry and balance index, BI. The latter was calculated as the summed product of standard deviations of cyclogram markers, i.e., start/end points, turning points, and intersection points of the COP. The geometry was assessed by the positions of, and distances between, these points, and the distance ratios (14 parameters in total). The BI of healthy and diabetic individuals differed significantly. Of the fifteen parameters (including the BI), three were suitable as classifiers to predict DPN, namely two distances and their ratio, with false negatives ranging from 1.8 to 12.5%, and false positives ranging from 2.9 to 7.1%. The standard metric of the cyclogram provided by the software packages failed as a classifier. While the BI captures both DPN-related balance and other balance disorders, the changing geometry of the cyclogram in diabetics appears to be DPN-specific. Full article

The intensifying challenges posed by global climate change and water scarcity necessitate enhancements in agricultural productivity and sustainability within arid regions. This review synthesizes recent advancements in genetic engineering, molecular breeding, precision agriculture, and innovative water management techniques aimed at improving crop drought resistance, soil health, and overall agricultural efficiency. By examining cutting-edge methodologies, such as CRISPR/Cas9 gene editing, marker-assisted selection (MAS), and omics technologies, we highlight efforts to manipulate drought-responsive genes and consolidate favorable agronomic traits through interdisciplinary innovations. Furthermore, we explore the potential of precision farming technologies, including the Internet of Things (IoT), remote sensing, and smart irrigation systems, to optimize water utilization and facilitate real-time environmental monitoring. The integration of genetic, biotechnological, and agronomic approaches demonstrates a significant potential to enhance crop resilience against abiotic and biotic stressors while improving resource efficiency. Additionally, advanced irrigation systems, along with soil conservation techniques, show promise for maximizing water efficiency and sustaining soil fertility under saline–alkali conditions. This review concludes with recommendations for a further multidisciplinary exploration of genomics, sustainable water management practices, and precision agriculture to ensure long-term food security and sustainable agricultural development in water-limited environments. By providing a comprehensive framework for addressing agricultural challenges in arid regions, we emphasize the urgent need for continued innovation in response to escalating global environmental pressures. Full article

Smart radiotherapy biomaterials (SRBs) include seed and liquid biomaterials designed to be employed as fiducial markers during radiotherapy while also delivering therapeutic drug payloads to enhance treatment outcomes. In this study, we investigate a novel Customizable Lyophilized Agent for Radiotherapy Imaging and TherapY (CLARITY) biomaterial, which can be loaded with immunoadjuvants (anti-CD40 monoclonal antibody or Caflanone (FBL-03G)) at the point of care. The CLARITY biomaterial was investigated in an animal model of pancreatic cancer using C57BL6 mice. Mice were imaged before and at different points of time post-treatment to evaluate the potential of CLARITY biomaterial to provide imaging contrast similar to fiducials. This study also used cadavers to assess CLARITY’s potential to provide imaging contrast in humans. Results showed imaging contrast from computed tomography (CT) and magnetic resonance imaging (MRI) modalities for up to 30 days post-treatment, demonstrating potential for use as fiducials. A significant increase in survival (***, p = 0.0006) was observed for mice treated with CLARITY biomaterial loaded with immunoadjuvant for up to 10 weeks post-treatment compared to those without treatment. These initial results demonstrate the potential of CLARITY biomaterial to serve as a smart multifunctional radiotherapy biomaterial and provide the impetus for further development and optimization as a point-of-care technology for combination radiotherapy and immunotherapy. Full article

Cancer cells are known to create an acidic microenvironment (the Warburg effect). At the same time, fluorescent dyes can be sensitive to pH, showing a sharp increase or decrease in fluorescence depending on pH. However, modern applications, such as confocal laser scanning microscopy (CLSM), set additional requirements for such fluorescent markers to be of practical use, namely, high quantum yield, low bleaching, minimal quenching in the cell environment, and minimal overlap with auto-fluorophores. R6G could be the perfect match for these requirements, but its fluorescence is not pH-dependent. We have attempted to develop an R6G conjugate with its FRET or PeT pair that would grant it pH sensitivity in the desired range (5.5–7.5) and enable the selective targeting of tumor cells, thus improving CLSM imaging. Covalent conjugation of R6G with NBD using a spermidine (spd) linker produced a pH-sensitive FRET effect but within the pH range of 7.0–9.0. Shifting this effect to the target pH range of 5.5–7.5 appeared possible by incorporating the R6G-spd-NBD conjugate within a “smart” polymeric micelle based on chitosan grafted with lipoic acid. In our previous studies, one could conclude that the polycationic properties of chitosan could make this pH shift possible. As a result, the micellar form of the NBD-spd-R6G fluorophore demonstrates a sharp ignition of fluorescence by 40%per1 pH unit in the pH range from 7.5 to 5. Additionally, “smart” polymeric micelles based on chitosan allow the label to selectively target tumor cells. Due to the pH sensitivity of the fluorophore NBD-spd-R6G and the selective targeting of cancer cells, the efficient visualization of A875 and K562 cells was achieved. CLSM imaging showed that the dye actively penetrates cancer cells (A875 and K562), while minimal accumulation and low fluorophore emission are observed in normal cells (HEK293T). It is noteworthy that by using “smart” polymeric micelles based on polyelectrolytes of different charges and structures, we create the possibility of regulating the pH dependence of the fluorescence in the desired interval, which means that these “smart” polymeric micelles can be applied to the visualization of a variety of cell types, organelles, and other structures. Full article

In the last decade, Smart Glasses (SG) and augmented reality (AR) technology have gained considerable interest in all production sectors. In the agricultural field, an SG can be considered a valuable device to support farmers and agricultural operators. SGs can be distinguished by technical specification, type of display, interaction system, and specific features. These aspects can affect their integration into farms, influencing users’ experience and the consequent level of performance. The aim of the study was to compare four SGs for AR with different technical characteristics to evaluate their potential integration in agricultural systems. This study analyzed the capability of QR code reading in terms of distance and time of visualization, the audio–video quality of image streaming during conference calls and, finally, the battery life. The results showed different levels of performance in QR code reading for the selected devices, while the audio–video quality in conference calls demonstrated similar results for all the devices. Moreover, the battery life of the SGs ranged from 2 to 7 h per charge cycle, and it was influenced by the type of usage. The findings also underlined the potential use and integration of SGs to support operators during farm management. Specifically, SGs might enable farmers to obtain fast and precise augmented information using markers placed at different points on the farm. In conclusion, the study highlights how the different technical characteristics of SG represent an important factor in the selection of the most appropriate device for a farm. Full article

The growing interest of the scientific community in surveying and monitoring submerged assets is motivated by the increasing demand for high-resolution products with certified accuracies. While many instrumental and methodological solutions for documenting, monitoring, and studying archaeological and cultural heritage through geomatics techniques are already available for the terrestrial environment, the challenge remains open to the underwater context. High-resolution capability and accurate positioning are still difficult to achieve in these environments. This paper discusses the limitations of positioning and georeferencing techniques in the underwater environment. It explores how existing methods and new instruments can be used to perform accurate topographic surveys of ground control points (GCPs) in very shallow waters (within 5 m depths), which can support the photogrammetric reconstruction of underwater assets. This research presents two innovative prototypes: a self-built plastic marker for topographic use in the underwater environment and a self-built aluminum pole for topographic use in the marine environment. The prototypes are tested and validated with a tilt-compensating smart antenna to reduce planar and altimetric errors when the pole is not perfectly level and to work independently of the shore proximity required when using a total station to perform said measurements. Full article

Maize (Zea mays L.) is an important cereal and is affected by climate change. Therefore, the production of climate-smart maize is urgently needed by preserving diverse genetic backgrounds through the exploration of their genetic diversity. To achieve this, 96 maize inbred lines were used to screen for phenotypic yield-associated traits and grain quality parameters. These traits were studied across two different environments (Anand and Godhra) and polymorphic simple sequence repeat (SSR) markers were employed to investigate the genetic diversity, population structure, and trait-linked association. Genotype–environment interaction (GEI) reveals that most of the phenotypic traits were governed by the genotype itself across the environments, except for plant and ear height, which largely interact with the environment. The genotypic correlation was found to be positive and significant among protein, lysine and tryptophan content. Similarly, yield-attributing traits like ear girth, kernel rows ear⁻¹, kernels row⁻¹ and number of kernels ear⁻¹ were strongly correlated to each other. Pair-wise genetic distance ranged from 0.0983 (1820194/T1 and 1820192/4-20) to 0.7377 (IGI-1101 and 1820168/T1). The SSRs can discriminate the maize population into three distinct groups and shortlisted two genotypes (IGI-1101 and 1820168/T1) as highly diverse lines. Out of the studied 136 SSRs, 61 were polymorphic to amplify a total of 131 alleles (2–3 per loci) with 0.46 average gene diversity. The Polymorphism Information Content (PIC) ranged from 0.24 (umc1578) to 0.58 (umc2252). Similarly, population structure analysis revealed three distinct groups with 19.79% admixture among the genotypes. Genome-wide scanning through a mixed linear model identifies the stable association of the markers umc2038, umc2050 and umc2296 with protein, umc2296 and umc2252 with tryptophan, and umc1535 and umc1303 with total soluble sugar. The obtained maize lines and SSRs can be utilized in future maize breeding programs in relation to other trait characterizations, developments, and subsequent molecular breeding performances for trait introgression into elite genotypes. Full article

Purpose: We aimed to evaluate the feasibility of using a novel device, the Smart Eye Camera (SEC), for assessing tear meniscus height (TMH) after fluorescein staining and the agreement of the results with measurements obtained using standard slit lamp examination. Methods: TMH was assessed using both SEC and conventional slit lamp examination. The images were analyzed using the software ImageJ 1.53t (National Institutes of Health, Bethesda, MD, USA). A common measurement unit scale was established based on a paper strip, which was used as a calibration marker to convert pixels into metric scale. A color threshold was applied using uniform parameters for brightness, saturation, and hue. The images were then binarized to black and white to enhance the representation of the tear menisci. A 2 mm area around the upper and lower meniscus in the central eye lid zone was selected and magnified 3200 times to facilitate manual measurement. The values obtained using SEC were compared with those obtained with a slit lamp. Results: The upper and lower TMH values measured using the SEC were not statistically different from those obtained with a slit lamp (0.209 ± 0.073 mm vs. 0.235 ± 0.085, p = 0.073, and 0.297 ± 0.168 vs. 0.260 ± 0.173, p = 0.275, respectively). The results of Bland–Altman analysis demonstrated strong agreement between the two instruments, with a mean bias of −0.016 mm (agreement limits: −0.117 to 0.145 mm) for upper TMH and 0.031 mm (agreement limits: −0.306 to 0.368 mm) for lower TMH. Conclusions: The SEC demonstrated sufficient validity and reliability for assessing TMH in healthy eyes in a clinical setting, demonstrating concordance with the conventional slit lamp examination. Full article

Accurately docking mobile robots to various workstations on the factory floor is a common and essential task. The existing docking methods face three major challenges: intricate deployment procedures, susceptibility to ambient lighting, and incapacity to recognize product information during the docking process. This paper devises a novel approach that combines the features of ArUco and Data Matrix to form a composite marker termed “DataMatrix-ArUco-Hybrid” (DAH). The DAH pattern serves as a fiducial marker capable of concurrently providing both target pose information and product information. Detection of the DAH pattern is conducted by a cost-effective fiducial marker reader system, called “FiMa-Reader”, which comprises an embedded processing unit and an infrared camera equipped with a 940 nm fill-light to overcome lighting issues. The FiMa-Reader system effectively detects the DAH pattern under both well-lit and dimly lit conditions. Additionally, the implementation of the FiMa-Reader system leads to significant improvements in positioning accuracy, including an 86.42% improvement on the x-axis, a 44.7% improvement on the y-axis, and an 84.21% improvement in angular orientation when compared to traditional navigation methods. The utilization of FiMa-Reader presents an economically viable system capable of guiding mobile robots’ positioning with high precision in various indoor lighting conditions. Full article

The smart cities paradigm has gained significant attention as a tool to address the multifaceted challenges posed by contemporary urban mobility systems. While cities are eager to integrate cutting-edge technologies to evolve into digital and intelligent hubs, they often deal with infrastructure and governance bottlenecks that prevent the rapid adoption of industry-driven innovations. This study introduces a three-step methodological approach to forecast a city’s innovation readiness in urban mobility, thus facilitating city-led innovation and identifying key areas within urban mobility systems that require attention. Initially, a comprehensive literature review was undertaken to ascertain the most impactful innovation indicators influencing a city’s ability to embrace new technologies. Subsequently, Principal Component Analysis (PCA) was applied to identify these indicators, highlighting the primary markers of innovation for each city. The final step involved the application of both random and fixed-effects regression models to quantify the influence of distinct unobserved variables—such as economic, cultural, and political factors—on the innovation readiness of various cities. The methodology’s effectiveness was tested using data from cities across diverse regions. The findings underscore that merely 7 out of 21 innovation indicators are critical for assessing a city’s innovation readiness. Moreover, the random-effects model was identified as the most suitable for capturing the nuances of unobserved variables in the studied cities. The innovation readiness scores at the city level revealed a diverse range, with cities like Madrid, Gothenburg, and Mechelen demonstrating high readiness, while others like Kalisz and Datong showed lower scores. This research contributes to the strategic planning for smart cities, offering a robust framework for policymakers to enhance innovation readiness and foster sustainable urban development, with a newfound emphasis on city-specific analysis. Full article