Search Results (157)

Building on our previous 310-h test of a larger MEMS sensor, this study develops and validates a miniaturized, lift-off-fabricated, and FPC-integrated three-in-one microsensor. In addition to extending the operation to 744 h, we introduce a wireless MQTT/Node-RED architecture to enable real-time IoT-level monitoring in factory HVAC ducts. The microsensor was fabricated using Micro-electro-mechanical systems (MEMS) technology and integrated with a flexible printed circuit (FPC) for improved mechanical compliance and ease of installation. To assess its durability and reliability, a 744-h long-term test was conducted in an industrial HVAC environment, where the performance of the microsensor was compared with that of two commercially available velocity sensors. The integrated sensor exhibited stable operation throughout the test and demonstrated effective measurement capabilities in the ranges of 10–40 °C for temperature, 60–90% RH for humidity, and 1.5–5.0 m/s for airflow velocity, with an overall accuracy of approximately ±3%. The results highlight the sensor’s potential for real-time environmental monitoring in factory HVAC systems, offering advantages in integration, adaptability, and cost-effectiveness compared to traditional single-function commercial sensors. Full article

This paper presents a novel hybrid and hierarchical architecture of a Convolutional Neural Network (CNN), based on MobileNetV2 and Support Vector Machines (SVM) for the classification of crop diseases (SVMobileNetV2). The system feeds from multispectral images captured by Unmanned Aerial Vehicles (UAVs) alongside data from IoT nodes. The primary objective is to improve classification performance in terms of both accuracy and precision. This is achieved by integrating contemporary Deep Learning techniques, specifically different CNN models, a prevalent type of artificial neural network composed of multiple interconnected layers, tailored for the analysis of agricultural imagery. The initial layers are responsible for identifying basic visual features such as edges and contours, while deeper layers progressively extract more abstract and complex patterns, enabling the recognition of intricate shapes. In this study, different datasets of tropical crop images, in this case banana crops, were constructed to evaluate the performance and accuracy of CNNs in detecting diseases in the crops, supported by transfer learning. For this, multispectral images are used to create false-color images to discriminate disease through spectra related to the blue, green and red colors in addition to red edge and near-infrared. Moreover, we used IoT nodes to include environmental data related to the temperature and humidity of the environment and the soil. Machine Learning models were evaluated and fine-tuned using standard evaluation metrics. For classification, we used fundamental metrics such as accuracy, precision, and the confusion matrix; in this study was obtained a performance of up to 86.5% using current deep learning models and up to 98.5% accuracy using the proposed hybrid and hierarchical architecture (SVMobileNetV2). This represents a new paradigm to significantly improve classification using the proposed hybrid CNN-SVM architecture and UAV-based multispectral images. Full article

Background/Objectives: Angioimmunoblastic T-cell lymphoma (AITL) is a rare peripheral T-cell lymphoma of follicular helper T-cell (TFH) origin, often associated with immune dysregulation and EBV-positive B-cell proliferation. Kaposi sarcoma (KS) is a vascular neoplasm caused by human herpesvirus 8 (HHV-8), typically arising in immunocompromised individuals. The synchronous occurrence of AITL and KS in HIV-negative patients is exceptionally rare, with only three cases previously reported worldwide. Case Presentation: We describe an 81-year-old HIV-negative Korean woman presenting with progressive generalized edema and dyspnea. Imaging revealed multifocal lymphadenopathy. Excisional biopsy of the inguinal lymph node showed two distinct but adjacent neoplastic processes. The AITL component demonstrated a polymorphous infiltrate of atypical TFH cells expressing CD3, CD4, CD10, PD-1, and Bcl-6, with monoclonal TCR-γ rearrangement and TET2 and RHOA mutations. The KS component comprised spindle cells with slit-like vascular spaces, red blood cell extravasation, and immunoreactivity for HHV-8, CD31, CD34, and ERG. The findings were consistent with a collision tumor. Despite supportive care, the patient’s condition deteriorated, and she was discharged with palliative care. Discussion: The coexistence of AITL and KS in an HIV-negative setting raises important pathogenetic considerations. AITL is characterized by profound immune dysregulation, with depletion of normal T-cell subsets, abnormal B-cell activation, and cytokine milieu changes that may favor latent viral reactivation. This immunologic environment may permit HHV-8 reactivation, thereby facilitating the development of KS even in the absence of overt immunodeficiency due to HIV infection. Our findings support the hypothesis that AITL-related immune dysfunction may create a permissive niche for HHV-8-driven neoplasia. Conclusions: This is the first reported case in Asia and the fourth worldwide of a collision tumor comprising AITL and KS in an HIV-negative patI dient. The case suggests that AITL-associated immune dysregulation may facilitate HHV-8 reactivation and KS development even in the absence of HIV infection. Awareness of this association is critical for accurate diagnosis and optimal patient management. Full article

To address the limitations of the Red-billed Blue Magpie Optimization algorithm (RBMO), such as its tendency to get trapped in local optima and its slow convergence rate, an enhanced version called MRBMO was proposed. MRBMO was improved by integrating Good Nodes Set Initialization, an Enhanced Search-for-food Strategy, a newly designed Siege-style Attacking-prey Strategy, and Lens-Imaging Opposition-Based Learning (LIOBL). The experimental results showed that MRBMO demonstrated strong competitiveness on the CEC2005 benchmark. Among a series of advanced metaheuristic algorithms, MRBMO exhibited significant advantages in terms of convergence speed and solution accuracy. On benchmark functions with 30, 50, and 100 dimensions, the average Friedman values of MRBMO were 1.6029, 1.6601, and 1.8775, respectively, significantly outperforming other algorithms. The overall effectiveness of MRBMO on benchmark functions with 30, 50, and 100 dimensions was 95.65%, which confirmed the effectiveness of MRBMO in handling problems of different dimensions. This paper designed two types of simulation experiments to test the practicability of MRBMO. First, MRBMO was used along with other heuristic algorithms to solve four engineering design optimization problems, aiming to verify the applicability of MRBMO in engineering design optimization. Then, to overcome the shortcomings of metaheuristic algorithms in antenna S-parameter optimization problems—such as time-consuming verification processes, cumbersome operations, and complex modes—this paper adopted a test suite specifically designed for antenna S-parameter optimization, with the goal of efficiently validating the effectiveness of metaheuristic algorithms in this domain. The results demonstrated that MRBMO had significant advantages in both engineering design optimization and antenna S-parameter optimization. Full article

Background and Objectives: Breast cancer is the most common malignant disease among women. The aim of this study is to compare clinical, histological, and radiological findings of breast cancer between younger and older women. Materials and Methods: This retrospective study enrolled 241 patients with histologically diagnosed breast cancer from January 2015 to December 2021. The patients were divided into two groups: the first group comprised young women aged 49 years or younger and the second group consisted of older women aged over 70 years. Because preventive mammograms were only performed on patients between the ages of 50 and 69 until 2025, we did not include this interval in the study; therefore, only younger and older women who had not undergone preventive mammograms were selected. All patients underwent radiological examinations, including mammography, ultrasound, and magnetic resonance imaging. The parameters were compared between the two groups to evaluate clinical, histological, and radiological features. Results: During the study period, a total of 241 patients were included in the final analysis, with 94 (39%) being younger women and 147 (61%) being older women. Clinical signs were analyzed, revealing that redness and swelling (19%) and pain (17.7%) were statistically significantly more common in the older women group compared to the younger group (p < 0.013 and p < 0.002, respectively). The hormone receptor status of patients in both cohorts did not differ significantly, except for human epidermal growth factor receptor 2 (HER2). Older patients had a significantly higher percentage of HER2-negative disease (83.7%) compared to younger patients (70.2%) (p < 0.013). Older women were statistically significantly more likely to have G2 (68.5%) and G3 (21.7%) tumors compared to younger women (G2—36.6% and G3—46.6%) (p < 0.001). Ki67 < 40% (61.2%) was statistically significantly more common in the older women group, while Ki67 ≥ 40% was more prevalent in the younger women group (p < 0.001). Lobular (19.7%) and ductal (62.6%) histological types of cancer were more common in the older women group (p < 0.001). Comparing cancer changes in MRI and ultrasound scans with the results of postoperative histology showed a sensitivity of 87.8% for MRI and 82.7% for ultrasound. Our study suggests that younger women have a higher percentage of proliferation index Ki67 > 40% (73.9%), which is statistically more significant than in the older women group with Ki67 < 40% (63%) (p < 0.001). Conclusions: All diagnostic tools are essential for early breast cancer detection. Malignant microcalcifications are typically identified through mammography. Breast MRI was found to be more sensitive in detecting breast cancer compared to mammography and ultrasound. While ultrasound is considered the most sensitive and specific diagnostic tool for axillary lymph node evaluation, it is unfortunately not sensitive enough to determine the exact extent of cancer spread. During our retrospective study, T1 and T2 histological sizes were identified most frequently; the earlier the diagnosis is made, the higher the chances of survival and improved quality of life. Full article

Rural regions face significant barriers to adopting IoT technologies, due to limited connectivity, energy constraints, and poor technical infrastructure. While urban environments benefit from advanced digital systems and cloud services, rural areas often lack the necessary conditions to deploy and evaluate secure and autonomous IoT solutions. To help overcome this gap, this paper presents the Smart Rural IoT Lab, a modular and reproducible testbed designed to replicate the deployment conditions in rural areas using open-source tools and affordable hardware. The laboratory integrates long-range and short-range communication technologies in six experimental scenarios, implementing protocols such as MQTT, HTTP, UDP, and CoAP. These scenarios simulate realistic rural use cases, including environmental monitoring, livestock tracking, infrastructure access control, and heritage site protection. Local data processing is achieved through containerized services like Node-RED, InfluxDB, MongoDB, and Grafana, ensuring complete autonomy, without dependence on cloud services. A key contribution of the laboratory is the generation of structured datasets from real network traffic captured with Tcpdump and preprocessed using Zeek. Unlike simulated datasets, the collected data reflect communication patterns generated from real devices. Although the current dataset only includes benign traffic, the platform is prepared for future incorporation of adversarial scenarios (spoofing, DoS) to support AI-based cybersecurity research. While experiments were conducted in an indoor controlled environment, the testbed architecture is portable and suitable for future outdoor deployment. The Smart Rural IoT Lab addresses a critical gap in current research infrastructure, providing a realistic and flexible foundation for developing secure, cloud-independent IoT solutions, contributing to the digital transformation of rural regions. Full article

In the context of industrial digitalization, the Industry 5.0 paradigm introduces digital twins as a cutting-edge solution. This study explores the concept of digital twins and their integration with the Industrial Internet of Things (IIoT), offering insights into how these technologies bring intelligence to industrial settings to drive both process optimization and sustainability. Industrial digitalization connects products and processes, boosting the productivity and efficiency of people, facilities, and equipment. These advancements are expected to yield broad economic and environmental benefits. As connected systems continuously generate data, this information becomes a vital asset, but also introduces new challenges for industrial operations. The work presented in this article aims to demonstrate the possibility of generating advanced tools for process optimization. This, which ultimately impacts the environment and empowers people in the processes, is achieved through data integration and the development of a digital twin using open tools such as NodeRed v4.0.9 and Python 3.13.5 frameworks, among others. The article begins with a conceptual analysis of IIoT and digital twin integration and then presents a case study to demonstrate how these technologies support the principles of the Industry 5.0 framework. Specifically, it examines the requirements for applying the golden batch concept within a biological production environment. The goal is to illustrate how digital twins can facilitate the achievement of quality standards while fostering a more sustainable production process. The results from the case study show that biomaterial concentration was optimized by approximately 10%, reducing excess in an initially overdesigned process. In doing so, this paper highlights the potential of digital twins as key enablers of Industry 5.0—enhancing sustainability, empowering operators, and building resilience throughout the value chain. Full article

Digital Twins (DTs) are transforming manufacturing by bridging the physical and digital worlds, enabling real-time insights, predictive analytics, and enhanced decision making. In Industry 4.0, DTs facilitate automation and data integration, while Industry 5.0 emphasizes human-centric, resilient, and sustainable production. However, implementing DTs in robotic metal additive manufacturing (AM) remains challenging because of the complexity of the wire-based laser metal deposition (LMD) process, the need for real-time monitoring, and the demand for advanced defect detection to ensure high-quality prints. This work proposes a structured DT architecture for a robotic wire-based LMD cell, following a standard framework. Three DT implementations were developed. First, a real-time 3D simulation in RoboDK, integrated with a 2D Node-RED dashboard, enabled motion validation and live process monitoring via MQTT (message queuing telemetry transport) telemetry, minimizing toolpath errors and collisions. Second, an Industrial IoT-based system using KUKA iiQoT (Industrial Internet of Things Quality of Things) facilitated predictive maintenance by analyzing motor loads, joint temperatures, and energy consumption, allowing early anomaly detection and reducing unplanned downtime. Third, the Meltio dashboard provided real-time insights into the laser temperature, wire tension, and deposition accuracy, ensuring adaptive control based on live telemetry. Additionally, a prescriptive analytics layer leveraging historical data in FireStore was integrated to optimize the process performance, enabling data-driven decision making. Full article

Background and Clinical Significance: Kaposi sarcoma (KS) is a rare angio-proliferative mesenchymal tumor that predominantly affects the skin and mucous membranes but may involve lymph nodes and visceral organs. Clinically, it manifests as red-purple-brown papules, nodules, or plaques, either painless or painful, often with disfiguring potential. The diagnosis is traditionally based on clinical and histopathological evaluation, although non-invasive imaging techniques are increasingly used to support diagnosis and treatment monitoring. We report a case of HHV-8-negative Kaposi sarcoma evaluated with multiple non-invasive imaging modalities to highlight their diagnostic utility. Case Presentation: An 83-year-old man presented with multiple painful, violaceous papulo-nodular lesions, some ulcerated, on the lateral aspect of his left foot. Dermoscopy revealed the characteristic rainbow pattern. Dynamic Optical Coherence Tomography (D-OCT) allowed real-time visualization of microvascular abnormalities, identifying large serpentine and branching vessels with clearly delineated capsules. Line-field Optical Coherence Tomography (LC-OCT) showed irregular dermal collagen, vascular lacunae, and the presence of spindle cells and slit-like vessels. Histological analysis confirmed the diagnosis of Kaposi sarcoma, revealing a proliferation of spindle-shaped endothelial cells forming angulated vascular spaces, with red blood cell extravasation and a mixed inflammatory infiltrate. Conclusions: Non-invasive imaging tools, including dermoscopy, D-OCT, and LC-OCT, have emerged as valuable adjuncts in the diagnosis and monitoring of KS. These techniques enable in vivo assessment of vascular architecture and tissue morphology, enhancing clinical decision-making while reducing the need for immediate biopsy. Dermoscopy reveals polychromatic vascular features, such as the rainbow pattern, while D-OCT and LC-OCT provide high-resolution insights into vascular proliferation, tissue heterogeneity, and cellular morphology. Dermoscopy, dynamic OCT, and LC-OCT represent promising non-invasive diagnostic tools for the assessment of Kaposi sarcoma. These technologies provide detailed morphological and vascular information, enabling earlier diagnosis and more personalized management. While histopathology remains the gold standard, non-invasive imaging offers a valuable complementary approach for diagnosis and follow-up, particularly in complex or atypical presentations. Ongoing research and technological refinement are essential to improve accessibility and clinical applicability. Full article

Background:Salmonella Typhimurium is a major zoonotic pathogen, in which type 1 fimbriae play a crucial role in intestinal colonization and immune modulation. This study aimed to improve the protective immunity of a previously developed growth-deficient strain—a double auxotroph for D-glutamate and D-alanine—by engineering the inducible expression of type 1 fimbriae. Methods: P_tetA-driven expression of the fim operon was achieved by λ-Red mutagenesis. fimA expression was quantified by qRT-PCR, and fimbriation visualized by transmission electron microscopy. Adhesive properties were evaluated through FimH sequence analysis, yeast agglutination, mannose-binding/inhibition assays, and HT-29 cell adherence. BALB/c mice were immunized orogastrically with IRTA ΔΔΔ or IRTA ΔΔΔ P_tetA::fim. Safety and immunogenicity were assessed by clinical monitoring, bacterial load, fecal shedding, ELISA tests, and adhesion/blocking assays using fecal extracts. Protection was evaluated after challenging with wild-type and heterologous strains. Results: IRTA ΔΔΔ P_tetA::fim showed robust fimA expression, dense fimbrial coverage, a marked mannose-sensitive adhesive phenotype and enhanced HT-29 attachment. Fimbrial overexpression did not alter intestinal colonization or translocation to mesenteric lymph nodes (mLNs). Immunization elicited a mixed IgG1/IgG2a, significantly increased IgA and IgG against type 1 fimbriae-expressing Salmonella, and enhanced the ability of fecal extracts to inhibit the adherence of wild-type strains. Upon challenge (IRTA wild-type/20220258), IRTA ΔΔΔ P_tetA::fim reduced infection burden in the cecum (−1.46/1.47-log), large intestine (−1.35/2.17-log), mLNs (−1.32/0.98-log) and systemic organs more effectively than IRTA ΔΔΔ. Conclusions: Inducible expression of type 1 fimbriae enhances mucosal immunity and protection, supporting their inclusion in next-generation Salmonella vaccines. Future work should assess cross-protection and optimize FimH-mediated targeting for mucosal delivery. Full article

Integrating building information modeling (BIM) with Internet of things (IoT) technologies significantly enhances facility management (FM) by enabling advanced real-time monitoring of indoor environmental quality (IEQ). However, technical complexity, proprietary limitations, high software costs, and unclear long-term benefits hinder practical adoption. This study suggests a way to combine BIM and IoT using open standards like IFC and JSON, simple programming tools like Node-RED, and secure cloud services. A case study of a six-story office building showed that real-time IEQ sensor data can be combined with organized BIM information, helping to make better decisions about maintaining, replacing, or upgrading heating, ventilation, and air conditioning (HVAC) systems. This integration offers essential data needed for using advanced analysis techniques, specifically tackling issues with compatibility, ease of use, and organizational challenges, which is especially advantageous for small-to-medium-sized office buildings. Nevertheless, this study faced limitations due to restricted real-time data access from existing building management systems and preliminary predictive analytic capabilities, highlighting a need for improved direct data integration and robust analytical methods in future implementations. Full article

Designed hen eggs enriched in DHA and EPA are an alternative source of essential phospholipids. This study assessed the effects of ovophospholipids on lymphocyte subsets in non-immunized mice and on the humoral immune response in sheep red blood cells (SRBC)-immunized mice. Ovophospholipids were administered orally for 14 days (once a day) at doses of 100, 10, and 1 mg/kg. Ovophospholipids increased the total lymphocyte count of in the lymphoid organs. At 10 and 1 mg/kg, ovophospholipids increased the subsets of CD4⁻CD8⁻ and CD4⁺CD8⁺ thymocytes but decreased the percentage of CD4⁺ and CD8⁺ thymocytes. A stimulating effect on splenocytes was particularly evident 24 h after administration of the 10 and 1 mg/kg doses. Ovophospholipids also elevated the absolute counts of CD3⁺ and CD19⁺ splenocytes. An increase in the absolute count of CD3⁺, CD4⁺, CD8⁺, and CD19⁺ lymphocytes of the mesenteric lymph nodes was observed 24 h after administration of the lowest dose. The increase in the percentage and absolute count of CD19⁺ cells and in the absolute count of CD3⁺ cells was still observed after 72 h. At all doses, ovophospholipids elevated the number of plaque-forming cells on day 4 and increased 2-mercaptoethanol-resistant antibody titer on day 7 after priming. In conclusion, ovophospholipids can modulate the immune response in mice. Full article

Okra (Abelmoschus esculentus) is a tropical vegetable with high nutritional and economic value. Rich in fiber, vitamins (C, K, and B9), and minerals (magnesium, potassium, calcium, and iron), it contributes to food security in many tropical regions. Global production is estimated at 11.5 million tons in 2023, 62% of which will come from India. Nigeria, Mali, Sudan, Pakistan, and Côte d’Ivoire are also among the major producers. Given its economic importance, optimizing its growth through controlled methods such as greenhouse cultivation and light-emitting diode (LED) lighting is a strategic challenge. Energy-efficient LED horticultural lighting offers promising prospects, but each plant variety reacts differently depending on the light spectrum, intensity, and duration of exposure (photoperiod). This study evaluated the effects of different LED spectra on okra’s flowering after 30 days of growth using B (blue, 445 nm) and R (red, 660 nm) LED lights and red-blue alternating in a three-day cycle (R3B3) by alternating the photoperiod from 14 to 10 h. Outdoor and greenhouse conditions served as controls. The results show that the R3B3 treatment improves germination in terms of both speed and percentage. However, plant growth (height, stem diameter, and leaf area) remains higher in the control group. R3B3 and red light stimulate leaf and node development. Flowering occurs earlier in the control group (51 days) and later under LED, particularly blue (73 days). Fruit diameter after petal fall was also larger in the control group. These results confirm the sensitivity of okra to photoperiod and light quality, and highlight the potential of spectral and photoperiod manipulation to regulate flowering in controlled-environment agriculture. Full article

This study aims to develop a real-time gas leak detection system for application in gas cylinder filling machines. To promptly recover gas during leakage incidents, the efficiency of the gas filling process was improved by reducing resource wastage. The system utilized a Raspberry Pi with a camera for image-based detection and employed the dark channel prior method to detect the presence of gas. The message queue system was used for the real-time data transmission of gas leak status, temperature, and humidity data. The system sent data to a central server via message queuing telemetry transport (MTQQ). Node-RED was used for data visualization and anomaly alerts. Machine learning methods such as support vector machines (SVMs) and decision trees were applied to analyze the correlation between gas leaks and other environmental parameters to predict leak incidents. This system effectively detected gas leakage and transmitted and analyzed the data, significantly improving the operational efficiency of the gas cylinder filling process. Full article

This research presents an advanced autonomous HVAC control system tailored for a chemical fiber factory, emphasizing the human-centric principles and collaborative potential of Industry 5.0. The system architecture employs several functional levels—actuator and sensor, process, model, critic, fault detection, and specification—to effectively monitor and predict indoor air pressure differences, which are critical for maintaining consistent product quality. Central to the system’s innovation is the integration of digital twins and physical AI, enhancing real-time monitoring and predictive capabilities. A virtual representation runs in parallel with the physical system, enabling sophisticated simulation and optimization. Development involved custom sensor kit design, embedded systems, IoT integration leveraging Node-RED for data streaming, and InfluxDB for time-series data storage. AI-driven system identification using Nonlinear Autoregressive with eXogenous inputs (NARX) neural network models significantly improved accuracy. Crucially, incorporating airflow velocity data alongside AHU output and past pressure differences boosted the NARX model’s predictive performance (R² up to 0.9648 on test data). Digital twins facilitate scenario testing and optimization, while physical AI allows the system to learn from real-time data and simulations, ensuring adaptive control and continuous improvement for enhanced operational stability in complex industrial settings. Full article