Search Results (4,542)

This paper focuses on two-dimensional (2-D) direction-of-arrival (DOA) estimation for an L-shaped array. While recent studies have explored sparse methods for this problem, most exploit only the cross-correlation matrix, neglecting self-correlation information and resulting accuracy degradation. We propose a multidimensional matrix completion method that employs joint sparsity and redundant correlation information embedded in the covariance matrix to reconstruct a structured matrix compactly coupling the two DOA parameters. A semidefinite program problem formulated via covariance fitting criteria is proved equivalent to the atomic norm minimization framework. The alternating direction method of multipliers is designed to reduce computational costs. Numerical results corroborate the analysis and demonstrate the superior estimation accuracy, identifiability, and resolution of the proposed method. Full article

The growing demand for sustainable urban logistics highlights the need for innovative, low-emission delivery solutions, particularly in the context of small-scale urban food producers. These producers often face logistical challenges in reaching consumers efficiently while minimizing environmental impacts. Green micro-mobility, such as electric cargo bikes and scooters, offers a promising last-mile delivery alternative that aligns with environmental and economic goals. This study addresses the integration of micromobility into urban food logistics, aiming to enhance both efficiency and sustainability. The authors develop a mathematical optimization model that supports real-time decision-making for last-mile deliveries from multiple local food producers to urban customers using micromobility vehicles. The model considers vehicle capacity constraints, and delivery time windows while minimizing greenhouse gas (GHG) emissions and total operational costs. Optimization results based on realistic urban scenario demonstrate that the proposed model significantly reduces GHG emissions compared to conventional delivery methods. Additionally, it enables a more cost-effective and streamlined delivery operation tailored to the specific needs of small producers. The findings confirm that green micromobility-based logistics, supported by optimized planning, can play a crucial role in building cleaner, more resilient urban food distribution systems. Full article

The increasing demand for sustainable alternatives to synthetic agrochemicals underscores the need for novel, naturally derived plant growth regulators (PGRs) with high specificity and minimal environmental impact. In this study, we propose agavenin (AG), a steroidal saponin from Agave species, as a promising candidate and evaluate its potential role in plant growth regulation through a comprehensive in silico approach. Using molecular docking, molecular dynamics simulations, ADME profiling, and FTIR spectroscopy, we analyzed the interaction of AG with three key protein receptors (KPRs) that regulate major hormonal pathways: GA3Ox2 (gibberellin), IAA7 (auxin), and BRI1 (brassinosteroid). AG showed strong and stable binding to GA3Ox2 and IAA7, with affinities comparable to their endogenous ligands, while exhibiting low interaction with BRI1—suggesting receptor selectivity. Molecular dynamics confirmed the stability of AG–GA3Ox2 and AG–IAA7 complexes over 100 ns, and ADME profiling highlighted favorable properties for bioavailability and transport. Collectively, these findings indicate that AG could function as a selective, receptor-targeted modulator of gibberellin and auxin signaling pathways. Beyond demonstrating the molecular basis of AG’s bioactivity, this work establishes a computational foundation for its future experimental validation and potential development as a sustainable, naturally derived growth regulator for plant biotechnology and agriculture. Full article

Objectives: Adolescence is a pivotal stage of development characterized by significant physical, psychological, and social changes. Establishing healthy lifestyle habits during this period is crucial for long-term health and the prevention of chronic diseases. Despite this, global trends show a marked decline in physical activity among adolescents, particularly girls, who are more susceptible to sedentary behaviors. One potential site for intervention to eliminate physical inactivity at the population level is the school educational setting during childhood. Traditional school-based physical exercise programs often adopt a one-size-fits-all approach, which may not address the diverse needs and interests of students, leading to reduced motivation and participation. Personalized physical exercise programs, tailored to individual capabilities and preferences, offer a promising alternative to enhance physical fitness and foster lifelong engagement in physical activity. Methods: A total of 170 Hungarian high school girls (mean age ≈ 15.3 years) were randomly assigned to either a personalized physical exercise group or a control group following the standard curriculum. The intervention spanned two academic years and consisted of five traditional gym classes per week (control group) or three traditional and two individually tailored classes with cardiorespiratory and resistance training per week (intervention group), each lasting 45–60 min. Individual goals were set based on baseline assessments, emphasizing self-referenced progress. Results: The personalized physical exercise group showed significant improvements in body mass index (BMI), body fat percentage, maximum oxygen uptake capacity (VO₂max), muscular strength, and flexibility (p < 0.05), while the control group exhibited minimal or negative changes. Conclusions: The personalized physical exercise program has been shown to be more effective in achieving higher cardiorespiratory performance and favorable body composition among adolescent girls than a traditional school physical education class, highlighting its potential role in school settings. Full article

Traditional dairy products remain an essential yet underutilized component of Lebanon’s food system. Amid economic instability, supply chain fragility, and heavy reliance on imported dairy inputs (≈80% of demand), these products offer resilient, low-input alternatives rooted in centuries-old practices. This review analyzes key traditional Lebanese dairy products, including Labneh, Labneh–Anbaris, Akkawi, Shanklish, Halloumi, Karishi, Pressed–Brined Karishi (Lebanese Double-Cream), Qishta, and Kishk, using Codex Alimentarius and Tetra Pak classification frameworks. It examines their compositional attributes, milk-to-product conversion efficiency, preservation methods, and economic characteristics. The findings reveal a continuum from high-yield fresh cheeses to lower-yield preserved forms with extended shelf life, demonstrating diversified strategies for food security and resilience. Unlike prior studies focused mainly on composition or culinary aspects, this review integrates classification systems with cultural geography to map Lebanon’s traditional dairy landscape. It highlights strategies grounded in rural milk availability and artisanal know-how, revealing overlooked food system functions. These practices exemplify circular models that valorize whey, minimize waste, and preserve quality without refrigeration, aligning with sustainability goal SDG-12.3. This review calls for integrating these products into national food strategies, regulatory frameworks, and innovation systems, recognizing traditional Lebanese dairy as both cultural heritage and a strategic resource for a more self-sufficient and resilient food sector. Full article

The widespread production and poor management of plastic waste have led to the pervasive presence of microplastics (MPs) in environmental and biological systems. Among various polymers, polyethylene (PE) is the most widely produced plastic globally, primarily due to its use in single-use packaging. Its persistence in ecosystems and resistance to degradation processes result in the continuous formation of PE-derived MPs. These particles have been detected in human biological matrices, including blood, lungs, placenta, and even the brain, raising increasing concerns about their bioavailability and potential health effects. Once internalized, PE MPs can interact with cellular membranes, induce oxidative stress, inflammation, and apoptosis, and interfere with epigenetic regulatory pathways. In vitro studies on epithelial, immune, and neuronal cells reveal concentration-dependent cytotoxicity, mitochondrial dysfunction, membrane disruption, and activation of pro-inflammatory cytokines. Moreover, recent findings suggest that PE MPs can induce epithelial-to-mesenchymal transition (EMT), senescence, and epigenetic dysregulation, including altered expression of miRNAs and DNA methyltransferases. These cellular changes highlight the potential role of MPs in disease development, especially in cardiovascular, metabolic, and possibly cancer-related conditions. Despite growing evidence, no standardized method currently exists for quantifying MPs in human samples, complicating comparisons across studies. Further, MPs can carry harmful additives and environmental contaminants such as bisphenols, phthalates, dioxins, and heavy metals, which enhance their toxicity. Global estimates indicate that humans ingest and inhale tens of thousands of MPs particles each year, yet long-term human research remains limited. Given these findings, it is crucial to expand research on PE MP toxicodynamics and to establish regulatory policies to reduce their release. Promoting alternative biodegradable materials and improved waste management practices will be vital in decreasing human exposure to MPs and minimizing potential health risks. Full article

The use of consumer-grade cameras for stereo vision provides a cost-effective, non-contact method for measuring three-dimensional displacement in civil engineering experiments. However, obtaining accurate 3D coordinates requires accurate temporal alignment of several unsynchronized cameras, which is often lacking in consumer-grade devices. Current synchronization software methods usually only achieve precision at the frame level. As a result, they fall short for high-frequency shake table experiments, where even minor timing differences can cause significant triangulation errors. To address this issue, we propose a novel image-based synchronization method and a graphical user interface (GUI)-based software for acquiring stereo videos during shake table testing. The proposed method estimates the time lag between unsynchronized videos by minimizing reprojection errors. Then, the estimate is refined to sub-frame accuracy using polynomial interpolation. This method was validated using a high-precision motion capture system (Mocap) as a benchmark through large- and small-scale experiments. The proposed method reduces the RMSE of triangulation by up to 78.79% and achieves maximum displacement errors of less than 1 mm for small-scale experiments. The proposed approach reduces the RMSE of displacement measurements by 94.21% and 62.86% for small- and large-scale experiments, respectively. The results demonstrate the effectiveness of the proposed method for precise 3D displacement measurement with low-cost equipment. This method offers a practical alternative to expensive vision-based measurement systems commonly used in structural dynamics research. Full article

Spot melting is an emerging alternative to traditional line melting in electron beam powder bed fusion, dividing a layer into thousands of individual spots. This method allows for an almost infinite number of spot arrangements and spot melting sequences to tailor material and part properties. To enhance the productivity of spot melting, the number of spots can be reduced by increasing the beam diameter. However, this results in rough surfaces due to the staircase effect. The classical approach to counteract these effects is to melt a contour that surrounds the infill area. Creating effective contours is challenging because the melted area ought to cover the artifacts from the staircase effect and avoid porosity in the transition area between the infill and contour, all while minimizing additional energy and melt time. In this work, we propose an algorithm for generating a spot melting sequence for contour lines surrounding the infill area. Additionally, we compare three different approaches for combining the spot melting of infill and contour areas, each utilizing a combination of large infill spots and small contour spots. The quality of the contours is evaluated based on optical inspection as well as the porosity between infill and contour using electron optical images, balanced against the additional energy input. The most suitable approach is used to build a complex brake caliper. Full article

Among other uses, DC-DC converters are employed in the auxiliary power modules (APMs) of electric vehicles (EVs), connecting the high-voltage traction battery to the low-voltage auxiliary system (AS). Traditionally, the APM is an isolated two-port, two-level (2L) DC-DC converter, and the auxiliary loads are fed at a fixed voltage level, e.g., 12 V in passenger cars. Dual-active-bridge (DAB) converters are commonly used for this application, as they provide galvanic isolation, high power density and efficiency, and bidirectional power flow capability. However, the auxiliary loads do not present a uniform optimum supply voltage, hindering overall efficiency. Thus, a more flexible approach, providing multiple supply voltages, would be more suitable for this application. Multiport DC-DC converters capable of feeding auxiliary loads at different voltage levels are a promising alternative. Multilevel neutral-point-clamped (NPC) DAB converters offer several advantages compared to conventional two-level (2L) ones, such as greater efficiency, reduced voltage stress, and enhanced scalability. The series connection of the NPC DC-link capacitors enables a multiport configuration without additional conversion stages. Moreover, the modular nature of the ML NPC DAB converter enables scalability while using semiconductors with the same voltage rating and without requiring additional passive components, thereby enhancing the converter’s power density and efficiency. This paper proposes a modulation strategy and decoupled closed-loop control strategy for the generalized multiport 2L-NL NPC DAB converter interfacing the EV traction battery with the AS, and its performance is validated through hardware-in-the-loop testing and simulations. The proposed modulation strategy minimizes conduction losses in the converter, and the control strategy effectively regulates the LV battery modules’ states of charge (SoC) by varying the required SoC and the power sunk by the LV loads, with the system stabilizing in less than 0.5 s in both scenarios. Full article

Global energy demand is steadily increasing, accompanied by a growing emphasis on clean and renewable energy sources. Serbia possesses significant solar energy potential, with solar radiation levels among the highest in Europe—about 40% above the European average. Within this context, rural depopulation clusters offer attractive opportunities for solar energy development due to the availability of underutilized land. This study aims to identify optimal locations for solar power installations in Serbia’s depopulated areas by applying multi-criteria decision-making methods under uncertainty. A hybrid framework, combining fuzzy Analytic Hierarchy Process (fuzzy AHP) and fuzzy MULTIMOORA, was employed to evaluate potential sites. Fuzzy AHP was used to determine the relative importance of criteria, while fuzzy MULTIMOORA ensured a robust ranking of alternatives by addressing the vagueness in data and expert judgments. The analysis identified several high-potential brownfield locations, with the most suitable land class covering 5.01% (16.94 km²) of the examined cluster area (311.3 km²). These areas are typically characterized by flat terrain, high solar irradiation, and minimal environmental constraints, providing favorable conditions for solar farms. Among the assessed sites, location no. 9 consistently ranked highest across all three fuzzy MULTIMOORA variants: FRPA (z = 0.0588), FRS (y = 0.2811), and FFMF (p = 1.6748). The findings confirm that the hybrid fuzzy AHP–MULTIMOORA approach offers valuable support for informed decision-making on solar energy deployment in depopulated rural regions. Moreover, the utilization of rural brownfield sites contributes to the expansion of renewable energy, rural revitalization, and sustainable land management in Serbia. Full article

The deep inferior epigastric perforator (DIEP) flap in breast reconstruction has been an evolution in providing an ideal autologous reconstruction while minimizing donor site morbidity. Innovations have continued to optimize the DIEP flap in multiple facets. Alternative flap designs, vasculature modifications, and conjoined and stacked flaps have improved the ability to increase flap volume and perfusion. Advancements in anatomic understanding of the abdomen have resulted in decreases in donor site morbidity and improved abdominal outcomes. Patient satisfaction regarding aesthetics has been enhanced through careful consideration of mastectomy techniques and recipient site modifications in addition to improved quality of life outcomes through sensory innervation. The study reviews the evolution and current state of abdominally-based breast reconstruction in its goal of optimizing aesthetic, patient-reported and quality-of-life outcomes while minimizing complications. Full article

Accurate prediction of a robot’s dynamic parameters, including mass and moment of inertia, is essential for adequate motion planning and control in autonomous systems. Traditional methods often depend on manual computation or physics-based modelling, which can be time-consuming and approximate for intricate, real-world environments. Recent advances in machine learning, primarily through artificial neural networks (ANNs), offer profitable alternatives. However, the potential of quantum-inspired models in this context remains largely uncharted. The current research assesses the predictive performance of a classical artificial neural network (CANN) and a quantum-inspired artificial neural network (QANN) in estimating a car-like mobile robot’s mass and moment of inertia. The predictive accurateness of the models was considered by minimizing a cost function, which was characterized as the RMSE between the predicted and actual values. The outcomes indicate that while both models demonstrated commendable performance, QANN consistently surpassed CANN. On average, QANN achieved a 9.7% reduction in training RMSE, decreasing from 0.0031 to 0.0028, and an 84.4% reduction in validation RMSE, dropping from 0.125 to 0.0195 compared to CANN. These enhancements highlight QANN’s singular predictive accuracy and greater capacity for generalization to unseen data. In contrast, CANN displayed overfitting tendencies, especially during the training phase. These findings emphasize the significance of quantum-inspired neural networks in enhancing prediction precision for involved regression tasks. The QANN framework has the potential for wider applications in robotics, including autonomous vehicles, uncrewed aerial vehicles, and intelligent automation systems, where accurate dynamic modelling is necessary. Full article

Background/Objectives: Minimally invasive cardiac surgical (MICS) approaches to the implantation of left ventricular assist devices (LVADs) have gained increasing interest as alternatives to full median sternotomy (FS), particularly in patients with prior cardiac surgeries or elevated surgical risk. However, evidence regarding their safety, feasibility, and clinical outcomes remains fragmented. This systematic review aimed to evaluate the effectiveness and safety of minimally invasive techniques for LVAD implantation in comparison to standard sternotomy, with a focus on mortality, perioperative complications, intensive care unit (ICU) stay, and infection rates. Methods: A comprehensive literature search was conducted in PubMed, Web of Science, Science Direct, Cochrane Library, and Google Scholar up to 1 January 2025. Studies were included if they reported on adult patients undergoing LVAD implantation via minimally invasive thoracotomy or sternotomy-sparing approaches, with or without comparator groups. Data were extracted and synthesized qualitatively; the Newcastle–Ottawa Scale (NOS) was applied to assess the methodological quality of the included cohort and retrospective comparative studies. Results: A total of 12 studies involving 1448 patients were included (584 received MICS and 862 received FS). MICS techniques have demonstrated comparable short and mid-term survival outcomes, with trends toward reduced ICU stay, fewer reoperations for bleeding, and lower incidence of driveline infections. Some studies reported longer operative and cardiopulmonary bypass times in the MICS group. Among high-risk cohorts, such as patients with prior sternotomies or significant comorbidities, MICS was associated with lower morbidity and acceptable safety profiles. However, heterogeneity in patient selection, surgical protocols, and outcome definitions limited quantitative synthesis. Conclusions: Minimally invasive LVAD implantation is a viable alternative to conventional sternotomy in selected patient populations. While current data suggest favorable perioperative outcomes and equivalent survival, high-quality prospective studies are needed to confirm long-term benefits and to guide patient selection. MICS approaches should be considered within multidisciplinary teams experienced in advanced heart failure surgery. Full article

The importance of managing goods delivery in urban areas has reached its peak in recent years, driven by the constant and rapid growth of online commerce. Under such conditions, where smaller quantities of goods are ordered, yet the number of shipments continues to rise, the question of last-mile delivery (LMD) efficiency becomes increasingly relevant. This paper addresses the issue of last-mile delivery zone efficiency through the application of a new methodological approach. First, the concept of measuring last-mile delivery productivity is defined using a specific example from an urban environment. Next, Key Performance Indicators (KPIs) are established to enable a proper assessment of urban zone efficiency in line with the LMD concept. The main contribution of this study is the development of the IRN OWCM (Interval Rough Number Opinion Weight Criteria Method), which is used to calculate the weights of the criteria. To assess suitable delivery zones in terms of efficiency based on the defined KPIs, the previously developed IRN OWCM method is integrated with IRN AROMAN (Alternative Ranking Order Method Accounting for Two-Step Normalization). The results identify delivery zones that are suitable in terms of meeting standardized user needs. The developed model demonstrated stability through additional verification tests and can be adequately applied in cases when it is needed to minimize subjectivity and uncertainties. Full article

Physics-informed neural networks (PINNs) have emerged as a promising alternative to purely data-driven neural networks (NNs) for surrogate modeling, particularly in data-scarce scenarios. This study evaluates the performance of hybrid-PINNs against traditional NNs for modeling the adsorption step of a Direct Air Capture (DAC) process. As the complexity of the modeled system increases, larger datasets and longer computational times are required for numerical methods. Therefore, the study aims to develop approaches that minimize data requirements while maintaining accuracy, which is crucial for efficient modeling of complex physical systems. While both AI models can achieve high accuracy with abundant data, the advantages of hybrid-PINNs become more evident as data becomes scarce. In the intermediate and low-data regimes, the physics constraints embedded in hybrid-PINNs significantly improve generalization and predictive accuracy. For extreme low-data conditions, a curriculum learning strategy is implemented, progressively enforcing physics constraints to mitigate underfitting and enhance model stability. Despite these benefits, hybrid-PINNs exhibit a computational cost approximately one order of magnitude higher than traditional NNs as enforcing physics constraints increases training complexity. The results suggest that PINNs hold potential for modeling complex multi-physics problems in DAC and beyond, provided challenges related to gradient balancing and computational efficiency are addressed. Full article