Search Results (1,187)

Background/Objectives: Military Service Members (SMs) require optimal nutrition to support health, readiness, and job performance. However, they often fall short of meeting nutrition guidelines. This study aimed to determine the impact and feasibility of implementing the U.S. Marine Corps (USMC) “Fueled to Fight^®” (F2F) nutrition program in non-appropriated fund (NAF) food venues. Objectives included evaluating changes in Military Nutrition Environment Assessment Tool (mNEAT) scores, feasibility of implementing and maintaining F2F strategies, and influence on customer purchasing patterns. Methods: Researchers conducted a pre-post interventional study from January to December 2024 at three NAF food venues across two USMC bases. F2F strategies, including identifying items using a stoplight color coding system (Green = healthy, Yellow = less healthy, Red = least healthy), menu revisions, food placement, promotion, and marketing, were implemented. Data included mNEAT assessments, sales reports, and stakeholder focus groups. Generalized Estimating Equations models were used to analyze sales data. Results: mNEAT scores increased across all venues post-intervention. Availability and sales of Green items increased, while sales of Red items decreased in some venues. Profit increased at all three food venues. Focus groups revealed feasibility and provided insights for future interventions. Conclusions: F2F interventions in NAF food venues are feasible and can positively impact the food environment and customer purchasing patterns without negatively affecting profit. This study highlights the importance of integrating nutrition programs into all military food venues, not just government-funded dining facilities, to support the nutritional fitness and readiness of SMs. Full article

This study presented a comprehensive investigation into the performance of object detection models tailored for edge devices, particularly in the context of Unmanned Aerial Vehicle swarms. Object detection plays a pivotal role in enhancing autonomous navigation, situational awareness, and target tracking capabilities within UAV swarms, where computing resources are constrained by the onboard low-cost computers. Initially, a thorough review of the existing literature was conducted to identify state-of-the-art object detection models suitable for deployment on edge devices. These models are evaluated based on their speed, accuracy, and efficiency, with a focus on real-time inference capabilities crucial for Unmanned Aerial Vehicle applications. Following the literature review, selected models undergo empirical validation through custom training using the Vision Meets Drone dataset, a widely recognized dataset for Unmanned Aerial Vehicle-based object detection tasks. Performance metrics such as mean average precision, inference speed, and resource utilization were measured and compared across different models. Lastly, the study extended its analysis beyond traditional object detection to explore the efficacy of instance segmentation and proposed an optimization to an object tracking technique within the context of unmanned Aerial Vehicles. Instance segmentation offers finer-grained object delineation, enabling more precise target or landmark identification and tracking, albeit at higher resource usage and higher inference time. Full article

Having in view the current global disruptive social and economic landscape, sustainability becomes more important than ever. As producers become more concerned about adopting more sustainable practices, customer awareness towards sustainable behavior must be the focus of all stakeholders. Within this context, the SHIFT framework (proposed in 2019) highlights the manner in which consumers’ traits and attitudes influence their propensity towards sustainable consumption. It consists of five factors considered to be relevant to consumer behavior: Social influence, Habit formation, Individual self, Feelings and cognition, and Tangibility. Different from previous studies, this research focuses on applying the SHIFT framework to the automotive industry, taking into consideration the contribution of digital technologies to fostering sustainable consumer behavior throughout the entire product lifecycle. Using a qualitative research approach, the most relevant digital technologies in the automotive industry were identified and mapped in relation to the three phases of consumption (choice, usage, and disposal). The research aimed to develop and test an original conceptual framework, starting from the SHIFT. The results of the study highlight the fact that the digital technologies, in their diversity, are integrated in different ways into each of the three phases, facilitating the adoption of sustainable consumption. To achieve sustainability, the two key stakeholders, consumers and producers, should share a common ground on capitalizing the opportunities offered by digital technologies. Full article

Unmanned aerial vehicles (UAVs) are increasingly used for road infrastructure inspection and monitoring. However, challenges such as scale variation, complex background interference, and the scarcity of annotated UAV datasets limit the performance of traditional segmentation models. To address these challenges, this study proposes UAV-YOLOv12, a multi-scale segmentation model specifically designed for UAV-based road imagery analysis. The proposed model builds on the YOLOv12 architecture by adding two key modules. It uses a Selective Kernel Network (SKNet) to adjust receptive fields dynamically and a Partial Convolution (PConv) module to improve spatial focus and robustness in occluded regions. These enhancements help the model better detect small and irregular road features in complex aerial scenes. Experimental results on a custom UAV dataset collected from national highways in Wuxi, China, show that UAV-YOLOv12 achieves F1-scores of 0.902 for highways (road-H) and 0.825 for paths (road-P), outperforming the original YOLOv12 by 5% and 3.2%, respectively. Inference speed is maintained at 11.1 ms per image, supporting near real-time performance. Moreover, comparative evaluations with U-Net show that UAV-YOLOv12 improves by 7.1% and 9.5%. The model also exhibits strong generalization ability, achieving F1-scores above 0.87 on public datasets such as VHR-10 and the Drone Vehicle dataset. These results demonstrate that the proposed UAV-YOLOv12 can achieve high accuracy and robustness in diverse road environments and object scales. Full article

Bacteriophages (phages), the most abundant biological entities on Earth, have long served as both model systems and therapeutic tools. Recent advances in synthetic biology and genetic engineering have revolutionized the capacity to tailor phages with enhanced functionality beyond their natural capabilities. This review outlines the current landscape of synthetic and functional engineering of phages, encompassing both in-vivo and in-vitro strategies. We describe in-vivo approaches such as phage recombineering systems, CRISPR-Cas-assisted editing, and bacterial retron-based methods, as well as synthetic assembly platforms including yeast-based artificial chromosomes, Gibson, Golden Gate, and iPac assemblies. In addition, we explore in-vitro rebooting using TXTL (transcription–translation) systems, which offer a flexible alternative to cell-based rebooting but are less effective for large genomes or structurally complex phages. Special focus is given to the design of customized phages for targeted applications, including host range expansion via receptor-binding protein modifications, delivery of antimicrobial proteins or CRISPR payloads, and the construction of biocontained, non-replicative capsid systems for safe clinical use. Through illustrative examples, we highlight how these technologies enable the transformation of phages into programmable bactericidal agents, precision diagnostic tools, and drug delivery vehicles. Together, these advances establish a powerful foundation for next-generation antimicrobial platforms and synthetic microbiology. Full article

Addressing the growing demand for affordable housing requires innovative solutions that strike a balance between cost efficiency and user-specific needs. Mass customization (MC) presents a promising approach that enables the creation of tailored housing solutions on a scale. In this context, this study introduces a model for mass customization of affordable single-family housing units in the city of Teresina, PI, Brazil. Our approach integrates algorithmic–parametric modeling and BIM technologies, facilitating the flow of information and enabling informed decision-making throughout the design process. Since the early design stages, the work has assumed that these integrated technologies provide real-time control over design variables and associated construction data. To develop the model, the method proceeded through the following phases: (1) analysis of the context and definition of the design language; (2) definition of the design process; (3) definition of the cost calculation method and estimation of construction time; (4) definition of the computing model based on the specified technologies; and (5) quantitative and qualitative evaluation of the computational model. As a result, this research aims to contribute to the state-of-the-art by formalizing the knowledge generated through the systematic description of the processes involved in this workflow, with a special focus on the Brazilian context, where the issue of social housing is a critical challenge. Full article

This study presents the design and development of a structurally optimized mobile robotic system with a machine learning-based energy-efficient path planning framework. Topology optimization (TO) and finite element analysis (FEA) were applied to reduce structural weight while maintaining mechanical integrity. The optimized components were manufactured using Fused Deposition Modeling (FDM) with ABS (Acrylonitrile Butadiene Styrene) material. A custom power analysis tool was developed to compare energy consumption between the optimized and initial designs. Real-world current consumption data were collected under various terrain conditions, including inclined surfaces, vibration-inducing obstacles, gravel, and direction-altering barriers. Based on this dataset, a path planning model was developed using machine learning algorithms, capable of simultaneously optimizing both energy efficiency and path length to reach a predefined target. Unlike prior works that focus separately on structural optimization or learning-based navigation, this study integrates both domains within a single real-world robotic platform. Performance evaluations demonstrated superior results compared to traditional planning methods, which typically optimize distance or energy independently and lack real-time consumption feedback. The proposed framework reduces total energy consumption by 5.8%, cuts prototyping time by 56%, and extends mission duration by ~20%, highlighting the benefits of jointly applying TO and ML for sustainable and energy-aware robotic design. This integrated approach addresses a critical gap in the literature by demonstrating that mechanical light-weighting and intelligent path planning can be co-optimized in a deployable robotic system using empirical energy data. Full article

Despite navigation advancements in enhanced sensor utilization and increased focus on maritime training and education, most marine accidents still involve collisions with high human involvement. Furthermore, navigators’ knowledge and application of the most often misunderstood Rule 10 Traffic Separation Schemes (TSS) according to the Convention on the International Regulations for Preventing Collisions at Sea (COLREG) represents the first focus in this study. To provide insight into the level of understanding and knowledge regarding COLREG Rule 10, a customized, worldwide survey has been created and disseminated among marine industry professionals. The survey results reveal a notable knowledge gap in Rule 10, where we initially assumed that more than half of the respondents know COLREG regulations well. According to the probability calculation and chi-square test results, all three categories (OOW, Master, and others) have significant rule misunderstanding. In response to the COLREG misunderstanding, together with the increasing density of maritime traffic, the implementation of Decision Support Systems (DSS) in navigation has become crucial for ensuring compliance with regulatory frameworks and enhancing navigational safety in general. This study presents a structural approach to vessel prioritization and decision-making within a DSS framework, focusing on the classification and response of the own vessel (OV) to bow-crossing scenarios within the TSS. Through the real-time integration of AIS navigational status data, the proposed DSS Architecture offers a structured, rule-compliant architecture to enhance navigational safety and the decision-making process within the TSS. Furthermore, implementing a Fall-Back Strategy (FBS) represents the key innovation factor, which ensures system resilience by directing operator response if opposing vessels disobey COLREG rules. Based on the vessel’s dynamic context and COLREG hierarchy, the proposed DSS Architecture identifies and informs the navigator regarding stand-on or give-way obligations among vessels. Full article

We live in an era where digitalization and omnichannel strategies significantly transform retail landscapes, and accurate spatial analytics from Geographic Information Systems (GIS) can deliver substantial competitive benefits. Nonetheless, despite evident practical advantages for specific targeting strategies and operational efficiency, the degree of GIS integration into academic marketing literature remains ambiguous. Clarifying this uncertainty is beneficial for advancing theoretical understanding and ensuring retail strategies fully leverage robust, data-driven spatial intelligence. To examine the intellectual development of the field, co-occurrence analysis, topic mapping, and citation structure visualization were performed on 4952 peer-reviewed articles using the Bibliometrix R package (version 4.3.3) within R software (version 4.4.1). The results demonstrate that although GIS-based methods have been effectively incorporated into fields like site selection and spatial segmentation, traditional marketing research has not yet entirely adopted them. One of the study’s key findings is the distinction between “author keywords” and “keywords plus,” where researchers concentrate on novel topics like omnichannel retail, artificial intelligence, and logistics. However, “Keywords plus” still refers to more traditional terms such as pricing, customer satisfaction, and consumer behavior. This discrepancy presents a misalignment between current research trends and indexed classification practices. Although the mainstream retail research lacks terminology connected to geomarketing, a theme evolution analysis reveals a growing focus on technology-driven and sustainability-related concepts associated with the Retail 4.0 and 5.0 paradigms. These findings underscore a conceptual and structural deficiency in the literature and indicate the necessity for enhanced integration of GIS and spatial decision support systems (SDSS) in retail marketing. Full article

On a worldwide scale, neurodegenerative diseases, including multiple sclerosis, Parkinson’s, and Alzheimer’s, face considerable healthcare challenges demanding the development of novel approaches to early detection and efficient treatment. With its ability to provide real-time patient monitoring, customized medical care, and advanced predictive analytics, artificial intelligence (AI) is fundamentally transforming the way healthcare is provided. Through the integration of wearable physiological sensors, motion sensors, and neurological assessment tools, the NeuroPredict platform harnesses AI and smart sensor technologies to enhance the management of specific neurodegenerative diseases. Machine learning algorithms process these data flows to find patterns that point out disease evolution. This paper covers the design and architecture of the NeuroPredict platform, stressing the ethical and regulatory requirements that guide its development. Initial development of AI algorithms for disease monitoring, technical achievements, and constant enhancements driven by early user feedback are addressed in the discussion section. To ascertain the platform’s trustworthiness and data security, it also points towards risk analysis and mitigation approaches. The NeuroPredict platform’s capability for achieving AI-driven smart healthcare solutions is highlighted, even though it is currently in the development stage. Subsequent research is expected to focus on boosting data integration, expanding AI models, and providing regulatory compliance for clinical application. The current results are based on incremental laboratory tests using simulated user roles, with no clinical patient data involved so far. This study reports an experimental technology evaluation of modular components of the NeuroPredict platform, integrating multimodal sensors and machine learning pipelines in a laboratory-based setting, with future co-design and clinical validation foreseen for a later project phase. Full article

This study explores the relationship between customer satisfaction and loyalty in mobile banking, emphasizing the moderating role of Technology Readiness. As mobile banking becomes increasingly central to financial service delivery, understanding the nuanced drivers of customer loyalty is essential for strategic growth. Drawing from the Technology Readiness Index, this study examines how four dimensions, optimism, innovativeness, discomfort, and insecurity, moderate the satisfaction–loyalty linkage. Data were collected via a structured survey from 258 mobile banking users in the United States, analyzed using partial least squares structural equation modeling (PLS-SEM). Results show that optimism and innovativeness positively moderate this relationship, while discomfort and insecurity act as negative moderators. Practically, this research introduces a segmented approach to mobile banking service design, underscoring the need for differentiated strategies that address varying levels of user readiness. Theoretically, this study addresses a gap in mobile banking literature by shifting the focus from adoption to sustained usage and satisfaction-based loyalty, enriching the discourse on customer behavior in digital finance. Full article

The increasing presence of non-cooperative targets poses significant challenges to the space environment and threatens the sustainability of aerospace operations. Accurate on-orbit perception of such targets, particularly those without cooperative markers, requires advanced algorithms and efficient system architectures. This study presents a hardware–software co-design framework for the pose estimation of non-cooperative targets. Firstly, a two-stage architecture is proposed, comprising object detection and pose estimation. YOLOv5s is modified with a Focus module to enhance feature extraction, and URSONet adopts global average pooling to reduce the computational burden. Optimization techniques, including batch normalization fusion, ReLU integration, and linear quantization, are applied to improve inference efficiency. Secondly, a customized FPGA-based accelerator is developed with an instruction scheduler, memory slicing mechanism, and computation array. Instruction-level control supports model generalization, while a weight concatenation strategy improves resource utilization during convolution. Finally, a heterogeneous DSP–FPGA system is implemented, where the DSP manages data pre-processing and result integration, and the FPGA performs core inference. The system is deployed on a Xilinx X7K325T FPGA operating at 200 MHz. Experimental results show that the optimized model achieves a peak throughput of 399.16 GOP/s with less than 1% accuracy loss. The proposed design reaches 0.461 and 0.447 GOP/s/DSP48E1 for two model variants, achieving a 2× to 3× improvement over comparable designs. Full article

Reducing setup costs and improving product quality are critical objectives in a sustainable production processes. The significance of these goals lies in their direct impact on efficiency. It affects competitiveness and customer satisfaction. Businesses can reduce setup costs to maximize resource usage. It can reduce downtime between production runs and improve overall operational agility. Sustained performance and expansion in contemporary manufacturing environments focus on setup cost reduction and product quality improvement. The present paper discusses a production inventory model for the product, which produces by-products as secondary products from the same manufacturing process. Setup cost is reduced for the setup of production and refining processes. A production process may change from being under control to an uncontrolled one. As a result of this, imperfect products are formed. This paper considers product quality improvement for both produced and processed items. The outcome shows that dealing with by-products helps make the system more profitable. Sensitivity analysis is performed for various costs and parameters. Mathematica 11 software was used for calculation and graphical work. Full article

With water scarcity becoming worse, and demand increasing, the urgency for the water industry to hit net-zero carbon is accelerating. Even as a multitude of utilities have pledged to reach net-zero by 2050, advancing beyond the energy–water nexus remains a heavy lift. This paper, using a systematic literature review that complies with Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA), aims to propose sustainable water resource management (SWRM) strategies that may assist water utilities in decarbonizing their value chains and achieving net-zero carbon. In total, 31 articles were included from SCOPUS, ResearchGate, ScienceDirect, and Springer. The findings show that water utilities are responsible for 3% of global greenhouse gas emissions and could reduce these emissions by more than 45% by employing a few strategies, including the electrification of transport fleets, the use of renewables, advanced oxidation processes (AOPs) and energy-efficient technologies. A broad-based case study from Scottish Water shows a 254,000-ton CO₂ reduction in the period since 2007, indicative of the potential of these measures. The review concludes that net-zero carbon is feasible through a mix of decarbonization, wastewater reuse, smart systems and policy-led innovation, especially if customized to both large and small utilities. To facilitate a wider and a more scalable transition, research needs to focus on development of low-cost and flexible strategies for underserved utilities. Full article

Aircraft icing and snow accumulation are significant threats to flight safety and operational efficiency, necessitating rapid and accurate detection methods. The aim of this study was to develop and comparatively evaluate artificial intelligence (AI) models for the real-time detection of ice and snow on aircraft surfaces using vision systems. A custom dataset of annotated aircraft images under various winter conditions was prepared and augmented to enhance model robustness. Two training approaches were implemented: an automatic process using the YOLOv8 framework on the Roboflow platform and a manual process in the Google Colab environment. Both models were evaluated using standard object detection metrics, including mean Average Precision (mAP) and mAP@50:95. The results demonstrate that both methods achieved comparable detection performance, with final mAP50 values of 0.25–0.3 and mAP50-95 values around 0.15. The manual approach yielded lower training losses and more stable metric progression, suggesting better generalization and a reduced risk of overfitting. The findings highlight the potential of AI-driven vision systems to support intelligent de-icing decision-making in aviation. Future research should focus on refining localization, minimizing false alarms, and adapting detection models to specific aircraft components to further enhance operational safety and reliability. Full article