Search Results (137)

Noise in long highway tunnels and underground interchanges poses a significant environmental concern, affecting both drivers and nearby residents. This research develops an acoustic finite element model of a long tunnel in Leuven Measurement Systems (LMS) Virtual Lab to characterize the tunnel noise field, and the effectiveness of different noise mitigation measures was also evaluated and optimized accordingly. The model is validated against in situ monitoring data, with deviations controlled within 3 dB(A) and strong agreement confirmed by the Kappa consistency test. Both simulations and measurements show that sound pressure levels (SPLs) are generally highest near the tunnel center and lower toward the portal, exhibiting periodic fluctuations rather than a monotonic decrease. The dominant noise energy is concentrated between 125 Hz and 500 Hz. SPLs at 1.8 m above the road surface are noticeably higher than at 1.2 m and 1.5 m, indicating greater noise exposure for drivers of large vehicles compared with smaller vehicles. Noise reduction performance is further assessed for different lining materials and pavement types. Installing sound-absorbing panels in the tunnel midsection provides effective attenuation, with expanded perlite panels, single-layer metal micro-perforated panels, and FC quiet perforated panels (FC-PP) performing best, while porous asphalt shows superior noise reduction compared with conventional dense-graded asphalt pavements. Full article

This paper presents a high-performance and resource-efficient Field Programmable Gate Array (FPGA)-based architecture for satellite telemetry transmission systems. The proposed design implements a flexible channel coding chain, including Reed–Solomon (R-S) encoding, convolutional encoding, symbol interleaving, pseudo-randomization, and Attached Synchronization Marker (ASM) insertion, in accordance with CCSDS recommendations. The architecture is fully integrated and configurable, allowing dynamic selection of coding schemes without requiring structural modifications. The system is implemented on a modern FPGA platform with a 32-bit AXI4-Stream interface at 110 MHz, reaching an effective throughput of up to 1.76 Gbps. Experimental results demonstrate reliable timing with positive setup and hold margins, allowing the system to operate at approximately 130 MHz. Power consumption is measured using Switching Activity Interchange Format (SAIF)-based switching activity, providing a realistic estimate of programmable logic power consumption. The total on-chip power is about 1.77 W for individual coding modes. It rises to 1.91 W in the concatenated setup, which is the worst-case scenario. The results show that the proposed architecture efficiently uses resources, runs reliably at high speeds, and exhibits predictable power consumption. This makes it well suited for high-reliability and energy-constrained satellite communication systems. resources are used. Full article

Background/Objectives: Large language models (LLMs) are increasingly used as decision support tools in clinical nutrition, including meal planning for individuals with type 2 diabetes mellitus (T2DM). However, the clinical safety, quantitative accuracy, and guideline adherence of AI-generated dietary plans remain uncertain. This study aimed to evaluate systematic bias and agreement between LLM-generated diets and a guideline-concordant reference diet, and to assess whether current LLMs can function as reliable clinical nutrition decision support tools in T2DM. Methods: Six widely used LLMs generated standardized three-day, 1800 kcal dietary plans for T2DM using an identical prompt. Each day was treated as an independent observation (n = 18). Energy and macronutrient contents were analyzed using professional nutrition software and compared with a dietitian-designed reference diet based on ADA, EASD, IDF, and national guidelines. Agreement was evaluated using Bland–Altman analysis, proportional bias assessment, and intraclass correlation coefficients. Guideline adherence and clinical appropriateness were independently scored by registered dietitians. Results: Most LLM-generated diets systematically deviated from the reference diet, with lower total energy, reduced carbohydrate and fiber content, and variable protein distribution. Bland–Altman analyses demonstrated significant bias and wide limits of agreement for key nutrients, indicating clinically meaningful discrepancies. Guideline adherence scores varied substantially across models, with only one model showing relatively consistent performance. Inter-rater reliability between dietitians was high (ICC = 0.806). Conclusions: Current LLMs exhibit systematic quantitative bias and inconsistent guideline adherence when used for T2DM meal planning. AI-generated dietary plans are not interchangeable with dietitian-guided medical nutrition therapy and may pose clinical risks if used without professional oversight. Careful validation, domain-specific fine-tuning, and integration within supervised clinical workflows are required before implementation in diabetes care. Full article

Petrochemical hybrid renewable energy systems (PHRESs), integrating renewable and fossil energy sources, have garnered more and more attention for sustainable manufacturing. However, achieving concurrent optimization of energy supply reliability and carbon mitigation in these complex systems remains a critical challenge. This study proposes an innovative bilateral optimization framework coupling supply-side energy management with demand-side flexibility. On the supply side, a scenario-based two-stage stochastic programming method synergizes with energy storage systems to address renewable energy intermittency, considering a time-of-day tariff from the grid. On the utilization side, heat energy-based and shaft work-based energy interchangeability are introduced and leveraged to enable both qualitative and quantitative flexibility in process unit requirements and thus obtain energy consumption relaxation models for relaxing the design boundaries of PHRESs. These dual strategies are then coupled in a two-stage mixed-integer programming model framework for the optimal design of PHRESs. Applied to a large-scale refinery incorporating carbon taxation and dynamic electricity price, the proposed methodology demonstrates superior performance through five comparative cases. Compared to the Base Case, the Optimal Case using the proposed method can reduce the total annual cost by 14.82%, and stochastic programming reveals over a 40% probability of carbon mitigation in the uncertain space. Full article

Fragmentation Resilience Energy Mass Spectrometry (FREMS) builds on the field of energy-resolved mass spectrometry and previously used methods, e.g., Survival Yield. It exploits breakdown energies at near “continuous” ramp (0.2% NCE increments) to offer higher resolution and a reliable method for compound differentiation, contaminant identification and structural elucidation. Implementation of FREMS involves acquiring ion breakdown/formation curves as collision energy is incrementally increased. These curves themselves can be analyzed by several means to give a single metric—Fragmentation Resilience (FR₅₀). This value has been shown to be experimentally interchangeable with the modified-Survival Yield (m-SY₅₀) and the Cross-Intersect (C-I). A full panel of testing on an LTQ-Orbitrap revealed that breakdown energies depend only on three controllable parameters—number of ions inside the ion trap, Maximum Inject time and Activation Time. A fairly linear relationship (R² > 0.95) with proposed FR₅₀, m-SY₅₀ and C-I metrics provides reliable adjustment mechanisms for these variables via calibrations. Consequently, this technique can be applied to ions produced by any atmospheric pressure ionization processes and treated as exclusively in vacuo experiments. Applications of FREMS to 4-chlorobenzylpyridinium ion revealed that under collisional activated dissociation (CAD) conditions, the rate of decomposition of precursor ion is equivalent to the rate of formation of its fragments, i.e., normalized breakdown and formation curves intersect at inflection points. Full article

This paper presents a low-cost, modular ankle–foot prosthesis that integrates an S-shaped compliant foot with a parallel spring–short-stroke actuator branch to balance energy return, impact attenuation, and rapid personalization. The design follows an FDM-oriented CAD/CAE workflow using PETG and interchangeable modules (foot, ankle unit, pylon adapter). Finite-element analyses of heel-strike, mid-stance, and toe-off load cases, supported by bench checks, show strain localization in intended flexural regions, a minimum safety factor of 15 for the housing, and peak-stress reduction after geometric refinements (increased transition radii and local ribs). The modular layout simplifies servicing and allows quick tuning of stiffness and damping without redesigning the load-bearing structure. The results indicate an engineeringly realistic path toward accessible prosthetics and provide a basis for subsequent upgrades toward semi-active control and sensor-assisted damping. Full article

The purpose of this article is to assess the impact of three major crises—the global economic and financial crisis of 2007–2009, the COVID-19 pandemic (2020), and the energy crisis induced by the war in Ukraine (2022)—on the condition of regional economies in Poland, both before and after their occurrence. These events, which may be regarded as black swan phenomena, were examined using several indicators of regional economic performance: the Herfindahl–Hirschman Index (HHI), the dynamics of Gross Domestic Product (GDP), industrial output sold, construction and assembly output, and retail sales. The study is based on Statistics Poland GUS data ranging from the first quarter of 2004 to the fourth quarter of 2024. The findings indicate that some Polish voivodeships (Since 1 May 2004, Poland has been divided into regions (NUTS I) and voivodeships (NUTS II). In this division, the region is statistical in nature, but in the presented research we refer exclusively to voivodeships, and therefore we also use the terms “region” and “NUTS II” interchangeably in this work. The interchangeable use of these terms also stems from the practice in Polish literature) (regions) experienced increases in industrial concentration over the study period. In 2009, during the global financial crisis, seven regions (Dolnośląskie, Lubuskie, Małopolskie, Mazowieckie, Opolskie, Podkarpackie, and Pomorskie) exhibited a relatively high degree of industrial diversification. Seven others (Kujawsko-Pomorskie, Lubelskie, Łódzkie, Śląskie, Świętokrzyskie, Wielkopolskie, and Zachodniopomorskie) showed moderate concentration. The two eastern regions—Warmińsko-Mazurskie (HHI = 194) and Podlaskie (HHI = 315)—had the highest concentration of industrial production in that year. By 2024, the overall pattern remained consistent: seven voivodeships displayed high concentration, eight moderate concentration, and one (Podlaskie) exceptionally high concentration. The degree of cyclical convergence across regions was generally high throughout the examined period. However, notable differences emerged under crisis conditions. Synchronization was strongest during the COVID-19 pandemic, when abrupt and unexpected shocks produced relatively similar regional responses. In contrast, the financial crisis of 2007–2009 and the energy crisis of 2022 generated more heterogeneous regional effects, resulting in lower and more varied levels of convergence. Full article

Peripheral nerve injuries, particularly those involving the sciatic nerve, remain a major clinical challenge due to incomplete functional recovery and the limited translation of preclinical advances into effective therapies. This review synthesizes current evidence on the phase-specific evaluation of sciatic nerve regeneration in preclinical models, integrating behavioral, sensory, electrophysiological, and morphological approaches across the acute, subacute (Wallerian degeneration), early regenerative, and late regenerative phases. By mapping functional readouts onto the underlying biological events of each phase, we highlight how tools such as the Sciatic Functional Index, Beam Walk test, Rotarod test, nerve conduction studies, and nociceptive assays provide complementary and often non-interchangeable information about motor, sensory, and neuromuscular recovery. We further examine emerging therapeutic strategies, including intraoperative electrical stimulation, immunomodulation, platelet-rich plasma, bioengineered scaffolds, conductive and piezoelectric conduits, exosome-based hydrogels, tacrolimus delivery systems, and small molecules, emphasizing the importance of aligning their mechanisms of action with the dynamic microenvironment of peripheral nerve repair. Despite substantial advancements in experimental models, an analysis of publication trends and registries reveals a persistent translational gap, with remarkably few clinical trials relative to the high volume of preclinical studies. To illustrate how mechanistic insights can be complemented by molecular-level characterization, we also present a targeted computational analysis of alpha-lipoic acid (ALA,) including frontier orbital energies, physicochemical descriptors, and docking interactions with IL-6, TGF-β, and a growth-factor receptor—performed solely for this molecule due to its documented structural availability and relevance. By presenting an integrated, phase-specific framework for functional assessment and therapeutic evaluation, this review underscores the need for standardized, biologically aligned methodologies to improve the rigor, comparability, and clinical relevance of future studies in sciatic nerve regeneration. Full article

Four benzyltrimethylammonium (BTMA) salts were successfully prepared: bis(benzyltrimethylammonium) hexachloroplatinate (1), benzyltrimethylammonium tetrachloroaurate (2), bis(benzyltrimethylammonium) tetrachlorocuprate (3), and bis(benzyltrimethylammonium) tetrabromocuprate (4) from benzyltrimethylammonium hydroxide (Triton B). Their crystal structures were determined by single-crystal X-ray diffraction, and the supramolecular architectures were characterized hierarchically. Extended Hirshfeld surface analysis, including enrichment ratio calculations, was performed to evaluate intermolecular interactions. Nonclassical hydrogen bonds, such as C–H^…Cl(Br), involving the anions, contribute to the formation of self-assembled architectures. Additional stabilization arises from π^…π and Cu–Br^…π interactions, particularly in crystals 2 and 4, respectively. Hirshfeld surface analysis showed that H^…H and C^…H/H^…C interactions are the dominant contributors in all crystals. According to enrichment ratio calculations, C^…H/H^…C interactions in 1, 3, and 4; Cl^…H/H^…Cl in 1 and 3; Cu^…H/H^…Cu in 3 and 4; and Br^…H/H^…Br and Br^…C/C^…Br in 4 are statistically favored in the crystal packing. Halogen bonding Cl^…Cl was observed in 1 but does not significantly influence packing. Energy framework calculations indicated that dispersive interactions are favorable in the analyzed crystals. A library of H-bonding supramolecular patterns, including interchangeable synthons, is provided and may guide the rational design of new derivatives with controllable features. Finally, the topology of intermolecular connections and the electronic structure of the benzyltrimethylammonium cation, investigated by quantum-chemical calculations, provide insights into its reactivity. Full article

In new wireless ecosystems, simultaneous wireless information and power transfer (SWIPT) and cooperative non-orthogonal multiple access (CNOMA) together make a potential design model. These systems enhance spectral efficiency (SE), energy efficiency (EE), and data interchange reliability by combining energy harvesting (EH), superposition coding (SC), and relay-assisted transmission. Despite this, CNOMA’s energy efficiency is still constrained by the fact that relay nodes servicing multiple users require a significant amount of power. Most previous studies look at performance as if imperfect successive interference cancellation (SIC) were possible. To solve these problems, this study presents a multiuser SWIPT-enabled cooperative wavelet NOMA (CWNOMA) framework that reduces imperfect SIC, inter-symbol interference (ISI), and inter-user interference. SWIPT-CWNOMA enhances overall energy efficiency (EE), keeps relays functional, and maintains data transmission strong for users by obtaining energy from received signals. The proposed architecture is evaluated against traditional CNOMA and orthogonal multiple access (OMA) in both perfect and imperfect scenarios with SIC. The authors derive closed-form formulas for EE, signal-to-interference-plus-noise ratio (SINR), and achievable rate to support the analysis. Residual error because of imperfect SIC for near users shows lower values in a varying range of SNR. Across 0–30 dB SNR, SWIPT-CWNOMA achieves, on average, $1.4$ times higher energy efficiency, approximately $4.7$ lower BER, and $1.9$ times higher achievable rate than OFDMA, which establishes SWIPT-CWNOMA as a promising candidate for next-generation energy-efficient wireless networks. Full article

Magnetic confinement nuclear fusion offers a promising solution to the world’s growing energy demands. The DEMO reactor presented here aims to bridge the gap between laboratory fusion experiments and practical electricity generation, posing unique challenges for magnetic plasma diagnostics due to limited space for diagnostic equipment. This study employs Bayesian inference and Gaussian process modeling to integrate data from pick-up coils, flux loops, and saddle coils, enabling a qualitative estimation of the plasma current density distribution relying on only external magnetic measurements. The methodology successfully infers total plasma current, plasma centroid position, and six plasma–wall gap positions, while adhering to DEMO’s stringent accuracy standards. Additionally, the interchangeability between normal pick-up coils and saddle coils was assessed, revealing a clear preference for saddle coils. Initial steps were taken to utilize Bayesian experimental design for optimizing the orientation (normal or tangential) of pick-up coils within DEMO’s design constraints to improve the diagnostic setup’s inference precision. Our approach indicates the feasibility of Bayesian integrated data analysis in achieving precise and accurate probability distributions of plasma parameter crucial for the successful operation of DEMO. Full article

One key aspect of the Global Goal on Adaptation is to examine transformational adaptation at different scales and sectors. While there are several adaptation assessment frameworks in the energy sector, there is still room for further development to properly capture and quantify the adaptability of energy infrastructure against climate change. Therefore, this study aims to define a definition for the adaptability of resilient energy infrastructure and develop the adaptive resilience curve to quantify it. The study hypothesized and confirmed definition boundaries for the resilience, adaptation, and adaptability of resilient energy infrastructure and an adaptive resilience curve. Definitions of resilience and adaptation are often interchangeably used, yet differences were found. Common keywords extracted from definitions of resilience and adaptation were utilized to define the adaptability of resilient energy infrastructure. The adaptive resilience curve was formulated, borrowing attributes from concepts contributing to adaptability, including global catastrophic risk, beyond design basis accident, and foresight. The definition for the adaptability of resilient energy infrastructure sets a common ground for the understanding of the concept, on which the adaptive resilience curve is developed to facilitate its visualization and quantification. The adaptive resilience curve can capture temporal change in the adaptability of resilient energy infrastructure under multiple scenarios using multiple figures-of-merit. Full article

Irradiation with ultraviolet light-emitting diodes (UV-LEDs) represents a promising method for viral inactivation, but a detailed understanding of the wavelength-dependent action spectra remains limited, particularly across different viral components. In this study, we established standardized UV action spectra for infectivity reduction in pathogenic viruses using a system equipped with interchangeable LEDs at 13 different peak wavelengths (250–365 nm). The reduction in viral infectivity induced by UV-LED exposure was strongly related to viral genome damage, whereas no significant degradation of viral structural proteins was detected. Peak virucidal efficiency was observed at 267–270 nm across all tested viruses, representing a slight shift from the traditionally expected 260 nm nucleic acid absorption peak. Enveloped RNA viruses, including influenza A virus, respiratory syncytial virus, and coronavirus, exhibited greater UV sensitivity than nonenveloped viruses such as feline calicivirus and adenovirus. These observations indicate that structural characteristics, such as the presence of an envelope and genome organization, influence UV susceptibility. The wavelength-specific action spectra established in this study provide critical data for optimizing UV-LED disinfection systems to achieve efficient viral inactivation while minimizing energy consumption in healthcare, food safety, and environmental sanitation. Full article

The interchanging energy systems are vital, as fossil fuel, on the other hand, is running out of time as its resources are facing scarcity and continue to endanger human life by having a huge contribution to climate change. The solar power still faces the challenge of not being easily sustainable after implementation, but promising investment soon. This study aims to investigate the possibilities of switching from fossil fuel to solar PV in the region of KwaZulu Natal. The study compares regression results of solar power in different categories to elaborate on the solar power performance. It further predicts solar power performance using a multiple regression method. The adopted multiple regression also revealed the high possibility of solar power performance of 0.75 r²-value with a tolerable error of 0.25. All challenges facing solar power are investigated using historical data. However, more work still needs to be done to investigate the potential growth of solar power in the region. Full article

Weeds cause significant yield and economic losses by competing with crops and increasing production costs. Compounding these challenges are labor shortages, herbicide resistance, and environmental pollution, making weed management increasingly difficult. In response, precision weed control (PWC) technologies, such as robots and unmanned aerial vehicles (UAVs), have emerged as innovative solutions. These tools offer farmers high precision (±1 cm spatial accuracy), enabling efficient and sustainable weed management. Herbicide spraying robots, mechanical weeding robots, and laser-based weeders are deployed on large-scale farms in developed countries. Similarly, UAVs are gaining popularity in many countries, particularly in Asia, for weed monitoring and herbicide application. Despite advancements in robotic and UAV weed control, their large-scale adoption remains limited. The reasons for this slow uptake and the barriers to widespread implementation are not fully understood. To address this knowledge gap, our review analyzes 155 articles and provides a comprehensive understanding of PWC challenges and needed interventions for scaling. This review revealed that AI-driven weed mapping in robots and UAVs struggles with data (quality, diversity, bias) and technical (computation, deployment, cost) barriers. Improved data (collection, processing, synthesis, bias mitigation) and efficient, affordable technology (edge/hybrid computing, lightweight algorithms, centralized computing resources, energy-efficient hardware) are required to improve AI-driven weed mapping adoption. Specifically, robotic weed control adoption is hindered by challenges in weed recognition, navigation complexity, limited battery life, data management (connectivity), fragmented farms, high costs, and limited digital literacy. Scaling requires advancements in weed detection and energy efficiency, development of affordable robots with shared service models, enhanced farmer training, improved rural connectivity, and precise engineering solutions. Similarly, UAV adoption in agriculture faces hurdles such as regulations (permits), limited payload and battery life, weather dependency, spray drift, sensor accuracy, lack of skilled operators, high initial and operational costs, and absence of standardized protocol. Scaling requires financing (subsidies, loans), favorable regulations (streamlined permits, online training), infrastructure development (service providers, hiring centers), technological innovation (interchangeable sensors, multipurpose UAVs), and capacity building (farmer training programs, awareness initiatives). Full article