Search Results (154)

Background/Objectives: The oral cavity represents a convenient route of administration for drugs that exhibit significant hepatic first-pass extraction. In this study, the mucosal permeation properties of selected active pharmaceutical ingredients (APIs) incorporated into oral cavity drug products that are approved by the U.S. Food and Drug Administration were quantified using the human-derived sublingual HO-1-u-1 and buccal EpiOral™ in vitro tissue models. Methods: Epithelial barrier properties were monitored using propranolol and Lucifer Yellow as prototypic transcellular and paracellular markers. APIs were dissolved in artificial saliva, pH 6.7, and transepithelial flux from the apical to the basolateral compartment was quantified using HPLC. Results: Apparent permeability coefficients (Papp) calculated for these APIs in the sublingual HO-1-u-1 tissue model varied from Papp = 2.72 ± 0.06 × 10⁻⁵ cm/s for asenapine to Papp = 6.21 ± 2.60 × 10⁻⁵ cm/s for naloxone. In contrast, the buccal EpiOral™ tissue model demonstrated greater discrimination power in terms of permeation properties for the same APIs, with values ranging from Papp = 3.31 ± 0.83 × 10⁻⁷ cm/s for acyclovir to Papp = 2.56 ± 0.68 × 10⁻⁵ cm/s for sufentanil. The tissue-associated dose fraction recovered at the end of the transport experiment was significantly increased in the buccal EpiOral™ tissue model, reaching up to 8.5% for sufentanil. Conclusions: Experimental permeation data collected for selected APIs in FDA-approved oral cavity products will serve as a training set to aid the development of predictive computational models for improving algorithms that describe drug absorption from the oral cavity. Following a robust in vitro–in vivo correlation analysis, it is expected that such innovative in silico modeling strategies will the accelerate development of generic oral cavity products by facilitating the utility of model-integrated evidence to support decision making in generic drug development and regulatory approval. Full article

The rapid growth of the private space industry has intensified the demand for lightweight, efficient, and cost-effective photovoltaic technologies. Metal halide perovskite solar cells (PSCs) offer high power conversion efficiency (PCE), mechanical flexibility, and low-temperature solution processability, making them strong candidates for next-generation space power systems. However, exposure to extreme thermal cycling, high-energy radiation, vacuum, and ultraviolet light in space leads to severe degradation. This study addresses these challenges by introducing three key design strategies: self-healing perovskite compositions that recover from radiation-induced damage, gradient buffer layers that mitigate mechanical stress caused by thermal expansion mismatch, and advanced encapsulation that serves as a multifunctional barrier against space environmental stressors. These approaches enhance device resilience and operational stability in space. The design strategies discussed in this review are expected to support long-term power generation for low-cost satellites, high-altitude platforms, and deep-space missions. Additionally, insights gained from this research are applicable to terrestrial environments with high radiation or temperature extremes. Perovskite solar cells represent a transformative solution for space photovoltaics, offering a pathway toward scalable, flexible, and radiation-tolerant energy systems. Full article

Colorimetric-based biosensors are practical detection devices that can detect the presence and concentration of biomarkers through simple color changes. Conventional laboratory-based tests are highly sensitive but require long processing times and expensive equipment, which makes them difficult to apply for on-site diagnostics. In contrast, the colorimetric method offers advantages for point-of-care testing and real-time monitoring due to its flexibility, simple operation, rapid results, and versatility across many applications. In order to enhance the color change reactions in colorimetric techniques, functional nanomaterials are often integrated due to their desirable intrinsic properties. In this review, the working principles of nanomaterial-based detection strategies in colorimetric systems are introduced. In addition, current signal amplification methods for colorimetric biosensors are comprehensively outlined and evaluated. Finally, the latest trends in artificial intelligence (AI) and machine learning integration into colorimetric-based biosensors, including their potential for technological advancements in the near future, are discussed. Future research is expected to develop highly sensitive and multifunctional colorimetric methods, which will serve as powerful alternatives for point-of-care testing and self-testing. Full article

Ammonia is gaining increasing attention as a zero-carbon fuel and hydrogen carrier, offering high energy density, mature liquefaction infrastructure, and strong compatibility with existing energy systems. This review presents a comprehensive summary of the recent advances in ammonia-based clean energy systems. It covers the fuel’s physicochemical properties, green synthesis pathways, storage and transport technologies, combustion behavior, NO_X formation mechanisms, emission control strategies, and safety considerations. Co-firing approaches with hydrogen, methane, coal, and DME are evaluated to address ammonia’s low reactivity and narrow flammability limits. This paper further reviews engineering applications across power generation, maritime propulsion, and long-duration energy storage, drawing insights from current demonstration projects. Key technical barriers—including ignition delay, NO_X emissions, ammonia slip, and economic feasibility—are critically examined. Finally, future development trends are discussed, highlighting the importance of integrated system design, low-NO_X combustor development, solid-state storage materials, and supportive policy frameworks. Ammonia is expected to serve as a strategic energy vector bridging green hydrogen production with zero-carbon end-use, facilitating the transition to a sustainable, secure, and flexible energy future. Full article

The increasing adoption of electric vehicles (EVs) is driving the need for more efficient, scalable, and flexible charging infrastructures. Among the most promising solutions are reconfigurable multi-point multi-power (MPMP) charging stations, which enable dynamic power allocation across multiple charging points operating at discrete power levels. This paper introduces a novel power management strategy for MPMP stations based on Pareto optimization, aiming to minimize the average charging time while ensuring fairness and efficiency. The method dynamically allocates power among charging points to minimize the average charging time across all connected EVs, while adhering to system constraints and the varying charging profiles required to preserve battery health. The proposed approach was validated through simulations in a dynamic scenario involving six EVs with heterogeneous battery capacities and charging profiles. Results demonstrated that the Pareto-based strategy achieved a significantly lower expected average charging time when compared to the first-come first-served strategy (FCFS). Full article

With the soaring demand for electric power and the limited spinning reserve in the power system in Taiwan, the comprehensive management of both thermal power generation and load demand turns out to be a key to achieving the robustness and sustainability of the power system. This paper aims to design a demand bidding (DB) mechanism to collaborate between customers and suppliers on demand response (DR) to prevent the risks of energy shortage and realize energy conservation. The concurrent integration of the energy, transmission, and reserve capacity markets necessitates a new formulation for determining schedules and marginal prices, which is expected to enhance economic efficiency and reduce transaction costs. To dispatch energy and reserve markets concurrently, a hybrid approach of combining dynamic queuing dispatch (DQD) with direct search method (DSM) is developed to solve the extended economic dispatch (ED) problem. The effectiveness of the proposed approach is validated through three case studies of varying system scales. The impacts of tie-line congestion and area spinning reserve are fully reflected in the area marginal price, thereby facilitating the determination of optimal load reduction and spinning reserve allocation for demand-side management units. The results demonstrated that the multi-area bidding platform proposed in this paper can be used to address issues of congestion between areas, thus improving the economic efficiency and reliability of the day-ahead market system operation. Consequently, this research can serve as a valuable reference for the design of the demand bidding mechanism. Full article

This study investigates the impact of modeling the aluminum (Al) metallization layer as an integrated part of the chip model, versus as an individual component, on the results of electrical–thermal analysis of power semiconductor packages using Finite Element Analysis (FEA), ANSYS 2024 R2. The results showed that modeling the aluminum metallization layer separately exhibited high consistency with actual thermal imaging data. Furthermore, based on these findings, we observed through simulations that the aluminum metallization layer plays a key role in improving the uniformity of current density and temperature distribution within the chip. Using the aluminum metallization layer model, we optimized the thickness, material, and design of the metallization layer, as well as the bonding wire material through the design of experiments (DOE) methodology. Under the optimized conditions, an optimal design is proposed to minimize the voltage–current ratio (V_DS/I_DS), maximum junction temperature, strain, and von Mises stress. This study systematically examines the influence of aluminum metallization layer modeling on FEA-based power semiconductor package simulations and is expected to serve as a valuable reference for future power device design utilizing finite element analysis. Full article

A McKibben artificial muscle is a soft actuator driven by air pressure, characterized by its flexibility, lightweight design, and high power-to-weight ratio. We have developed a smart artificial muscle that is capable of sensing its motion. To enable this sensing function, an optical fiber was integrated into the sleeve consisting of multiple fibers and serving as a component of the McKibben artificial muscle. By measuring the macrobending loss of the optical fiber, the length of the smart artificial muscle is expected to be estimated. However, experimental results indicated that the sensor’s characteristics depend not only on the length but also on the load and the applied air pressure. This dependency arises because the stress applied to the optical fiber increases, causing microbending loss. In this study, we employed a machine learning model, primarily composed of Long Short-Term Memory (LSTM) neural networks, to estimate the length of the smart artificial muscle. The experimental results demonstrate that the length estimation obtained through machine learning exhibits a smaller error. This suggests that machine learning is a feasible approach to enhancing the length measurement accuracy of the smart artificial muscle. Full article

This review article is the continuation of a previous publication, by the same author, on one dimensional theory of space charge effect and virtual cathode. The virtual cathode is known to be unstable. However, the process of virtual cathode oscillation is very complicated both physically and mathematically. No satisfactory theoretical model exists that can fully describe the oscillatory behavior of the virtual cathode. On the other hand, computer simulations allow us to numerically observe this phenomenon and establish certain relations between the electron beam parameters and the virtual cathode characteristics. This article explains the detailed procedure of numerical modeling by dealing with the one-dimensional case as an example. A sample code written in the C language is attached at the end following the main text. This article is expected to serve as a reference for young researchers and students who are interested in computer simulations of intense particle beams and high-power microwave generation. Full article

The urgency to retrofit buildings for energy efficiency highlights the need for effective financing mechanisms. Energy Performance Contracts (EPCs) present a viable solution by financing building retrofits based on anticipated energy savings. Reliable baseline models are essential to quantifying these savings accurately. EPCs facilitate retrofits by allowing Energy Service Companies (ESCOs) to cover the upfront costs of energy-saving measures, with repayment derived from the cost savings generated by the reduced energy consumption. This performance-based approach demands accurate and reliable baseline models to predict the expected savings. This study introduces a white-box calibration methodology that accurately estimates energy consumption even with limited monitoring data, making it valuable for cases with scarce or incomplete historical data. In addition to addressing data limitations, the research examines scenarios with restricted control parameters, demonstrating that indoor temperature and energy demand are essential to obtaining a robust baseline model. The present work focuses on performing the calibration process through a single-stage approach that operates on EnergyPlus’ Ideal Loads component and the building-envelope parameters simultaneously. The paper demonstrates that it is possible to accurately assess the building’s energy performance and capture its indoor climate while reducing the time and resources required to train the model. This method achieved a Coefficient of Variation of Mean Square Error (CV(RMSE)) of 26.40% and a Normalized Mean Bias Error (NMBE) of −8.49% during training, with stability maintained during the checking period. The resulting calibrated white-box model serves as a powerful tool for EPCs, enabling reliable prediction of energy savings and offering a predictive framework for building management. By incorporating both energy and temperature, the model supports more informed decision-making and proactive energy management, enhancing the overall sustainability and efficiency of building operations. The methodology is limited to air-based HVAC systems and depends on high-resolution data and monitoring infrastructure. Additionally, the methodology was tested on a single demonstration site, and further research is needed to assess its adaptability to diverse building types and HVAC configurations. Full article

This study explores how Chang-Rae Lee, a Korean American writer, adeptly reworks the generic elements of spy fiction to serve as a conduit for interweaving his semi-autobiographical elements into Native Speaker, ultimately yielding a literary precondition sought by literary cosmopolitanism. The examination engages in a continuous search to identify literary preconditions that can address the challenges posed by prevailing power imbalances in discourse systems, which persist in impeding the progress of comparative exchanges toward a genuinely cosmopolitan literary ecology. It positions Lee’s literary practice within the landscape of U.S. literature, where he navigates similar challenges posed by the American publishing industry and the expectations of the reading public for ethnic writers to conform to formulaic representations that reinforce essentialist notions of identity. Analyzing Lee’s literary construction within this context reveals how his formal blend, in and of itself, not only subverts the role of genre as an ideological reinforcer but also empowers him to convey his authentic personal narratives without being reduced to a simplistic representation. This approach, therefore, ensures the preservation of authentic selfhood before embarking on further comparative literary exchanges. Full article

Clinacanthus nutans is a valuable traditional medicinal plant that contains enriched active compounds such as triterpenoids and flavonoids. Understanding the accuulation process of these secondary metabolites in C. nutans requires exploring gene expression regulation under abiotic stresses and hormonal stimuli. qRT-PCR is a powerful method for gene expression analysis, with the selection of suitable reference genes being paramount. However, reports on stably expressed reference genes in C. nutans and even across the entire family Acanthaceae are limited. In this study, we evaluated the expression stability of 12 candidate reference genes (CnUBQ, CnRPL, CnRPS, CnPTB1, CnTIP41, CnACT, CnUBC, CnGAPDH, Cn18S, CnCYP, CnEF1α, and CnTUB) in C. nutans across different tissues and under abiotic stresses and MeJA treatment using three programs (geNorm, NormFinder, and BestKeeper). The integrated ranking results indicated that CnUBC, CnRPL, and CnCYP were the most stably expressed genes across different tissues. Under abiotic stress conditions, CnUBC, CnRPL, and CnEF1α were the most stable, while under MeJA treatment, CnRPL, CnEF1α, and CnGAPDH exhibited the highest stability. Additionally, CnRPL, CnUBC, and CnEF1α were the most stable reference genes across all tested samples, whereas CnGAPDH was the least stable. CnRPL, consistently ranking among the top three most stable genes, may therefore serve as an ideal reference gene for qRT-PCR analysis in C. nutans. To further validate the selected reference genes, we assessed the expression of two key biosynthetic genes, CnPAL and CnHMGR. The results confirmed that using the most stable reference genes yielded expression patterns consistent with biological expectations, while using unstable reference genes led to significant deviations. These findings offer valuable insights for accurately quantifying target genes via qRT-PCR in C. nutans, facilitating investigations into the mechanisms underlying active compound accumulation. Full article

A micro electric vehicle (micro-EV) is a small electric car with one or two seats designed for short-to-medium-distance trips. Micro-EVs produce relatively less pollution during operation and, due to their compact size, offer greater mobility in narrow areas compared to conventional transportation. These advantages have led to a continuous increase in the number of micro-EVs. However, their small battery capacity results in a limited driving range per charge, and restrictions on power and speed lead to lower driving performance. Due to these drawbacks, micro-EVs still hold a small share of the overall vehicle market. Therefore, it is necessary to evaluate the strengths of micro-EVs and analyze how they should be utilized to promote their widespread adoption. Therefore, this study analyzed the strengths of micro-EVs and identified the types of services where they can be effectively utilized to promote the use of micro-EVs as a smart mobility option. This study focused on micro-EVs used as a shared transport service, delivery service, and in public service, as part of an R&D project on micro-EVs conducted by the Ministry of Trade, Industry, and Energy. A total of 106 micro-EVs were deployed for each service type: 57 for shared transport, 13 for delivery, and 36 for public service. Each micro-EV was equipped with a GPS device, and the analysis was conducted using GPS data collected from January 2021 to October 2021. Micro-EVs with missing data due to GPS device malfunctions were excluded from the analysis. As a result, two micro-EVs from the shared transport service and one from the public service were excluded. The study compared the travel characteristics of micro-EVs across the three different service types. Additionally, a comparative analysis of the driving characteristics of micro-EVs and conventional vehicles was conducted to assess the advantages of micro-EVs over traditional vehicles. The results of the analyses showed that micro-EVs were more utilized for the delivery service type than other service types in terms of daily usage time and travel distance (3.5 h/day and 38.5 km/day, respectively), trip amounts (24.1 trips/day), and number of trips per trip chain (9.4 trips/trip chain). Moreover, micro-EVs have their strengths compared to other modes of transportation when traveling narrow roads. Analysis of the roads around the areas where micro-EVs were located showed that the micro-EVs were exposed to narrow roads with a width of under 5 m (among the total road link extensions, 57% consisted of road links with a width of less than 5 m), especially the micro-EVs used for delivery service. It is expected that the findings of this study will serve as a foundational resource for developing strategies to promote the adoption of micro electric vehicles. Full article

This article provides an in-depth analysis of blockchain research in the energy sector, focusing on projects funded by the U.S. Department of Energy (DOE) and comparing them with industry-funded initiatives. A total of 110 funded activities within the U.S. power industry were successfully tracked and mapped into a newly developed categorization framework. This framework is designed to help research agencies to systematically understand their funded portfolio. Such characterization is expected to help them make effective investments, identify research gaps, measure impact, and advance technological progress to meet national goals. In line with this need, the proposed framework proposes a 2-D categorization matrix to systematically classify blockchain efforts within the energy sector.Under the proposed framework, the Energy System Domain serves as the primary classification dimension, categorizing use cases into 30 distinct applications. The second dimension, Blockchain Properties, captures the specific needs and functionalities provided by Blockchain technology. The aim was to capture blockchain’s applicability and functionality: where and why blockchain? Principles behind the selection of the viewpoint dimensions were carefully defined based on consensus obtained through the Blockchain for Optimized Security and Energy Management (BLOSEM) project. The mapped results show that activities within the Grid Automation, Coordination, and Control (31.8%), Marketplaces and Trading (25.5%), Foundational Blockchain Research (19.1%), and Supply Chain Management (17.3%) domains have been actively pursued to date. The three leading specific use case applications were identified as Transactive Energy Management for Marketplaces and Trading, Asset Management for Supply Chain Management, and Fundamental Blockchain for Foundational Blockchain Research. The Marketplaces and Trading and Retail Services Enablement domains stood out as being favored by industry by a factor greater than 2 (2.3 and 2.6, respectively), yet there seemed to be little to zero investment from DOE. Approximately 76% of the total projects prioritized Immutability, Identity Management, and Decentralization and/or Disintermediation compared to Asset Digitization and/or Tokenization, Automation, and Privacy and/or Anonymity. The greatest discrepancies between DOE and industry were in Asset Digitization and/or Tokenization and Automation. The industry efforts (36% in Asset Digitization/Tokenization and 22% in Automation) was 14 times and 2.4 times, respectively, more intensive than the DOE-sponsored efforts, indicating a significant discrepancy in industry versus government priorities. Overall, quantifying DOE-sponsored projects and industry activities through mapping provides clarity on portfolio investments and opportunities for future research. Full article

The integration of electromobility with its required charging infrastructures into the existing power grid, which is demanded by politics and society, is an enormous challenge for electrical power grid operators. Especially considering further challenges, such as the electrification of heat supply systems and sector coupling, it is to be expected that the power grid’s capacity will be strongly overstrained. On the other hand, grid expansion is an extremely expensive and time-consuming method of ensuring that the existing grid is not overloaded, and sufficient grid capacity is available. A suitable grid operations management approach can enable comprehensive and grid-serving control of flexibility, especially charging processes. This article presents a cluster-based and incentive-oriented grid operation management concept and describes the integration of the system into the current German regulatory framework. In addition, the structural integration of charging infrastructures for electromobility into a grid-oriented control system is presented. The suitability of grid charges and their dynamization for stimulating grid-oriented behavior is analyzed. Furthermore, the derivation of additional costs arising from the utilization-dependent thermal aging of grid assets and their imputed integration into the incentive system is described. Full article