High Efficiency Research Articles

FS2M: fuzzy smart IoT device pairing protocol via speak to microphone

Purpose This paper aims to provide a secure and efficient pairing protocol for two devices. Due to the large amount of data involving sensitive information transmitted in Internet of Things (IoT) devices, generating a secure shared key between smart devices for secure data sharing becomes essential. However, existing smart devices pairing schemes require longer pairing time and are difficult to resist attacks caused by context, as the secure channel is established based on restricted entropy from physical context. Design/methodology/approach This paper proposes a fuzzy smart IoT device pairing protocol via speak to microphone, FS2M. In FS2M, the device pairing is realized from the speaking audio of humans in the environment around the devices, which is easily implemented in the vast majority of Internet products. Specifically, to protect the privacy of secret keys and improve efficiency, this paper presents a single-round pairing protocol by adopting a recently published asymmetric fuzzy encapsulation mechanism (AFEM), which allows devices with similar environmental fingerprints to successfully negotiate the shared key. To instantiate AFEM, this paper presents a construction algorithm, the AFEM-ECC, based on elliptic curve cryptography. Findings This paper analyzes the security of the FS2M and its pairing efficiency with extensive experiments. The results show that the proposed protocol can achieve a secure device pairing between two IoT devices with high efficiency. Originality/value In FS2M, a novel cryptographic primitive (i.e., AFEM-ECC) are designed for IoT device pairing by using a new context-environment (i.e., human voice) . The experimental results show that FS2M has a good performance in both communication cost (i.e., 130 KB) and running time (i.e., 10 S).

Hypercube Pooling for Visual Semantic Embedding

Visual Semantic Embedding ( VSE ) is a primary model for cross-modal retrieval, wherein the global feature aggregator is a crucial component of the VSE model. In recent research, the General Pooling Operator ( GPO ) aggregator, which weighs the features reconstructed from the local feature set to aggregate, facilitates the related models to achieve good retrieval performance. However, the reason for the effectiveness remains to be explored. To enhance the rationality of aggregator designs, we analyze the reason from the perspective of feature space. Indeed, for each data, the local feature set forms a hypercube containing abundant data information, and the feature learned by GPO measures the hypercube, thereby representing the data. The geometric structure of the hypercube implies that the set containing all points within the hypercube is a convex set, so the feature learned by weighted aggregation is an interior point of the hypercube. However, using the interior point to measure the hypercube leads to some problems in feature representation and model optimization, as well as the reduction of retrieval efficiency caused by weight computation. For example, the related pair’s features may be far, while the unrelated ones may be close. To measure the hypercube more clearly and alleviate the problems mentioned above, we propose Hypercube Pooling ( HCP ) aggregator. Specifically, HCP concatenates the Max and Min Pooling features as the global features. This aggregation method has multiple advantages, e.g., the learned global feature represents all hyperplanes of the hypercube that contain critical information and hypercube geometric structure. Moreover, HCP adds normalization-before-concatenation and reduces the usual setting of margin in the loss function by half to avoid gradient loss caused by the difference in the feature value and dimensionality doubling. The experimental results on the Flickr30K and MSCOCO datasets show that the HCP model has excellent performance with high efficiency, confirming the correctness of the spatial analysis and the effectiveness of the HCP aggregator.

Risk minimizing framework for solar photovoltaics

The complexity of risk assessment and the challenges in decision-making often impede the application of various models to renewable energy systems. This study introduces a comprehensive framework designed to streamline this process, facilitating informed decisions regarding the estimation of risks associated with solar photovoltaic (PV) technologies. Leveraging data and information available in the literature, the framework is particularly useful for manufacturers in selecting materials that balance low environmental risk with high efficiency. The framework emphasizes early-stage risk minimization by integrating changes during PV development, thereby promoting cleaner production systems. It’s interconnected components encompass various approaches to risk assessment, control, and management, providing a structured methodology for risk reduction. Based on available information, the defined steps guide users through evaluating and mitigating risks. Applying risk minimization by metal substitution approach lowers the oral-ingestion and dermal-contact risk by a magnitude of four and six times, respectively. This framework will guide regulatory bodies throughout each step of the product life cycle, suggesting necessary changes and assessment strategies aligned with the perspectives of various stakeholders. By facilitating the identification and implementation of the most effective risk management strategies, the framework aims to advance the development of sustainable and safe PV technologies.

A small molecule K-3 promotes PDX1 expression and potentiates the differentiation of pluripotent stem cells into insulin-producing pancreatic β cells

Abstract Insulin-producing pancreatic β-like cells derived from human pluripotent stem cells (PSCs) are anticipated as a novel cell source for cell replacement therapy for diabetes patients. Here, we describe the identification of small molecule compounds that promote the differentiation of the PSCs into insulin-producing cells by high throughput screening with a chemical library composed of 55,000 compounds. The initial hit compound K-1 and one derivative K-3 increased the proportion of PSC-derived insulin-positive endocrine cells and their glucose-stimulated insulin secretory (GSIS) functions. K-3 preferentially acts on stage 3 pancreatic progenitor cells and increases the population expressing high levels of PDX1. As a result, the ratios of the PSC-derived PDX1 / NKX6.1 double-positive endocrine progenitor and INS / NKX6.1 double-positive mono-hormonal endocrine cells were increased. K-3 enhances the expression of functional pancreatic β cell markers and affects biological processes concerning organ development. K-3 also increased the yield of endocrine cells at the end of stage 5. The novel compound is a beneficial new tool for efficiently generating PSC-derived insulin-producing cells with high functionality and differentiation efficiency.

A Systematic Review of Fast, Scalable, and Efficient Hardware Implementations of Elliptic Curve Cryptography for Blockchain

Blockchain technology entered the enterprise domain under the name of permissioned blockchains and hybrid or verifiable database systems, as they provide a distributed solution that allows multiple distrusting parties to share common information. One drawback of these systems is the overhead added by the cryptographic functions which impacts the throughput in terms of transactions per second and increases the latency of transaction processing. Many of the cryptographic functions and protocols used in blockchains are based on Elliptic Curve Cryptography (ECC). Unfortunately, ECC operations such as modulo inverse or scalar point multiplication have considerable latency which causes the slowdown of the entire system. In such situations, reconfigurable computing architectures, such as FPGAs, can be used to offload these tasks to overcome the performance loss. This survey analyzes the current state-of-the-art designs and implementations of ECC from a hardware perspective. We use a PRISMA-based approach to filter recent publications and to reduce their number from over 16,000 to only 43 highly relevant designs. In the end, we show that very few designs are able to fulfill all three properties of high performance, scalability, and efficiency.

The Study of Nitrogen and Phosphorus Removal Efficiency in Urbanized River Systems Using Artificial Wetland Systems with Different Substrates

This study evaluated the effectiveness of five commercial substrates (zeolite, volcanic rock, gravel, magic rack, and ceramic pellets) in removing nitrogen (N) and phosphorus (P) from urban river systems using constructed wetlands. By employing X-ray CT and NGS technologies, we analyzed the physical structure of the substrates and the microbial communities they harbor. The results indicated that volcanic rock and ceramic pellets, due to their high porosity and specific surface area, performed exceptionally well in nitrogen and phosphorus removal. Specifically, the microbial systems with these two substrates achieved ammonia nitrogen removal rates of 89.86% and 88.45%, total nitrogen removal rates of 78.78% and 74.97%, and total phosphorus removal rates of 92.67% and 80.82%, respectively, within a 7-day period. Furthermore, the microbial communities on volcanic rock and ceramic pellets were more diverse, which correlated with their high pollutant removal efficiency. The study further elucidated the synergistic role of substrate characteristics and microbial community structure and function in nitrogen and phosphorus removal, enhancing the understanding of the purification mechanisms in constructed wetlands. These findings provide a scientific basis for the ecological restoration of urban rivers and are significant for improving the quality of urban water environments.

Performance-optimized and phase purity-improved high-n quasi-two-dimensional perovskite for green light-emitting diodes

Quasi-two-dimensional (quasi-2D) perovskite has the advantage of enlarging exciton binding energy and is more suitable for efficient perovskite light-emitting diodes (PeLEDs). However, the quasi-2D perovskite films deposited with solution methods are usually mixtures of multiple phases with different inorganic layer numbers (n), unfavorable to obtaining high emission efficiency. In this study, we selected formamidinium lead bromide (FAPbBr3) as the light emitter and (2-phenylethyl)ammonium cation (PEA+) as the long-chain organic spacer cation to prepare high-n (n = 9) quasi-2D perovskite films with improved phase purity. Based on the multiple cations mixed engineering, the quality of these films improved obviously by partly replacing FA+ with minute quantities of cesium cation (Cs+). The improvement focused on remarkably enhanced photoluminescence, few low-n phases, and decreased grain sizes. The green PeLED based on the performance-optimized and phase purity-improved high-n quasi-2D perovskite reached a high brightness of 28 960 cd/m2 together with a maximum current efficiency of 44.8 cd/A and a maximum external quantum efficiency of 9.99%.

Rational strategies for improving the efficiency of design and discovery of nanomedicines

AbstractThe rise of rational strategies in nanomedicine development, such as high-throughput methods and computer-aided techniques, has led to a shift in the design and discovery patterns of nanomedicines from a trial-and-error mode to a rational mode. This transition facilitates the enhancement of efficiency in the preclinical discovery pipeline of nanomaterials, particularly in improving the hit rate of nanomaterials and the optimization efficiency of promising candidates. Herein, we describe a directed evolution mode of nanomedicines driven by data to accelerate the discovery of nanomaterials with high delivery efficiency. Computer-aided design strategies are introduced in detail as one of the cutting-edge directions for the development of nanomedicines. Ultimately, we look forward to expanding the tools for the rational design and discovery of nanomaterials using multidisciplinary approaches. Rational design strategies may potentially boost the delivery efficiency of next-generation nanomedicines.

Modelling and optimization for material removal rate while processing bio-compatible Ti-6Al-7Nb using wire electric discharge machining (WEDM)

Abstract This study utilizes machine learning methodologies, such as artificial neural networks (ANN) and TLBO (teaching Learning Based optimisation), to develop a model and Optimisation of the Material Removal Rate (MRR) in wire electrical discharge machining (WEDM) of Ti-6Al-7Nb. The material removal rate was determined by conducting WEDM experiments with different levels of control parameters, including spark on time, spark off time, peak current, servo voltage, and wire feed rate using a Full Factorial approach through 81 runs. The most effective architecture for the ANN model was 4–10–1, and the parameters were adjusted depending on R2. The Artificial Neural Network (ANN) predictions were compared to those produced by the Multiple Linear Regression (MLR) model. The performance of these models was assessed by calculating the correlation between the experimental values and predicted values by models (R2). MRR value is optimised using TLBO (Teaching Learning Based Optimisation), keeping the relation developed by MLR as the objective function and leading to an improved material removal rate. The proposed method ANN & TLBO would help accurately predict and optimise MRR while processing Ti-6Al-7Nb. These machine learning-based methods significantly enhance complex machining processes by providing predictive capabilities & optimizing parameters, hence playing a vital role in achieving higher efficiency, quality, and adaptability in manufacturing environments.

Physical, biochemical, and biological characterization of olive-derived lipid nanovesicles for drug delivery applications

AbstractExtracellular vesicles (EVs) have shown great promise as drug delivery system (DDS). However, their complex and costly production limit their development for clinical use. Interestingly, the plant kingdom can also produce EV-like nanovesicles that can easily be isolated and purified from a large quantity of raw material at a high yield. In this study, olive-derived nanovesicles (ODNVs) were isolated from raw fruits using serial centrifugations and their physical and biological features characterized to demonstrate their promising potential to be used as a DDS. Nanotracking particle analysis indicated an average size of 109.5 ± 3.0 nm and yield of 1012 ODNVs/mL for the purest fraction. Microscopy imaging, membrane fluidity assay and lipidomics analysis showed the presence of a rich lipid bilayer that significantly varied between different sources of ODNVs but showed a distinct signature compared to human EVs. Moreover, ODNVs were enriched in PEN1 and TET8 compared to raw fruits, suggesting an extracellular origin. Interestingly, ODNVs size and yield stayed unchanged after exposure to high temperature (70 °C for 1 h), wide pH range (5–10), and 50–100 nm extrusion, demonstrating high resistance to physical and chemical stresses. This high resistance allowed ODNVs to stay stable in water at 4 °C for a month, or with the addition of 25 mM trehalose for long-term freezing storage. Finally, ODNVs were internalized by both 2D and 3D cell culture without triggering significant cytotoxicity and immunogenicity. Importantly, the anticancer drug doxorubicin (dox) could be loaded by passive incubation within ODNVs and dox-loaded ODNVs decreased cell viability by 90% compared to only 70% for free dox at the same concentration, indicating a higher efficiency of drug delivery by ODNVs. In addition, this high cytotoxicity effect of dox-loaded ODNVs was shown to be stable after a 2-week storage at 4 °C. Together, these findings suggested that ODNVs represent a promising candidate as drug nanocarrier for various DDS clinical applications, as demonstrated by their biocompatibility, high resistance to stress, good stability in harsh environment, and improvement of anticancer drug efficacy.

A sustainable HPTLC approach for green assessment of Tyrosine to phenylalanine ratio in chronic kidney disease

AbstractThe kidney is considered the main site for the net release of Tyrosine (Tyr) to the circulation through hydroxylation of Phenylalanine (Phe) using phenylalanine hydroxylase enzyme. In chronic kidney disease (CKD) patients the enzyme activity is impaired, therefore the serum concentration ratio of Tyr/Phe is reduced compared to healthy individuals. This motivates us to develop a cost effective, green, simple, sensitive, and selective HPTLC method to measure the levels of Tyr and Phe in serum samples. The analysis was carried out using acetonitrile: ethanol: 25% ammonia solution: ethyl acetate (6.5: 1.5: 1: 0.5) as a mobile phase. Rf values were 0.55 ± 0.05 for Phe and 0.39 ± 0.05 for Tyr. Densitometry scanning was performed using UV detector and dual wavelengths of 210 and 225 nm were obtained. A linear correlation was observed between the levels of Phe and Tyr, ranging from 50 to 700 ng band− 1 and 50 to 600 ng band− 1, respectively, under the optimum conditions. The method selectivity, linearity, precision, accuracy, and robustness were all confirmed in accordance with ICH recommendations. Calculations of the separation and resolution factors, number of theoretical plates, and height equivalent to theoretical plates prove to the chromatographic system accuracy and high separation efficiency. The developed method exhibits an acceptable eco-scale when measuring the method greenness using AGREE and GAPI softwares. It was applied for the determination of Phe and Tyr concentrations in human serum samples.

Brayton-Moser passivity based controller for constant power load with interleaved boost converter.

A DC microgrid with renewable energy sources can achieve reduced current ripple, higher efficiency, faster dynamics, high voltage gain, and less operational stress by interfacing with an interleaved boost converter (IBC). The stability of an IBC linked to a DC microgrid supplying a constant power load (CPL) can be imperceptibly guaranteed by a conventional controller. A tightly regulated CPL with nonlinear and negative incremental impedance characteristics will lead to stability issues. Uncertainties such as load and line variations will further affect the stability of the system. A nonlinear passivity-based control algorithm requires more attention than a traditional controller to achieve the stability of power converters. This article explains the Brayton-Moser (BM) passivity-based controller (PBC) for a 2-level interleaved boost converter (IBC) interfaced DC microgrid with CPL. The suggested controller can achieve high signal stability by injecting a series-connected virtual impedance. The stability of the proposed controller has been assessed using the Lyapunov stability approach. A BM passivity-based controller for a 2-level IBC with CPL has been derived and investigated under various operating modes using MATLAB and Simulink. It was also observed that the proposed system achieves at least improvement in efficiency and reduction in current ripple. To evaluate the performance of BM Passivity-based controller, a comparative analysis was performed between the suggested controller and the traditional PI controller, which is also included in this paper.

Asymmetric Palladium Migration for Synthesis of Chiral‐at‐Cage o‐Carboranes

AbstractMetal migration strategy can offer BH functionalization of o‐carboranes at different positions from where initial bond activation occurs to achieve bifunctionalized o‐carboranes in one reaction. We report in this article an enantioselective 3,4‐bifunctionalization of o‐carboranes via asymmetric Pd migration with a high efficiency and up to 98 % ee. This asymmetric catalysis has a broad substrates scope, leading to the preparation of a class of chiral‐at‐cage o‐carborane derivatives. The enantiocontrol model is suggested on the basis of density functional theory (DFT) results, where the chiral Trost ligand plays a crucial role in this enantioselective Pd migration from exo‐alkenyl sp2 C to the cage B(4) position of o‐carborane.

Stable Surface Contact with Tailored Alkylamine Pyridine Derivatives for High-Performance Inverted Perovskite Solar Cells.

Formamidinium-cesium lead triiodide (FA1-xCsxPbI3) perovskite holds great promise for perovskite solar cells (PSCs) with both high efficiency and stability. However, the defective perovskite surfaces induced by defects and residual tensile strain largely limit the photovoltaic performance of the corresponding devices. Here, the passivation capability of alkylamine-modified pyridine derivatives for the surface defects of FA1-xCsxPbI3 perovskite is systematically studied. Among the studied surface passivators, 3-(2-aminoethyl)pyridine (3-PyEA) with the suitable size is demonstrated to be the most effective in reducing surface iodine impurities and defects (VI and I2) through its strong coordination with Npyridine. Additionally, the tail amino group (─NH2) from 3-PyEA can react with FA+ cations to reduce the surface roughness of perovskite films, and the reaction products can also passivate FA vacancies (VFA), and further strengthen their binding interaction to perovskite surfaces. These merits lead to suppressed nonradiative recombination loss, the release of residual tensile stress for the perovskite films, and a favorable energy-level alignment at the perovskite/[6,6]-phenyl-C61-butyric acid methyl ester interface. Consequently, the resulting inverted FA1-xCsxPbI3 PSCs obtain an impressive power conversion efficiency (PCE) of 25.65% (certified 25.45%, certified steady-state efficiency 25.06%), along with retaining 96.5% of the initial PCE after 1800 h of 1-sun operation at 55 °C in air.

Dual‐Site NiO/Bi3O4Br Heterojunction Catalyst for High‐Efficiency CO2‐to‐CH4 Conversion

AbstractSolar light‐driven conversion of CO2 into chemicals with high calorific value is regarded as an upcoming carbon utilization technology for alleviating the energy crisis. This technique faces the challenge of low CO2 conversion and product yield due to charge recombination in the catalyst. In this paper, a dual‐reaction site heterojunction catalyst was designed and fabricated by combining NiO with Bi3O4Br nanosheets, for the separation of CO2 reduction and H2O oxidation semireactions, which effectively promoted electrons and holes separation. The resulting catalyst exhibited a high CH4 production rate (8.12 µmol/g) and selectivity (94.69%). Electron distribution and band structure were investigated to explain the satisfactory catalytic performance. In addition, combining the in‐situ DRIFTS results, a formic acid‐intermediated CO2‐to‐CH4 reaction pathway was proposed. This work aims to pave the way for CH4 production via CO2 photoreduction with high efficiency and selectivity.

Towards precision medicine: design considerations for nanozymes in tumor treatment.

Since the discovery of Fe3O4 nanoparticles with enzyme-like activity in 2007, nanozymes have emerged as a promising class of catalysts, offering advantages such as high catalytic efficiency, low cost, mild reaction conditions, and excellent stability. These properties make nanozymes highly suitable for large-scale production. In recent years, the convergence of nanomedicine and nanocatalysis has highlighted the potential of nanozymes in diagnostic and therapeutic applications, particularly in tumor therapy. Despite these advancements, the clinical translation of nanozymes remains hindered by the lack of designs tailored to specific tumor characteristics, limiting their effectiveness in targeted therapy. This review addresses the mechanisms by which nanozymes induce cell death in various tumor types and emphasizes the key design considerations needed to enhance their therapeutic potential. By identifying the challenges and opportunities in the field, this study aims to provide a foundation for future nanozyme development, ultimately contributing to more precise and effective cancer treatments.

Stabilizing Wide‐Bandgap Perovskite with Nanoscale Inorganic Halide Barriers for Next‐Generation Tandem Technology

AbstractWide‐bandgap (WBG) perovskite solar cells (PSCs) play a crucial role in advancing perovskite‐based tandem solar cells. In WBG perovskite films, grain boundary (GB) defects are the main contributors to open‐circuit voltage (VOC) deficits and performance degradation. This report presents an effective strategy for passivating GBs by incorporating an inorganic protective layer and reducing the density of GBs in perovskite films. This is achieved by integrating potassium thiocyanate (KSCN) into I‐Br mixed halide WBG perovskites. It is reported for the first time that the incorporation of KSCN creates band‐shaped barriers along the GBs. In addition, KSCN enlarges the grains of perovskite film. Elemental and structural analyses reveal that these barriers are composed of potassium lead halide. Incorporating KSCN significantly enhances the fill factor and VOC of WBG single‐junction PSCs by reducing trap density. This results in high power conversion efficiencies of 19.22% (bandgap of 1.82 eV), 20.45% (1.78 eV), and 21.54% (1.70 eV) with a C60/bathocuproine electron transport layer, and 18.51% (1.82 eV) with a C60/SnO2. Furthermore, both operational and shelf stabilities are significantly improved due to reduced light‐induced halide segregation. By using inorganic‐halide‐passivated WBG sub‐cells, a monolithic all‐perovskite tandem solar cell with an efficiency of 27.04% is demonstrated.

Energy-efficient instruction compression with programmable dictionaries

AbstractTo improve the energy efficiency of computation, accelerators trade off performance and energy consumption for flexibility. Fixed-function accelerators reach high energy efficiency, but are inflexible. Adding programmability via an instruction set architecture (ISA) incurs an energy consumption overhead, as instructions are fetched and decoded. To reduce it, hardware-controlled instruction caches and software-controlled components, such as loop buffers and (programmable) dictionaries improve the energy efficiency of instruction streams in embedded processors. Reducing the instruction overhead with code compression is well established and dictionary compression has been an effective approach due to its simplicity. Compared to static dictionaries, adding programmability improves the effectiveness. However, run-time-programmable dictionary compression and its effect on energy consumption has not been thoroughly studied. We describe a scheme to target energy efficiency by using fine-grained programmable dictionaries in embedded compute devices. Guided by compile-time analysis, the dictionary contents are changed during execution. On CHStone and Embench suites, our method reduces energy consumption on average by 11.4% and 3.8% with negligible run-time overhead. The addition of a loop buffer further reduces the energy consumption by 19.8% and 4.5% in the two suites. Our results indicate that programmable dictionary compression allows further energy reductions over an already highly tuned instruction stream.

Carbon Nanocage Supported Asymmetrically Coordinated Nickle Single‐Atom for Enhanced CO2 Electroreduction in Membrane Electrode Assembly

AbstractDesigning efficient catalysts for operating CO2 electroreduction in membrane electrode assembly (MEA) faces significant obstacles. Herein, we propose an asymmetrically coordinated Ni single‐atom catalyst featuring axial Br coordination at NiN4Br sites anchoring onto hollow Br/N co‐doped carbon nanocages, achieved through a NaBr‐assisted confined‐pyrolysis strategy. The Ni‐NBr‐C catalyst exhibits a high CO Faradaic efficiency (FECO>97 %) over the current density range of 50 to 350 mA cm−2 in the MEA device. Furthermore, Ni‐NBr‐C shows a stable cell voltage of 2.66±0.2 V while delivering a large current density of 350 mA cm−2 over an 85‐hour long‐term operation, demonstrating its potential for industrial‐scale applications. Advanced characterization techniques and theoretical calculations reveal that the coordination and doping of Br not only enhance the intrinsic activity but also highlight that the unique pore structure improves mass transfer efficiency.

Anatase‐reinforced PtZn@Silicalite‐1 structured catalysts boosting propane dehydrogenation

AbstractStructured catalysts exhibit the advantages of high diffusion efficiency and low heat transfer resistance, which have attracted increasing attention to non‐adiabatic gas–solid process. However, the metal‐supported coating catalysts face the problems of weaker bond strength and severe sintering, especially under the conditions of large flow rate and high temperature. Herein, metal@Silicalite‐1 structured catalysts with high adhesion and thermal stability were successfully prepared by hydroxylating the substrate with anatase. Rich surface Ti‐OH significantly strengthened the adhesion stability of the zeolite coating. In propane dehydrogenation reaction, the optimized PtZn@S‐1‐15Ti showed a high specific activity of 49.6 molC3H6·molPt−1·s−1 with propylene selectivity above 99% at 600°C. The introduction of anatase accelerated the aggregation of silicon sources and induced nucleation with growth content of zeolite increased by 3.6 times. It breaks the inherent contradiction between high loading amount and strong binding ability of coated catalysts, which broadens the avenues for industrial applications.