Powerful Technology Research Articles

Implementation of Accurate Parameter Identification for Proton Exchange Membrane Fuel Cells and Photovoltaic Cells Based on Improved Honey Badger Algorithm.

Predicting the system efficiency of green energy and developing forward-looking power technologies are key points to accelerating the global energy transition. This research focuses on optimizing the parameters of proton exchange membrane fuel cells (PEMFCs) and photovoltaic (PV) cells using the honey badger algorithm (HBA), a swarm intelligence algorithm, to accurately present the performance characteristics and efficiency of the systems. Although the HBA has a fast search speed, it was found that the algorithm's search stability is relatively low. Therefore, this study also enhances the HBA's global search capability through the rapid iterative characteristics of spiral search. This method will effectively expand the algorithm's functional search range in a multidimensional and complex solution space. Additionally, the introduction of a sigmoid function will smoothen the algorithm's exploration and exploitation mechanisms. To test the robustness of the proposed methodology, an extensive test was conducted using the CEC'17 benchmark functions set and real-life applications of PEMFC and PV cells. The results of the aforementioned test proved that with regard to the optimization of PEMFC and PV cell parameters, the improved HBA is significantly advantageous to the original in terms of both solving capability and speed. The results of this research study not only make definite progress in the field of bio-inspired computing but, more importantly, provide a rapid and accurate method for predicting the maximum power point for fuel cells and photovoltaic cells, offering a more efficient and intelligent solution for green energy.

Human NQO1 as a Selective Target for Anticancer Therapeutics and Tumor Imaging.

Human NAD(P)H-quinone oxidoreductase1 (HNQO1) is a two-electron reductase antioxidant enzyme whose expression is driven by the NRF2 transcription factor highly active in the prooxidant milieu found in human malignancies. The resulting abundance of NQO1 expression (up to 200-fold) in cancers and a barely detectable expression in body tissues makes it a selective marker of neoplasms. NQO1 can catalyze the repeated futile redox cycling of certain natural and synthetic quinones to their hydroxyquinones, consuming NADPH and generating rapid bursts of cytotoxic reactive oxygen species (ROS) and H2O2. A greater level of this quinone bioactivation due to elevated NQO1 content has been recognized as a tumor-specific therapeutic strategy, which, however, has not been clinically exploited. We review here the natural and new quinones activated by NQO1, the catalytic inhibitors, and the ensuing cell death mechanisms. Further, the cancer-selective expression of NQO1 has opened excellent opportunities for distinguishing cancer cells/tissues from their normal counterparts. Given this diagnostic, prognostic, and therapeutic importance, we and others have engineered a large number of specific NQO1 turn-on small molecule probes that remain latent but release intense fluorescence groups at near-infrared and other wavelengths, following enzymatic cleavage in cancer cells and tumor masses. This sensitive visualization/quantitation and powerful imaging technology based on NQO1 expression offers promise for guided cancer surgery, and the reagents suggest a theranostic potential for NQO1-targeted chemotherapy.

Epigenome editing technologies for discovery and medicine.

Epigenome editing has rapidly evolved in recent years, with diverse applications that include elucidating gene regulation mechanisms, annotating coding and noncoding genome functions and programming cell state and lineage specification. Importantly, given the ubiquitous role of epigenetics in complex phenotypes, epigenome editing has unique potential to impact a broad spectrum of diseases. By leveraging powerful DNA-targeting technologies, such as CRISPR, epigenome editing exploits the heritable and reversible mechanisms of epigenetics to alter gene expression without introducing DNA breaks, inducing DNA damage or relying on DNA repair pathways.

Predicting the economic feasibility of solar-based net-zero emission buildings (NZEBs) in the United States non-residential sector

Buildings have significantly contributed to high energy demand (30.3%) and GHG emissions (26.1%) worldwide. Consequently, many developed countries have set carbon-neutral targets for 2050, mandating that all new constructions thereafter be designed as net-zero emission buildings (NZEBs). To achieve this goal, one of the most practical sources of renewable energy, solar power, is employed in this study to estimate the economic feasibility of implementing NZEBs in the non-residential sector of the United States. While past studies have typically estimated the economic payback period of solar power technology by assuming fixed values for “PV energy conversion rates (%)” and “investment costs ($USD)”, this paper carefully investigated the economic feasibility of “solar-based NZEBs” by precisely tracking these dynamic future changes in PV panels. These techno-economic variables were carefully predicted using a statistical technique known as the five-parameter logistic (5 PL) function. The results show that Site 4 is the only solar region suitable for implementing “PV-integrated NZEBs”, reaching a payback period of 8.44 years in 2040 (Scenario 1). Furthermore, with small technological improvements in PV energy efficiency (%), buildings located in Site 3 and Site 4 can meet the net-zero emission target with payback periods of less than 10 years in 2034 (Scenario 2). Finally, starting in 2044, the “PV-integrated system” will have a payback period of approximately 7 years in most study locations, even without federal support for the solar investment tax credit (SITC). Accordingly, federal support for solar power generation will be more effective if the SITC rate is gradually reduced by 2.5% annually starting in 2033. In conclusion, this paper suggests the economic feasibility of implementing solar-based NZEBs in the United States non-residential sector by considering the synergetic effect of technological improvement and cost reduction in PV-integrated systems.

Thermodynamic evaluation of decarbonized power production based on solar energy integration

This study aims to create an environmentally friendly system by utilizing solar and biomass energies as renewable sources, employing high temperature and pressure ranges for analysis. A new energy system is introduced, using biomass to produce hydrogen and other valuable commodities. The study evaluates power and hydrogen production technologies from a thermodynamic perspective, including conventional biomass gasification, calcium and iron thermochemical looping cycles, and gasification with supercritical water, incorporating carbon capture techniques. It assesses and compares the performance of locally available biomass material, such as cotton stalk, during gasification. Results show that employing the supercritical water gasification cycle yields the highest hydrogen production (8330 kg/h) and carbon capture rate (92.91 %), with potential for significant reduction in CO2 emissions. Supercritical water gasification also demonstrates electricity, heating, and CO2 production rates of 51.48 MW, 23.3 MW, and 16.6 MW, respectively. Integrating supercritical water gasification with solar energy enhances syngas productivity and system efficiency. Additionally, the highest performance is demonstrated in the SCWG case, which exhibits the lowest irreversibility at 320.32 MW. In conclusion, the system with supercritical water gasification emerges as the most efficient biomass conversion method, with higher energy and exergy efficiencies of 67.59 % and 49.74 %, respectively, under specific operating conditions. The developed model facilitates integration of solar energy into biomass gasification, enabling prediction and comparison of performance in terms of energy and exergy efficiencies.

Experimental relationship between the Seebeck and Peltier effects in thermoelectric modules based on Fe and Al metals

Thermoelectric modules (TEMs) based on resource-rich metals with a high thermoelectric performance are expected to be environmentally friendly technologies for electric power and heat generation. The relationship between the Seebeck and Peltier effects in Fe−Al TEMs was investigated to clarify the thermoelectric mechanism of metal-based TEMs. The TEMs with various Seebeck coefficients ranging from 1.8 to 12.7 μV/K were fabricated. The Peltier effect was analyzed based on a model established using the current direction and temperature difference. During the application of current from Fe to Al and from Al to Fe, heat absorption and generation were observed at the Fe−Al interface, respectively. The quasi-Peltier coefficients of the TEMs, calculated from the proposed model, were highly proportional to the Seebeck coefficient. These results suggest that the number of interfacial charges between the Fe and Al metals determines the thermoelectric performance. The tailoring of these charges could be an avenue to improve the thermoelectric performance in the design of high-performance metal-based TEMs.

Review of the occurrence, treatment technologies, and detection methods for saxitoxins in freshwaters.

The increasing occurrence of saxitoxins in freshwaters is becoming a concern for water treatment facilities owing to its structural properties which make it resistant to oxidation at pH < 8. Hence, it is crucial to be able to monitor these toxins in surface and drinking water to protect public health. This review aims to outline the current state of knowledge related to the occurrence of saxitoxins in freshwaters and its removal strategies and provide a critical assessment of the detection methods to provide a basis for further development. Temperature and nutrient content are some of the factors that influence the production of saxitoxins in surface waters. A high dose of sodium hypochlorite with sufficient contact time or activated carbon has been shown to efficiently remove extracellular saxitoxins to meet the drinking water guidelines. While HILIC-MS has proven to be a powerful technology for more sensitive and reliable detection of saxitoxin and variants after solid phase extraction, ELISA is cost-effective and easy to use and is used by Ohio EPA for surveillance with a limit of detection of 0.015 μg/L. However, there is a need for the development of cost-effective and sensitive techniques that can quantify the variants of saxitoxin.

Context of Digital Transformation Integration of Digital Technology Development and Innovation Entrepreneurship Education in Higher Education

In the process of education digitisation, novel and powerful digital technologies have been widely innovated, developed and applied, which have greatly changed the technological environment of innovation and entrepreneurship education in colleges and universities. As the main position of talent input, colleges and universities should take full advantage of the east wind of the rapid development of digital technology to inject strong vitality and impetus into innovation and entrepreneurship education, and keep exploring new paths of dual-creation education. This paper aims to provide theoretical and practical guidance for the continuous optimisation of the higher education system. It analyses the necessity of carrying out innovation and entrepreneurship education in colleges and universities in the context of the new era, explores the relationship and role of the development of digital technology in colleges and universities and innovation and entrepreneurship education, and finally puts forward the strategies and paths to promote the development of innovation and entrepreneurship education.

Differentially expressed heterogeneous overdispersion genes testing for count data.

The mRNA-seq data analysis is a powerful technology for inferring information from biological systems of interest. Specifically, the sequenced RNA fragments are aligned with genomic reference sequences, and we count the number of sequence fragments corresponding to each gene for each condition. A gene is identified as differentially expressed (DE) if the difference in its count numbers between conditions is statistically significant. Several statistical analysis methods have been developed to detect DE genes based on RNA-seq data. However, the existing methods could suffer decreasing power to identify DE genes arising from overdispersion and limited sample size, where overdispersion refers to the empirical phenomenon that the variance of read counts is larger than the mean of read counts. We propose a new differential expression analysis procedure: heterogeneous overdispersion genes testing (DEHOGT) based on heterogeneous overdispersion modeling and a post-hoc inference procedure. DEHOGT integrates sample information from all conditions and provides a more flexible and adaptive overdispersion modeling for the RNA-seq read count. DEHOGT adopts a gene-wise estimation scheme to enhance the detection power of differentially expressed genes when the number of replicates is limited as long as the number of conditions is large. DEHOGT is tested on the synthetic RNA-seq read count data and outperforms two popular existing methods, DESeq2 and EdgeR, in detecting DE genes. We apply the proposed method to a test dataset using RNAseq data from microglial cells. DEHOGT tends to detect more differently expressed genes potentially related to microglial cells under different stress hormones treatments.

Discovery of the first selective, small-molecule GFRα2/3 inhibitors through DNA-encoded library technology

Studies have shown that disrupting the formation of the ligand-RET-GFRα complex could be an effective way of treating pain and itch. Compared to traditional high-throughput screens, DNA encoded libraries (DELs) have distinguished themselves as a powerful technology for hit identification in recent years. The present work demonstrates the use of DEL technology identifying compound 16 as the first GFRa2/GFRa3 small molecule inhibitor (0.1/0.2 μM respectively) selective over RET. This molecule represents an opportunity to advance the development of small-molecule inhibitors targeting the GFRα-RET interface for the treatment of pain and itch.

Cross-technology legitimacy feedback: The politics of policy-led innovation for complementarity in concentrating solar power

Solar photovoltaic and wind power generation is expanding fast globally, fuelled by technological progress and rapid cost reductions. Other renewable power technologies fare much worse: deployment stagnates despite substantial technological progress. Here, we explore why these technologies fall off political agendas although they are improving, proposing that negative cross-technology feedback from more dynamic, faster deployed technologies reduce the legitimacy of laggard technologies. This generates political pressure to cancel or adapt support schemes, which in turn may push the laggard technology to change and become more complementary to the dynamic technologies. We illustrate our propositions with a case study of concentrating solar power (CSP) policy and deployment in three countries. We show how negative legitimacy feedback from the dynamic diffusion of photovoltaics and wind power in the 2010s led to both policy termination and technological adaptation towards complementarity, changing CSP from a generation to a storage and balancing technology.

Thermal stability of magneto-hybridized silicon and aluminum oxides nanoparticle in C3H8O2-Williamson exothermic reactive fluid with thin radiation for perovskite solar power

The need to increase thermal power stability and energy conservation have spurred the interest in various renewable energies. A diverse range of fabrication techniques and architectures have been developed to meet the global energy demand. Perovskite solar power technologies are the next emerging generation of photovoltaic thermal power systems for an enhanced and stable power supply. Thus, this project examines the thin radiation thermal stability of combined magneto-hybrid silicon oxide (SiO2) and aluminum oxide (Al2O3) nanoparticles in exothermic propylene glycol (C3H8O2)-Williamson fluid for perovskite thermal cells improvement. Without particles agglomeration, the fluid flow is influenced by lower wall velocity, Joule heating and Williamson shear stress in a bounded domain. An invariant coupled differential model is obtained through the similarity transformation of the governing model. The solutions to the invariant model is provided using semi-discretized finite difference method. The outcomes revealed that nanoparticles thermal propagation for perovskite power generation is strengthened with rising Brinkman number, radiation, and Frank-Kamenetskii terms. Also, criticality is raised at the unstable thermal region but damped at the stable thermal regime.

Optimizing Tourism Accommodation Offers by Integrating Language Models and Knowledge Graph Technologies

Online platforms have become the primary means for travellers to search, compare, and book accommodations for their trips. Consequently, online platforms and revenue managers must acquire a comprehensive comprehension of these dynamics to formulate a competitive and appealing offerings. Recent advancements in natural language processing, specifically through the development of large language models, have demonstrated significant progress in capturing the intricate nuances of human language. On the other hand, knowledge graphs have emerged as potent instruments for representing and organizing structured information. Nevertheless, effectively integrating these two powerful technologies remains an ongoing challenge. This paper presents an innovative deep learning methodology that combines large language models with domain-specific knowledge graphs for classification of tourism offers. The main objective of our system is to assist revenue managers in the following two fundamental dimensions: (i) comprehending the market positioning of their accommodation offerings, taking into consideration factors such as accommodation price and availability, together with user reviews and demand, and (ii) optimizing presentations and characteristics of the offerings themselves, with the intention of improving their overall appeal. For this purpose, we developed a domain knowledge graph covering a variety of information about accommodations and implemented targeted feature engineering techniques to enhance the information representation within a large language model. To evaluate the effectiveness of our approach, we conducted a comparative analysis against alternative methods on four datasets about accommodation offers in London. The proposed solution obtained excellent results, significantly outperforming alternative methods.

Assessing the usage of ChatGPT on life satisfaction among higher education students: The moderating role of subjective health

OpenAI's ChatGPT is a widely used artificial intelligence tool that has recently experienced rapid growth and widespread adoption. ChatGPT enhances digital accessibility, performance in communication, and supports the creation of digital content, which can be a powerful assistive technology for the education industry. However, the role of ChatGPT for higher education students remains a topic of contention. Therefore, this study is undertaken to investigate how ChatGPT usage can enhance information and communication technology (ICT) accessibility and performance by influencing life satisfaction among higher education students by proposing a unique conceptual model. The primary data were collected from 305 respondents, and quantitative methodology was used to analyse the data. The results indicate that interaction with ChatGPT increases freedom and productivity by producing understandable and relevant responses that meet emotional needs, which are positively correlated with happiness. ChatGPT usage provides unique experiences to students that evoke their feelings to strengthen academic engagement, and such feelings positively influence their perceptions and behaviour towards academic buoyancy. However, the concerns about the biased response, limited knowledge, and lack of emotional intelligence of ChatGPT limit trustworthiness and cause disengagement, which have been deemed the most significant weaknesses in improving quality of life.

Multi-omics analysis reveals that Cas13d contributes to PI3K-AKT signaling and facilitates cell proliferation via PFKFB4 upregulation

The CRISPR-Cas system is a powerful gene editing technology, the clinical application of which is currently constrained due to safety concerns. A substantial body of safety research concerning Cas9 exists; however, scant attention has been directed toward investigating the safety profile of the emergent Cas13 system, which confers RNA editing capabilities. In particular, uncertainties persist regarding the potential cellular impacts of Cas13d in the absence of reliance on a cleavage effect. In this study, we conducted an initial exploration of the effects of Cas13d on HeLa cells. Total RNA and protein samples were extracted after transfection with a Cas13d-expressing plasmid construct, followed by transcriptomic and proteomic sequencing. Differential gene expression analysis identified 94 upregulated and 847 downregulated genes, while differential protein expression analysis identified 185 upregulated and 231 downregulated proteins. Subsequently, enrichment analysis was conducted on the transcriptome and proteome sequencing data, revealing that the PI3K-Akt signaling pathway is a common term. After intersecting the differentially expressed genes enriched in the PI3K-Akt signaling pathway with all the differentially expressed proteins, it was found that the expression of the related regulatory gene PFKFB4 was upregulated. Moreover, western blot analysis demonstrated that Cas13d can mediate PI3K-Akt signaling upregulation through overexpression of PFKFB4. CCK-8 assay, colony formation, and EdU experiments showed that Cas13d can promote cell proliferation. Our data demonstrate, for the first time, that Cas13d significantly impacts the transcriptomic and proteomic profiles, and proliferation phenotype, of HeLa cells, thus offering novel insights into safety considerations regarding gene editing systems.

Neurobiology of Obsessive-Compulsive Disorder from Genes to Circuits: Insights from Animal Models.

Obsessive-compulsive disorder (OCD) is a chronic, severe psychiatric disorder that has been ranked by the World Health Organization as one of the leading causes of illness-related disability, and first-line interventions are limited in efficacy and have side-effect issues. However, the exact pathophysiology underlying this complex, heterogeneous disorder remains unknown. This scenario is now rapidly changing due to the advancement of powerful technologies that can be used to verify the function of the specific gene and dissect the neural circuits underlying the neurobiology of OCD in rodents. Genetic and circuit-specific manipulation in rodents has provided important insights into the neurobiology of OCD by identifying the molecular, cellular, and circuit events that induce OCD-like behaviors. This review will highlight recent progress specifically toward classic genetic animal models and advanced neural circuit findings, which provide theoretical evidence for targeted intervention on specific molecular, cellular, and neural circuit events.

Demystifying Machine Learning

This paper delves into the rapidly evolving domain of Artificial Intelligence (AI), with a particular focus on Machine Learning (ML), a dynamic and influential subset of AI. It explores how ML empowers computers to learn from data, identify patterns, and make decisions with minimal human intervention. The manuscript examines the broad utility of ML across various real-world scenarios, emphasizing its critical role in enabling organizations to evolve and maintain a competitive edge in the fast-paced technological landscape. It discusses the necessity for organizations to adopt new ways of working and embrace the opportunities presented by AI to remain viable in the global, online marketplace. The paper reviews the evolution of ML, evaluates its advantages and disadvantages, and contemplates the future directions ML could lead organizations willing to integrate this powerful technology. The overarching theme is the transformative potential of ML in reshaping organizational strategies and operations for a more interconnected and intelligent future.

Task execution latency minimization for energy-sensitive IoTs in wireless powered mobile edge computing: A DRL-based method

Wireless powered mobile edge computing (MEC) has become a vital component of future 6G networks, offering efficient computational capabilities to internet of things (IoT) devices and ensuring a stable energy supply. In this paper, we propose an innovative design for a wireless powered MEC-assisted energy-sensitive IoT systems. The system integrates wireless power technology (WPT), allowing IoT devices to efficiently perform tasks under wireless power supply without consuming their battery energy. Therefore, we formulate a dynamic computational task execution latency minimization (DCTELM) problem to tackle the impacts of unpredictable energy requirements and potential communication latency on resource allocation. We also analyze the differences between time division multiple access and non-orthogonal multiple access communication protocols. Given the complexity and dynamic nature of this problem, we define it as a mixed integer nonlinear programming problem. To address this problem, we introduced an enhanced deep reinforcement learning (DRL) algorithm. First, the DCTELM problem is transformed into a Markov decision process (MDP), which simplifies the structure of the problem. Then, after determining the offloading decision, we reveal the convex relationship between resource allocation and task latency. Furthermore, the action space of MDP is further reduced, and a multi-head proximal policy optimization (PPO) algorithm is proposed to deal with the simplified MDP problem. This process reduces the complexity of problem solving, decreases the consumption of computational resources. Simulation results demonstrate that our method outperforms other baselines in terms of computation latency, particularly with the introduction of an exploration strategy during the training phase, which significantly reduces task latency.

Assessing the social life cycle impacts of the Spanish electricity mix: A decadal analysis

Power generation systems are crucial to national energy transitions, such as Spain's, which stands as a notable example. However, this profound transformation could have multifaceted implications, leading to unintended consequences on society. The present work is the first to understand the social impacts of the Spanish power sector and their technology supply chains using the social life cycle sssessment methodology. The functional unit is 1 kWh of electricity produced by the technologies in the Spanish electricity mix. A cradle-to-gate approach is taken using a supply chain protocol to complete the system boundaries. The social life cycle inventory, comprising data on national suppliers, working hours and social flows, was integrated into the PSILCA database to derive the social profile of each power technology and, consequently, to obtain a comprehensive view of the Spanish power sector. The results reveal that social impact associated with the Spanish electricity mix has increased or remained stable from 2010 to 2022. Analysis of four indicators (child labour, contribution of the sector to the economic development, frequency of forced labour and women in the sectoral labour force) reveals significant differences, highlighting three main social hotspots: i) solar PV panel production in East and Southeast Asia, particularly China, ii) natural gas extraction and refining in North Africa, concentrated in Algeria, for natural gas combined cycle and cogeneration plants, and iii) construction and operation of hydropower and nuclear plants in Spain. This study demonstrates that current strategies for Spain's power sector transition may not guarantee a favourable social performance, emphasizing the need for balanced environmental and social considerations in energy policy making, aligned with the Sustainable Development Goals.

Application of genome editing in plant reproductive biology: recent advances and challenges.

This comprehensive review underscores the application of genome editing in plant reproductive biology, including recent advances and challenges associated with it. Genome editing (GE) is a powerful technology that has the potential to accelerate crop improvement by enabling efficient, precise, and rapid engineering of plant genomes. Over the last decade, this technology has rapidly evolved from the use of meganucleases (homing endonucleases), zinc-finger nucleases, transcription activator-like effector nucleases to the use of clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein (CRISPR/Cas), which has emerged as a popular GE tool in recent times and has been extensively used in several organisms, including plants. GE has been successfully employed in several crops to improve plant reproductive traits. Improving crop reproductive traits is essential for crop yields and securing the world's food supplies. In this review, we discuss the application of GE in various aspects of plant reproductive biology, including its potential application in haploid induction, apomixis, parthenocarpy, development of male sterile lines, and the regulation of self-incompatibility. We also discuss current challenges and future prospects of this technology for crop improvement, focusing on plant reproduction.