Powerful Technology Research Articles

Three-Dimensional Hydrodynamic and CFD Modeling of a Tidal Barrage Power Plant without Sluicing in Buenaventura, Colombia

Recent research revealed the potential of tidal energy in the central coastal region of the Colombian Pacific. Buenaventura City, located in the Valle del Cauca department in Colombia, has an important opportunity to develop tidal power technologies near its marine coastal areas. This research implemented a 3D hydrodynamic model for simulating the hydrodynamics of the Buenaventura Bay to provide data as input for evaluating the hydraulics of a tidal barrage without sluicing through CFD modeling. According to the results, the velocities across the gates during Syzygy (April 2021) showed impressive velocities between 9 and 11 m/s, which suggest a high possibility of producing electricity through tidal turbines. The mean behavior of velocities in the gates pointed to values of 3 and 5 m/s in most of the cases. The results during the Stoa condition were interesting because flow velocities higher than 1 m/s were not expected. This is promising because the plant might produce electricity even during the Stoa condition. For the first time, the results of this research suggest that there exists a high possibility of implementing tidal barrage plants in Buenaventura City, Colombia.

Bids and asks: Blockchain-based peer-to-peer trading platform

In residential microgrids, rooftop solar power generation is becoming increasingly common. A new concept in electricity markets, such as peer-to-peer (P2P) auctions, is also emerging. In these markets, consumers and prosumers can exchange locally generated power directly with each other without relying on an intermediary. This approach promotes sustainable development. However, data security is a major concern in energy trading. Consequently, the use of blockchain technology in power markets has become more widespread. Blockchain can facilitate the trading of power from P2P sources. This paper presents the design and algorithms for automating energy trading through smart contracts, utilizing Home Energy Management Systems (HEMS) and Smart Meter Gateways (SM) for prosumers, consumers, and utilities. Simulations across various microgrid setups validated the peer-to-peer (P2P) energy market on the blockchain network. This framework increases profits for prosumers and consumers trading with each other, the necessity of blockchain operators for updating smart contracts and managing participants, the economic impracticality of current high gas costs on Ethereum, and the benefit of a closer bidding range for profit generation in P2P energy markets.

Comparing the Efficacy and Efficiency of Human and Generative AI: Qualitative Thematic Analyses.

Qualitative methods are incredibly beneficial to the dissemination and implementation of new digital health interventions; however, these methods can be time intensive and slow down dissemination when timely knowledge from the data sources is needed in ever-changing health systems. Recent advancements in generative artificial intelligence (GenAI) and their underlying large language models (LLMs) may provide a promising opportunity to expedite the qualitative analysis of textual data, but their efficacy and reliability remain unknown. The primary objectives of our study were to evaluate the consistency in themes, reliability of coding, and time needed for inductive and deductive thematic analyses between GenAI (ie, ChatGPT and Bard) and human coders. The qualitative data for this study consisted of 40 brief SMS text message reminder prompts used in a digital health intervention for promoting antiretroviral medication adherence among people with HIV who use methamphetamine. Inductive and deductive thematic analyses of these SMS text messages were conducted by 2 independent teams of human coders. An independent human analyst conducted analyses following both approaches using ChatGPT and Bard. The consistency in themes (or the extent to which the themes were the same) and reliability (or agreement in coding of themes) between methods were compared. The themes generated by GenAI (both ChatGPT and Bard) were consistent with 71% (5/7) of the themes identified by human analysts following inductive thematic analysis. The consistency in themes was lower between humans and GenAI following a deductive thematic analysis procedure (ChatGPT: 6/12, 50%; Bard: 7/12, 58%). The percentage agreement (or intercoder reliability) for these congruent themes between human coders and GenAI ranged from fair to moderate (ChatGPT, inductive: 31/66, 47%; ChatGPT, deductive: 22/59, 37%; Bard, inductive: 20/54, 37%; Bard, deductive: 21/58, 36%). In general, ChatGPT and Bard performed similarly to each other across both types of qualitative analyses in terms of consistency of themes (inductive: 6/6, 100%; deductive: 5/6, 83%) and reliability of coding (inductive: 23/62, 37%; deductive: 22/47, 47%). On average, GenAI required significantly less overall time than human coders when conducting qualitative analysis (20, SD 3.5 min vs 567, SD 106.5 min). The promising consistency in the themes generated by human coders and GenAI suggests that these technologies hold promise in reducing the resource intensiveness of qualitative thematic analysis; however, the relatively lower reliability in coding between them suggests that hybrid approaches are necessary. Human coders appeared to be better than GenAI at identifying nuanced and interpretative themes. Future studies should consider how these powerful technologies can be best used in collaboration with human coders to improve the efficiency of qualitative research in hybrid approaches while also mitigating potential ethical risks that they may pose.

Evaluating batch correction methods for image-based cell profiling

High-throughput image-based profiling platforms are powerful technologies capable of collecting data from billions of cells exposed to thousands of perturbations in a time- and cost-effective manner. Therefore, image-based profiling data has been increasingly used for diverse biological applications, such as predicting drug mechanism of action or gene function. However, batch effects severely limit community-wide efforts to integrate and interpret image-based profiling data collected across different laboratories and equipment. To address this problem, we benchmark ten high-performing single-cell RNA sequencing (scRNA-seq) batch correction techniques, representing diverse approaches, using a newly released Cell Painting dataset, JUMP. We focus on five scenarios with varying complexity, ranging from batches prepared in a single lab over time to batches imaged using different microscopes in multiple labs. We find that Harmony and Seurat RPCA are noteworthy, consistently ranking among the top three methods for all tested scenarios while maintaining computational efficiency. Our proposed framework, benchmark, and metrics can be used to assess new batch correction methods in the future. This work paves the way for improvements that enable the community to make the best use of public Cell Painting data for scientific discovery.

The Telecommunications Act, 2023: Solidarity Between Democracy and Totalitarianism

ABSTRACT With the coming of a surveillance culture, even minute aspects of human life are being observed, collected, analyzed, and stored by institutions in charge of surveillance. On the other hand, the same does not apply to the institutions which are surveillant of the lives of individuals. Such phenomenon can be observed via various technologies of power prevalent in societies. One such technology of power is legislation passed by the State to access, control, analyze, disseminate, or store the personal information of citizens. This article analyses the Telecommunications Act, 2023 which has been hailed as a move towards decolonization by repealing the former colonial laws over telecom services. The article discusses various attributes of the 2023 Act to analyze the means through which the usage of biometric systems of identification leads to violation of the fundamental right to privacy, as envisaged by the apex court of India in the landmark judgment of Justice K S Puttaswamy (Retd) v Union of India and other judgments before and after. The article also argues how the works of Giorgio Agamben and Didier Bigo on a permanent state of emergency being materialized through surveillance of minute aspects of human life and consequently marks a shift towards totalitarian tendencies of governance.

A review of concentrated solar power status and challenges in India

Concentrated Solar Power (CSP) technology has emerged as a promising renewable energy solution, offering a sustainable and efficient means of electricity generation and thermal energy storage. India, endowed with abundant solar irradiance, has made significant strides in promoting CSP technology as part of its renewable energy portfolio. With a growing focus on reducing greenhouse gas emissions and enhancing energy security, the Indian government has initiated numerous policies, incentives, and projects to encourage CSP adoption. Parabolic trough collectors, a type of linear concentrating system, are currently in widespread use. Power or solar towers are the most common type of point concentrating CSP technology currently in use. India aims to achieve a renewable energy capacity of 175 GW by 2022, with solar power constituting 100 GW of the overall target. Concentrated solar power technology is slated to grow 87% during 2018–2023, 32% faster than in the previous five-year period 2012–2017 and reach 4.3 GW in 2023. In future, present review paper can be regarded as a valuable resource for researchers, policymakers and industry professionals seeking to comprehend the present condition of concentrated solar power in India. It can aid in the development of strategies to address obstacles and advance sustainable and efficient solar energy solutions.

Imagining beyond a statist imaginary

AbstractThe chilling infiltration by technologies of state power that make up modern governance is brought home by each of the articles in AE’s “Citizenship, Solidarity, and Nonbelonging” forum. In reflecting on them, I pose the question: Can we move beyond descriptions of human agency entirely within the cracks and fissures of state governance? Or can we develop a richer futural imagination that goes beyond a statist imaginary? If we are to sustain such an endeavor, we need not only imaginative analytical frameworks, but also forms of language that resist being reduced to “thinking like the state.”

Control Strategies for Mitigating Power Quality Issues in Renewable Energy coordinated Microgrid - A Comprehensive Review

Abstract Renewable Energy technologies are becoming more and more common for generating electricity because they are environmentally friendly and can meet local electricity needs. They reduce network congestion and also lighten the load on conventional based power plants. Over the past few decades, grid-connected renewable energy systems have seen a rise in the significance of Power Quality issues, particularly as a result of the extensive usage of nonlinear electronics and the intermittent nature of these systems. With the emergence of power electronic converters through powerful control technology, renewable energy systems be interconnected to a large extent with the power grid or used as isolation systems in remote areas. Using additional competent control strategies can enhance not only improve the concert of these systems, but also caliber the quality of energy generated, distributed and used at the load side of Power System. Hence, this paper reviews various control strategies adopted for alleviation of power quality staples of renewable energy coordinated microgrid system and further recommendations are provided to solve power quality issues as future research.

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.