Viable Alternatives Research Articles

Evaluation of local substrates as rice straw alternatives for oyster mushroom (Pleurotus ostreatus) cultivation in resource-constrained Darchula, Nepal

Oyster mushroom cultivation, though cost-effective, faces constraints due to seasonality and substrate availability. An experimental study was conducted in the resource-constrained Darchula district of Nepal from February to May 2022. The aim was to identify and recommend economically sustainable alternatives to rice straw for oyster mushroom production using local substrates in regions with limited resources. Six treatments; rice straw (T1), banana leaves and pseudostem (T2), maize cob (T3), sawdust (T4), grass (Eulaliopsis sp.) (T5), and spent mushroom substrates (T6); were employed in a completely randomized design with four replications. Statistical analysis of growth and yield parameters revealed significant results (P values ranging from P>0.001 to P>0.05) across all parameters. The maize cob treatment exhibited a shorter spawn run period (20.50 days) and the earliest pinhead formation (25 days). The highest total yield (3.14 kg) across three flushes was obtained from paddy straw, followed by T2 and T5, yielding 2.05 kg and 1.43 kg, respectively. Sawdust, despite its larger stalk (1.23 cm) and pileus diameter (7.72 cm), had the lowest production (0.63 kg). Maximum biological efficiency was recorded for T1 (139.63%), followed by T2, T5, and T3, respectively. Economically, T1 resulted in the highest gross margin per 10 kg of substrate (NRs.1845.22) and the highest B:C ratio (2.51), followed by T5 and T2. These findings highlight the promise of locally abundant substrates such as banana leaves, pseudo stems, Eulaliopsis, and maize cobs as economically viable alternatives to rice straw in regions with limited straw availability or unsuitable climates for rice cultivation.

Thyme, Oregano, and Garlic Essential Oils and Their Main Active Compounds Influence Eimeria tenella Intracellular Development.

Coccidiosis poses a significant challenge in poultry production and is typically managed with ionophores and chemical anticoccidials. However, the emergence of drug resistance and limitations on their use have encouraged the exploration of alternative solutions, including botanical compounds and improvements in in vitro screening methods. Prior research focused only on the impact of these alternatives on Eimeria invasion, with intracellular development in cell cultures receiving limited attention. This study assessed the impact of thyme (Thymus vulgaris), oregano (Origanum vulgare), and garlic (Allium sativum) essential oils, as well as their bioactive compounds, on the initial phase of schizogony in Madin-Darby bovine kidney cells, comparing their effectiveness to two commercially used anticoccidial drugs. Using image analysis and quantitative PCR, the study confirmed the efficacy of commercial anticoccidials in reducing invasion and schizont formation, and it found that essential oils were equally effective. Notably, thymol and carvacrol exhibited mild inhibition of intracellular replication of the parasite but significantly reduced schizont numbers, implying a potential reduction in pathogenicity. In conclusion, this research highlights the promise of essential oils and their bioactive components as viable alternatives to traditional anticoccidial drugs for mitigating coccidiosis in poultry, particularly by disrupting the intracellular development of the parasites.

Real-time diagnostic synthesis of nitrogen-doped carbon-coated VC@C(Nx) core-shell nanoparticles by DC arc plasma as efficient ORR electrocatalysts

The high complexity and cost of synthesis methods, along with the instability of catalytic activity, pose significant challenges in the search for viable non-precious metal alternatives to commercial Pt/C catalysts for oxygen reduction reaction (ORR). In this study, we employed a highly efficient and cost-effective method, namely, DC arc plasma, to synthesize carbon-coated vanadium carbide (VC@C) core-shell nanoparticles in a one-step process. The synthesized VC@C material was subsequently nitrogen-doped using urea as a nitrogen (N) source in a tube furnace, resulting in the formation of N-doped carbon-coated VC [VC@C(Nx)] catalysts for ORR electrocatalysis in alkaline media. Real-time optical emission spectroscopy (OES) analysis offers an in-depth comprehension of the energy states involved in the synthesis process of VC@C nanocrystalline powders. Highlighting the plasma states at various stages throughout the synthesis, provides a solid basis for dissecting the formation of VC@C nanoparticles and elucidating the intricate nature of their core-shell structures. Compared to the VC@C catalyst, the VC@C(N10) catalyst exhibited a significant improvement in both half-wave and onset potential, approaching the performance levels comparable to Pt/C catalysts. Moreover, VC@C(N10) demonstrated superior ORR kinetics, stability, and methanol tolerance. The exceptional performance of VC@C(N10) could be primarily attributed to the introduction of nitrogen doping, which generated more active sites and enhanced the synergistic cooperation between the VC core and the nitrogen-doped carbon shell. Additionally, the protective effect from the carbon shell prevented corrosion and oxidation of the VC core. This study represents a new exploration in the quest for the development of stable and efficient non-precious metal catalysts for ORR applications.

ENHANCING SENTIMENT ANALYSIS WITH CHATBOTS: A COMPARATIVE STUDY OF TEXT PRE-PROCESSING

Text pre-processing plays a crucial role in the Sentiment Analysis process. Machine Learning models like Chat GPT-3.5 by OpenAI and Google Bard serve as alternative methods for text pre-processing. This study aims to evaluate the capabilities of both Chatbots in the text pre-processing stage while assessing their performance using a dataset obtained by crawling from source X. The study involves a comparison of Chat GPT-3.5 and Google Bard using Decision Tree and Naïve Bayes algorithms. The validation process employs K-Fold Cross Validation with a K value of 10. Additionally, three sampling methods, namely Linear, Shuffled, and Stratified Sampling, are utilized. The findings reveal that Chat GPT-3.5 performs best when using the Decision Tree algorithm with a K-Fold Cross value of 10, and employing Stratified Sampling, achieving an Accuracy of 90.68%, Precision of 90.63%, and Recall of 100%. On the other hand, Google Bard's optimal performance is achieved with the Decision Tree algorithm, a K-Fold Cross value of 10, and Shuffled Sampling, resulting in an Accuracy of 74.00%, Precision of 72.73%, and Recall of 98.77%. The study concludes that Chat GPT-3.5 and Google Bard are viable alternatives for text pre-processing in Sentiment Analysis. Performance measurements indicate that Chat GPT-3.5 outperforms Google Bard, achieving an Accuracy of 90.68%, Precision of 90.63%, and Recall of 100%. These results were validated by comparing them to human annotations, which achieved an accuracy score of 85.20%, Precision of 85.71%, and Recall of 99.03% when using the Decision Tree algorithm with a K-Fold Cross value of 10 and employing Stratified Sampling. This suggests that Chat GPT-3.5's text pre-processing performance is on par with human annotations.

An in-Depth Exploration of the Electrocatalytic Stability of Sn-Based Electrocatalysts for the Electrochemical CO2 Reduction Towards Formate

Over the past decades, the electrochemical CO2 reduction (eCO2R) into industrially valuable products has become one of the most promising technologies to valorize anthropogenic CO2 emission, while also providing a means of energy storage for intermittent renewable sources. A wide variety of products, such as formic acid/formate (HCOOH/HCOO-), carbon monoxide (CO), methane (CH4), methanol (CH3OH), ethylene (C2H4) etc., can be obtained. The eCO2R towards formic acid/formate (FA), a 2-electron transfer process, has the potential to generate the highest revenue per mole of consumed electrons, which originates from the fact that FA can be produced with high Faradaic efficiencies on cheap and earth abundant metals, such as Sn, Bi, Pb, Hg, In and Cd. State-of-the-art Sn-based electrocatalysts have been reported to reach selectivities approaching 100% at industrially relevant current densities. Looking at the long-term performance of these state-of-the-art Sn-based electrocatalysts, only seven have a minimum reported stability of 72 hours, with the maximum being 100 days (2400 h) of stable operation. Even though Bi-based catalysts currently outperform Sn-based electrocatalysts in terms of stability, Sn-based catalysts are still believed to be viable alternatives if an extended stability of over 80 000 hours can be achieved. Sn-based electrocatalyst stability thus remains inadequate and appears to be a crucial piece to the puzzle. 1 In this research, we elucidate the major degradation pathways that impair long-term electrocatalytic performance, by determining the chemical and physical phenomena that occur during the electrochemical reduction reaction on the surface and in the bulk of Sn-based catalysts. Simultaneously, the possibilities of a variety of mitigation strategies are explored, and insight into stability issues related to Sn-based electrocatalysts and CO2 electrolysers is gathered.Firstly, Sn was incorporated into a more open, carbon based supporting material (N-doped ordered mesoporous carbon) in an attempt to significantly increase the stability by inhibiting agglomeration and nanoparticle detachment. An N-doped carbon supporting material was chosen for its high surface area and in order to enhance the initial adsorption of CO2, which is commonly referred to as the rate-limiting step, for the eCO2R towards formate, in literature. These novel electrocatalysts provide us with valuable insights into the influence of the supporting material on their electrochemical performance and highlighted the potential of the particle confinement strategy to increase the morphological electrocatalyst stability during electrolysis.In a next step, we demonstrated the successful application of the recently developed pomegranate-structured SnO2 (Pom. SnO2) and SnO2@C (Pom. SnO2@C) nanocomposite electrocatalysts for the efficient electrochemical conversion of CO2 to formate. With an initial selectivity of 83 and 86% towards formate and an operating potential of -0.72 V and -0.64 V vs. RHE, respectively, these pomegranate SnO2 electrocatalysts are able to compete with most of the current state-of-the-art Sn-based electrocatalysts in terms of activity and selectivity. Given the importance of electrocatalyst stability, long-term experiments (24 h) were performed and a temporary loss in selectivity was noticed for the Pom. SnO2@C electrocatalyst. Ex situ XRD and XPS were used to link this temporary selectivity loss of the Pom. SnO2@C electrocatalyst to the in situ SnO2 reduction to metallic Sn. While this electrochemical degradation occurs in both electrocatalysts, it is more pronounced in the Pom. SnO2@C electrocatalyst since it isn’t offset by the morphological electrocatalyst degradation revealing new and selective SnOx active sites, as suspected for the Pom. SnO2. Furthermore, we were able to largely restore its selectivity upon drying and exposure to air. Of all the used (24 h) electrocatalysts, the pomegranate SnO2@C had the highest selectivity over a time period of one hour, reaching an average recovered FE of 85%, while the commercial SnO2 and bare pomegranate SnO2 electrocatalysts reached an average of 79 and 80% FE towards formate, respectively. Finally, the pomegranate structure of Pom. SnO2@C was largely preserved due to the presence of the heterogeneous carbon shell, which acts as a protective layer, physically inhibiting particle segregation/pulverisation and agglomeration.Utilizing the particle confinement strategy, we increased the morphological stability of pomegranate structured SnO2 electrocatalysts and demonstrated the reversible nature of the in situ SnO2 reduction. Van Daele, K. et al. Sn-Based Electrocatalyst Stability: A Crucial Piece to the Puzzle for the Electrochemical CO2Reduction toward Formic Acid. ACS Energy Lett. 6, 4317–4327 (2021). Figure 1

Unlocking hidden knowledge: Harnessing AI, technology, and design for accessible research

This paper is based upon a presentation at the APE conference 2023, during the ‘STM’s New Dotcoms to Watch session’ by CPO and Co-Founder Alejandro Garcia Garcia. It discussed the limitations of the existing format of published journal articles and the barriers it creates. It discusses how this can be tackled using a combination of AI, design innovation and software to create viable alternatives, and extend the reach of the content to readers outside of Academia.

(Invited) Electrochemical Deposition for Applications in the Semiconductor Industry

This year (2023) marks the 25th anniversary of the “Damascene copper electroplating for chip interconnections” publication by P.C. Andricacos et al [1] detailing the dual-damascene process and Cu superfilling of complex structures. And even though this was neither the only electrochemistry contribution to the semiconductor industry nor the first time to report successful plating of Cu structures, it signaled that electrochemical deposition would play prominent role in downscaling race [2]. The authors described Cu superfilling of a feature having characteristic dimensions of about 1 micron. Since then, electrochemists have focused intensely on electrolyte development, optimization of deposition parameters, and improvements in plating tools and process chambers, which went hand in hand with downscaling of features to be filled. Cu was plated on resistive substrates and alternative seeds, various surface pretreatments were applied, and nucleation and growth phenomena studied [3-6], until the focus shifted to alternative metals, such as Co [7]. While Chemical vapor deposition (CVD) and Atomic layer deposition (ALD) offer viable alternatives to electrochemical deposition (ECD) in fabrication of interconnects having width of several nanometers, new challenges and opportunities in 3D packaging and stacking appeared on the horizon. This field resembles more a ‘landscape’ than a ‘one-way street’ that damascene nano-interconnects were. While on one hand downscaling is gaining momentum here, too, there is also upscaling. For example, electrochemically deposited mega-pillars have diameters and heights exceeding hundreds of microns. More than a decade ago, a competitive race started between chemistry suppliers, tool manufacturers and scientists in the field to achieve bottom-up Cu fill of through-Si-vias (TSV) [8], structures meant to provide interconnections between vertically stacked integrated circuits. These structures had characteristic sizes an order of magnitude larger than the ones described in [1]. After demonstrating that superfilling of TSV was indeed possible, it became imperative to satisfy a plethora of production-line requirements, such as optimization of plating time or control of the so-called Cu pumping, a plastic deformation of Cu upon annealing. Plastic deformation leads to an uneven top surface and challenges in stacking layers on top of each other and is, therefore, undesirable. Today, researchers are contemplating possible benefits of the plastic deformation in Cu structures to establish a more robust and reliable Cu-to-Cu bonding process. Achieving defect-free fill of features was never the only requirement to be satisfied, but the ability to control plastic deformation in Cu features, or fabricate structures with specific texture, grain size, crystal structure [9, 10] on sub-micron scale, might require more thorough modifications of the current Cu plating processes. We will review this and other current trends in electrochemical deposition for applications in ‘classical’ semiconductor industry. We will also consider possibilities in technologies for Quantum computing, where a new spectrum of non-traditional candidate elements for plating could emerge [11, 12]. There, lessons learned from the downscaling of traditional interconnects could prove very useful.

Sculpting the future of meat: Biomaterial approaches and structural engineering for large‐scale cell‐based production

AbstractNovel foods are an increasing reality (and necessity) for our global food system and need to be protein‐rich for good nutrition. Cell‐based meats use the edible biomass of in vitro cultured animal cells harvested from the muscle tissue of live animals, removing the need to raise and slaughter animals. It is a viable alternative that is environmentally friendly, lowers resource consumption, and reduces health risks associated with traditional livestock farming. The cell‐based meat industry has boomed over the last 5 years with dozens of start‐ups being founded and millions of dollars in capital raised. While cell‐based meat faces technical, socio‐political, and regulatory challenges, it is a nascent technology with key technical challenges such as cell line stability, non‐animal‐derived culture media development, and bioprocessing for commercial‐scale uses. Here, we review the current field of cell‐based meat production at a lab scale and assess the feasibility and scalability of commercial production and the challenges that these production methods face. Moreover, we discuss the advancements in technologies for large‐scale manufacturing of cell‐based meat covering aspects including optimized culture media formulations, edible scaffold designs, and bioreactors for high‐density cell culture to reduce production costs.

Pb2+ biosorption by Serratia marcescens CCMA 1010 and its relation with zntR gene expression and ZntA efflux pump regulation

Global concerns about the preservation and restoration of aquatic environments are rising and pollution related to heavy metals is one of the main worries. Indeed, this issue has been a challenge for the metallurgical industry and other activities associated with metal contamination. Thus, over time, several physical and chemical methods have been developed and applied to remove metals from water. However, these methods can be associated with high costs, and bioremediation using plants, fungi, and bacteria is considered a viable alternative. This paper reports experiments on lead removal from an aqueous medium using active and inactive Serratia marcescens CCMA 1010 and a study on the effect of lead in this bacterial strain regarding the expression of the zntR gene, which produces the zntR protein known to have a role as a regulator of the ZntA efflux pump of metals (Pb2+, Cd2+, Zn2+). The amount of removed Pb2+ by active biomass remained below ∼25 mg/L for the initial concentrations tested up to 120 mg/L but increased to removals of ∼70 and ∼167 mg/L for the tested concentrations of 220 and 300 mg/L, respectively. On the other hand, the removal of Pb2+ by inactive biomass increased in direct relation to the initial tested concentration, with removed percentages around 25 %. Interestingly, in cultures with 15 and 60 mg/L Pb2+ the expression of zntR was 27 to 74 and 87 to 177 times lower (respectively) than in cultures without Pb2+, while in cultures with 120 mg/L Pb2+, the zntR expression was just 1.3 to 9.3 times lower than in the absence of Pb2+. The results confirm the potential of S. marcescens CCMA 1010 for Pb2+ biosorption, the presence of Pb2+ resistance mechanisms in this strain, and contribute to a better understanding of the ZntA transmembrane protein regulation.

Endophytic fungi: versatile partners for pest biocontrol, growth promotion, and climate change resilience in plants

Plant-associated endophytic fungi (EFs) are emerging as a promising solution to advancing modern agriculture and fostering environmental sustainability, especially in the face of climate change scenarios. These fungi, either naturally residing in plants or introduced through artificial inoculation techniques, improve agricultural production due to their various roles in protecting and supporting host plants. The majority of EFs serve as natural biocontrol agents for a variety of agricultural pests, such as insects, phytopathogens, nematodes, and weeds. Notably, EFs produce secondary metabolites, trigger immune responses, modify plant defense gene expression, confer host plant resistance and/or tolerance, and regulate pest growth, populations, and survival to combat agricultural pests. Beyond controlling pests, EFs promote optimal plant growth, development, and resilience by aiding in the synthesis of vital compounds such as phytohormones and bioactive metabolites, nutrient acquisition, and fortifying plants against environmental stresses and climatic changes. Moreover, the mostly nonpathogenic nature of EFs, coupled with their high yield potential, environmental safety, and cost effectiveness, positions them as eco-friendly and economically viable alternatives to synthetic agrochemicals amidst rapid climate change scenarios. As a result, the promising horizon of EFs in agricultural production necessitates interdisciplinary study and microbial modulation approaches to optimize symbiotic plant-EF relationships and their potential for improved productivity. This review provides current and comprehensive insights into the practical applications and multifaceted benefits of EFs in pest management, plant growth promotion, and climate change resilience for future agricultural production improvements. The analysis reveals the potential of developing EFs into innovative bioformulations such as biofertilizers, biostimulants, and biopesticides, thereby paving the way for their integration into a sustainable and more resilient future agricultural system.

Advising at Scale: Automated Guidance of the Role Players Influencing Student Success

Although student advising is known to improve student success, its application is often inadequate in institutions that are resource constrained. Given recent advances in large language models (LLMs) such as Chat Generative Pre-trained Transformer (ChatGPT), automated approaches such as the AutoScholar Advisor system affords viable alternatives to conventional modes of advising at scale. This article focuses on the AutoScholar Advisor system, a system that continuously analyzes data using modern methods from the fields of artificial intelligence (AI), data science, and statistics. The system connects to institutional records, evaluates a student’s progression, and generates advice accordingly. In addition to serving large numbers of students, the term “advising at scale” refers to the various role players: the executives (whole-institution level), academic program managers (faculty and discipline levels), student advisors (faculty level), lecturers (class level), and, of course, the students (student level). The form of advising may also evolve to include gamification elements such as points, badges, and leaderboards to promote student activity levels. Case studies for the integration with academic study content in the form of learning pathways are presented. We therefore conclude with the proposition that the optimal approach to advising is a hybrid between human intervention and automation, where the automation augments human judgment.

Spatially-aware station based car-sharing demand prediction

In recent years, car-sharing services have emerged as viable alternatives to private individual mobility, promising more sustainable and resource-efficient, but still comfortable transportation. Research on short-term prediction and optimization methods has improved operations and fleet control of car-sharing services; however, long-term projections and spatial analysis are sparse in the literature. We propose to analyze the average monthly demand in a station-based car-sharing service with spatially-aware learning algorithms that offer high predictive performance as well as interpretability. Our study utilizes a rich set of socio-demographic, location-based (e.g., POIs), and car-sharing-specific features as input, extracted from a large proprietary car-sharing dataset and publicly available datasets. We first compare the performance of different modeling approaches and find that a global Random Forest with geo-coordinates as part of input features achieves the highest predictive performance with an R-squared score of 0.87 on test data. While a local linear model, Geographically Weighted Regression, performs almost on par in terms of out-of-sample prediction accuracy. We further leverage the models to identify spatial and socio-demographic drivers of car-sharing demand. An analysis of the Random Forest via SHAP values, as well as the coefficients of GWR and MGWR models, reveals that besides population density and the car-sharing supply, other spatial features such as surrounding POIs play a major role. In addition, MGWR yields exciting insights into the multiscale heterogeneous spatial distributions of factors influencing car-sharing behaviour. Together, our study offers insights for selecting effective and interpretable methods for diagnosing and planning the placement of car-sharing stations.

Comprehensive data-driven methods for estimating the thermal conductivity of biodiesels and their blends with alcohols and fossil diesels

Biodiesels offer viable alternatives to fossil diesels as renewable and clean energy sources. Understanding the thermal conductivity of biodiesels is crucial for engine design and heat transfer rate calculations in thermal systems. However, existing literature lacks validated predictive tools for this property. To address this, a study utilized 2552 data points from 18 published studies with uncertainties less than 2 %, covering 26 fatty acid alkyl ester biodiesels, 12 biodiesel-alcohol mixtures, and 9 biodiesel-fossil diesel blends across various pressure and temperature conditions. Three machine learning algorithms were employed, using input variables of reduced temperature, reduced pressure, and average molecular weight. Models were trained on 80 % of the data and evaluated on the remaining 20 %. Statistical error metrics and graphical analysis confirmed high precision in predicting biodiesel thermal conductivity. The Gaussian Process Regression model showed the best consistency, with an average absolute relative error (AARE) of 0.36 % and determination coefficient (R2) of 99.69 % on the testing dataset. These models were versatile, applicable to different biodiesel mixtures, accurately capturing thermal conductivity behavior under diverse operating conditions. In contrast, existing literature correlations were limited to specific conditions and required complex calculations. Additionally, a sensitivity analysis determined the relative importance of factors influencing thermal conductivity.

Global trends in antimicrobial resistance on organic and conventional farms

The important hypothesis that organic livestock management reduces the prevalence of antimicrobial resistance is either fiercely supported or bitterly contested. Yet, empirical evidence supporting this view remains fragmentary, in part because relationships between antimicrobial use and drug resistance vary dramatically across contexts, hosts, pathogens, and country-specific regulations. Here, we synthesize global policies and definitions of ‘organic’ and ask if organic farming results in notable reductions in the prevalence of antimicrobial resistance when directly examined alongside conventional analogs. We synthesized the results of 72 studies, spanning 22 countries and five pathogens. Our results highlight substantial variations in country-specific policies on drug use and definitions of ‘organic’ that hinder broad-scale and generalizable patterns. Overall, conventional farms had slightly higher levels of antimicrobial resistance (28%) relative to organic counterparts (18%), although we found significant context-dependent variation in this pattern. Notably, environmental samples from organic and conventional farms often exhibited high levels of resistance to medically important drugs, underscoring the need for more stringent and consistent policies to control antimicrobial contaminants in the soil (particularly on organic farms, where the application of conventional manure could faciliate the spread antimicrobial resistance). Taken together, these results emphasize the challenges inherent in understanding links between drug use and drug resistance, the critical need for global standards governing organic policies, and greater investment in viable alternatives for managing disease in livestock.

Use of Bio-Epoxies and Their Effect on the Performance of Polymer Composites: A Critical Review.

This study comprehensively examines recent developments in bio-epoxy resins and their applications in composites. Despite the reliability of traditional epoxy systems, the increasing demand for sustainability has driven researchers and industries to explore new bio-based alternatives. Additionally, natural fibers have the potential to serve as environmentally friendly substitutes for synthetic ones, contributing to the production of lightweight and biodegradable composites. Enhancing the mechanical properties of these bio-composites also involves improving the compatibility between the matrix and fibers. The use of bio-epoxy resins facilitates better adhesion of natural composite constituents, addressing sustainability and environmental concerns. The principles and methods proposed for both available commercial and especially non-commercial bio-epoxy solutions are investigated, with a focus on promising renewable sources like wood, food waste, and vegetable oils. Bio-epoxy systems with a minimum bio-content of 20% are analyzed from a thermomechanical perspective. This review also discusses the effect of incorporating synthetic and natural fibers into bio-epoxy resins both on their own and in hybrid form. A comparative analysis is conducted against traditional epoxy-based references, with the aim of emphasizing viable alternatives. The focus is on addressing their benefits and challenges in applications fields such as aviation and the automotive industry.

Extreme value analysis of wind droughts in Great Britain

Due to the growing proportion of wind energy in Great Britain’s energy mix, prolonged periods of low wind power generation have become a significant challenge for decarbonising the electricity system. As such, characterising drought severity and duration is important for ensuring the reliability of the electricity system. Employing concepts derived from hydrology, an extreme value analysis was carried out on wind drought events in Great Britain based on 72 years of ERA5 reanalysis data. The application of pooling procedures was found to be beneficial in robustly identifying wind droughts in cases where the capacity factor is not constantly below an arbitrary threshold. The sequent peak algorithm pooling was found to have particular relevance for electricity systems where energy storage technologies are used to compensate for low wind power generation. The Pearson-III distribution was identified as a suitable model to represent extreme wind droughts, while the Lognormal and Generalised Pareto distributions are also viable alternatives. Sustained periods of low wind power generation with a duration of 14 days were estimated to have a return period of five years and the longest event on record of approximately 26 days is expected to occur once every 100 years. The investigation of these wind droughts from a hydrological perspective has thus shown that they may not be particularly rare occurrences.

Using TransR to enhance drug repurposing knowledge graph for COVID-19 and its complications

MotivationThe COVID-19 pandemic has been spreading globally for four years, yet specific drugs that effectively suppress the virus remain elusive. Furthermore, the emergence of complications associated with COVID-19 presents significant challenges, making the development of therapeutics for COVID-19 and its complications an urgent task. However, traditional drug development processes are time-consuming. Drug repurposing, which involves identifying new therapeutic applications for existing drugs, presents a viable alternative. ResultIn this study, we construct a knowledge graph by retrieving information on genes, drugs, and diseases from databases such as DRUGBANK and GNBR. Next, we employ the TransR knowledge representation learning approach to embed entities and relationships into the knowledge graph. Subsequently, we train the knowledge graph using a graph neural network model based on TransR scoring. This trained knowledge graph is then utilized to predict drugs for the treatment of COVID-19 and its complications. Based on experimental results, we have identified 15 drugs out of the top 30 with the highest success rates associated with treating COVID-19 and its complications. Notably, out of these 15 drugs, 10 specifically aimed at treating COVID-19, such as Torcetrapib and Tocopherol, has not been previously identified in the knowledge graph. This finding highlights the potential of our model in aiding healthcare professionals in drug development and research related to this disease.

Synthesizing small-sized monodisperse molybdenum nanoparticles as potential precursors for medical radioisotopes

Molybdenum-99 (99Mo) is used to produce technetium-99 m (99mTc), one of the most used medical radioisotopes in the world. 99Mo is currently created by fission of uranium-235 (235U) in a small number of research nuclear fission reactors. Several problems related to the use of 235U as primary source is causing a global 99Mo generation crisis. Therefore, we are facing the need of finding new viable and safe production alternatives. Neutron activation of molybdenum is a promising candidate in which the ideal target for 99Mo production are small monodisperse molybdenum nanoparticles (Mo NPs). However, the reported procedures for the obtention of Mo NPs present either the lack of simplicity of the employed synthetic methods or the inappropriate size and monodispersity of the synthesized Mo NPs. Here, we report a simple synthetic procedure for the obtention of small-sized monodisperse metallic Mo NPs. In the studied conditions, thermal decomposition of Mo(CO)6 produced oleylamine coated Mo NPs. The oleylamine layer was included with the purpose of avoiding oxidation. Although an oxide patina was still formed on the surface of the Mo NPs, the core remains in a metallic phase after several weeks from its production, as it was confirmed by X-ray photoelectron spectroscopy analysis (XPS). The described synthetic procedure provides a simple method for the obtention of Mo NPs with optimum characteristics as non-fission method for 99Mo production.

Heterogeneous Integration of Chiplets, Lego-like IP for More than Moore

The semiconductor industry is facing an inflection point as higher cost, lower yield, and reticle size limitations drive the need for viable alternatives to traditional monolithic solutions, which are hitting or coming close to the limits of manufacturing and physics. This is driving an emerging trend to disaggregate what typically would be implemented as an SoC into solid, fabricated IP blocks, otherwise known as chiplets. These chiplets typically provide a specific function implemented in an optimal chip process node. Several chiplets and an optional, custom SOC device mounted and interconnected in a single packing using high speed/bandwidth interfaces that will then deliver monolithic or greater performance at a reduced cost, higher yield, and with only a slightly larger area as a heterogeneous integrated advanced package. As fabless semiconductor companies begin to bring these disaggregated chiplets to market, their successful adoption requires the fabless semiconductor industry to standardize on a set of interface protocols that will offer Lego-like plug and play compatibility between different suppliers chiplets creating a truly open ecosystem and supply chain. When it comes to the physical heterogeneous integration of these chiplets it is typically achieved using a high-performance silicon-like interposer substrate. Such substrates typically require silicon-like design rules and design techniques along with their own specific analysis and verification requirements. The supply chain for these substrates does not change significantly, it is still typically the leading semiconductor foundries and OSATs however the level of interaction, requirements, and signoff approvals does change bringing often new demands and challenges. Is this a revolution or an evolution, it depends on your starting point? Does it need an entirely new set of workflows and design tools? Can we evolve in some stepwise linear process from where we are today with monolithic or traditional system-in-package type designs into this next-generation design environment? This paper will explore these new challenges and outline 5 key workflows that address and manage them. These 5 workflows span interlinked domain areas starting with architecture definition before progressing to design that incorporates planning, prototyping, and technology co-optimization of the chosen architecture as well as the detailed physical implementation of substrates before moving to multi-physics analysis, device-level test, and then manufacturing. This paper will then recommend workflow adoption focus areas that we have seen provide immediate heterogeneous integration capability benefits while providing a managed methodology adoption and migration process that minimizes disruption, risk, and of course cost that can bring heterogeneous integration based chiplet design within reach of the mainstream instead of only being accessible to the mega iDMs and fabless semi’s.

Bioactive fractions from Allium humile alleviate the risk of high fat diet induced atherosclerosis in albino Wistar rats by inhibiting protein kinase C

Atherosclerosis is a global concern that worsens with age, and plants that are effective medicinal herbs can give a viable alternative. PKC is a key factor in cardiovascular and other disorders; targeting it can reduce the risk of these diseases. We evaluated Allium humile for PKC inhibition and therapeutic efficacy against atherosclerosis. Soxhlet extraction was done to obtain extracts (hexane, ethyl acetate, methanol, ethanol and aqueous) and then tested for DPPH radical scavenging and PKC inhibitory activity. The methanolic extract was more active than the other extracts, so it was subjected to column chromatography, and seventeen fractions were obtained. Only 11, 12, and 15 showed good activity against PKC. Wistar rats were divided into six groups and each group received high fat diet for 30 days. Then the three potent fractions (10 mg/kg) were administered for 15 days along with high fat diet. Fraction II had the highest effectiveness (P < 0.0001) in decreasing lipid levels, lipid peroxidation, reducing IL-6 and TNF-α expression, and raising nitric oxide. This also demonstrated a decrease in PKC activity, as well as a decrease in the formation of the lipoidal layer in the aorta wall and rupture of the intima and media as validated by histological analysis. The two compounds, phytol acetate and cyanidin 3-(6″-o-malonyllaminaribioside) were characterised in fraction II by NMR and HRMS and cyanidin 3-(6″-o-malonyllaminaribioside) inhibited PKC more efficiently. Thus, Allium humile has strong anti-atherogenic activity as well as the ability to inhibit PKC both in vitro and in vivo.