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Peptides are crucial in biological processes and therapeutic applications. Given their importance, advancing our ability to predict peptide properties is essential. In this study, we introduce Multi-Peptide, an innovative approach that combines transformer-based language models with graph neural networks (GNNs) to predict peptide properties. We integrate PeptideBERT, a transformer model specifically designed for peptide property prediction, with a GNN encoder to capture both sequence-based and structural features. By employing a contrastive loss framework, Multi-Peptide aligns embeddings from both modalities into a shared latent space, thereby enhancing the transformer model’s predictive accuracy. Evaluations on hemolysis and nonfouling data sets demonstrate Multi-Peptide’s robustness, achieving state-of-the-art 88.057% accuracy in hemolysis prediction. This study highlights the potential of multimodal learning in bioinformatics, paving the way for accurate and reliable predictions in peptide-based research and applications.

AbstractThis study reports the first case of lung and fungus ball lesions caused by Cladosporium subcinereum A48. A rural diabetic woman infected by C. subcinereum A48, presented with fever and hemoptysis. CT scans, bronchoalveolar lavage, culture and molecular methods were used to evaluate and confirm the disease. Our patient had not taken insulin during the last few months of her life which probably caused acidosis and a decrease in the patient’s immunity level, resulting in penetration and formation of a fungal in the lung.

Transportation researchers often rely on test networks to evaluate the performance of traffic assignment algorithms, analyze resilience and criticality, study emission productions, investigate transportation economics, and address various other challenges. Existing test networks have limited diversity and might not fully represent the topological features of real-world transportation networks. To address this issue, we proposed a novel approach that leverages real-life road networks extracted from OpenStreetMap to generate synthetic test networks with desired topological properties. We developed a graph neural network model that learns from the extracted networks’ topological patterns and predicts binary adjacency matrices for the generated networks. The proposed model creates synthetic networks resembling real-life networks, demonstrating its effectiveness in generating realistic test networks. The resulting synthetic networks offer a diverse and comprehensive set of test networks for various transportation studies, enabling improved simulations, analyses, and decision making for transportation researchers and practitioners. We have made our source code publicly available for collaboration and further enhancements in the transportation domain.

In this work, a new modeling approach is presented to obtain a two-dimensional transport lattice of a biological cellular system for the calculation of the potential distribution throughout the system and investigation of the corresponding membrane electroporation. The presented model has been obtained by a modified bilayer model of the cell membrane. This modified membrane model allows for an effective inclusion of the shape of the cell membrane in the potential calculation. The results of the model have shown good agreement with the results of the well-known Schwan equation and COMSOL Multiphysics for the circular cell. The simulation results show that both membranes of a mitochondrion can be simultaneously electroporated by an alternating voltage source with frequencies between 1 MHz and 1 GHz.

Chiral amino acids (AAs) are essential in metabolism and understanding physiological processes, and they could be used as biomarkers for the diagnosis of different diseases. In this study, chiral Cdots@Van were prepared by postmodifying an achiral Cdots core with vancomycin for recognizing and determining the enantiomeric excess (ee) of tyrosine (Tyr) enantiomers. The fluorescence response of Cdots@Van is based on an “on–off” strategy, with different quenching percentages for d- and l-tyrosine. Interestingly, the circular dichroism (CD) spectrum of Cdots@Van responded to only one form of Tyr enantiomer, specifically d-Tyr, and remained nearly unchanged upon the addition of l-Tyr. Quantum mechanical (QM) calculations were in excellent agreement with the experimental results, confirming the stronger binding affinity of Cdots@Van for d-Tyr compared to l-Tyr. We further investigated the chiral recognition ability of the interconnected vancomycin particles, which was synthesized using the EDC/NHS coupling reaction between vancomycin molecules without a Cdots core. Surprisingly, unlike free vancomycin molecules, interconnected vancomycin displayed an enantiomeric recognition ability by CD spectroscopy, similar to what was observed for Cdots@Van. Crucially, this chiral probe has been successfully utilized for cell imaging applications.

The use of blockchain technology has been proposed to provide auditable access control for individual resources. Unlike the case where all resources are owned by a single organization, this work focuses on distributed applications such as business processes and distributed workflows. These applications are often composed of multiple resources/services that are subject to the security and access control policies of different organizational domains. Here, blockchains provide an attractive decentralized solution to provide auditability. However, the underlying access control policies may have event-driven constraints and can be overlapping in terms of the component conditions/rules as well as events. Existing work cannot handle event-driven constraints and does not sufficiently account for overlaps leading to significant overhead in terms of cost and computation time for evaluating authorizations over the blockchain. In this work, we propose an automata-theoretic approach for generating a cost-efficient composite access control policy. We reduce this composite policy generation problem to the standard weighted set cover problem. We show that the composite policy correctly captures all the local access control policies and reduces the policy evaluation cost over the blockchain. We have implemented the initial prototype of our approach using Ethereum as the underlying blockchain and empirically validated the effectiveness and efficiency of our approach. Ablation studies were conducted to determine the impact of changes in individual service policies on the overall cost.

Background Generalized anxiety disorder (GAD) is a prevalent anxiety disorder characterized by uncontrollable worry, trouble sleeping, muscle tension, and irritability. Cognitive behavioural therapy (CBT) is one of the first-line treatments that has demonstrated high efficacy in reducing symptoms of anxiety. Electronically delivered CBT (e-CBT) has been a promising adaptation of in-person treatment, showing comparable efficacy with increased accessibility and scalability. Finding further scalable interventions that can offer benefits to patients requiring less intensive interventions can allow for better resource allocation. Some studies have indicated that weekly check-ins can also lead to improvements in GAD symptoms. However, there is a lack of research exploring the potential benefits of online check-ins for patients with GAD.Objective This study aims to investigate the effects of weekly online asynchronous check-ins on patients diagnosed with GAD and compare it with a group receiving e-CBT.Methods Participants (ne-CBT = 45; ncheck-in = 51) with GAD were randomized into either an e-CBT or a mental health check-in program for 12 weeks. Participants in the e-CBT program completed pre-designed modules and homework assignments through a secure online delivery platform where they received personalized feedback from a trained care provider. Participants in the mental health check-in condition had weekly asynchronous messaging communication with a care provider where they were asked structured questions with a different weekly theme to encourage conversation.Results Both treatments demonstrated statistically significant reductions in GAD—7-item questionnaire (GAD-7) scores over time, but when comparing the groups there was no significant difference between the treatments. The number of participants who dropped out and baseline scores on all questionnaires were comparable for both groups.Conclusions The findings support the effectiveness of e-CBT and mental health check-ins for the treatment of GAD.Plain Language Summary Title Comparing the Effectiveness of Electronically Delivered Therapy (e-CBT) to Weekly Online Mental Health Check-ins for Generalized Anxiety Disorder—A Randomized Controlled Trial

Porous membranes, either polymeric or two-dimensional materials, have been extensively studied because of their outstanding performance in many applications such as water filtration. Recently, inspired by the significant success of machine learning (ML) in many areas of scientific discovery, researchers have started to tackle the problem in the field of membrane design using data-driven ML tools. In this Mini Review, we summarize research efforts on three types of applications of machine learning in membrane design, including (1) membrane property prediction using ML, (2) gaining physical insight and drawing quantitative relationships between membrane properties and performance using explainable artificial intelligence, and (3) ML-guided design, optimization, or virtual screening of membranes. On top of the review of previous research, we discuss the challenges associated with applying ML for membrane design and potential future directions.

Exploring the potential energy surface (PES) of molecular systems is important for comprehending their complex behaviors, particularly through the identification of various metastable states. However, the transition between these states is often hindered by substantial energy barriers, demanding prolonged molecular simulations that consume considerable computational resources. Our study introduces the gradient-based navigation (GradNav) algorithm, which accelerates the exploration of the energy surface and enables proper reconstruction of the PES. This algorithm employs a strategy of initiating short simulation runs from updated starting points derived from prior observations to effectively navigate across potential barriers and explore new regions. To evaluate GradNav’s performance, we introduce two metrics: the deepest well escape frame (DWEF) and the search success initialization ratio (SSIR). Through applications on Langevin dynamics within Müller-type PESs and molecular dynamics simulations of the Fs-peptide protein, these metrics demonstrate GradNav’s enhanced ability to escape deep energy wells and its reduced reliance on initial conditions, as denoted by the reduced DWEF values and increased SSIR values, respectively. Consequently, this improved exploration capability enables more precise energy estimations from simulation trajectories.

Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period.

The generation rescheduling is described as the power generation shifting from one or more generators to one or more other generators as a preventive action to improve and maintain the security of the power system. Since, there is a direct link between security improvement and the lines overload under contingencies, by rescheduling generation, the transmission lines become more flexible and thus, the overload can be relieved. In this paper, contingency constrained optimal power flow (CCOPF) problem based on generation rescheduling by considering the uncertainty of photovoltaic (PV), wind turbine (WT), and plug-in hybrid electric vehicle (PHEV) have been addressed. Water cycle algorithm (WCA) using its potential in finding optimal solution has been used in order to reschedule the generators and optimize the total fuel cost, power losses under contingency scenario, and system security. Moreover, stochastic approach has been proposed for taking into account the uncertainty of PV, WT, and PHEV. Overall performance index including the power and the voltage severity indices have been provided for determining overloaded transmission lines due to the lines’ outage and consequently elimination of overloaded lines. The efficiency of the proposed algorithm has been evaluated on two IEEE-30 and IEEE 118-bus systems. The results are compared with the results of other classical and metaheuristic optimization algorithms. The simulations reveal that the WCA outperforms the other reported optimizers, and is more efficient and effective in improving security for power systems. In addition, the obtained numerical results show that renewable energy sources can significantly reduce fuel costs.

This study aims to synthesize akermanite (Ca2MgSi2O7) using an eco-friendly and fast microwave-assisted method and understand the effect of using citric acid (CA) as a chelating agent to optimize the calcination temperature for synthesizing pure akermanite nanoparticles (along with a trace amount of merwinite) at low calcination temperatures. This study developed a new predictive model by machine learning-assisted modeling to advance the understanding of relationship between synthesis parameters and the resulting purity and particle size of akermanite. To evaluate the reliability of the developed model in predicting the purity and characteristics of the resultant Ak powder, the crystallization behavior and phase formation sequence of citric acid-containing samples and those without citric acid were experimentally investigated by differential thermal analysis (DTA) and X-ray diffraction (XRD) techniques. Following validation and training, the developed model indicates promising effects of citric acid on reducing crystallization temperature, enhancing purity, and decreasing particle size. The synthesized powders were further characterized by field emission scanning electron microscopy (FESEM), energy-dispersive X-ray spectroscopy (EDS), and transmission electron microscopy (TEM) analyses. For the first time, our results addressed the microwave-assisted synthesis as an effective method via experimental evidence and machine learning-assisted modeling that allows for the production of akermanite containing a trace amount of merwinite with the aid of citric acid at a relatively low calcination temperature of 950 °C. In vitro bioactivity evaluation supported the potential suitability of Ak powders for bone tissue engineering applications.

Textile dyes, including methylene blue (MB) and methyl orange (MO), pose a significant threat to water quality. This study delves into the competitive adsorption mechanisms pivotal for the concurrent removel of cationic MB and anionic MO dyes using nitrogen-rich seaweed-based carbon adsorbents. Synthesized through the pyrolysis and NaOH activation of Enteromorpha seaweed biomass, the preparation method is both uncomplicated and cost-effective, holding promising applications in industry. Characterization unveiled a commendable specific surface area of 911 m2/g, accompanied by abundant nitrogen- and oxygen-containing functional groups. Batch adsorption experiments showcased exceptional removal efficiencies surpassing 99.8% for both dyes. Molecular dynamics simulations offered valuable insights into the dynamic multi-stage adsorption behavior, while density functional theory calculations clarified the chemisorption-based interaction mechanism. Notably, MB displayed superior adsorption affinity compared to MO. An integrated adsorption mechanism was proposed, encompassing electrostatic attraction, migration, and enhanced chemisorptive binding of dye aggregates at active sites on the adsorbent surface. The innovative adsorption mechanism adopted by this carbon-based adsorbent prepared from nitrogen-rich seaweed can effectively remove cationic and anionic dyes, laying a solid foundation for future research on real water bodies with complex compositions.

In this study, various types of dye molecules, including natural, organic, and metal-free organic dyes, designed for application in dye-sensitized solar cells (DSSCs), were investigated using various computational chemistry approaches. These sensitizers show promising potential for enhancing the photovoltaic performance of DSSCs. Additionally, multiple aspects of molecular properties and charge transfer processes, crucial for solar cells, were analyzed. Different structural configurations of dye molecules were considered, encompassing (D)l–(π)m–(A)n, D–π–π–A, D–D–π–A, D–A–π–A, D–π–A–π–A, A–D–π–D–A, A–D–A–D–A, D–D–π–π–A, A–π–D–π–A, and (D)2 –A–π–A. Each component within these molecular structures within a given material type significantly impacts the analysis of quantum chemistry properties and the performance of DSSCs. Furthermore, the effects of various π-bridges, electron donors, electron acceptors, and anchor groups within the photosensitizer's structure on their photovoltaic properties were discussed. The remarkable electron-donating and electron-withdrawing capabilities of donor and acceptor units, the extension of π-conjugated systems, and the incorporation of anchor groups into the backbone were found to enhance DSSC performance. These findings recommend their application in solar cell technology.

With the rise of transformers and large language models (LLMs) in chemistry and biology, new avenues for the design and understanding of therapeutics have been opened up to the scientific community. Protein sequences can be modeled as language and can take advantage of recent advances in LLMs, specifically with the abundance of our access to the protein sequence data sets. In this letter, we developed the GPCR-BERT model for understanding the sequential design of G protein-coupled receptors (GPCRs). GPCRs are the target of over one-third of Food and Drug Administration-approved pharmaceuticals. However, there is a lack of comprehensive understanding regarding the relationship among amino acid sequence, ligand selectivity, and conformational motifs (such as NPxxY, CWxP, and E/DRY). By utilizing the pretrained protein model (Prot-Bert) and fine-tuning with prediction tasks of variations in the motifs, we were able to shed light on several relationships between residues in the binding pocket and some of the conserved motifs. To achieve this, we took advantage of attention weights and hidden states of the model that are interpreted to extract the extent of contributions of amino acids in dictating the type of masked ones. The fine-tuned models demonstrated high accuracy in predicting hidden residues within the motifs. In addition, the analysis of embedding was performed over 3D structures to elucidate the higher-order interactions within the conformations of the receptors.

Urea electrolysis is a promising energy-efficient hydrogen production process with environmental benefits, but the lack of efficient and sustainable ampere-level current density electrocatalysts fabricated through simple methods is a major challenge for commercialization. Herein, we present an efficient and stable heterostructure electrocatalyst for full urea and water electrolysis in a convenient and time-efficient preparation manner. Overall, superhydrophilic/superaerophobic CoMn/CuNiP/NF exhibits exceptional performance for the hydrogen evolution reaction (HER) (−33.8, −184.4, and −234.8 mV at −10, −500, and −1000 mA cm–2, respectively), urea electro-oxidation reaction (UOR) [1.28, 1.43, and 1.51 V (vs RHE) at 10, 500, and 1000 mA cm–2, respectively], and oxygen evolution reaction (OER) [1.45, 1.67, and 1.74 V (vs RHE) at 10, 500, and 1000 mA cm–2, respectively]. Moreover, the superaerophobic CoMn/CuNiP/NF demonstrates promising potential in full urea (1.33, 1.57, and 1.60 V at 10, 500, and 1000 mA cm–2, respectively) and water (1.46 V, 1.78, and 1.86 at 10, 500, and 1000 mA cm–2, respectively) electrolysis. Based on X-ray photoelectron spectroscopy results, it was determined that the surface of the CoMn/CuNiP electrode was rich in redox pairs such as Ni2+/Ni3+, Cu+/Cu2+, Co2+/Co3+, and Mn2+/Mn3+, which are crucial for the formation of active sites for the OER and UOR, such as NiOOH, MnOOH, and CoOOH, thereby enhancing the catalytic activity. Besides, the in situ assembled CoMn/CuNiP/NF displayed highly stable performance for HER, OER, and UOR with high Faradaic efficiency for over 500 h. This research offers a simple and efficient method for manufacturing a high-efficiency and stable trifunctional electrocatalyst capable of delivering ampere-level current density in urea-assisted hydrogen production. Our density functional theory calculations reveal the potential of CoMn/CuNiP as an effective catalyst, enhancing the electronic properties and catalytic performance. The near-zero Gibbs free-energy change for HER underscores its promise, while reduced CO2desorption energies and charge redistribution support efficient UOR. These findings signify CoMn/CuNiP’s potential for electrochemical applications.

In recent years, machine learning (ML), especially graph neural network (GNN) models, has been successfully used for fast and accurate prediction of material properties. However, most ML models rely on relaxed crystal structures to develop descriptors for accurate predictions. Generating these relaxed crystal structures can be expensive and time-consuming, thus requiring an additional processing step for models that rely on them. To address this challenge, structure-agnostic methods have been developed, which use fixed-length descriptors engineered based on human knowledge about the material. However, the fixed-length descriptors are often hand-engineered and require extensive domain knowledge and generally are not used in the context of learnable models which are known to have a superior performance. Recent advancements have proposed learnable frameworks that can construct representations based on stoichiometry alone, allowing the flexibility of using deep learning frameworks as well as leveraging structure-agnostic learning. In this work, we propose three different pretraining strategies that can be used to pretrain these structure-agnostic, learnable frameworks to further improve the downstream material property prediction performance. We incorporate strategies such as self-supervised learning (SSL), fingerprint learning (FL), and multimodal learning (ML) and demonstrate their efficacy on downstream tasks for the Roost architecture, a popular structure-agnostic framework. Our results show significant improvement in small data sets and data efficiency in the larger data sets, underscoring the potential of our pretrain strategies that effectively leverage unlabeled data for accurate material property prediction.

Ovary vitrification is a way for the preservation of fertility in women undergoing chemotherapy and for protecting the valuable or the endangered species. However, cryopreservation of complex tissues, which are composed of different cells and materials, encountered various challenges including oxidative stress damage.

This study aims to establish easy-to-fabricate and novel structures for the synthesis of highly active and enduring electrocatalysts for the hydrogen evolution reaction (HER) and urea oxidation reaction (UOR). Gradient electrodeposition and four different time regimes were utilized to synthesize Ni–S 3D patterns with the optimization of electrodeposition time. Pulse electrodeposition was employed for the synthesis of Ni–Fe–S nanosheets at three different frequencies and duty cycles to optimize the pulse electrodeposition parameters. The sample synthesized at 13 min of gradient electrodeposition with a 1 Hz frequency and 0.7 duty cycle for pulse electrodeposition demonstrated the best electrocatalytic performance. The optimized electrode further showed remarkable performance for HER and UOR reactions, requiring only 54 mV and 1.25 V to deliver 10 mA cm–2for HER and UOR, respectively. Moreover, the overall cell voltage of the two-electrode system in 1 M KOH and 0.5 M urea was measured at 1.313 V, delivering 10 mA cm–2. Constructing Ni–Fe–S nanosheets on 3D Ni–S significantly increased the electrochemical surface area from 51 to 278 for the Ni–S and Ni–Fe–S layers. Tafel slopes were measured as 138 and 182 mV dec–1for the HER and UOR for the Ni–S coating layer and 97 mV dec–1for the HER and 131 mV dec–1for the UOR for the optimal Ni–Fe–S nanosheets on Ni–S. Minimal changes in the potential were observed at 100 mA cm–2in 50 h regarding the HER and UOR, signifying exceptional electrocatalytic stability. This study provides economically viable, highly active, and long-lasting electrocatalysts suitable for HER and UOR applications.

Purpose: Behavioral characteristics and the existence of personal knowledge and skills that are essential in forming a capable manager with specialized insight, is one of the most important evaluation processes in appointing a manager. Wisdom as a trait of a set of personal characteristics and cognitive knowledge of a capable manager will gain competitive advantages for the company and external stakeholders. The purpose of this study is to present the framework of managerial financial wisdom through qualitative and quantitative research methodologies. Design/methodology/approach: The study uses grounded theory methodology to identify factors for managerial financial wisdom and uses a MICMAC process to evaluate the power of driving and dependence among factors. MaxQDA software was used for data coding and analysis. The MICMAC process by determining the location of each of the sub-themes in the four-dimensional matrix, seeks to determine the most effective criterion in the context of the study. The method of data collection is mixed and in terms of philosophical nature, this study is inductive/comparative. Findings: The results of the research in the qualitative part indicate the presentation of a framework based on two categories, four components, and thirty themes. The results of the quantitative part of the study also showed that the most important factor of managerial financial wisdom is market-oriented values. Finally, it was found that the social trust caused by managerial financial wisdom is considered a factor in gaining more shares in the capital market due to the positive stimulus in this dimension. Originality/value: The study is relevant for both practitioners and academia and has significant implications. For practitioners, the structural linkages identified will help enhance managerial wisdom effectiveness and drive the critical metrics for financial decisions and shareholders' rights protection. The study may help financial market practitioners better understand future policies. Academia and researchers can take reference from methodologies used in this study for exploring factors of interest and developing matrix linkages among them.

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ABSTRACTThe article aims to deepen knowledge concerning the continuity of the Safavid system of central bureaucracy following the fall of the Safavids through the presentation of the administrative history of a high-ranking office, majlis-nawis, and a type of royal decree, raqam. By introducing their functions and unique interrelation between them during the seventeenth century, and their development and transformation in the post-Safavid period, the paper sheds light on the continuous, albeit changed, presence of this administrative position and document form in the eighteenth century. Furthermore, it will be shown that the raqambecame the most dominant form of decree in the chancelleries of post-Safavid dynasties, whereas the majlis-nawislost its relevance in the second half of the eighteenth century.

Metallurgical slags are produced at a massive rate of over 750 Mt/year, and carry a thermal energy equivalent to 40 Mt/year of coal. The potential mineral and thermal energy values of slags are in the order of $22 b and $3–6 b per year. Such attractive figures, together with tightening legislation on disposal of slag and the carbon footprint associated with the loss of mineral and energy values of slags have created a significant thrust in the past decade to develop sustainable approaches for full utilization of slag and its waste heat. As a result of extensive R&D in this area, the slag-processing technologies are slowly converging around dry granulation combined with heat recovery. In this paper, the slag granulation and heat recovery options are reviewed and given a critical evaluation, aiming to provide a basis for future directions in slag handling.

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Background: Multiple sclerosis (MS) is a devastating autoimmune disorder characterized by oligodendrocytes (OLGs) loss and demyelination. In this study, we have examined the effects of metformin (MET) on the oligodendrogenesis, redox signaling, apoptosis, and glial responses during a self-repairing period (1-week) in the animal model of MS. Methods: For induction of demyelination, C57BL/6 J mice were fed a 0.2% cuprizone (CPZ) for 5 weeks. Thereafter, CPZ was removed for 1-week and molecular and behavioral changes were monitored in the presence or absence of MET (50 mg/kg body weight/day). Results: MET remarkably increased the localization of precursor OLGs (NG2+/O4+cells) and subsequently the renewal of mature OLGs (MOG+cells) in the corpus callosum via AMPK/mammalian target of rapamycin (mTOR) pathway. Moreover, we observed a significant elevation in the antioxidant responses, especially in mature OLGs (MOG+/nuclear factor erythroid 2-related factor 2 (Nrf2+) cells) after MET intervention. MET also reduced brain apoptosis markers and lessened motor dysfunction in the open-field test. While MET was unable to decrease active astrogliosis (GFAP mRNA), it reduced microgliosis by down-regulation of Mac-3 mRNA a marker of pro-inflammatory microglia/macrophages. Molecular modeling studies, likewise, confirmed that MET exerts its effects via direct interaction with AMPK. Conclusions: Altogether, our study reveals that MET effectively induces lesion reduction and elevated molecular processes that support myelin recovery via direct activation of AMPK and indirect regulation of AMPK/Nrf2/mTOR pathway in OLGs. These findings facilitate the development of new therapeutic strategies based on AMPK activation for MS in the near future.

In this article, a modified version of the Lee model is utilized to research the effect of variation of the geometric parameters of the electrode system on the amount of the soft X-ray radiation (SXR) yield. The parameters are the anode radius and length, the cathode radius, and the number of rods in the cathode. It will be seen that increasing the anode radius causes an increase in the SXR yield. This is because of the constancy of the ratios of the pinch radius and length to the anode radius and length, respectively. Since the SXR yield scales to the pinch current, increasing the anode length brings a reduction in the yield. When the number of the cathode rods is increased, then the formed pinch will have a smaller temperature and lifetime that reduce the amount of the SXR. The influences of the geometrical parameters on the effective charge number of the working gas have also been investigated.

•Antimicrobial resistance and biofilm formation are becoming the most important public health.•Nanomaterials are much more effective than conventional antibiotics by increasing the surface area.•Carbon-based nanomaterials have been identified as potential antimicrobial agents.•C-dots & ciprofloxacin significantly reduced the growth and inhibited biofilm formation.•The expression of pslA, pelA, and ppyRgenes was significantly reduced after exposure to C-dots.

This paper is focused on the problem of the pollutant source localisation in streams in other words the solution of the inverse problem of pollution spreading with in an extensive open channel network structure, i.e. in a complex system of rivers, channels and creeks in natural catchments or sewer systems in urban catchments. The design of the overall localisation procedure is based on the requirement that the entire localization system be operative and fast enough to enable quick operative interventions and help prevent the spread of pollution. The proposed model, as well as, the overall localisation procedure was calibrated and tested on a real sewer system, which represents in this case an extensive open channel network structure with free surface flow. The test results are successful and confirmed applicability of proposed localization tool in simple real conditions. However, the localisation procedure has pros and cons, which are discussed in the paper.

Dynamics of individual amino acids play key roles in the overall properties of proteins. However, the knowledge of protein structural features at the residue level is limited due to the current resolutions of experimental and computational techniques. To address this issue, we designed a novel machine-learning (ML) framework that uses Molecular Dynamics (MD) trajectories to identify the major conformational states of individual amino acids, classify amino acids switching between two distinct modes, and evaluate their degree of dynamic stability. The Random Forest model achieved 96.94% classification accuracy in identifying switch residues within proteins. Additionally, our framework distinguishes between the stable switch (SS) residues, which remain stable in one angular state and jump once to another state during protein dynamics, and unstable switch (US) residues, which constantly fluctuate between the two angular states. This study also illustrates the correlation between the dynamics of SS residues and the protein’s global properties.

In this report, a nickel nanoscaled morphology was synthesized by two-step cathodic electrodeposition on a microporous copper template. The resulting morphology, nanosheets formed on 3D micropores, offers incredible cyclic stability of almost 100% and facilitates transport mechanisms while significantly preserving the active surface area. The origin of the nanosheets is assumed to be the presence of a small amount of iron cations in the electrolyte bath during the final deposition step. By altering the deposition current density of this step, three samples were prepared and compared in terms of the resulting morphology, chemical composition, surface area, wettability, and activation toward the methanol oxidation Reaction. Results show that an increase in the deposition current density in the range of this study produces finer and denser nanosheets, a higher content of reduced iron, a larger surface area, and greater activity toward MOR. The current density for methanol oxidation was exceptional among all other studies on nickel-containing electrocatalysts, yielding a steady-state current density of 135 mA cm–2at 600 mV versus SCE. All samples offered superhydrophilicity.

Energy hubs, as the prospect of future energy systems, are efficient and reliable frameworks for the generation, conversion, storage, and consumption of energy in multicarrier energy systems. Existing urban facilities in smart cities (SCs) can be considered as micro energy hubs (MIEHs), whereas the SC itself can be regarded as a macro energy hub incorporating its MIEHs. Moreover, the interconnection of MIEHs is particularly important to achieve high-reliability and economical smart city energy systems (SCESs). This article presents a framework for reliability-oriented expansion planning of multicarrier energy systems in SCESs. The proposed framework tends to improve energy not supplied and customer interruption cost indices for both heat and electrical loads, as well as the energy index of reliability. The proposed multicarrier energy system facilitates interoperability between different energy carrier systems to supply electricity and heat demand simultaneously. The interconnection of MIEHs is modeled through linearized alternating current optimal power flow and natural gas network constraints, whereas full interaction of the electricity and gas networks is realized by the power-to-gas systems and combined heat and power systems. The effectiveness of the proposed mixed-integer linear programming planning model is demonstrated in the Dättwil district (Switzerland) through four scenarios, analyzing the effect of MIEH interconnection, as well as the gas and electricity interaction within an MIEH. Simulation results confirm the effectiveness of the proposed method due to a significant reduction in total investment cost and a considerable improvement in reliability indices.

Utilization of Distributed Generation (DG) resources has increased dramatically in power systems due to economic and environmental benefits. Risk-based planning methods could manage the operational and economic risks at acceptable levels. In this paper a new probabilistic multi-objective risk-based DG planning method is proposed. In the proposed method, operation and investment costs minimization, reliability improvement, losses minimization, and operational risks minimization are considered. Uncertainties of wind generation and demand are modeled by using scenario tree and two stage method. The multi-objective problem is solved by using a combination of lexicographic optimization technique and hybrid augmented weighted ε-constraint approach. In addition, fuzzy satisfying criterion is used for decision making among Pareto solutions. Analyzing the numerical results validated that the proposed method has an appropriate and accurate performance in achieving the set of Pareto solutions. By analyzing the Pareto solutions, it is proved that by more investment on DG allocation, reduction of loss and operation risk could be appropriately achieved. Comprehensive risk analysis of wind energy generation and the curtailment of wind power are conducted under different cases. It is concluded that when the wind power injection increases the negative effects on the power system increase. Moreover, in case of considering wind power curtailment, the risk factor significantly reduced.

The current design of aerodynamic shapes, like airfoils, involves computationally intensive simulations to explore the possible design space. Usually, such design relies on the prior definition of design parameters and places restrictions on synthesizing novel shapes. In this work, we propose a data-driven shape encoding and generating method, which automatically learns representations from existing airfoils and uses the learned representations to generate new airfoils. The representations are then used in the optimization of synthesized airfoil shapes based on their aerodynamic performance. Our model is built upon VAEGAN, a neural network that combines Variational Autoencoder with Generative Adversarial Network and is trained by the gradient-based technique. Our model can (1) encode the existing airfoil into a latent vector and reconstruct the airfoil from that, (2) generate novel airfoils by randomly sampling the latent vectors and mapping the vectors to the airfoil coordinate domain, and (3) synthesize airfoils with desired aerodynamic properties by optimizing learned features via a genetic algorithm. Our experiments show that the learned features encode shape information thoroughly and comprehensively without predefined design parameters. By interpolating/extrapolating feature vectors or sampling from Gaussian noises, the model can automatically synthesize novel airfoil shapes, some of which possess competitive or even better aerodynamic properties as compared to airfoils used for model training purposes. By optimizing shapes on the learned latent domain via a genetic algorithm, synthesized airfoils can evolve to target aerodynamic properties. This demonstrates an efficient learning-based airfoil design framework that encodes and optimizes the airfoil on the latent domain and synthesizes promising airfoil candidates for required aerodynamic performance.Graphical Abstract

Abstract The pathogenesis of nasal polyps has been debated for many years. The lymphocytes that infiltrate nasal polyps have been identified as predominantly memory T cells in an activated state, [...]

Fungal sinusitis is a widespread infection that affects both healthy and immunocompromised individuals. Reports of sinus fungal infections have increased due to recent advances in diagnosis. Furthermore, susceptible and immune-compromised patients play an important role in increasing the number of reported cases. Infections with lesser-known fungi have been reported infrequently around the world. This paper describes a Cladosporium tenuissimuminfection caused by chronic fungal sinusitis in a woman who had traveled to several countries. We used morphological and molecular methods to confirm the infection. The infection is most likely caused by the use of sulfasalazine, which is related to the patient’s rheumatism. Sulfasalazine inhibits neutrophilic chemoattractant lipid synthesis in neutrophils, which play a key role in antifungal immunity. The patient is also undergoing root canal therapy and has several upper jaw implants, which may have contributed to the development of sinusitis.

In this study, to improve pulverization of Fe–Si alloy, the evolution of its microstructure under container and containerless solidification conditions were investigated using electromagnetic levitation melting technology. The results indicated that the microstructure changed from dendritic crystals during container solidification to equiaxed crystals with containerless solidification. With an undercooling of 280Kobtained by containerless solidification, the pulverizing sensitive elements (Al, P) disperse better in FeSi2phase. The equiaxed Si grains with relatively uniform grain size obtained under ΔT=280Kare distributed in random orientation. High angle grain boundaries are significantly more present with the increase of misorientation within FeSi2grains. This texture is closely related to recrystallization in favor of grain refinement, which is attributed to the melt oscillation and dendrites remelting-recrystallization effects occurring in containerless solidification.

The rapid development of optical technologies, such as optical manipulation, data processing, sensing, microscopy, and communications, necessitates new degrees of freedom to sculpt optical beams in space and time beyond conventionally used spatially homogenous amplitude, phase, and polarization. Structuring light in space and time has been indeed shown to open new opportunities for both applied and fundamental science of light. Rapid progress in nanophotonics has opened up new ways of “engineering” ultra-compact, versatile optical nanostructures, such as optical two-dimensional metasurfaces or three-dimensional metamaterials that facilitate new ways of optical beam shaping and manipulation. Here, we review recent progress in the field of structured light–matter interactions with a focus on all-dielectric nanostructures. First, we introduce the concept of singular optics and then discuss several other families of spatially and temporally structured light beams. Next, we summarize recent progress in the design and optimization of photonic platforms, and then we outline some new phenomena enabled by the synergy of structured light and structured materials. Finally, we outline promising directions for applications of structured light beams and their interactions with engineered nanostructures.

Metal–organic frameworks (MOFs) are materials with a high degree of porosity that can be used for many applications. However, the chemical space of MOFs is enormous due to the large variety of possible combinations of building blocks and topology. Discovering the optimal MOFs for specific applications requires an efficient and accurate search over countless potential candidates. Previous high-throughput screening methods using computational simulations like DFT can be time-consuming. Such methods also require the 3D atomic structures of MOFs, which adds one extra step when evaluating hypothetical MOFs. In this work, we propose a structure-agnostic deep learning method based on the Transformer model, named as MOFormer, for property predictions of MOFs. MOFormer takes a text string representation of MOF (MOFid) as input, thus circumventing the need of obtaining the 3D structure of a hypothetical MOF and accelerating the screening process. By comparing to other descriptors such as Stoichiometric-120 and revised autocorrelations, we demonstrate that MOFormer can achieve state-of-the-art structure-agnostic prediction accuracy on all benchmarks. Furthermore, we introduce a self-supervised learning framework that pretrains the MOFormer via maximizing the cross-correlation between its structure-agnostic representations and structure-based representations of the crystal graph convolutional neural network (CGCNN) on >400k publicly available MOF data. Benchmarks show that pretraining improves the prediction accuracy of both models on various downstream prediction tasks. Furthermore, we revealed that MOFormer can be more data-efficient on quantum-chemical property prediction than structure-based CGCNN when training data is limited. Overall, MOFormer provides a novel perspective on efficient MOF property prediction using deep learning.

Chronic thromboembolic pulmonary hypertension (CTEPH) is caused by the obstruction of the main pulmonary artery due to thrombosis and vascular remodeling. Regarding the need for anticoagulant therapy in CTEPH patients, this study aimed to compare rivaroxaban with warfarin in terms of its efficacy and safety in patients undergoing endarterectomy surgery.

Quantum system attributes, notably entanglement, are indispensable in manipulating quantum information tasks. Ergo, machine learning applications to help harness the sophistication of quantum information theory have surged. However, relying on unsuited prototypes and not bridling quantum information data for usual processors has often resulted in sub-optimal efficaciousness and inconvenience. We develop a custom deep convolutional neural network, XpookyNet, which is streamlined with respect to the interrelationships of density matrices of two-qubit systems to underpin three-qubit systems, breaking new ground to more qubit systems by their subsystems. Comparative implementation of XpookyNet provides instantaneous and meticulous results with an accuracy of 98.53% in merely a few epochs. XpookyNet effectively handles the inherent complexity of quantum information, equipping deeper insights into many-body systems as a bedrock. The study also investigates quantum features and their relation to the purity of a density matrix directly related to noise and system decoherence in NISQ-era quantum computation. Preparing the density matrix in a compact and compatible format with customary convolutional neural networks plays a determining role in dissecting quantum features. The procedure renders a convex criterion that detects entanglement and is a yardstick for quantum system coherence.

Maintaining coverage, power consumption, and network lifetime are the most fundamental challenges for wireless sensor networks. Since it is impossible to replace or recharge the battery of the sensor nodes, discharging the battery will end the sensor node’s life. With the death of some sensor nodes and disconnecting, the network coverage is also violated. This paper presents a method for detecting and recovering coverage holes in the wireless sensor network. In the proposed method, the network is cellulated first, and a node is selected as an agent for each cell. Then, the degree of overlap of each node’s sensing area by its neighbors is calculated to schedule sensor nodes. Based on the node overlap information, the cell agent determines cell coverage and detects holes. Finally, mobile nodes and the grasshopper optimization algorithm are used to recover the holes. The simulation results reveal that the proposed method leads to a decrease in the network’s energy consumption, an increase in network lifetime, and improved coverage in the network.

Transition metal dichalcogenide (TMDC) nanomaterials have become excellent candidates for next-generation functional membranes. In the past few years, tremendous encouraging experimental and theoretical studies have been conducted on TMDC-based nanomaterials, which opened up a new direction for developing highly selective and permeable membranes. Herein, we systematically summarize the recent advances in 2D TMDC nanomaterials and their application in water purification. The physical and chemical properties of TMDC-based functional membranes that facilitate water purification are stated in detail. Simultaneously, the theoretical investigations used to understand the desalination process of the TMDC-based membranes are also summarized. Furthermore, we sum up the synthesis method and discuss the modification strategies for TMDC-based membrane in water purification application. Finally, the challenges and perspectives on the future development of TMDC-based functional membrane for water purification, as well as the future research direction for functionalization of TMDC-based membrane, are proposed.

Background/AimsThe protection motivation theory is a common framework illustrating the use of protective behaviours. The aim of this study was to develop and evaluate the psychometric properties of the protection motivation theory questionnaire for fall prevention in older people in Iran.MethodsThis study used a psychometric design. A literature review was initially performed and developed a 35-item questionnaire consisting of eight sub-constructs of the protection motivation theory. A panel of experts and a group of older people aged 60 years or above were invited to assess the content and face validity of the questionnaire. Thereafter, construct validity was evaluated by using confirmatory factor analysis. The reliability of the questionnaire was examined using test–retest reliability and internal consistency. Data analysis was performed using the partial least squares path modelling version three software.ResultsA total of 300 people living in retirement centres completed the questionnaire. In the expert panel review, all tasks received a content validity index above 0.90 and a content validity ratio above 0.80, representing good content validity. In total, 32 items remained in the final version of the questionnaire. Confirmatory factor analysis confirmed an acceptable eight-factor model fit of the protection motivation theory sub-constructs with factor loadings from 0.59 to 0.93. The discriminant validity was verified using the Fornell–Larcker criterion. The Cronbach's alpha ranged from 0.68 to 0.90 and the test–retest reliability ranged from 0.78 to 0.90, displaying an acceptable result.ConclusionsThe results of this study suggest that the protection motivation theory questionnaire had good validation and can be used to measure the psychological predictors of fall behaviour in older people. Further studies are needed to evaluate the validity in various settings.

Massive MIMO has emerged as a key-enabler to deliver the high data-rates in current and next-generation systems. Many massive MIMO deployments use time-division duplex (TDD), thereby opening the door for the potential use of full-duplex at base-stations. In this paper, we present LensFD, a scalable method to enable cost-effective massive MIMO full-duplex operation in sub-6 GHz frequencies with a focus on the CBRS band of 3.5 GHz. LensFD uses a low-cost lens array combined with transmit precoding to reshape the effective beam pattern of antennas in massive MIMO arrays to reduce self-interference. We tested three lens configurations in LensFD experimentally with a software-defined 40-antenna base-station and demonstrate a significant improvement in self-interference reduction and hence overall increased system capacity to 1.6× over the TDD counterpart. As an important contribution, we will open-source the large measurement dataset from our experiments.

Federal regulations require employees to protect themselves from electrical hazards when working at substations. Such protections, commonly called personal protective equipment (PPE), vary with the hazard types and nature of exposure or delivery. Over the past decades, personal injuries and fatalities from electrical hazards have remained relatively common despite regular risk assessments and controls. One reason for this is that adequate PPE is not appropriately used. Easy-to-deploy strategies to detect proper use of PPE for electrical hazards are not available. Here, an intelligent detection model is developed to check whether PPE is appropriately worn or not; warning alarms would be triggered when the usage does not follow safety regulations. Arc-flash analysis is employed to determine a reasonable and safe PPE guideline. Eight types of PPE are considered, which cover the major PPE categories utilized in practice, including medical masks recommended for the Covid-19 pandemic. The model's framework utilizes a few-shot based graph neural network (GNN) technique to detect PPE. In contrast to prior data-driven models, only 50 images were collected for each PPE type, a relatively small number compared with state-of-the-art research. The proposed model was trained with diversified samples within multiple environments, resulting in a robust, efficient, intelligent detection model with probability of similarity in the range of 79%–100%. To tackle the existing issues of computer-vision based PPE detection models, some technical suggestions on preserving personal privacy and PPE labels are provided.

The study aims to identify fire patterns in Mashhad, the second-most populous city in Iran, between 2015 and 2019. Spatial scan statistics were utilized to determine the spatiotemporal patterns of 29,889 fire events in the research area. There were four primary types of fires: (1) structural fires (39%), (2) vehicle fires (11%), (3) green and open space fires (19%), and (4) others (31%). The interval from 12:00 to 23:00 h was identified as the high-risk period for all fire incidents. Fires were common in the nearby city core. Additionally, three significant hourly spatial-temporal clusters of firefighting operations were identified: the western part of the city between 12:00 and 23:00, the city center between 11:00 and 22:00, and the southeastern part between 11:00 and 22:00. Population density, illiteracy ratio, unemployment ratio, youth ratio, low-income population, and the number of old buildings might be socio-economic criteria that contribute to the spatiotemporal pattern of urban fires. Urban planners might prioritize high-risk neighborhoods when allocating resources for fire safety. Future research could specifically investigate high-risk regions to identify relevant characteristics in these areas.

L1 instruction (L1-I) cache misses are a source of performance bottleneck. While many instruction prefetchers have been proposed over the years, most of them leave a considerable potential uncovered. In 2011, Proactive Instruction Fetch (PIF) showed that a hardware prefetcher could effectively eliminate all instruction-cache misses. However, its enormous storage cost makes it an impractical solution. Consequently, reducing the storage cost was the main research focus in instruction prefetching in the past decade. Several instruction prefetchers, including RDIP and Shotgun, were proposed to offer PIF-level performance with significantly lower storage overhead. However, our findings show that there is a considerable performance gap between these proposals and PIF. While these proposals use different mechanisms for instruction prefetching, the performance gap is mainly not because of the mechanism, and instead, is due to not having sufficient storage. In this paper, we make the case that the key to designing a powerful and cost-effective instruction prefetcher is choosing a metadata record and microarchitecting the prefetcher to minimize the storage. We propose MANA, which offers PIF-level performance with 15.7× lower storage cost. MANA outperforms RDIP and Shotgun by 12.5 and 29%, respectively. We also evaluate a version of MANA with no storage overhead and show that it offers 98% of the peak performance benefits.

Background: Substance use is a serious public health problem in the world, and the relapse rate among abstinent substance users is very high. The purpose of this study was to clarify the factors affecting substance use relapse based on the ecological approach by reviewing the relevant literature. Methods: Applying different search strategies, we searched electronic databases, including Scopus, PubMed, Web of Science, Psychinfo, Embase, Science Direct as well as Magiran, Iran Medex, and SID up to June 2018. The Joanna Briggs Institute Critical Appraisal Checklist was employed for critical appraisal. All searches were limited to studies published in English and Persian. Results: The literature search identified 3481 articles. After screening titles, abstracts, and full texts, 39 studies met the inclusion criteria. The findings were classified based on ecological model (intrapersonal, interpersonal, organizational, environment, community, public policy). The most frequent factors affecting substance use relapse at any level of the ecological model included unpleasant emotion (at the intrapersonal level), addicted friends and family disputes (at the interpersonal level), poor follow up (at the organizational level), society rejection (at the community level), drug availability and unhealthy environment (at the environmental level). Conclusion: According to the findings of the present study, it is indicated that designing interventions approaches based on the ecological model to prevent relapse of substance use seems to be useful.

Previous research has identified the socio-economic, built environment, and psychological barriers to children walking to school. Long journeys, however, have been identified as a crucial constraint reducing the likelihood of walking to school. Determining a threshold to distinguish between long and short journeys, the present study sought to examine whether the contribution of the factors reducing walking to school differs between short and long journeys. Questionnaires were distributed among 7 to 12-year-old children in a neighborhood of Tehran (n= 272) to be completed by parents. In addition, instead of walkability at the neighborhood level that considers walkability within a radial buffer from home or destination, we measured walkability along the school routes. ROC (Receiver Operating Characteristic) curve analysis found a threshold of 12.5 min to differentiate between long and short journeys. Walking journeys longer or shorter than the threshold were classified to be long or short, respectively. We developed two separate models, one to estimate walking for all (including long and short) journeys; and the other for only long journeys. Compared with all journeys, long walking journeys were explained by more built-environment variables. Parental attitudes toward walking were also significant in long walking journeys. However, analysis of interaction effects between walking time to school and the study variables confirmed that among all variables examined, the contribution of only environmental factors of crosswalks, walkways, and physically permeable frontages was significantly different between long and short walking journeys. Policy implications of the findings have been discussed.