Single Entity Research Articles

Research on elastic governance strategy of urban public safety based on entropy weighted discrete clustering method

<p>Currently, there are problems in the governance of urban public safety, such as a single entity, outdated governance concepts, and immature governance technologies. This article combines big data analysis technology and utilizes intelligent emergency mechanisms to conduct in-depth research on governance strategies to enhance the resilience of urban public safety to disasters. This article first integrates big data analysis technologies, such as the Internet of Things and cloud computing, into UPS (urban public safety) and then builds a UPS system based on this. Combining the entropy-weighted dispersion clustering method, evaluate the values of urban public safety indicators. In order to verify the effectiveness of the intelligent emergency mechanism based on big data analysis, this article conducted experimental analysis on it. Under the intelligent emergency mechanism algorithm, the average seismic compliance rate of buildings in various cities has reached 88.57%. The conclusion indicates that an intelligent emergency mechanism based on big data analysis can enhance the adaptability of urban public safety governance strategies, improve the seismic and fire warning monitoring capabilities of urban buildings, reduce the occurrence of traffic accidents, and provide more guarantees for urban fire safety.</p>

Optimal scheduling of electricity-hydrogen coupling virtual power plant considering hydrogen load response

With the rapid development of hydrogen production by water electrolysis, the coupling between the electricity-hydrogen system has become closer, providing an effective way to consume surplus new energy generation. As a form of centralized management of distributed energy resources, virtual power plants can aggregate the integrated energy production and consumption segments in a certain region and participate in electricity market transactions as a single entity to enhance overall revenue. Based on this, this paper proposes an optimal scheduling model of an electricity-hydrogen coupling virtual power plant (EHC-VPP) considering hydrogen load response, relying on hydrogen to ammonia as a flexibly adjustable load-side resource in the EHC-VPP to enable the VPP to participate in the day-ahead energy market to maximize benefits. In addition, this paper also considers the impact of the carbon emission penalty to practice the green development concept of energy saving and emission reduction. To validate the economy of the proposed optimization scheduling method in this paper, the optimization scheduling results under three different operation scenarios are compared and analyzed. The results show that considering the hydrogen load response and fully exploiting the flexibility resources of the EHC-VPP can further reduce the system operating cost and improve the overall operating efficiency.

Unraveling the Heterogeneity of ALS-A Call to Redefine Patient Stratification for Better Outcomes in Clinical Trials.

Despite tremendous efforts in basic research and a growing number of clinical trials aiming to find effective treatments, amyotrophic lateral sclerosis (ALS) remains an incurable disease. One possible reason for the lack of effective causative treatment options is that ALS may not be a single disease entity but rather may represent a clinical syndrome, with diverse genetic and molecular causes, histopathological alterations, and subsequent clinical presentations contributing to its complexity and variability among individuals. Defining a way to subcluster ALS patients is becoming a central endeavor in the field. Identifying specific clusters and applying them in clinical trials could enable the development of more effective treatments. This review aims to summarize the available data on heterogeneity in ALS with regard to various aspects, e.g., clinical, genetic, and molecular.

Maximizing Analytical Performance in Biomolecular Discovery with LC-MS: Focus on Psychiatric Disorders.

In this review, we discuss the cutting-edge developments in mass spectrometry proteomics and metabolomics that have brought improvements for the identification of new disease-based biomarkers. A special focus is placed on psychiatric disorders, for example, schizophrenia, because they are considered to be not a single disease entity but rather a spectrum of disorders with many overlapping symptoms. This review includes descriptions of various types of commonly used mass spectrometry platforms for biomarker research, as well as complementary techniques to maximize data coverage, reduce sample heterogeneity, and work around potentially confounding factors. Finally, we summarize the different statistical methods that can be used for improving data quality to aid in reliability and interpretation of proteomics findings, as well as to enhance their translatability into clinical use and generalizability to new data sets.

Octahedron in a Cubic Nanoframe: Strong Near-Field Focusing and Surface-Enhanced Raman Scattering.

Here, we describe the synthesis of a plasmonic particle-in-a-frame architecture in which a solid Au octahedron is enclosed by a Au cubic nanoframe. The octahedra are positioned inside and surrounded by outer Au cubic nanoframes, creating intra-nanogaps within a single entity. Six sharp vertexes in the Au octahedra point toward the open (100) facets of the cubic nanoframes. This allows not only efficient interactions with the surroundings but also tip-enhanced electromagnetic near-field focusing at the sharp tips of the octahedra, combined with intraparticle coupling. The solid core-frame shell structure enhances near-field focusing, giving rise to a heightened concentration of "hot spots". This effect enables highly sensitive detection of 2-naphthalenethiol and thiram, indicating these substrates for use in surface-enhanced Raman spectroscopy-related applications.

Evaluation of the Embrittlement in Reactor Pressure-Vessel Steels Using a Hybrid Nondestructive Electromagnetic Testing and Evaluation Approach.

The nondestructive determination of the neutron-irradiation-induced embrittlement of nuclear reactor pressure-vessel steel is a very important and recent problem. Within the scope of the so-called NOMAD project funded by the Euratom research and training program, novel nondestructive electromagnetic testing and evaluation (NDE) methods were applied to the inspection of irradiated reactor pressure-vessel steel. In this review, the most important results of this project are summarized. Different methods were used and compared with each other. The measurement results were compared with the destructively determined ductile-to-brittle transition temperature (DBTT) values. Three magnetic methods, 3MA (micromagnetic, multiparameter, microstructure and stress analysis), MAT (magnetic adaptive testing), and Barkhausen noise technique (MBN), were found to be the most promising techniques. The results of these methods were in good agreement with each other. A good correlation was found between the magnetic parameters and the DBTT values. The basic idea of the NOMAD project is to use a multi-method/multi-parameter approach and to focus on the synergies that allow us to recognize the side effects, therefore suppressing them at the same time. Different types of machine-learning (ML) algorithms were tested in a competitive manner, and their performances were evaluated. The important outcome of the ML technique is that not only one but several different ML techniques could reach the required precision and reliability, i.e., keeping the DBTT prediction error lower than a ±25 °C threshold, which was previously not possible for any of the NDE methods as single entities. A calibration/training procedure was carried out on the merged outcome of the testing methods with excellent results to predict the transition temperature, yield strength, and mechanical hardness for all investigated materials. Our results, achieved within the NOMAD project, can be useful for the future potential introduction of this (and, in general, any) nondestructive evolution method.

Carbon storage units and carbon storage obligations: A review of policy approaches

Policies that incentivize the capture and geological storage of CO2 from large point sources (‘CCS’; and more recently, atmospheric removals), have so far tended to reward the resultant avoided CO2 emissions (or CO2 removals) achieved by operators at the point of capture. The expectation has been that geological CO2 storage sites, as well as the connecting infrastructure, will be developed and operated based upon the funding delivered from the single point of incentive (e.g. under an emissions trading system or a carbon tax, only CO2 capture installation operators are absolved of the obligation to acquire and surrender CO2 emission rights or to pay the tax). Project-based crediting mechanisms (e.g. in the voluntary carbon market) tend to treat the entire chain of operations as a single entity to be supplied with emission reduction or removal credits. Consequently, there has been minimal explicit financial incentive to store CO2. Yet a multiple gigaton-per-year scale capture and storage industry requires complex CO2 networks to evolve, with multiple sources connecting to multiple sinks, with installations owned by different operators with different technical expertise, climate mitigation goals and obligations. This multiplicity of goals and incentives can create agency problems and cross-chain risks, which impact negatively upon investment decisions. In this paper, we review the history, evolution and potential of geological net zero (GNZ) and carbon storage (or takeback) obligation concepts applied to fossil carbon producers and suppliers as a means to address these risks alongside policies aimed at atmospheric net zero (ANZ) and fossil carbon emitters.

A Holistic Perspective on Living Aggregate.

Aggregate is one of the most extensive existing modes of matters in the world. Besides the research objectives of inanimate systems in physical science, the entities in life science can be regarded as living aggregates, which are far from being thoroughly understood despite the great advances in molecular biology. Molecular biology follows the research philosophy of reductionism, which generally reduces the whole into parts to study. Although reductionism benefits the understanding of molecular behaviors, it encounters limitations when extending to the aggregate level. Holism is another epistemology comparable to reductionism, which studies objectives at the aggregate level, emphasizing the interactions and synergetic/antagonistic effects of a group of composed single entities in determining the characteristics of a whole. As a representative of holism, aggregation-induced emission (AIE) materials have made great achievements in the past two decades in both physical and life science. In particular, the unique properties of AIE materials endow them with in situ and real-time visual methods to investigate the inconsistency between microscopic molecules and macroscopic substances, offering researchers excellent toolkits to study living aggregates. The applications of AIE materials in life science are still in their infancy and worth expanding. In this Perspective, we summarize the research progress of AIE materials in unveiling some phenomena and processes of living systems, aiming to provide a general research approach from the viewpoint of holism. At last, insights into what we can do in the near future are also raised and discussed.

How to make a SPE resolution strategy work in the EU

Abstract When dealing with the resolution of a banking group under EU regulation there are two different approaches: Single point of entry (SPE) versus Multiple Point of Entry (MPE). In a MPE strategy, the resolution authority is taking separate resolution actions at the level of individual legal entities of the group. In a SPE strategy, the resolution authority is taking a resolution action at the level of a single entity, usually the parent undertaking, which, consequently, becomes the so called “resolution entity” building together with its subsidiaries the “resolution group”. The general idea in a SPE strategy is, that losses at subsidiary level are first streamed up to the parent undertaking (resolution entity), i. e. capital is streamed down from the parent undertaking to the subsidiary to cover its losses and ensure its recapitalisation. This leads to losses at the resolution entity level, which, if the resolution entity meets the conditions for resolution, are taken care of by applying resolution tools to the resolution entity. This procedure is not necessarily a two-step approach but could also be completed in one go. In the following, we focus on different ways to upstream losses from the subsidiary to the parent undertaking and to recapitalise the subsidiary.

A fog-edge-enabled intrusion detection system for smart grids

The Smart Grid (SG) heavily depends on the Advanced Metering Infrastructure (AMI) technology, which has shown its vulnerability to intrusions. To effectively monitor and raise alarms in response to anomalous activities, the Intrusion Detection System (IDS) plays a crucial role. However, existing intrusion detection models are typically trained on cloud servers, which exposes user data to significant privacy risks and extends the time required for intrusion detection. Training a high-quality IDS using Artificial Intelligence (AI) technologies on a single entity becomes particularly challenging when dealing with vast amounts of distributed data across the network. To address these concerns, this paper presents a novel approach: a fog-edge-enabled Support Vector Machine (SVM)-based federated learning (FL) IDS for SGs. FL is an AI technique for training Edge devices. In this system, only learning parameters are shared with the global model, ensuring the utmost data privacy while enabling collaborative learning to develop a high-quality IDS model. The test and validation results obtained from this proposed model demonstrate its superiority over existing methods, achieving an impressive percentage improvement of 4.17% accuracy, 13.19% recall, 9.63% precision, 13.19% F1 score when evaluated using the NSL-KDD dataset. Furthermore, the model performed exceptionally well on the CICIDS2017 dataset, with improved accuracy, precision, recall, and F1 scores reaching 6.03%, 6.03%, 7.57%, and 7.08%, respectively. This novel approach enhances intrusion detection accuracy and safeguards user data and privacy in SG systems, making it a significant advancement in the field.

Label-Free Optical Imaging of Nanoscale Single Entities.

The advancement of optical microscopy technologies has achieved imaging of nanoscale objects, including nanomaterials, virions, organelles, and biological molecules, at the single entity level. Recently developed plasmonic and scattering based optical microscopy technologies have enabled label-free imaging of single entities with high spatial and temporal resolutions. These label-free methods eliminate the complexity of sample labeling and minimize the perturbation of the analyte native state. Additionally, these imaging-based methods can noninvasively probe the dynamics and functions of single entities with sufficient throughput for heterogeneity analysis. This perspective will review label-free single entity imaging technologies and discuss their principles, applications, and key challenges.

The Antitubercular Activities of Natural Products with Fused-Nitrogen-Containing Heterocycles.

Tuberculosis (TB) is notorious as the leading cause of death worldwide due to a single infectious entity and its causative agent, Mycobacterium tuberculosis (Mtb), has been able to evolve resistance to all existing drugs in the treatment arsenal complicating disease management programs. In drug discovery efforts, natural products are important starting points in generating novel scaffolds that have evolved to specifically bind to vulnerable targets not only in pathogens such as Mtb, but also in mammalian targets associated with human diseases. Structural diversity is one of the most attractive features of natural products. This review provides a summary of fused-nitrogen-containing heterocycles found in the natural products reported in the literature that are known to have antitubercular activities. The structurally targeted natural products discussed in this review could provide a revealing insight into novel chemical aspects with novel biological functions for TB drug discovery efforts.

Advancements in microemulsion-based fabrication of upconversion-mediated multifunctional materials

Upconversion nanoparticles (UCNPs) have experienced significant advancements, finding applications in diverse fields over the past decade. The growing demand for UCNP-based nanoplatforms with multifunctionality to address complex scenarios has led to the emergence of the microemulsion confined self-assembly method, which allows for the integration of different UCNPs or UCNPs with additional functional materials within a single entity, resulting in a nanoplatform that possesses a wide range of properties suitable for specific applications. This comprehensive review aimed to summarize recent developments in the design of UCNP assemblies using the microemulsion confined self-assembly method, which focused on exploring their applications in critical areas such as color encoding, bioimaging, and programmable therapeutics. Furthermore, the review acknowledged the existing limitations associated with the microemulsion confined self-assembly method and provided an in-depth discussion of potential solutions to overcome these challenges, aiming to foster further progress and innovation in the design and application of UCNP assemblies.

Discordance between Criteria-diagnosed and clinically-diagnosed Asthma COPD Overlap (ACO) among hospitalized patients in an Indian referral hospital.

Background and Objectives: Asthma-COPD-overlap (ACO), as a single entity, has been widely contested over the past few years. Most ACO diagnostic criteria do not agree with each other when applied to respiratory patients. From 2016 to 2020, GINA described a syndromic approach for diagnosing ACO, which was replaced with a broader approach after 2021. We intended to learn if the GINA criteria and the CSMA Screening tool agreed with a clinical diagnosis of ACO. Methods: We performed a retrospective cross-sectional analysis of inpatients with a clinical diagnosis of ACO from 2014 to 2019. We reclassified these patients into Not-ACO and ACO using the GINA syndromic approach 2019 (GSA 2019), Modified GINA syndromic approach 2019 (MGSA 2019), GINA 2021 and the CSMA tool. We used Kappa statistics to compare the performance of various tools. Results: 83 clinically diagnosed ACO patients were included. According to GINA syndromic approach 2019, only 41 (49.39%) patients would be classified as ACO. GINA 2021 picked up 57 (68.67%) patients as ACO. The modified GINA syndromic approach 2019, which we proposed, identified 67 (80.72%) patients as ACO. The CSMA tool identified 63 (75.90%) patients as ACO. The GINA 2019 criteria, when interpreted liberally as described by us, have a better agreement with the CSMA tool with a sensitivity of 87.3% and specificity of 40%, with a 'k' agreement of 29.3%. Conclusion: The standard GINA syndromic approach 2019 Tool is stringent and may sometimes miss the ACO diagnosis. The original GINA syndromic approach table 2019 should be brought back with modifications.

Surface Plasmon Resonance Biosensors: A Review of Molecular Imaging with High Spatial Resolution.

Surface plasmon resonance (SPR) is a powerful tool for determining molecular interactions quantitatively. SPR imaging (SPRi) further improves the throughput of SPR technology and provides the spatially resolved capability for observing the molecular interaction dynamics in detail. SPRi is becoming more and more popular in biological and chemical sensing and imaging. However, SPRi suffers from low spatial resolution due to the imperfect optical components and delocalized features of propagating surface plasmonic waves along the surface. Diverse kinds of approaches have been developed to improve the spatial resolution of SPRi, which have enormously impelled the development of the methodology and further extended its possible applications. In this minireview, we introduce the mechanisms for building a high-spatial-resolution SPRi system and present its experimental schemes from prism-coupled SPRi and SPR microscopy (SPRM) to surface plasmonic scattering microscopy (SPSM); summarize its exciting applications, including molecular interaction analysis, molecular imaging and profiling, tracking of single entities, and analysis of single cells; and discuss its challenges in recent decade as well as the promising future.

The last stage of development: The restructuring and plasticity of the cortex during adolescence especially at puberty.

There is considerable evidence of reorganization in the prefrontal cortex during adolescence in humans, as well as in rodents, where the cellular basis can be explored. Studies from my laboratory in the rat medial prefrontal cortex are reviewed here. In general, growth predominates before puberty. Pruning mainly occurs at puberty and after with decreases in the number of synapses, dendrites, and neurons. Perineuronal nets, extracellular structures that control plasticity, are pruned peripubertally only in female rats, which may further open the adolescent prefrontal cortex to environmental influences. This is supported by our recent evidence that exposure to mild stress early, but not late, in adolescence decreases prepulse inhibition. Additionally, exposure to methamphetamine in females early in adolescence increases the number of a major class of inhibitory interneurons, parvalbumin neurons, while the opposite occurs late in adolescence. In females, even estrogen receptor beta mRNA decreases at puberty in the prefrontal cortex. Interestingly, rats of both sexes perform better after puberty on a test of cognitive flexibility in the water maze. Thus, evidence is accruing that adolescence is not a single entity but rather an ongoing set of processes, and environmental effects will differ depending on timing and sex.

Synthesis of Novel Artemisinin, Ciprofloxacin, and Norfloxacin Hybrids with Potent Antiplasmodial Activity.

The synthesis and antiplasmodial evaluation of new hybrids combining the pharmacophore structures of artemisinin, ciprofloxacin or norfloxacin, and 7-chloroquinoline are reported in this study. The first step for all of the syntheses is the obtainment of key piperazine esters intermediates bearing the drugs ciprofloxacin and norfloxacin. Using these platforms, 18 final compounds were synthesized through a multistep procedure with overall yields ranging between 8 and 20%. All compounds were screened for their antiplasmodial activity against the chloroquine-resistant Plasmodium falciparum FcB1 strain. Compounds 20, 21, 22, and 28, bearing an artesunate fragment with ciprofloxacin, exhibited IC50 values in the range of 3.5-5.4 nM and excellent selectivity indices. Among the compounds bearing the artesunate moiety on the norfloxacin, two of them, 23 and 24, afforded IC50 values of 1.5 nM and 1.9 nM, respectively. They also showed excellent selectivity indices. The most potent compounds were also evaluated against the CQ-resistant Dd2 strain of Plasmodium falciparum, demonstrating that those compounds incorporating the artesunate fragment were the most potent. Finally, the combination of artesunate with either ciprofloxacin or norfloxacin moieties in a single molecular entity proved to substantially enhance the activity and selectivity when compared to the administration of the unconjugated counterparts artesunate/ciprofloxacin and artesunate/norfloxacin.

Flexible pressure sensor constructed by polyurethane composite conductive sponge

As the main core component of wearable devices, flexible strain sensors have broad application prospects in health monitoring, motion monitoring, human-machine interface, rehabilitation, entertainment technology and other fields. In this paper, a rectangular sandwich resistive pressure sensor is constructed with porous conductive sponge, and its working mechanism is analyzed. The linearity of the sensor is improved and the stress range is increased by gel modification. Through experimental tests, it can withstand more than 80% compressive strain, and shows a sensitivity of 0.398 kPa−1 in the range of 6 ∼ 11 kPa; the maximum range is close to 40 kPa, and the minimum detection limit is 20 Pa; under constant loading/releasing speed, the response/recovery time is about 133/150 ms; it also shows good linearity and stability. With the help of a single sensor entity, Morse code can be sent, and some human activity signals can be measured, such as speech recognition, weighing measurement, limb movement; and 8 sensors create an interesting smart insole for gait recognition. The results show that piezoresistive sensors with porous composite materials have broad application prospects in motion monitoring and human-computer interaction.

Modeling Magnitude Discrimination: Effects of Internal Precision and Attentional Weighting of Feature Dimensions.

Given a rich environment, how do we decide on what information to use? A view of a single entity (e.g., a group of birds) affords many distinct interpretations, including their number, average size, and spatial extent. An enduring challenge for cognition, therefore, is to focus resources on the most relevant evidence for any particular decision. In the present study, subjects completed three tasks-number discrimination, surface area discrimination, and convex hull discrimination-with the same stimulus set, where these three features were orthogonalized. Therefore, only the relevant feature provided consistent evidence for decisions in each task. This allowed us to determine how well humans discriminate each feature dimension and what evidence they relied on to do so. We introduce a novel computational approach that fits both feature precision and feature use. We found that the most relevant feature for each decision is extracted and relied on, with minor contributions from competing features. These results suggest that multiple feature dimensions are separately represented for each attended ensemble of many items and that cognition is efficient at selecting the appropriate evidence for a decision.

“Zero Cost” Majority Attacks on Permissionless Proof of Work Blockchains

The core premise of permissionless blockchains is their reliable and secure operation without the need to trust any individual agent. At the heart of blockchain consensus mechanisms is an explicit cost (e.g., mining cost) for participation in the network and the opportunity to add blocks to the blockchain. A key rationale for that cost is to make attacks on the network, which could be theoretically carried out if a majority of nodes were controlled by a single entity, too expensive to be worthwhile. We demonstrate that a majority attacker can successfully attack with a negative net cost when accounting for the mining rewards the attacker collects during the attack. This shows that the protocol mechanisms are insufficient to create a secure network, emphasizing the importance of socially driven mechanisms external to the protocol. At the same time, negative cost enables a new type of majority attack that is more likely to elude external scrutiny. This paper was accepted by David Simchi-Levi, finance.