Complex Protocols Research Articles

Continuous-time quantum harmonic oscillator state engineering

The center of mass motion of trapped ions and neutral atoms is suitable for approximation by a time-dependent driven quantum harmonic oscillator whose frequency and driving strength may be controlled with high precision. We show the time evolution for these systems with continuous differentiable time-dependent parameters in terms of the three basic operations provided by its underlying symmetry, rotation, displacement, and squeezing, using a Lie algebraic approach. Our factorization of the dynamics allows for the intuitive construction of protocols for state engineering, for example, creating and removing displacement and squeezing, as well as their combinations, optimizing squeezing, or more complex protocols that work for slow and fast rates of change in the oscillator parameters.

Multi-domain and complex protein structure prediction using inter-domain interactions from deep learning

Accurately capturing domain-domain interactions is key to understanding protein function and designing structure-based drugs. Although AlphaFold2 has made a breakthrough on single domain, it should be noted that the structure modeling for multi-domain protein and complex remains a challenge. In this study, we developed a multi-domain and complex structure assembly protocol, named DeepAssembly, based on domain segmentation and single domain modeling algorithms. Firstly, DeepAssembly uses a population-based evolutionary algorithm to assemble multi-domain proteins by inter-domain interactions inferred from a developed deep learning network. Secondly, protein complexes are assembled by means of domains rather than chains using DeepAssembly. Experimental results show that on 219 multi-domain proteins, the average inter-domain distance precision by DeepAssembly is 22.7% higher than that of AlphaFold2. Moreover, DeepAssembly improves accuracy by 13.1% for 164 multi-domain structures with low confidence deposited in AlphaFold database. We apply DeepAssembly for the prediction of 247 heterodimers. We find that DeepAssembly successfully predicts the interface (DockQ ≥ 0.23) for 32.4% of the dimers, suggesting a lighter way to assemble complex structures by treating domains as assembly units and using inter-domain interactions learned from monomer structures.

Fraud detection through data sharing using privacy‐preserving record linkage, digital signature (EdDSA), and the MinHash technique: Detect fraud using privacy preserving record links

AbstractFraud is a persistent and increasing problem in the telecom industry. Telcos work in isolation to prevent fraud. Sharing information is critical for detecting and preventing fraud. The primary constraint on sharing information is privacy preservation. Several techniques have been developed to share data while preserving privacy using privacy‐preserving record linkage (PPRL). Most of the PPRL techniques use a similarity measure like Jacquard similarity on homologous datasets, which are all prone to graph‐based attacks, rendering existing methods insecure. Many complex and slow techniques use the Bloom filter implementation, which can be compromised in a cryptanalysis attack. This paper proposes an attack‐proof PPRL method using existing infrastructure of a telco without a complex multistep protocol. First, a novel way of matching two non‐homologous datasets using attack‐proof digital signature schemes, like the Edwards‐curve digital signature algorithm is proposed. Here, Jaccard similarity can only be estimated using this method and not on the datasets directly. Second, two parties transact with a simple request–reply method. To validate the match accuracy, privacy preservation, and performance of this approach, it was tested on a large public dataset (North Carolina Voter Database). This method is secure against attacks and achieves 100% match accuracy with improved performance.

EPIoT: Enhanced privacy preservation based blockchain mechanism for internet-of-things

With the increasing popularity of the Internet of things (IoT) and giving the end users the opportunity of collecting and analyzing the data by these IoT devices give rise to ultimate privacy concern and is attracting significant attention nowadays. These IoT devices may contain highly sensitive data and data sharing processes which may lead to security and privacy concerns. To surmount these issues, the interaction of IoT with blockchain for a secure transaction is accepted as a candidate solution. However, the innate behavior of blockchain containing complex mathematical proofs and consensus protocol requires high computational power making it less favorable for IoT devices to be connected with. Motivated by a private by-design framework and emphasizing greater control and setting of privacy preferences by the data owner, this paper complements our previous work on privacy preservation in IoT networks. In this paper, we design and propound a complete blockchain-based privacy-preserving framework by deploying service-oriented layers concepts and low computation cryptography, and a less complex consensus protocol to address the privacy concern. Moreover, this paper will unravel the complete end-to-end architecture of IoT-based blockchain purposely build for secure transactions in IoT networks. Security analysis is conducted using AVISPA tool to show that the proposed algorithms attain the desired security goals. This is followed by extensive simulation experiments and ultimate output results cultivating it much favorably for the deployment of IoT applications in real life.

Optimising a landscape visual quality protocol. A Method for reducing respondent fatigue and obtaining site-specific indicators

Evaluation of landscape visual quality is crucial for policymaking and planning but is still challenging. A wide range of visual assessment protocols is available, but there is still no consensus on appropriate indicators or approaches. Also, evaluation protocols can encompass many indicators, being exhaustive and complex and making the evaluation lengthy. Furthermore, protocols tend to be catered to a particular type of landscape or site-specific, and it can be tricky to ensure the protocol developed is adequate for the landscape under study. This paper proposes a methodology to optimise the selection of indicators for landscape visual assessments. There are two main goals: i) reduce the evaluation time to avoid respondent fatigue, and ii) make the protocol site-specific, choosing indicators that perform better and avoiding redundant indicators.•The presented method optimises the selection of indicators in expert visual assessments;•Indicators are rated in situ on a 5-point scale and go through a performance and redundancy test;•It helps to adapt complex evaluation protocols to the study landscape and to choose robust indicators in a supported and scientific way.

Integrating operant behavior and fiber photometry with the open-source python library Pyfiber

Despite the popularity of fiber photometry (FP), its integration with operant behavior paradigms is progressing slowly. This can be attributed to the complex protocols in operant behavior – resulting in a combination of diverse non-predictable behavioral responses and scheduled events, thereby complicating data analysis. To overcome this, we developed Pyfiber, an open-source python library which facilitates the merge of FP with operant behavior by relating changes in fluorescent signals within a neuronal population to behavioral responses and events. Pyfiber helps to 1. Extract events and responses that occur in operant behavior, 2. Extract and process the FP signals, 3. Select events of interest and align them to the corresponding FP signals, 4. Apply appropriate signal normalization and analysis according to the type of events, 5. Run analysis on multiple individuals and sessions, 6. Collect results in an easily readable format. Pyfiber is suitable for use with many different fluorescent sensors and operant behavior protocols. It was developed using Doric lenses FP systems and Imetronic behavioral systems, but it possesses the capability to process data from alternative systems. This work sets a solid foundation for analyzing the relationship between different dimensions of complex behavioral paradigms with fluorescent signals from brain regions of interest.

Rapid and efficient generation of a transplantable population of functional retinal ganglion cells from fibroblasts.

Glaucoma and other optic neuropathies lead to progressive and irreversible vision loss by damaging retinal ganglion cells (RGCs) and their axons. Cell replacement therapy is a potential promising treatment. However, current methods to obtain RGCs have inherent limitations, including time-consuming procedures, inefficient yields and complex protocols, which hinder their practical application. Here, we have developed a straightforward, rapid and efficient approach for directly inducing RGCs from mouse embryonic fibroblasts (MEFs) using a combination of triple transcription factors (TFs): ASCL1, BRN3B and PAX6 (ABP). We showed that on the 6th day following ABP induction, neurons with molecular characteristics of RGCs were observed, and more than 60% of induced neurons became iRGCs (induced retinal ganglion cells) in the end. Transplanted iRGCs had the ability to survive and appropriately integrate into the RGC layer of mouse retinal explants and N-methyl-D-aspartic acid (NMDA)-damaged retinas. Moreover, they exhibited electrophysiological properties typical of RGCs, and were able to regrow dendrites and axons and form synaptic connections with host retinal cells. Together, we have established a rapid and efficient approach to acquire functional RGCs for potential cell replacement therapy to treat glaucoma and other optic neuropathies.

Cryptocurrencies from Islamic perspective

Purpose This paper aims to derive a compatible Shariah opinion on the permissibility of using cryptocurrencies by Muslims by reviewing the opinions expressed by Shariah scholars on the permissibility of cryptocurrencies. Design/methodology/approach This is a qualitative desk review research where the opinions expressed by the Shariah scholars on the permissibility of cryptocurrencies and the issues related to it have been analyzed using the literature. All the Shariah parameters checked pertaining to currencies have been studied and assessed to derive the Shariah opinion. Findings The research findings suggest that cryptocurrencies do not fully meet the characteristics of money according to Shariah principles. Scholars debate their classification as a medium of exchange due to concerns about volatility, intrinsic value and governance. The treatment of cryptocurrencies varies, and their decentralized nature prevents monopolization. Governance and resistance to manipulation are facilitated by blockchain technology. Classifying cryptocurrencies as hard money and their recognition as the primary unit of account face challenges. While they can be a store of value, price volatility and regulations must be considered. The network effect is crucial for their success, and their supply is controlled through complex protocols. These findings have implications for policymakers in Islamic finance. Originality/value The differences in Shariah opinions on using cryptocurrencies have been a major debate in the Islamic financial industry. A clear and comprehensive study is not found on the differences in the Shariah opinions on their reasonings, which is important for researchers and professionals in the field. Therefore, this research provides valuable insights for policymakers, scholars and practitioners in Islamic finance, contributing to the understanding of applying Islamic principles to cryptocurrencies.

Distributed Consensus Seeking With Different Convergence Performance Requirements: A Unified Control Framework.

In this article, we investigate distributed consensus seeking with multiple convergence performance requirements for single-integrator multiagent systems under undirected graphs. A unified distributed control framework is proposed to ensure consensus or practical consensus as well as performance time requirements, which contains most existing complex protocol schemes as special cases. In the proposed framework, three functions with specific properties in the controller play different roles and can be freely designed to achieve desired convergence performances, which guarantee a high-level scalability for multiple control requirements in addition to convergence time. For highlighting the compatibility and flexibility of the proposed method, four typical scenarios are discussed to reach exponential, finite-time, fixed-time, and appointed-time consensus seeking, respectively. Finally, numerical simulations are carried out to verify the effectiveness of the theoretical analysis.

Trifluoromethylation in the Design and Synthesis of High-Performance Wide Bandgap Polymer Donors for Quasiplanar Heterojunction Organic Solar Cells.

New strategies for the molecular design to construct efficient electron-deficient units for D-A-type donor copolymers are urgently needed. Halogenation of electron-deficient units (A) has been shown to be the most effective strategy reported to date with which to produce high-performance donor polymers. Herein, we have constructed two different trifluoromethyl-substituted polymer donors, PBQP-CF3 and PBQ-CF3. The trifluoromethylation process typically involves complex protocols, which are not widely used in the synthesis of polymer donors. Accordingly, we have developed a single-step, one-pot synthesis of the new trifluoromethyl-substituted electron-deficient unit (A) of PBQ-CF3. The strong electron-withdrawing ability of the trifluoromethyl group ensures deeper highest occupied molecular orbital (HOMO) energy levels, and the non-covalent bonding interactions of the fluorine atoms are beneficial to the regulation of aggregation properties. Thus, both of the trifluoromethyl-substituted polymer donors obtained much higher power conversion efficiency (PCE) than PBDP-H (6.66%). PBQ-CF3 exhibits a deeper HOMO energy level, better aggregation behavior, and higher hole mobility than PBQP-CF3. PBQ-CF3-based quasiplanar heterojunction (Q-PHJ) devices therefore achieve simultaneously enhanced open-circuit voltage (VOC), short-circuit current density (JSC), and fill factor (FF) and an impressive PCE (16.02%), which is much higher than that obtained by PBQP-CF3-based devices (12.57%). This work reveals a promising path to synthesis of the trifluoromethylation polymer donors and demonstrates that the trifluoromethylation strategy can be used to enhance the photovoltaic performance.

Echocardiographic image collection and evaluation in infants with CHD: lessons learned from the imaging core lab for the Residual Lesion Score study.

Many factors affect patient outcome after congenital heart surgery, including the complexity of the heart disease, pre-operative status, patient specific factors (prematurity, nutritional status and/or presence of comorbid conditions or genetic syndromes), and post-operative residual lesions. The Residual Lesion Score is a novel tool for assessing whether specific residual cardiac lesions after surgery have a measurable impact on outcome. The goal is to understand which residual lesions can be tolerated and which should be addressed prior to leaving the operating room. The Residual Lesion Score study is a large multicentre prospective study designed to evaluate the association of Residual Lesion Score to outcomes in infants undergoing surgery for CHD. This Pediatric Heart Network and National Heart, Lung, and Blood Institute-funded study prospectively enrolled 1,149 infants undergoing 5 different congenital cardiac surgical repairs at 17 surgical centres. Given the contribution of echocardiographic measurements in assigning the Residual Lesion Score, the Residual Lesion Score study made use of a centralised core lab in addition to site review of all data. The data collection plan was designed with the added goal of collecting image quality information in a way that would permit us to improve our understanding of the reproducibility, variability, and feasibility of the echocardiographic measurements being made. There were significant challenges along the way, including the coordination, de-identification, storage, and interpretation of very large quantities of imaging data. This necessitated the development of new infrastructure and technology, as well as use of novel statistical methods. The study was successfully completed, but the size and complexity of the population being studied and the data being extracted required more technologic and human resources than expected which impacted the length and cost of conducting the study. This paper outlines the process of designing and executing this complex protocol, some of the barriers to implementation and lessons to be considered in the design of future studies.

Massive transfusion protocol

Hemorrhage from trauma, surgery, obstetric emergencies, and gastrointestinal bleeds remain a cause of potentially preventable deaths for patients. However, transfusions of large amounts of blood products may lead to complications. The development of Massive Transfusion Protocols (MTP), a proactive treatment protocol to manage profound blood loss, has shown improved outcomes for patients requiring immediate and multiple blood transfusions. These guidelines help to recognize hemorrhagic patients in need of an MTP as well as how to best manage this complex protocol.

Accurate Modeling of CCS Combo Type 1 Cable and Its Communication Performance Analysis for High-Speed EV-EVSE Charging System

This paper addresses the issue of electromagnetic interference (EMI) in electric vehicle supply equipment (EVSE) charging cables, which can disrupt the communication signal for the real-time monitoring of the charging status, leading to the termination of charging. We propose a dedicated measurement jig for the Combined Charging System Combo Type 1 (CCS-CT1) cable structure and models its electrical characteristics of the jig using the impedance peeling technique for de-embedding. The obtained pure S-parameters of CCS-CT1 are then used to conduct a simulation of the signal integrity problem caused by Gaussian noise, which is the worst-case scenario that can occur in a typical charging system. This paper suggests that the root cause of this problem may be related to the high-power AC/DC conversion device included in the EVSE, which uses a switch-mode power conversion (SMPC) method that involves nonlinear operation and can result in increased harmonic noise and a more complex signal protocol for precise control. Finally, this study provides insights into the challenges of implementing high-speed charging systems and offers a solution for obtaining the accurate electromagnetic characteristics of charging cables.

A robotic system for automated genetic manipulation and analysis of Caenorhabditis elegans.

The nematode Caenorhabditis elegans is one of the most widely studied organisms in biology due to its small size, rapid life cycle, and manipulable genetics. Research with C. elegans depends on labor-intensive and time-consuming manual procedures, imposing a major bottleneck for many studies, especially for those involving large numbers of animals. Here, we describe a general-purpose tool, WormPicker, a robotic system capable of performing complex genetic manipulations and other tasks by imaging, phenotyping, and transferring C. elegans on standard agar media. Our system uses a motorized stage to move an imaging system and a robotic arm over an array of agar plates. Machine vision tools identify animals and assay developmental stage, morphology, sex, expression of fluorescent reporters, and other phenotypes. Based on the results of these assays, the robotic arm selectively transfers individual animals using an electrically self-sterilized wire loop, with the aid of machine vision and electrical capacitance sensing. Automated C. elegans manipulation shows reliability and throughput comparable with standard manual methods. We developed software to enable the system to autonomously carry out complex protocols. To validate the effectiveness and versatility of our methods, we used the system to perform a collection of common C. elegans procedures, including genetic crossing, genetic mapping, and genomic integration of a transgene. Our robotic system will accelerate C. elegans research and open possibilities for performing genetic and pharmacological screens that would be impractical using manual methods.

Extending the limitations in the prediction of PAMPA permeability with machine learning algorithms

Gastrointestinal absorption is a key factor amongst the ADME-related (absorption, distribution, metabolism and excretion) pharmacokinetic properties; therefore, it has a major role in drug discovery and drug safety determinations. The Parallel Artificial Membrane Permeability Assay (PAMPA) can be considered as the most popular and well-known screening assay for the measurement of gastrointestinal absorption. Our study provides quantitative structure-property relationship (QSPR) models based on experimental PAMPA permeability data for almost four hundred diverse molecules, which is a great extension of the applicability of the models in the chemical space. Two- and three-dimensional molecular descriptors were applied for the model building in every case. We have compared the performance of a classical partial least squares regression (PLS) model with two major machine learning algorithms: artificial neural networks (ANN) and support vector machine (SVM). Due to the applied gradient pH in the experiments, we have calculated the descriptors for the model building at pH values of 7.4 and 6.5, and compared the effect of pH on the performance of the models. After a complex validation protocol, the best model had an R2=0.91 for the training set, and R2= 0.84 for the external test set. The developed models are capable for the robust and fast prediction of new compounds with an excellent accuracy compared to the previous QSPR models.

Digitizing protocols into single reactors for the one-pot synthesis of nanomaterials

Digitizing protocols into single reactors for the one-pot synthesis of nanomaterials

Hypersensitivity Reactions to Native E. coli L-asparaginase in Children With Acute Lymphoblastic Leukemia Treated in Trial ALL-BFM 2000: Impact of Treatment Schedule and Type of Glucocorticoid in Induction.

Hypersensitivity Reactions to Native E. coli L-asparaginase in Children With Acute Lymphoblastic Leukemia Treated in Trial ALL-BFM 2000: Impact of Treatment Schedule and Type of Glucocorticoid in Induction.

DDoS attacks and machine‐learning‐based detection methods: A survey and taxonomy

AbstractDistributed denial of service (DDoS) attacks represent a significant cybersecurity challenge, posing a critical risk to computer networks. Developing an effective defense mechanism against these attacks is crucial but challenging, given their diverse attack types, network and computing platform heterogeneity, and complex communication protocols. Moreover, the emergence of innovative DDoS attack methods presents a formidable threat to existing countermeasures. Various machine learning techniques have shown promise in detecting DDoS attacks with low false‐positive rates and high detection rates. This survey paper offers a comprehensive taxonomy of machine learning‐based methods for detecting DDoS attacks, reviewing supervised, unsupervised, hybrid approaches, and analyzing the related challenges. Further, we explore relevant datasets, highlighting their strengths and limitations, and propose future research directions to address the current gaps in this domain. This paper aims to provide a profound understanding of DDoS attack detection mechanisms, aiding researchers, and practitioners in developing effective cybersecurity approaches against such attacks. This research is essential because DDoS attacks are diverse and pose a formidable threat to computer networks, and various machine learning techniques have shown promise in detecting them. Its implications include providing insights that can inform the development of robust defense mechanisms against DDoS attacks.

Wearable devices: underrepresentation in the ageing society

Wearable devices: underrepresentation in the ageing society

Alternating Magnetic Field-Promoted Nanoparticle Mixing: The On-Chip Immunocapture of Serum Neuronal Exosomes for Parkinson's Disease Diagnostics.

The analysis of cargo proteins in exosome subpopulations has considerable value in diagnostics but a translatable impact has been limited by lengthy or complex exosome extraction protocols. We describe herein a scalable, fast, and low-cost exosome extraction using an alternating (AC) magnetic field to support the dynamic mixing of antibody-coated magnetic beads (MBs) with serum samples within 3D-printed microfluidic chips. Zwitterionic polymer-coated MBs are, specifically, magnetically agitated and support ultraclean exosome capture efficiencies >70% from <50 μL of neat serum in 30 min. Applied herein to the immunocapture of neuronal exosomes using anti-L1CAM antibodies, prior to the array-based assaying of α-synuclein (α-syn) content by a standard duplex electrochemical sandwich ELISA, sub pg/mL detection was possible with an excellent coefficient of variation and a sample-to-answer time of ∼75 min. The high performance and semiautomation of this approach hold promise in underpinning low-cost Parkinson's disease diagnostics and is of value in exosomal biomarker analyses more generally.