General Applicability Research Articles

Generalized Performance of LSTM in Time-Series Forecasting

ABSTRACT Optimizing the time-series forecasting performance is a multi-objective problem which enables the comparison of general applicability of methods across multiple use cases such as finance and demographics. Libra, a time-series forecasting framework which shifts the problem of optimization from minimizing single to multiple evaluation measures and use cases, is used as a benchmark to evaluate the performance of the Long Short-Term Memory (LSTM) neural network. LSTMs with parameter tuning have been shown to perform well with time-series forecasting. This paper applies LSTMs (mostly with standard parameters and variations of some of them) to the Libra framework and concludes that due to data characteristic variance and without increased hardware and time constraints LSTMs do not outperform the median measures of Libra.

Generating grid double-scroll attractors from magnetized SC-CNN and its application in image encryption

Cellular neural network has attracted widespread attention due to its easy hardware implementation and general application in information processing. In this paper, a new memristive State-Controlled-CNN (MSC-CNN) model is established by introducing two novel flux-controlled memristors into a three-cell network, where one of which simulates the electromagnetic radiation and the other characterizes the coupling weight. The present MSC-CNN can generate multi-double-scroll attractors (MDSAs) in one direction or grid multi-double-scroll attractors (GMDSAs). Stability analysis shows that each asymptotically stable equilibrium point of the memristors serves as index-1 saddle-focus in the MSC-CNN, which induces the corresponding connecting bond orbits between two scrolls. Thus, the number of GMDSAs can be flexibly adjusted by varying the number of the asymptotically stable equilibrium points of the memristors. Moreover, the memristive coupling strength also controls the number of MDSAs. Through numerical simulations, complex dynamic behaviors such as extreme multistability and amplitude control are also observed. Then, an image encryption scheme based on the proposed MSC-CNN is designed from the application perspective. Finally, a microcontroller-based hardware system is implemented to verify the effectiveness of numerical simulations.

Ligand Characterization and DNA Intercalation of Ru(II) Polypyridyl Complexes: A Local Vibrational Mode Study.

We investigated in this work ruthenium-ligand bonding across the RuN framework in 12 Ru(II) polypyridyl complexes in the gas phase and solution for both singlet and triplet states, in addition to their affinity for DNA binding through π-π stacking interactions with DNA nucleobases. As a tool to assess the intrinsic strength of the ruthenium-ligand bonds, we determined local vibrational force constants via our local vibrational mode analysis software. We introduced a novel local force constant that directly accounts for the intrinsic strength of the π-π stacking interaction between DNA and the intercalated Ru(II) complex. According to our findings, [Ru(phen)2(dppz)]2+ and [Ru(phen)2(11-CN-dppz)]2+ provide an intriguing trade-off between photoinduced complex excitation and the strength of the subsequent π-π stacking interaction with DNA. [Ru(phen)2(dppz)]2+ displays a small singlet-triplet splitting and a strong π-π stacking interaction in its singlet state, suggesting a favorable photoexcitation but potentially weaker interaction with DNA in the excited state. Conversely, [Ru(phen)2(11-CN-dppz)]2+ exhibits a larger singlet-triplet splitting and a stronger π-π stacking interaction with DNA in its triplet state, indicating a less favorable photoinduced transition but a stronger interaction with DNA postexcitation. We hope our study will inspire future experimental and computational work aimed at the design of novel Ru-polypyridyl drug candidates and that our new quantitative measure of π-π stacking interactions in DNA will find a general application in the field.

Enhanced in situ spatial proteomics by effective combination of MALDI imaging and LC-MS/MS

Highly specialized cells are fundamental for proper functioning of complex organs. Variations in cell-type specific gene expression and protein composition have been linked to a variety of diseases. Investigation of the distinctive molecular makeup of these cells within tissues is therefore critical in biomedical research. Although several technologies have emerged as valuable tools to address this cellular heterogeneity, most workflows lack sufficient in situ resolution and are associated with high cost and extremely long analysis times. Here, we present a combination of experimental and computational approaches that allows a more comprehensive investigation of molecular heterogeneity within tissues than by either shotgun LC-MS/MS or MALDI imaging alone. We applied our pipeline on mouse brain, which contains a wide variety of cell types that not only perform unique functions but also exhibit varying sensitivities to insults. We explored the distinct neuronal populations within the hippocampus, a brain region crucial for learning and memory that is involved in various neurological disorders. As an example, we identified the groups of proteins distinguishing the neuronal populations of dentate gyrus (DG) and the cornu ammonis (CA) in the same brain section. Most of the annotated proteins matched the regional enrichment of their transcripts, thereby validating the method. As the method is highly reproducible, the identification of individual masses through the combination of MALDI-IMS and LC-MS/MS methods can be used for the much faster and more precise interpretation of MALDI-IMS measurements only. This greatly speeds up spatial proteomic analyses and allows the detection of local protein variations within the same population of cells. The method's general applicability has the potential to be used to investigate different biological conditions and tissues and a much higher throughput than other techniques making it a promising approach for clinical routine applications.

Expanding neutrino oscillation parameter measurements in NOvA using a Bayesian approach

NOvA is a long-baseline neutrino oscillation experiment that measures oscillations in charged-current νμ→νμ (disappearance) and νμ→νe (appearance) channels, and their antineutrino counterparts, using neutrinos of energies around 2 GeV over a distance of 810 km. In this work we reanalyze the dataset first examined in our previous paper [] using an alternative statistical approach based on Bayesian Markov chain Monte Carlo. We measure oscillation parameters consistent with the previous results. We also extend our inferences to include the first NOvA measurements of the reactor mixing angle θ13, where we find 0.071≤sin22θ13≤0.107, and the Jarlskog invariant, where we observe no significant preference for the CP-conserving value J=0 over values favoring CP violation. We use these results to examine the effects of constraints from short-baseline measurements of θ13 using antineutrinos from nuclear reactors when making NOvA measurements of θ23. Our long-baseline measurement of θ13 is shown to be consistent with the reactor measurements, supporting the general applicability and robustness of the Pontecorvo-Maki-Nakagawa-Sakata framework for neutrino oscillations. Published by the American Physical Society 2024

Empty-bucket techniques for spill-quality improvement at the CERN Super Proton Synchrotron

Synchrotrons can provide long spills of particles by employing resonant extraction where the circulating beam is slowly ejected over thousands to millions of turns by exploiting the amplitude growth caused by a transverse resonance. In the CERN Super Proton Synchrotron (SPS), this method is used to satisfy the experimental requests of the North Area. However, the extracted particle flux is modulated by power-converter ripple, an issue shared across all sychrotrons that perform resonant extraction. In order to suppress such modulations, empty-bucket techniques can be employed, which take advantage of chromaticity to quickly accelerate particles into resonant motion by using a longitudinal rf system. This paper explores empty-bucket techniques via theory, simulation, and measurement, providing a systematic characterization with general applicability to any machine. Additionally, the operational implementation in the SPS is detailed, where the impact on the beam profile and extracted intensity is addressed. Published by the American Physical Society 2024

The Lecturer as a Researcher in a Tertiary Level Technical Vocational Education and Training (TVET) Institution

This article explores the issue of the lecturer as a researcher in the context of a tertiary level technical and vocational education and training (TVET) institution in Jamaica. Much of what is said, however, will find easy application to TVET institutions in small developing countries in the Commonwealth Caribbean and elsewhere. The article first explores, broadly, the concept of research in education and its general application to the field of TVET. It then addresses a number of issues more specifically related to the lecturer as a researcher in a TVET institution.

Temporal-Quality Ensemble Technique for Handling Image Blur in Packaging Defect Inspection

Despite achieving numerous successes with surface defect inspection based on deep learning, the industry still faces challenges in conducting packaging defect inspections that include critical information such as ingredient lists. In particular, while previous achievements primarily focus on defect inspection in high-quality images, they do not consider defect inspection in low-quality images such as those containing image blur. To address this issue, we proposed a noble inference technique named temporal-quality ensemble (TQE), which combines temporal and quality weights. Temporal weighting assigns weights to input images by considering the timing in relation to the observed image. Quality weight prioritizes high-quality images to ensure the inference process emphasizes clear and reliable input images. These two weights improve both the accuracy and reliability of the inference process of low-quality images. In addition, to experimentally evaluate the general applicability of TQE, we adopt widely used convolutional neural networks (CNNs) such as ResNet-34, EfficientNet, ECAEfficientNet, GoogLeNet, and ShuffleNetV2 as the backbone network. In conclusion, considering cases where at least one low-quality image is included, TQE has an F1-score approximately 17.64% to 22.41% higher than using single CNN models and about 1.86% to 2.06% higher than an average voting ensemble.

Empty-bucket techniques for spill-quality improvement at the CERN Super Proton Synchrotron

Synchrotrons can provide long spills of particles by employing resonant extraction where the circulating beam is slowly ejected over thousands to millions of turns by exploiting the amplitude growth caused by a transverse resonance. In the CERN Super Proton Synchrotron (SPS), this method is used to satisfy the experimental requests of the North Area. However, the extracted particle flux is modulated by power-converter ripple, an issue shared across all sychrotrons that perform resonant extraction. In order to suppress such modulations, empty-bucket techniques can be employed, which take advantage of chromaticity to quickly accelerate particles into resonant motion by using a longitudinal rf system. This paper explores empty-bucket techniques via theory, simulation, and measurement, providing a systematic characterization with general applicability to any machine. Additionally, the operational implementation in the SPS is detailed, where the impact on the beam profile and extracted intensity is addressed. Published by the American Physical Society 2024

The Lecturer as a Researcher in a Tertiary Level Technical Vocational Education and Training (TVET) Institution

This article explores the issue of the lecturer as a researcher in the context of a tertiary level technical and vocational education and training (TVET) institution in Jamaica. Much of what is said, however, will find easy application to TVET institutions in small developing countries in the Commonwealth Caribbean and elsewhere. The article first explores, broadly, the concept of research in education and its general application to the field of TVET. It then addresses a number of issues more specifically related to the lecturer as a researcher in a TVET institution.

Temporal-Quality Ensemble Technique for Handling Image Blur in Packaging Defect Inspection

Despite achieving numerous successes with surface defect inspection based on deep learning, the industry still faces challenges in conducting packaging defect inspections that include critical information such as ingredient lists. In particular, while previous achievements primarily focus on defect inspection in high-quality images, they do not consider defect inspection in low-quality images such as those containing image blur. To address this issue, we proposed a noble inference technique named temporal-quality ensemble (TQE), which combines temporal and quality weights. Temporal weighting assigns weights to input images by considering the timing in relation to the observed image. Quality weight prioritizes high-quality images to ensure the inference process emphasizes clear and reliable input images. These two weights improve both the accuracy and reliability of the inference process of low-quality images. In addition, to experimentally evaluate the general applicability of TQE, we adopt widely used convolutional neural networks (CNNs) such as ResNet-34, EfficientNet, ECAEfficientNet, GoogLeNet, and ShuffleNetV2 as the backbone network. In conclusion, considering cases where at least one low-quality image is included, TQE has an F1-score approximately 17.64% to 22.41% higher than using single CNN models and about 1.86% to 2.06% higher than an average voting ensemble.

Genetic Cache: A Machine Learning Approach to Designing DRAM Cache Controllers in HBM Systems

DRAM memory controller plays a critical role in maximizing the performance of High Bandwidth Memory by efficiently managing data transfers between the CPU and the memory modules. Thus, they are suitable for low-power data-intensive applications. However, the complexity of DRAM command scheduling combined with tag management overheads in the cache makes the design of in-package cache controllers significantly challenging. Traditional memory controllers often face challenges with static, inflexible access scheduling designed for general applications, leading to suboptimal performance in dynamic environments. On the other hand, while some advanced controllers employing reinforcement learning offer adaptability to workload fluctuations, they tend to introduce hardware complexity and incur longer training latencies. Our approach aims to design low-power and efficient DRAM cache controllers using a machine learning model that dynamically adjusts to workload changes with optimized hardware efficiency and reduced training time. Therefore, we propose a machine learning approach to design low-power and efficient DRAM cache controllers that will leverage the trained model to produce optimal cache command schedules at runtime. The model considers several conditions for each request queue, and chooses the best response among the options. The simulation results show the superiority of our proposed design over the previous algorithms in a set of twelve data-intensive applications; our model is able to improve the performance up to 40% in some cases, and an average of 15% in performance and 10% in power consumption.

Design Considerations and Flow Characteristics for Couette-Type Blood-Shear Devices

Cardiovascular prosthetic devices, stents, prosthetic valves, heart-assist pumps, etc., operate in a wide regime of flows characterized by fluid dynamic flow structures, laminar and turbulent flows, unsteady flow patterns, vortices, and other flow disturbances. These flow disturbances cause shear stress, hemolysis, platelet activation, thrombosis, and other types of blood trauma, leading to neointimal hyperplasia, neoatherosclerosis, pannus overgrowth, etc. Couette-type blood-shearing devices are used to simulate and then clinically measure blood trauma, after which the results can be used to assist in the design of the cardiovascular prosthetic devices. However, previous designs for such blood-shearing devices do not cover the whole range of flow shear, Reynolds numbers, and Taylor numbers characteristic of all types of implanted cardiovascular prosthetic devices, limiting the general applicability of clinical data obtained by tests using different blood-shearing devices. This paper presents the key fluid dynamic parameters that must be met. Based on this, Couette device geometric parameters such as diameter, gap, flow rate, shear stress, and temperature are carefully selected to ensure that the device’s Reynolds numbers, Taylor number, operating temperature, and shear stress in the gap fully represent the flow characteristics across the operating range of all types of cardiovascular prosthetic devices. The outcome is that the numerical data obtained from the presented device can be related to all such prosthetic devices and all flow conditions, making the results obtained with such shearing devices widely applicable across the field. Numerical simulations illustrate that the types of flow patterns generated in the blood-shearing device meet the above criteria.

Mechanoresponsive Protein Crystals for NADH Recycling in Multicycle Enzyme Reactions.

NAD(H)-dependent enzymes play a crucial role in the biosynthesis of pharmaceuticals and fine chemicals, but the limited recyclability of the NAD(H) cofactor hinders its more general application. Here, we report the generation of mechano-responsive PEI-modified Cry3Aa protein crystals and their use for NADH recycling over multiple reaction cycles. For demonstration of its practical utility, a complementary Cry3Aa protein particle containing genetically encoded and co-immobilized formate dehydrogenase for NADH regeneration and leucine dehydrogenase for catalyzing the NADH-dependent l-tert-leucine (l-tert-Leu) biosynthesis has been produced. When combined with the PEI-modified Cry3Aa crystal, the resultant reaction system could be used for the efficient biosynthesis of l-tert-Leu for up to 21 days with a 10.5-fold improvement in the NADH turnover number.

Effectiveness of a mobile application for independent computerized cognitive training in patients with mild cognitive impairment: study protocol for the NeNaE Study, a randomized controlled trial

BackgroundMild cognitive impairment (MCI) involves cognitive decline beyond typical age-related changes, but without significant daily activity disruption. It can encompass various cognitive domains as the causes of MCI are diverse. MCI as well as frequent comorbid neuropsychiatric conditions like depression and anxiety affect individuals’ quality of life. Early interventions are essential, and computerized cognitive training (cCT) is an established treatment method. This paper presents the protocol for the NeuroNation MED Effectiveness Study, evaluating the self-administered mobile cCT intervention (“NeuroNation MED”) in individuals with MCI to assess training effects on cognitive domains, health competence, neuropsychiatric symptoms, psychological well-being, and the general application usability.MethodsThis study protocol presents a single-blinded multicenter randomized controlled trial that will be carried out in six study centers in Germany and Luxembourg. We included adults with MCI (existing F06.7 ICD-10-GM diagnosis and TICS ≥ 21 and ≤ 32). The intervention group will use a mobile, multi-domain cCT (“NeuroNation MED”) for 12 weeks. Meanwhile, the wait list control group will receive standard medical care or no care. The eligibility of volunteers will be determined through a telephone screening. After completion of the baseline examination, patients will be randomly assigned to one of the experimental conditions in a 2:1 ratio. In total, 286 participants will be included in this study. The primary outcome is the change of cognitive performance measured by the index score of the screening module of the Neuropsychological Assessment Battery. Secondary outcomes are changes in the Cognitive Failures Questionnaire, Hospital Anxiety and Depression Scale, Health-49, Health Literacy Questionnaire, among others. All of the primary and secondary outcomes will be assessed at baseline and after the 12-week post-allocation period. Furthermore, the intervention group will undergo an assessment of the System Usability Scale, and the training data of the NeuroNation MED application will be analyzed.DiscussionThis study aims to assess the effectiveness of a mobile self-administered cCT in enhancing cognitive abilities among individuals diagnosed with MCI. Should the findings confirm the effectiveness of the NeuroNation MED app, it may confer possible benefits for the care management of patients with MCI, owing to the accessibility, cost-effectiveness, and home-based setting it provides. Specifically, the cCT program could provide patients with personalized cognitive training, educational resources, and relaxation techniques, enabling participants to independently engage in cognitive training sessions at home without further supervision.Trial registrationGerman Clinical Trials Register DRKS00025133. Registered on November 5, 2021.

A scalable synergy-first backbone decomposition of higher-order structures in complex systems

In the last decade, there has been an explosion of interest in the field of multivariate information theory and the study of emergent, higher-order interactions. These “synergistic” dependencies reflect information that is in the “whole” but not any of the “parts.” Arguably the most successful framework for exploring synergies is the partial information decomposition (PID). Despite its considerable power, the PID has a number of limitations that restrict its general applicability. Subsequently, other heuristic measures, such as the O-information, have been introduced, although these measures typically only provide a summary statistic of redundancy/synergy dominance, rather than direct insight into the synergy itself. To address this issue, we present an alternative decomposition that is synergy-first, scales much more gracefully than the PID, and has a straightforward interpretation. We define synergy as that information encoded in the joint state of a set of elements that would be lost following the minimally invasive perturbation on any single element. By generalizing this idea to sets of elements, we construct a totally ordered “backbone” of partial synergy atoms that sweeps the system’s scale. This approach applies to the entropy, the Kullback-Leibler divergence, and by extension, to the total correlation and the single-target mutual information (thus recovering a “backbone” PID). Finally, we show that this approach can be used to decompose higher-order interactions beyond information theory by showing how synergistic combinations of edges in a graph support global integration via communicability. We conclude by discussing how this perspective on synergistic structure can deepen our understanding of part-whole relationships in complex systems.

Spatially asymmetrical copper dimer in ceria as an efficiently synergistic oxidation catalyst

Accurate and facile construction of atomically dispersed active sites is fundamentally important in heterogeneous catalysis. Herein, by applying a unique H2-He pretreatment, we successfully synthesize spatially asymmetrical dimeric Cu stabilized by CeO2. Integrated microscopic and spectroscopic characterizations, together with DFT calculations have revealed the redispersion of Cu species on CeO2 support is accompanied by the formation of Cu dimers with one Cu atom embedded into CeO2 lattice and the neighboring Cu atom supported on-top. The obtained dual-Cu atom catalyst demonstrates its general applicability in emission control scenarios or hydrogen fuel cell for automobile applications with excellent performances. Ab initio calculations reveal that such dimeric Cu sites on CeO2 promote reactant adsorption and lattice oxygen activation, owing to the synergistic effect of unique dimeric Cu structure. This work highlights the great potential of gas pretreatment in the fabrication of dual-atom catalysts, which is widely applicable to active site modulation in advanced oxidation catalysts.

Geographic and demographic trends with the initiation of virtual interviews in general surgery: A 7-program evaluation

BackgroundAlthough most general surgery residency interviews remain virtual, the effect of this format remains understudied. Single-institution data have shown an increase in the number of applications received and interviews conducted with virtual interviewing but no change in the geographic backgrounds of interviewed or matched applicants. This study sought to compare national trends in geographic characteristics of general surgery applicants, interviewed applicants, and matched applicants between in-person and virtual application cycles. Study DesignA retrospective review of 7 general surgery residency programs from application years 2016–2019 (in-person) and 2020–2021 (virtual) was conducted. Data collected included birth year, sex, race, medical school state, and contact location at the time of application. Data were analyzed using generalized mixed effects linear models. ResultsA total of 52,742 applicants, 4,550 interviewed applicants, and 329 matched applicants were included. During virtual application cycles, there were no increases in the average number of applicants (P = .25), interviewed applicants (P = .36), or matched (P = .84) applicants per year. Virtual cycles were associated with a larger proportion of interviews conducted with applicants from out-of-state medical schools (P < .01) and listing out-of-state contact locations (P < .01) compared with in-person application cycles. There were no significant geographic differences in matched applicants between virtual and in-person application cycles. ConclusionVirtual application cycles had greater geographic diversity among interviewed applicants. However, similar differences were not seen in the geographic diversity of matched applicants. Additional efforts should focus on why no changes in the geographic diversity of matched applicants were identified.

Framework for Designing of Wireless Sensor Network Using OPNET and Effect of Transport Protocols on Simulation

A Wireless sensor network can be defined as a network of devices that can communicate the information gathered from a monitored field through wireless links. The data is forwarded through multiple nodes, and with a gateway, the data is connected to other networks like wireless Ethernet. It is intended for a general application and should be able to collect data from many nodes, every node connected to several sensors. One possible application for such a network is in the medical area. At first the network will be used to display the evolution of blood pressure; the body temperature, the ambient temperature and pressure and the level of the transfusion liquid can be collected as well. The network coordinator will transmit the data to a local server for further processing. The simulation will be used to evaluate the general parameters of the wireless network and to optimize it. In this paper, we aim to evaluate the performance of a ZigBee wireless sensor network using OPNET and also study the effects of TCP and UDP on the performance of the application based on OPNET simulation software. Keywords-Sensor, wireless sensor network, ZigBee, OPNET, TCP, UDP.

Insights into the Versatility of Using Atomic Absorption Spectrometry in Antibacterial Research.

The ongoing development of bacterial resistance to antibiotics is a global challenge. Research in that field is thus necessary. Analytical techniques are required for such a purpose. From this perspective, the focus was on atomic absorption spectrometry (AAS). Although it is old, AAS often offers unexpected potential. Of course, this should be exploited. The aim was therefore to demonstrate the versatility of the technique in antibacterial research. This is illustrated by various examples of its practical application. AAS can be used, for example, to confirm the identity of antibacterial compounds, for purity controls, or to quantify the antibiotics in pharmaceutical preparations. The latter allowed analysis without laborious sample preparation and without interference from other excipients. In addition, AAS can help elucidate the mode of action or resistance mechanisms. In this context, quantifying the accumulation of the antibiotic drug in the cell of (resistant) bacteria appears to play an important role. The general application of AAS is not limited to metal-containing drugs, but also enables the determination of some organic chemical antibiotics. Altogether, this perspective presents a range of applications for AAS in antibacterial research, intending to raise awareness of the method and may thus contribute to the fight against resistance.