Specific Scenarios Research Articles

Core CSF Biomarker Profile in Cerebral Amyloid Angiopathy: Updated Meta-Analysis.

There is a clear need to characterize and validate molecular biomarkers of cerebral amyloid angiopathy (CAA), in an effort to improve diagnostics, especially in the context of patients with Alzheimer disease (AD) receiving immunotherapies (for whom underlying CAA is the driver of amyloid-related imaging abnormalities). We performed an updated meta-analysis of 5 core CSF biomarkers (Aβ42, Aβ40, Aβ438, total tau [T-tau], and phosphorylated tau [P-tau]) to assess which of these are most altered in sporadic CAA. We systematically searched PubMed for eligible studies reporting data on CSF biomarkers reflecting APP metabolism (Aβ42, Aβ40, Aβ38), neurodegeneration (T-tau), and tangle pathology (P-tau), in symptomatic sporadic CAA cohorts (based on the Boston criteria) vs control groups and/or vs patients with AD. Biomarker performance was assessed in random-effects meta-analysis based on ratio of mean (RoM) biomarker concentrations in (1) patients with CAA to controls and (2) CAA to patients with AD. RoM >1 indicates higher biomarker concentration in CAA vs comparison population, and RoM <1 indicates higher concentration in comparison groups. 8 studies met inclusion criteria: a total of 11 CAA cohorts (n = 289), 9 control cohorts (n = 310), and 8 AD cohorts (n = 339). Overall included studies were of medium quality based on our assessment tools. CAA to controls had lower mean level of all amyloid markers with CSF Aβ42, Aβ40, and Aβ38 RoMs of 0.46 (95% CI 0.38-0.55, p < 0.0001), 0.70 (95% CI 0.63-0.78, p < 0.0001), and 0.71 (95% CI 0.56-0.89, p = 0.003), respectively. CSF T-tau and P-tau RoMs of patients with CAA to controls were both greater than 1: 1.56 (95% CI 1.32-1.84, p < 0.0001) and 1.31 (95% CI 1.13-1.51, p < 0.0001), respectively. Differentiation between CAA and AD was strong for CSF Aβ40 (RoM 0.76, 95% CI 0.69-0.83, p < 0.0001) and Aβ38 (RoM 0.55, 95% CI 0.38-0.81, p < 0.0001), but not Aβ42 (RoM 1.00; 95% CI 0.81-1.23, p = 0.970). For T-tau and P-tau, average CSF ratios in patients with CAA vs AD were 0.64 (95% CI 0.58-0.71, p < 0.0001) and 0.64 (95% CI 0.58-0.71, p < 0.0001), respectively. Specific CSF patterns of Aβ42, Aβ40, Aβ38, T-tau, and P-tau might serve as molecular biomarkers of CAA, in research and clinical settings, offering the potential to improve the clinical diagnostic approach pathway in specific scenarios.

Transcatheter non-acute retrieval of the tine-based leadless ventricular pacemaker.

We report our single-centre experience of mid-term to long-term retrieval and reimplantation of a tine-based leadless pacemaker [Micra transcatheter pacing system (TPS)]. The TPS is a clinically effective alternative to transvenous single-chamber ventricular pacemakers. Whereas it is currently recommended to abandon the TPS at the end of device life, catheter-based retrieval may be favourable in specific scenarios. We report on nine consecutive patients with the implanted TPS who subsequently underwent transcatheter retrieval attempts. The retrieval system consists of the original TPS delivery catheter and an off-the-shelf single-loop 7 mm snare. The procedure was guided by fluoroscopy and intracardiac echocardiography. After an implantation duration of 3.1 ± 2.8 years (range 0.4-9.0), the overall retrieval success rate was 88.9% (8 of 9 patients). The mean procedure time was 89 ± 16 min, and the fluoroscopy time was 18.0 ± 6.6 min. No procedure-related adverse device events occurred. In the one unsuccessful retrieval, intracardiac echocardiography revealed that the TPS was partially embedded in the ventricular tissue surrounding the leadless pacemaker body in the right ventricle. After retrieval, three patients were reimplanted with a new TPS device. All implantations were successful without complications. A series of transvenous late retrievals of implanted TPS devices demonstrated safety and feasibility, followed by elective replacement with a new leadless pacing device or conventional transvenous pacing system. This provides a viable end-of-life management alternative to simple abandonment of this leadless pacemaker.

Vanadium-dioxide-assisted multifunctional switchable terahertz metamaterial devices

A multifunctional, switchable terahertz (THz) metamaterial (MM) device with wideband absorption and polarization conversion capabilities has been developed, based on the insulator-metal phase transition of vanadium dioxide (VO2). In its metallic state, the device operates as a wideband absorber within the range of 2.56–6.74 THz, achieving a bandwidth of 4.18 THz and an absorption rate of ≥90%. The wideband absorption is insensitive to both oblique incident angles and polarization. When VO2 is in its insulating state, the device switches to a polarization converter, facilitating linear-to-cross polarization (LTX) conversion between 1.04 and 4 THz, and linear-to-circular polarization (LTC) conversion between 1 and 1.04 THz, with a conversion efficiency exceeding 90%. Additionally, the effects of incident angle and polarization angle on the device’s performance were analyzed. This THz device offers advantages of wide angle, wide bandwidth, and high efficiency, making it a valuable reference for research into new multifunctional THz devices. It has great potential applications in short-range wireless THz communication, ultrafast optical switches, high-temperature resistant switches, transient spectroscopy, and optical polarization control devices. In specific application scenarios, particularly in fields requiring efficient detection, transmission, and analysis—such as security and non-destructive testing, secure communication systems, imaging and sensing, multidimensional spectral analysis, pollutant detection, smart stealth coatings, dynamic optical control devices, and integrated optical systems—these devices offer multifunctional capabilities. They enhance system performance and flexibility, meeting diverse application needs.

Association between telehealth use in oncology and downstream utilization at a large academic health system.

While telemedicine has been beneficial in oncology by reducing infectious exposure and improving access for patients with poor functional status, it also has intrinsic limitations, including the inability to perform a physical exam, which could lead to increased downstream utilization in this population at high risk of medical decompensation. We conducted a retrospective cohort study investigating the relationship between telemedicine use in oncology and subsequent outpatient oncology encounters, emergency department (ED) visits, and hospitalizations. We included outpatient oncology encounters, including telemedicine and in-person visits, occurring between 1 January 2018 and 31 December 2022 at a large academic health system. Unadjusted descriptive statistics and multiple linear regressions were used to estimate subsequent outpatient oncology visits, ED visits, and hospitalizations within 30 days of an index visit based on modality (telemedicine versus in-person). The multiple regressions were adjusted for various demographic and clinical characteristics, including palliative care visits, baseline utilization, recent chemotherapy, and comorbidities. Our cohort included 63,722 patients with 689,356 outpatient encounters, of which 639,217 (92.7%) were in-person and 50,139 (7.3%) were telemedicine visits. Patients on average had 0.91 outpatient oncology visits, 0.04 ED visits, and 0.05 hospitalizations within 30 days following an index encounter. In our adjusted analyses, telemedicine was associated with 13.7 fewer downstream outpatient oncology visits (95% CI 12.5-14.9; p < 0.001) per 100 index encounters, 0.7 fewer ED visits (95% CI 0.4-1.0; p < 0.001) per 100 index encounters and 0.9 fewer hospitalizations (95% CI 0.6-1.3; p < 0.001) per 100 index encounters compared to in-person visits. Contrary to our hypothesis, oncology patients who had a telemedicine visit had fewer follow-up outpatient oncology encounters, ED visits and hospitalizations after 30 days than those with in-person visits. Future studies should further investigate the efficacy of telemedicine in oncology and outline specific scenarios for appropriate use in this and other populations.

Similar Data Points Identification with LLM: A Human-in-the-Loop Strategy Using Summarization and Hidden State Insights

This study introduces a simple yet effective method for identifying similar data points across non-free text domains, such as tabular and image data, using Large Language Models (LLMs). Our two-step approach involves data point summarization and hidden state extraction. Initially, data is condensed via summarization using an LLM, reducing complexity and highlighting essential information in sentences. Subsequently, the summarization sentences are fed through another LLM to extract hidden states, serving as compact, feature-rich representations. This approach leverages the advanced comprehension and generative capabilities of LLMs, offering a scalable and efficient strategy for similarity identification across diverse datasets. We demonstrate the effectiveness of our method in identifying similar data points on multiple datasets. Additionally, our approach enables non-technical domain experts, such as fraud investigators or marketing operators, to quickly identify similar data points tailored to specific scenarios, demonstrating its utility in practical applications. In general, our results open new avenues for leveraging LLMs in data analysis across various domains.

Methods for Assessing the Effectiveness of Modern Counter Unmanned Aircraft Systems

Given the growing threat posed by the widespread availability of unmanned aircraft systems (UASs), which can be utilised for various unlawful activities, the need for a standardised method to evaluate the effectiveness of systems capable of detecting, tracking, and identifying (DTI) these devices has become increasingly urgent. This article draws upon research conducted under the European project COURAGEOUS, where 260 existing drone detection systems were analysed, and a methodology was developed for assessing the suitability of C-UASs in relation to specific threat scenarios. The article provides an overview of the most commonly employed technologies in C-UASs, such as radars, visible light cameras, thermal imaging cameras, laser range finders (lidars), and acoustic sensors. It explores the advantages and limitations of each technology, highlighting their reliance on different physical principles, and also briefly touches upon the legal implications associated with their deployment. The article presents the research framework and provides a structural description, alongside the functional and performance requirements, as well as the defined metrics. Furthermore, the methodology for testing the usability and effectiveness of individual C-UAS technologies in addressing specific threat scenarios is elaborated. Lastly, the article offers a concise list of prospective research directions concerning the analysis and evaluation of these technologies.

Should sports professionals consider their adversary’s strategy? A case study of match play in golf

AbstractThis study explores strategic considerations in professional golf’s Match Play format. Leveraging Professional Golfers’ Association Tour data, we investigate the impact of factoring in an adversary’s strategy. Our findings suggest that while slight strategy adjustments can be advantageous in specific scenarios, the overall benefit of considering an opponent’s strategy remains modest. This confirms the common wisdom in golf, reinforcing the recommendation to adhere to optimal stroke-play strategies due to challenges in obtaining precise opponent statistics. The methodology employed here is generic and could offer valuable insights into whether opponents’ performances should also be considered in other two-player or team sports, such as tennis, darts, soccer, volleyball, etc. We hope that this research will pave the way for new avenues of study in these areas.

Investigating the performance of a gas turbine system combined with steam methane reforming as a thermochemical recuperator in a power, cooling, freshwater, and hydrogen production multigeneration system

Depletion of fossil fuel resources at a high speed and increasing demand for energy supply for various purposes in today's life integrated with industry has led to collective contemplation for the optimal and maximum utilization of fossil fuels through the implementation of multigeneration systems. In the presented study, a multigeneration system for cooling, power, freshwater, and Hydrogen production is proposed and investigated from exergy, energy, and economic viewpoints through a specific scenario. A case study is performed, revealing that the system can deliver a net output power of 16,251.8 kW, a cooling load of 15,905.0 kW, produce freshwater at a rate of 3.891 kg/s, and generate hydrogen at 0.284 kg/h. The energy and exergy efficiencies of the system are 60.48 % and 32.55 %, respectively. An economic analysis shows a payback period of 0.474 years. A parametric evaluation is performed to perceive the system operation in various conditions. Subsequently, a multi-objective optimization using the MOPSO-LINMAP method is conducted to identify the optimal operating conditions for the system. The optimization results are compared with the case study findings, demonstrating further improvements in performance metrics. The optimized system achieves a slightly higher energy efficiency of 61.62 % and a lower payback period of 0.40 years, underscoring the benefits of the optimization process. This study highlights the potential of the proposed system to efficiently and economically meet the demands for power, hydrogen, cooling, and freshwater in various applications.

Covered Stent Graft for Treatment of Carotid Blowout Syndrome in Patients With Head and Neck Cancer

Carotid blowout syndrome (CBS) is a rare life-threatening complication of head and neck cancer that requires either surgical or endovascular treatment such as a carotid occlusion procedure or stent-graft placement. CBS outcomes and complications of its management using endovascular stent-graft placement remain unclear due to limited published data. Given that this treatment approach is increasingly used, understanding its efficacy and associated risks is paramount. To evaluate the periprocedural and postprocedural complications and outcomes associated with stent-graft treatment for CBS in patients with head and neck cancer. This case series study conducted at the Institut Gustave Roussy (Villejuif, France) between January 2006 and December 2021 included all eligible patients with head and neck cancer who experienced CBS and were referred for endovascular treatment. Risk factors and periprocedural and postprocedural data were collected retrospectively from medical records. Data analyses were performed from July 2022 to July 2024. Endovascular stent-graft placement for the treatment of CBS in patients with head and neck cancer. Periprocedural and postprocedural complications of stent-graft placement. Secondary outcomes were technical success, defined as immediate control of hemorrhage; 30-day and overall survival rates; and risk factors for mortality and rebleeding. Risk factors considered were body mass index (BMI), CBS presentation, hemodynamic status, tumor stage, and radiation dose. In all, 67 CBS-related stent-graft procedures were performed in 62 patients (mean [SD] age, 55.4 [10.1] years; 10 [16.1%] females and 52 [83.9%] males), most with advanced-stage head and neck cancer, over 15 years. The most frequently observed clinical complications were rebleeding (16 patients [38.8%]) and stroke (9 patients [13.4%]). Immediate hemostasis was achieved in 100% of cases. The survival rate was 77.3% (51 participants) at 30 days postprocedure, with a median (IQR) overall survival time of 59 (32-141.5) days. Acute CBS presentation (risk ratio, 4.30; 95% CI, 1.11-28.23) and BMI (risk ratio, 0.88; 95% CI, 0.77-0.99) showed a statistically significant association with 30-day mortality in univariate analysis. The findings of the case-series study indicate that CBS can be safely managed with endovascular stent-graft placement that preserves carotid patency; however, it carries significant risks of ischemia and rebleeding. These findings suggest that stent grafts should be used only in specific clinical scenarios. Despite achieving a high rate of technical success in controlling hemorrhage, the overall and 30-day survival outcomes underscore the critical implications of CBS in patients with cancer and its associated therapeutic challenges.

Discrepancies in Euclidean Operator Radii in Hilbert C∗-Modules

In this research, we establish precise limits for the Euclidean operator radius of two bounded linear operators operating within a Hilbert C∗-module over A. Furthermore, our work establishes a connection between these limits and recent research findings that provide accurate upper and lower bounds for the numerical radius of linear operators. The primary objective of this investigation is to explore various specific scenarios of interest and extend existing inequalities found in the literature to encompass the Euclidean radius of two operators in a Hilbert A-module. Additionally, our study presents conclusions that reveal relationships between the operator norm, the typical numerical radius of a composite operator, and the Euclidean operator radius. Furthermore, we introduce several new inequalities involving the Euclidean numerical radius and Euclidean operator norm of 2-tuple operators. These inequalities offer both lower and upper bounds for the Euclidean numerical radius of 2-tuple operators, as well as for the sum and product of 2-tuple operators. We also delve into the study of Euclidean numerical radius inequalities for 2×2 operator matrices whose entries consist of 2-tuple operators, leading to the derivation of some Euclidean operator radius inequalities. Additionally, we establish an inequality for the Euclidean operator norm of 2×2 operator matrices.

Transient voltage stability assessment and margin calculation based on disturbance signal energy feature learning

With the increasing variation of the network topology and the high complexity of the processing measurement data, the transient voltage stability assessment of the new power system is facing significant challenges in low accuracy and high time costs. To address the shortcomings of the existing method and apply it to online assessment, this paper proposes an assessment method based on feature learning for disturbance signal energy (DSE) from bus voltages. Firstly, the relationship between DSE and system transient voltage stability is established, and the calculation of DSE from bus voltage time series is detailed. Subsequently, a transient voltage stability assessment method based on the ID3 Decision Tree algorithm and DSE is proposed. Finally, by employing the Support Vector Machine (SVM) to construct the optimal boundary in the feature space formed by the key buses, the transient voltage stability margin (TVSM) for specific scenarios is proposed. Simulation results on the IEEE 39-bus system demonstrate that the proposed method can rapidly and accurately assess the transient voltage stability of the system and calculate the stability margin, providing intuitive and interpretable results with high engineering application value.

A-334 Vital one: a point-of-care platform for rapid, comprehensive, central-lab quality blood testing

Abstract Background Diagnostic tools are fundamental to informed healthcare decisions, but the current state of in vitro diagnostics presents barriers to providing timely access to a wide menu of tests. This poster discusses a method for overcoming these challenges with a novel and miniaturized point-of-care (PoC) solution, the VitalOneTM. While many PoC solutions have limited test menus tailored for specific scenarios, Vital Biosciences introduces a comprehensive test menu in a compact PoC format that uses centrifugal microfluidic workflows to deliver quantitative results for over 50 analytes spanning hematology, clinical chemistry, and immunoassay modalities, ensuring central-lab quality results for a wide variety of use cases in a 20-min time frame (or 15 min for a Comprehensive Metabolic Panel (CMP) lipid panel, and liver panel). The VitalOneTM is designed to perform a complete blood count (CBC), a comprehensive panel with a full array of immunoassays and clinical chemistry assays, returning over 50 results with as little as 300-750 µL of whole blood. This poster provides a comprehensive and transparent view of both our results and underlying methods of operation. By comparing the VitalOneTM system with established benchmarks like Roche, Sysmex, and Beckman Coulter instrument systems, we demonstrate consistent assay performance, resilience to interference, and performance that is compliant with CLIA Total Allowable Error standards. Methods A method comparison analysis of measurements on VitalOneTM platform was performed against predicate instruments that are commonly used in centralized laboratories (Roche Cobas, Beckman AU480, Beckman Coulter DxH500, Beckman Access 2, and Sysmex 140). Additionally, a 5-8 point calibration curve was generated for each analyte across the desired assay measuring range (AMR) using commercially available validation kits on three separate days, resulting in a master calibration curve. To check for sensitivity to potential interferences that may be present in patient samples, endogenous and exogenous interference testing was performed following Clinical &amp; Laboratory Standards Institute (CLSI) guidelines. Lastly, robustness testing was carried out to mimic the extremes of variances in volumes, times, or temperatures that may be encountered when running an assay protocol. Results The method comparison analysis (n = 840 samples) and precision studies revealed that the VitalOne exhibited a strong correlation with the benchmark instruments and were in compliance with CLIA's total allowable error limits, reinforcing the reliability and clinical relevance of our diagnostic system. Assays across Hematology, Clinical Chemistry, and immunoassays demonstrated &amp;gt;95% of data within CLIA TEa% and run-to-run imprecision ≤ 5%. Additionally, linearity studies revealed that each analyte AMR spans across physiologically relevant concentrations, defined by medical decision levels and reference intervals for each analyte. Our robustness and endogenous and exogenous interference testing performed as per CLSI guidelines has demonstrated that the VitalOneTM system closely matches, and in certain parameters, such as biotin interference, even surpasses that of the predicate instruments. Conclusions In summary, the VitalOneTM platform represents a significant advancement in point-of-care diagnostics, offering a comprehensive, accurate, and efficient alternative to traditional laboratory testing. This innovation is poised to transform the way clinicians approach diagnostics, enabling more informed decision-making and ultimately enhancing patient outcomes.

Investigating the potential of optical metamaterials with highly dispersive solitons in twin couplers with stochastic perturbations and white noise effects

This study emphasizes a unique contribution as we investigate the dimensionless version of the perturbed stochastic NLSE within an optical metamaterial-designed coupler. We employ Kudryashov's sextic-power law nonlinearity to present this formulation, and, for the first time, we introduce multiplicative white noise using the Ito interpretation. This study aims to bridge the gap in understanding the influence of stochastic effects on optical metamaterials and offers valuable insights for future research in this field. The methods introduced include enhancements to Kudryashov's approach and extensions to the modified sub-ODE technique. These methodologies are leveraged to derive a wide array of solutions, such as bright, dark, singular, and combined solitons, with some solutions presented using Jacobi and Weierstrass elliptic functions. The effectiveness of these methods in addressing various nonlinear partial differential equations is demonstrated through the results. The study further delves into how random noise impacts optical solitons and analyzes soliton behavior when subjected to different conditions. It is observed that white noise plays a crucial role in the formation of optical solitons within the coupler and that these solitons demonstrate highly dispersive properties under specific scenarios. These insights are pivotal for designing and innovating optical metamaterials for diverse technological uses.

Framework for LLM applications in manufacturing

In the era of Industry 4.0, the proliferation of data within manufacturing environments has presented both unprecedented opportunities and challenges. This paper introduces a framework that capitalizes on the capabilities of Large Language Models (LLMs) to revolutionize data integration and decision-making processes in manufacturing systems. Addressing the critical need for efficient data management, our framework streamlines the consolidation, processing, and generation of responses to essential inquiries, thus enhancing manufacturers’ capabilities to extract valuable insights. The focus of this paper is twofold. First to establish a framework for the use of LLM applications in manufacturing settings. Secondly, to provide an overview of the manufacturing connection between data, AI, and chat-bots, while also addressing a few pain points identified from the manufacturing literature. The paper then introduces FILLIS (Factory Integrated Logic and Language Interface System), a Large Language Model assistant, through a compelling case study. FILLIS showcases remarkable versatility, excelling in tasks ranging from elucidating machine operations to language translation. The study underscores FILLIS’s proficiency in handling specific contexts, answering questions from uploaded documents with precision. However, inherent limitations surface in tasks involving mathematical operations, emphasizing the need for external agents in specific scenarios. This pivotal opportunity is explored in the proposed framework as it advocates for integrating external agents alongside LLMs, creating a more versatile and comprehensive assistant tool. The findings of this paper and proposed framework position LLMs as transformative tools for intelligent data processing.

TRANSITIONING FROM AN INSTITUTIONALLY DEFINED CANDIDATE SEARCH TO THE ANALYTIC HIERARCHY PROCESS: IMPROVING FACULTY SELECTION

This article presents a comprehensive model using the Analytic Hierarchy Process (AHP) for faculty search and selection at a regional university. After developing and applying evaluation criteria based on generalized position descriptions, the search committee chose to revise the criteria using the AHP. The revision allowed for more detailed and relevant assessments of candidate competencies and experience. While full implementation of the AHP was time-constrained, outcomes revealed greater consensus regarding preferred qualifications and presented a clearly defined rationale to support recommendations. In the decision-making process, the main criteria included degree, teaching, student engagement, research, service, experience, and diversity. Some of the main criteria had associated detailed sub-criteria. Although the AHP is typically used early in the faculty selection process, in this specific scenario, the initial elimination of candidates from the pool was made simpler by clearly defining the required qualifications and then closely evaluating the preferred qualifications. This article aims to assist higher education institutions in prioritizing and ranking their selection criteria, which have been found to be similar across different institutions.

Design of voice command recognition chip based on heterogeneous acceleration

Abstract Speech, as one of the earliest forms of communication used by humans, can effectively convey information. However, the current deep neural network models for speech recognition are generally large in scale and can only be deployed in the cloud, which imposes high deployment environment requirements and power consumption, thereby limiting their implementation on embedded devices. In the context of end-to-end speech recognition, a series of challenges are encountered, including power consumption constraints, computing power limitations, network dependencies, privacy protection, bandwidth restrictions, and communication delays. To address these issues, this paper proposes the design of an end-to-end voice command recognition chip based on deep neural networks specifically for recognizing voice commands in specific scenarios. This chip achieves low power consumption and minimal delay in recognition. Additionally, we introduce a weighted, overloadable chip architecture to enable seamless scene migration, ultimately aiming to resolve the aforementioned challenges.

Efficient license plate recognition in unconstrained scenarios

Automatic license plate recognition (ALPR) is a critical technology for intelligent transportation systems. Most existing ALPR methods are focused on specific application scenarios. Although there are a few methods that focus on unconstrained scenarios, they are very time-consuming. In this work, we propose an efficient ALPR (EALPR) framework, where we can handle distorted license plates (LP) caused by perspective problems with high efficiency. We design a light LPD structure based on efficient object detection methods and use anchor-free strategies for LPD to alleviate the problem of expensive costs. Benefitting from these optimizations and a united framework structure, the proposed EALPR has real-time efficiency. We evaluate our method on five datasets and the results show that our method achieves state-of-the-art accuracy: 98.15% on OpenALPR(EU), 95.61% on OpenALPR(BR), 99.51% on AOLP(RP), 88.81% on SSIG, 79.41% on CD-HARD. Additionally, our method achieves an impressive speed of 74.9 FPS (Frames Per Second), outperforming existing approaches and demonstrating its efficiency. Our source code can be accessed at https://github.com/wechao18/Efficient-alpr-unconstrained.

Feasibility of vehicle-to-grid (V2G) implementation in Japan: A regional analysis of the electricity supply and demand adjustment market

Vehicle-to-Grid (V2G) technology forms a critical bridge between electric vehicles (EVs) and renewable energy, playing a transformative role in the global shift towards a low-carbon future. Despite its promise, the impact of regional differences on V2G potential, particularly within specific application scenarios, remains largely unexplored. This study employs an agent-based modeling approach to evaluate V2G potential across Japan's 47 prefectures, with a focus on its integration into an energy reserve service business model. Our analysis reveals that EV ownership is the dominant factor influencing regional variability, with the Tokyo Metropolitan Area, Aichi, and Kanagawa emerging as top candidates for V2G deployment. Interestingly, when EV ownership is excluded from consideration, regions like Okinawa, Kagoshima, and Ibaraki show leadership in V2G potential. These findings highlight the existence of regional priorities for V2G application. Moreover, a ‘High Demand Disparity’ is observed in the power supply and demand adjustment market, where V2G systems are often either underutilized or overwhelmed, leaving many idle. The study underscores the need for region-specific strategies and the integration of diverse business models to fully capitalize on V2G's potential in various demand adjustment scenarios.

Spatio-temporal neural networks for monitoring and prediction of CO[formula omitted] plume migration from measurable field data

Carbon capture, utilization, and storage (CCUS) technologies are crucial for mitigating greenhouse gas emissions. The success of CCUS projects hinges on accurate prediction and monitoring of the CO2 plume migration during and after injection. To address the computational burden of traditional numerical simulation methods, previous studies have successfully used neural networks as proxy models to expedite the prediction of the CO2 plume migration. However, these models rely on uncertain inputs, such as the distribution of heterogeneous permeability and porosity maps, which can lead to erroneous predictions and pose a significant hurdle for their adoption in real-world applications. To address this issue, this study introduces a framework for reconstruction and short-term prediction of CO2 plume migration based solely on field measurements, which can directly provide information on CO2 plumes, thus eliminating the dependency on uncertain geological information as model input. The framework trains a spatio-temporal neural network model using simulated data under various geologic scenarios to capture the plume evolution dynamics without constraining it to specific geological scenarios. Once trained, the model integrates global and local field measurements from multiple sources to reconstruct the CO2 plume and predict its spatio-temporal evolution. The effectiveness of the proposed framework is tested using two case studies: one using a synthetic dataset and another with data generated from a model of a real field in the Southern San Joaquin Basin. The results show that the proposed framework can accurately reconstruct and perform short-term predictions of the CO2 plume migration by integrating various forms of field measurements.

A Dynamic Sandbox Detection Technique in a Private Cloud Environment

In specific private cloud scenarios, how to defend against malicious software and ensure data security is one of the current research hotspots, and sandbox is an important detection method. This paper proposes a dynamic behavior detection technique based on sandboxing, which real-time monitors and analyzes malicious software behavior. By improving the sandbox behavior weight, integrating virtual resources, and designing fine-grained access control, the detection accuracy and efficiency are enhanced based on zero trust access control system. The simulated attacks are identified on the testing platform, drawing knowledge graphs, achieving effective discovery and tracing. Meanwhile, this paper verified through experiments that the system consumption of the detection method is within an acceptable range, expanding the detection range and reducing the missed detection rate.