Practical Limitations Research Articles

Major axes of variation in tree demography across global forests

The future trajectory of global forests is closely intertwined with tree demography, and a major fundamental goal in ecology is to understand the key mechanisms governing spatio‐temporal patterns in tree population dynamics. While previous research has made substantial progress in identifying the mechanisms individually, their relative importance among forests remains unclear mainly due to practical limitations. One approach to overcome these limitations is to group mechanisms according to their shared effects on the variability of tree vital rates and quantify patterns therein. We developed a conceptual and statistical framework (variance partitioning of Bayesian multilevel models) that attributes the variability in tree growth, mortality, and recruitment to variation in species, space, and time, and their interactions – categories we refer to as organising principles (OPs). We applied the framework to data from 21 forest plots covering more than 2.9 million trees of approximately 6500 species. We found that differences among species, the species OP, proved a major source of variability in tree vital rates, explaining 28–33% of demographic variance alone, and 14–17% in interaction with space, totalling 40–43%. Our results support the hypothesis that the range of vital rates is similar across global forests. However, the average variability among species declined with species richness, indicating that diverse forests featured smaller interspecific differences in vital rates. Moreover, decomposing the variance in vital rates into the proposed OPs showed the importance of unexplained variability, which includes individual variation, in tree demography. A focus on how demographic variance is organized in forests can facilitate the construction of more targeted models with clearer expectations of which covariates might drive a vital rate. This study therefore highlights the most promising avenues for future research, both in terms of understanding the relative contributions of groups of mechanisms to forest demography and diversity, and for improving projections of forest ecosystems.

Construction of an Elastin-like Polypeptide Gene in a High Copy Number Plasmid Using a Modified Method of Recursive Directional Ligation

Elastin-like polypeptides (ELPs) are popular biomaterials due to their reversible, temperature-dependent phase separation and their tunability, which is achievable by evolving procedures in recombinant technology. In particular, recursive direction ligation by plasmid reconstruction (PRe-RDL) is the predominant cloning technique used to generate ELPs of varying lengths. Pre-RDL provides precise control over the number of (VPGXG)n repeat units in an ELP due to the selection of type IIS restriction enzyme (REs) sites in the reconstructed pET expression plasmid, which is a low-to-medium copy number plasmid. While Pre-RDL can be used to seamlessly repeat essentially any gene sequence and overcome limitations of previous cloning practices, we modified the Pre-RDL technique, where a high copy number plasmid (pBluescript II SK(+)—using a new library of type IIS REs) was used instead of a pET plasmid. The modified technique successfully produced a diblock ELP gene of 240 pentapeptide repeats from 30 pentapeptide “monomers” composed of alanine, tyrosine, and leucine X residues. This study found that the large, GC-rich ELP gene compromised plasmid yields in pBluescript II SK(+) and favored higher plasmid yields in the pET19b expression plasmid. Additionally, the BL21 E. coli strain expression consistently provided a higher transformation efficiency and higher plasmid yield than the high cloning efficiency strain TOP10 E. coli. We hypothesize that the plasmid/high GC gene ratio may play a significant role in these observations, and not the total plasmid size or the total plasmid GC content. While expression of the final gene resulted in a diblock ELP with a phase separation temperature of 34.5 °C, future work will need to investigate RDL techniques in additional plasmids to understand the primary driving factors for improving yields of plasmids with large ELP-encoding genes.

Transforming equipment management in oil and gas with AI-Driven predictive maintenance

The oil and gas industry faces significant challenges in managing equipment maintenance due to the complexity and criticality of its assets. Traditional maintenance approaches are often reactive and inefficient, leading to costly downtime and safety risks. However, the emergence of artificial intelligence (AI) and predictive maintenance technologies offers a transformative solution to these challenges. This paper explores the role of AI-driven predictive maintenance in revolutionizing equipment management in the oil and gas sector. AI-driven predictive maintenance leverages machine learning algorithms to analyze equipment data and predict when maintenance is required before a breakdown occurs. By monitoring equipment performance in real-time, AI can identify potential issues early, allowing operators to take proactive maintenance actions. This approach helps minimize downtime, reduce maintenance costs, and improve overall equipment reliability and safety. The implementation of AI-driven predictive maintenance requires a comprehensive strategy that includes data collection, analysis, and integration with existing maintenance practices. Successful adoption of AI-driven predictive maintenance can lead to significant benefits for oil and gas companies, including increased equipment uptime, extended asset lifespan, and enhanced operational efficiency. This paper reviews the current landscape of equipment management in the oil and gas industry, highlighting the limitations of traditional maintenance practices and the need for a more proactive approach. It then examines the principles and benefits of AI-driven predictive maintenance, showcasing real-world examples of its successful implementation. Finally, the paper discusses the challenges and considerations for implementing AI-driven predictive maintenance and provides recommendations for oil and gas companies looking to transform their equipment management practices. Keywords: Transforming Equipment; Management; Oil and Gas; AI-Driven; Predictive Maintenance.

Practical fracture limit function considering data flexibility under plane-stress conditions in Lagrangian formulation

Ductile fracture limits have been extensively studied from the perspectives of material science and mechanics. While these studies have provided scientifically significant insights into ductile fracture limits, the influence of the evolution of anisotropy observed in actual experimental data suggests a need to account for diversity in the experimental data. To address this, it may be possible to further develop the model by investigating more influences from the field of materials science. However, from an engineering perspective, it can be effective to represent the diversity of acquired experimental data as practical functions for use, rather than solely focusing on the material science aspect. This paper presents mathematical formulations representing the strain- and stress-based fracture limit surfaces of ductile materials, to consider diverse data set for the evolution of anisotropy, under plane-stress conditions within the framework of the Lagrangian formulation. The model introduces a strain-based fracture limit surface (FLS), which has an additional anisotropic profile function based on the commonly used two-dimensional strain-based fracture limit curve (FLC). The anisotropic profile function is capable of capturing the evolving anisotropic profile based on the deformation mode and principal loading direction to represent more flexible shapes. With this function, a three-dimensional strain-based FLS, which can present the variation in fracture strains in the space of the deformation mode and principal loading direction, can be constructed. Then, the proposed model transformed this strain-based FLS into a stress-based FLS by converting the presented state vector in the former into the corresponding vector in the latter in the Lagrangian formulation. To achieve this, this study developed a formulation that considers the nonlinear evolution of stress anisotropy independent of the strain anisotropy during stress evolution. The two proposed models were validated by experimental results on a dual-phase steel. These models provide flexible fracture limit functions for ductile materials under varying stress and strain conditions in engineering applications.

One-step preparation of large-size 1.5-μm-thick robust YSZ electrolyte for high CH4 conversion SOFCs at 600 °C

Ultrathin electrolytes can lower the operating temperature of yttria-stabilized zirconia (YSZ)-based solid oxide fuel cells (SOFCs) to below 600 °C, accelerating their commercialization. However, current technologies for fabricating robust electrolytes with large areas and a thickness of a few microns for SOFCs suffer from substantial practical limitations, including high manufacturing costs, intricate technical demands, and multistep fabrication processes. This study introduces a low-cost, high-yield, and facile one-step phase inversion technology to fabricate large-area and shape-variable ultrathin YSZ electrolyte-based SOFCs (disc diameter >12 cm and area of 10 cm × 10 cm). An integrated form of SOFCs with dendritic YSZ microchannels supporting a 1.5-μm-thick dense YSZ thin film was fabricated. The ultrathin electrolyte layer lowered ohmic and polarization losses, enhancing SOFC electrochemical performance. The integrated anode/electrolyte structure overcame the interfacial thermal mismatch, resulting in more than 210 h of long-term stability even with cells operating in rapidly changing environments. The single cell also exhibited a high CH4 conversion efficiency (55%), which was attributed to the fast gas diffusion and excellent catalytic activity within the integrated anode. In addition, our 4 cm × 4 cm flat SOFCs exhibited a high power output (5.1 W) at 600 °C, paving the way for the mass production of ultrathin and robust YSZ electrolytes.

A statistical resolution measure of fluorescence microscopy with finite photons

First discovered by Ernest Abbe in 1873, the resolution limit of a far-field microscope is considered determined by the numerical aperture and wavelength of light, approximately λ2NA\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\frac{\\lambda }{2{NA}}$$\\end{document}. With the advent of modern fluorescence microscopy and nanoscopy methods over the last century, this definition is insufficient to fully describe a microscope’s resolving power. To determine the practical resolution limit of a fluorescence microscope, photon noise remains one essential factor yet to be incorporated in a statistics-based theoretical framework. We proposed an information density measure quantifying the theoretical resolving power of a fluorescence microscope in the condition of finite photons. The developed approach not only allows us to quantify the practical resolution limit of various fluorescence and super-resolution microscopy modalities but also offers the potential to predict the achievable resolution of a microscopy design under different photon levels.

Using technologies to evaluate response style in the competency crisis recommendations for evaluators

ABSTRACT It is possible that a portion of the competency crisis may be due to some defendants being misclassified as incompetent, secondary to evaluations of questionable thoroughness and quality, and perhaps over-reliance on interviews and lesser acquisition of collateral data. Meanwhile, a review of descriptive statistics suggests that, as criminal penalties have become increasingly harsh, requests for competency evaluations have proportionately increased. Thus, there is a risk that lengthy waiting lists, mounting time pressures, and attorney tactics may push evaluators in the direction of expediency, possibly at the expense of higher quality forensic work. Evaluators may therefore consider acquiring and perhaps more significantly relying upon relevant collateral informational sources, including police recorded interviews, jail recorded calls, social media content, and prior court records. While an evaluation of present mental state does not necessarily require significant background data, better use of technologies in an evaluation of competency may provide a more objective representation of competency-relevant, mental health-related history. So doing may help to solve a portion of the competency crisis that may be due to evaluations including inadequate collateral data. Best practice approaches, anecdotes, and limitations are presented.

AI-based diagnosis of chronic obstructive pulmonary disease from low-dose CT images

Background: Chronic obstructive pulmonary disease (COPD) is a group of diseases characterized by airflow blockage. It is one of the leading causes of global mortality and is primarily attributed to smoking. COPD patients are usually diagnosed by spirometry test. Although regarded as the gold standard for COPD diagnosis, the spirometry test carries contraindications, thus prompting the development of low-dose computed tomography (low-dose CT) scan as an alternative for COPD screening. However, a practical limitation of diagnosing COPD from CT images is its reliance on the expertise of a skilled radiologist. Objective: To address this limitation, we aimed to develop a deep-learning model for the automated classification of COPD and non-COPD from low-dose CT images. Materials and methods: We examined the potential of a convolutional neural network for identifying COPD. Our dataset consisted of 10,000 low-dose CT images obtained from a lung cancer screening program, involving both ex-smokers and current smokers deemed at high risk of lung cancer. Spirometry data served as the ground truth for defining COPD. We used 90% of the datasets for training and 10% for testing. Results: Our developed model achieved notable performance metrics: an area under the receiver operating characteristic curve (AUC) of 0.97, an accuracy of 0.89, a precision of 0.85, a recall of 0.96, and an F1-score of 0.90. Conclusion: Our study demonstrates the potential of deep learning models to augment clinical assessments and improve the diagnosis of COPD, thereby enhancing diagnostic accuracy and efficiency. The findings suggest the feasibility of integrating this technology into routine lung cancer screening programs for COPD detection.

Simulation of gamma spectra from soil samples by using MCNP: A case study

A comparison between the MCNP simulated gamma spectrums based on the nuclear data from NuDat with the current version 3.0 and Nucléide-Lara against measured spectra based on the IAEA reference samples has been performed to assess the influence of nuclear data of photon decay processes and cross-section of photon interactions on the quality of the simulated spectra. As the results, the appearance of some abnormal energy peaks; namely 215 keV, 571 keV, 675 keV, 1227 keV of the NuDat-based simulated spectra and 90 keV, 94 keV, 106 keV, 416 keV of the Nucléide-Lara-based simulated spectra, which were present in neither the measured spectrum nor remaining simulated spectra, indicating issues with accuracy and completeness of these dataset. In addition, the good correlation between the combined dataset-based simulated spectra and reference samples-based measured spectra within the range of 50 keV to 2620 keV suggests that this MCNP simulation configuration can be used to generate a large simulated dataset for Machine Learning (ML) models that automatically identify and qualify radioactive isotope from gamma-ray spectra, overcoming the practical limitation of number of reference samples to sufficiently generate data for training and testing ML algorithms in the field of environmental radiation [1].

Leveraging a realistic synthetic database to learn Shape-from-Shading for estimating the colon depth in colonoscopy images

Colonoscopy is the choice procedure to diagnose, screening, and treat the colon and rectum cancer, from early detection of small precancerous lesions (polyps), to confirmation of malign masses. However, the high variability of the organ appearance and the complex shape of both the colon wall and structures of interest make this exploration difficult. Learned visuospatial and perceptual abilities mitigate technical limitations in clinical practice by proper estimation of the intestinal depth. This work introduces a novel methodology to estimate colon depth maps in single frames from monocular colonoscopy videos. The generated depth map is inferred from the shading variation of the colon wall with respect to the light source, as learned from a realistic synthetic database. Briefly, a classic convolutional neural network architecture is trained from scratch to estimate the depth map, improving sharp depth estimations in haustral folds and polyps by a custom loss function that minimizes the estimation error in edges and curvatures. The network was trained by a custom synthetic colonoscopy database herein constructed and released, composed of 248400 frames (47 videos), with depth annotations at the level of pixels. This collection comprehends 5 subsets of videos with progressively higher levels of visual complexity. Evaluation of the depth estimation with the synthetic database reached a threshold accuracy of 95.65%, and a mean-RMSE of 0.451cm, while a qualitative assessment with a real database showed consistent depth estimations, visually evaluated by the expert gastroenterologist coauthoring this paper. Finally, the method achieved competitive performance with respect to another state-of-the-art method using a public synthetic database and comparable results in a set of images with other five state-of-the-art methods. Additionally, three-dimensional reconstructions demonstrated useful approximations of the gastrointestinal tract geometry. Code for reproducing the reported results and the dataset are available at https://github.com/Cimalab-unal/ColonDepthEstimation.

Performance Potential for Locally Contacted Perovskite Solar Cells

In perovskite solar cells (PSCs), a common characteristic of highly effective interface passivation materials is low conductivity. Gains in voltage are thus often disproportionately offset by resistive losses. Local contact approaches can minimize this trade‐off and have a proven track record in conventional silicon photovoltaics. Indeed, recent record efficiencies for centimeter‐scale PSCs exploit architectures where the passivation layer partially covers the perovskite‐transport layer interface. Herein, a three‐dimensional numerical device model is used to determine practical performance limits to local contact geometries and consider both the optimum contact dimensions and the trade‐offs involved in relaxing these dimensions for ease of fabrication. It is observed that the potential for substantial power conversion efficiency (PCE) increases with local contacts. In devices where power loss occurs solely through recombination at the contacted interface, PCE can be enhanced by up to 10% absolute compared to a full‐area contact. However, optimum PCEs depend on contacts on the order of nanometers. It is shown that more fabrication‐friendly micrometer‐scale contacts still boost PCE, but the absolute enhancement falls short due to the relatively low bulk perovskite charge carrier diffusion length. This may ultimately motivate methods of interface engineering that produce “effective” local contact geometries at nanometer scales, such as via self‐forming layers.

Comparison of routine office blood pressure measurement versus standardized attended manually activated oscillometric office blood pressure measurement in patients with chronic kidney disease.

Erroneous blood pressure measurement could lead to improper treatment and hence progression of chronic kidney disease (CKD). In routine clinical practice, there is poor adherence to the various steps to be followed during blood pressure measurement. Automated oscillometric BP measurement is difficult to perform in routine clinical practice due to several practical limitations. To evaluate the quality of blood pressure measurement and to compare routine office blood pressure measurement with standardized attended manually activated oscillometric blood pressure measurement in patients with CKD attending the nephrology outpatient department (OPD) of a tertiary care referral center. This cross-sectional study was conducted in patients aged more than 18 years with CKD stage 3-5ND, and previously diagnosed hypertension, in the nephrology OPD of a tertiary care referral center between July 2022 and September 2022. The quality of blood pressure measurement was evaluated using a questionnaire. The study participants had their blood pressure checked by both methods-routine office blood pressure and standardized attended manually activated oscillometric blood pressure. Standardized attended manually activated oscillometric blood pressure measurement yielded a significantly higher systolic blood pressure (SBP) compared to routine office blood pressure measurement (Mean SBP: 139.53 ± 29.1 vs 132.57 ± 23.59; P < 0.001). However, the diastolic blood pressure did not differ significantly between the two methods of measurement. Standardized attended manually activated oscillometric BP measurement yields a higher systolic BP compared to routine office BP measurement. Further studies are required to compare the standardized attended oscillometric BP measurement used in this study with unattended automated oscillometric BP measurement and ambulatory BP measurement.

A nondestructive high-resistance fault diagnosis method of XLPE medium voltage cables based on Chirp-TDR

Diagnosing and locating the high resistance fault (HIF) of XLPE medium voltage cables without causing secondary damage has been a trouble for power maintainers. Among the previous methods proposed by researchers, the frequency domain reflection method has the strongest sensitivity, which still cannot meet the broad dynamic range requirement of HIF detection. This paper proposes a chirp-TDR high resistance fault detection method, which fully taps the signal processing gain of the chirp signal after pulse compression. The method increases the practical detection limit to hundreds of kΩ. A prototype is implemented, and the performance is simulated. The influence of incidence and terminal reflection signals on fault point reflection signal detection is reduced by common mode filtering and access matching impedances. The selected detection signal with 5 ms duration and 20 MHz bandwidth can produce a signal processing gain of 50 dB. A signal coaxial cable and an XLPE power cable with artificial simulated defects were installed as test specimens. The test results show that the method is feasible and has strong anti-noise ability. The technology is compared with FDR, which also uses the chirp signal. The proposed method has a performance improvement of about 12 dB, showing better fault point resolution ability.

Novel risk index integrating practical operation limits enhances probabilistic contingency ranking for large-scale photovoltaic plant planning

This research addresses the pressing need for heightened grid security amid increasing uncertainties in photovoltaic PV generation. The research problem lies in the limitations of conventional contingency analysis metrics, failing to adequately consider both contingency occurrences and uncertainties inherent in PV generation. In response, a comprehensive algorithm is proposed that introduces a novel severity function framework, enhancing traditional contingency ranking metrics. This approach incorporates grid remedial actions and refines line and bus voltage classification by considering available correction time, aiming to offer a more robust security assessment. Motivated by the imperative to address uncertainty in PV generation, the proposed work builds on established analysis tools. A probabilistic load flow algorithm manages PV generation uncertainties, utilizing historical data for contingency incidence uncertainty. Additionally, a probabilistic model for PV plants integrates historical solar data, deriving hourly probability density functions to meet grid code requirements, including reactive power considerations. The justification for this work lies in the algorithm's demonstrated efficacy, validated on the IEEE 14-bus network. Results highlight its ability to identify risks associated with line overloading and bus voltage breaches. Comparative evaluations underscore proper coupling buses for security, favoring distributed capacity to mitigate line overloading risks. The study's key results emphasize voltage risk amplification with reactive power omission, stressing the significance of compensation strategies. This research addresses a critical problem, presenting a comprehensive algorithmic solution to enhance grid security amidst uncertainties in PV integration. Findings offer valuable insights for strategically interaction between large scale PV plants and electrical grid, contributing to an improved grid security paradigm in a dynamic and uncertain energy model.

A Redox-Electrodialysis Model with Zero Fitting Parameters: Insights into Process Limitations, Design, and Material Interventions

Redox-Electrodialysis (r-ED) is an electrochemical desalination cell architecture that has recently received considerable interest, due to its low energy demand relative to electrochemical desalination technologies that rely on electrode-based ion removal. To further improve the energy efficiency of r-ED, we developed a lumped mathematical model with no adjustable parameters to investigate the various sources of overpotential within the cell. Existing models of electrodialysis and r-ED cells either do not accurately incorporate all phenomena contributing to the overpotential or utilize empirical fitting parameters. The model developed here indicates that ohmic overpotentials, especially in the diluate chamber, are the most significant contributors to energy losses. Based on this insight, we hypothesized that adding an ion exchange resin wafer in the diluate compartment would increase the ionic conductivity and decrease the energy demand. Experimental results showed an 18% reduction in specific energy use while achieving the same degree of salt removal (20 mM to 12 mM). Furthermore, the resin wafer enabled complete desalination to potable drinking levels at a current density previously unachievable within practical operating voltage limits (4.93 mA cm−2). We also expanded the model to explore differences in r-ED energy use between configurations using multiple cells and a single cell with increased area.

Removing the barriers to prehospital blood: A roadmap to success.

This review describes the necessity, evolution, and current state of prehospital blood programs in the United States. Less than 1% of 9-1-1 ground emergency medical service agencies have been able to successfully implement prehospital blood transfusions as part of a resuscitation strategy for patients in hemorrhagic shock despite estimates that annually between 54,000 and 900,000 patients may benefit from its use. The use of prehospital blood transfusions as a tool for managing hemorrhagic shock has barriers to overcome to ensure it becomes widely available to patients throughout the United States. Barriers include (1) current state Emergency Medical Services clinicians' scope of practice limitations; (2) program costs and reimbursement of blood products; (3) no centralized data collection process for prehospital hemorrhagic shock and patient outcomes; (4) collaboration between prehospital agencies, blood suppliers, and hospital clinicians and transfusion service activities. The following article identifies barriers and a proposed roadmap to reduce death due to prehospital hemorrhage.

A high-accuracy intelligent fault diagnosis method for aero-engine bearings with limited samples

As a crucial component supporting aero-engine functionality, effective fault diagnosis of bearings is essential to ensure the engine's reliability and sustained airworthiness. However, practical limitations prevail due to the scarcity of aero-engine bearing fault data, hampering the implementation of intelligent diagnosis techniques. This paper presents a specialized method for aero-engine bearing fault diagnosis under conditions of limited sample availability. Initially, the proposed method employs the refined composite multiscale phase entropy (RCMPhE) to extract entropy features capable of characterizing the transient signal dynamics of aero-engine bearings. Based on the signal amplitude information, the composite multiscale decomposition sequence is formulated, followed by the creation of scatter diagrams for each sub-sequence. These diagrams are partitioned into segments, enabling individualized probability distribution computation within each sector, culminating in refined entropy value operations. Thus, the RCMPhE addresses issues prevalent in existing entropy theories such as deviation and instability. Subsequently, the bonobo optimization support vector machine is introduced to establish a mapping correlation between entropy domain features and fault types, enhancing its fault identification capabilities in aero-engine bearings. Experimental validation conducted on drivetrain system bearing data, actual aero-engine bearing data, and actual aerospace bearing data demonstrate remarkable fault diagnosis accuracy rates of 99.83%, 100%, and 100%, respectively, with merely 5 training samples per state. Additionally, when compared to the existing eight fault diagnosis methods, the proposed method demonstrates an enhanced recognition accuracy by up to 28.97%. This substantiates its effectiveness and potential in addressing small sample limitations in aero-engine bearing fault diagnosis.

Towards Machine-FAIR: Representing software and datasets to facilitate reuse and scientific discovery by machines

ObjectiveTo use software, datasets, and data formats in the domain of Infectious Disease Epidemiology as a test collection to evaluate a novel M1 use case, which we introduce in this paper. M1 is a machine that upon receipt of a new digital object of research exhaustively finds all valid compositions of it with existing objects. MethodWe implemented a data-format-matching-only M1 using exhaustive search, which we refer to as M1DFM. We then ran M1DFM on the test collection and used error analysis to identify needed semantic constraints. ResultsPrecision of M1DFM search was 61.7%. Error analysis identified needed semantic constraints and needed changes in handling of data services. Most semantic constraints were simple, but one data format was sufficiently complex to be practically impossible to represent semantic constraints over, from which we conclude limitatively that software developers will have to meet the machines halfway by engineering software whose inputs are sufficiently simple that their semantic constraints can be represented, akin to the simple APIs of services. We summarize these insights as M1-FAIR guiding principles for composability and suggest a roadmap for progressively capable devices in the service of reuse and accelerated scientific discovery. ConclusionAlgorithmic search of digital repositories for valid workflow compositions has potential to accelerate scientific discovery but requires a scalable solution to the problem of knowledge acquisition about semantic constraints on software inputs. Additionally, practical limitations on the logical complexity of semantic constraints must be respected, which has implications for the design of software.

Optimal autonomous truck platooning with detours, nonlinear costs, and a platoon size constraint

Autonomous trucks offer a promising avenue for enhancing the efficiency and reducing the environmental impact of road freight transportation. This paper examines a transitional phase towards a fully unmanned truck fleet, focusing on a platooning approach with a lead driver. We develop a novel optimization model for autonomous truck platooning that simultaneously considers platoon formation, scheduling, and routing to minimize costs related to labor and fuel. The model incorporates the possibility of detours, nonlinear fuel savings due to air-drag reduction, and the practical platoon size limit. We present an enhanced column generation method, termed the platoon-generation-and-updating approach, which demonstrates high effectiveness in reducing computational time and complexity. Our numerical analysis, based on the Hong Kong highway network, demonstrates the substantial cost advantages of autonomous truck platooning. It also investigates how platooning efficiency is influenced by various operating factors, including truck fleet size, platoon size restrictions, labor-to-fuel cost ratio, and the strictness of delivery time windows, with practical implications interwoven into the discussion.

Chirality-Induced Spin Selectivity in Composite Materials: A Device Perspective.

ConspectusMagnetism is an area of immense fundamental and technological importance. At the atomic level, magnetism originates from electron "spin". The field of nanospintronics (or nanoscale spin-based electronics) aims to control spins in nanoscale systems, which has resulted in astronomical improvement in data storage and magnetic field sensing technologies over the past few decades, recognized by the 2007 Nobel Prize in Physics. Spins in nanoscale solid-state devices can also act as quantum bits or qubits for emerging quantum technologies, such as quantum computing and quantum sensing.Due to the fundamental connection between magnetism and spins, ferromagnets play a key role in many solid-state spintronic devices. This is because at the Fermi level, electron density of states is spin-polarized, which permits ferromagnets to act as electrical injectors and detectors of spins. Ferromagnets, however, have limitations in terms of low spin polarization at the Fermi level, stray magnetic fields, crosstalk, and thermal instability at the nanoscale. Therefore, new physics and new materials are needed to propel spintronic and quantum device technologies to the true atomic limit. Emerging new phenomena such as chirality induced spin selectivity or CISS, in which an intriguing correlation between carrier spin and medium chirality is observed, could therefore be instrumental in nanospintronics. This effect could allow molecular-scale, chirality controlled spin injection and detection without the need for any ferromagnet, thus opening a fundamentally new direction for device spintronics.While CISS finds a myriad of applications in diverse areas such as chiral separation, recognition, detection, and asymmetric catalysis, in this focused Account, we exclusively review spintronic device results of this effect due to its immense potential for future spintronics. The first generation of CISS-based spintronic devices have primarily used chiral bioorganic molecules; however, many practical limitations of these materials have also been identified. Therefore, our discussion revolves around the family of chiral composite materials, which may emerge as an ideal platform for CISS due to their ability to assimilate various desirable material properties on a single platform. This class of materials has been extensively studied by the organic chemistry community in the past decades, and we discuss the various chirality transfer mechanisms that have been identified, which play a central role in CISS. Next, we discuss CISS device studies performed on some of these chiral composite materials. Emphasis is given to the family of chiral organic-carbon allotrope composites, which have been extensively studied by the authors of this Account over the past several years. Interestingly, due to the presence of multiple materials, CISS signals from hybrid chiral systems sometimes differ from those observed in purely chiral systems. Given the sheer diversity of chiral composite materials, CISS device studies so far have been limited to only a few varieties, and this Account is expected to draw increased attention to the family of chiral composites and motivate further studies of their CISS applications.