Intrinsic Limitations Research Articles

ChatGPT Undermines Human Reflexivity, Scientific Responsibility and Responsible Management Research

AbstractWith ChatGPT being promoted to and by academics for writing scholarly articles more effectively, we ask what kind of knowledge does ChatGPT produce, what this means for our reflexivity as responsible management educators/researchers, and how an absence of reflexivity disqualifies us from shaping management knowledge in responsible ways. We urgently need to grasp what makes human knowledge distinct compared with knowledge generated by ChatGPT et al. Thus, we first explain how ChatGPT operates and unpack its intrinsic epistemological limitations. Using high‐probability choices that are derivative, ChatGPT has no stake in the knowledge it produces and is thus likely prone to offering irresponsible outputs. By contrast, genuine human thinking—embodied in a contingent socio‐cultural setting—uses low‐probability choices both ‘inside’ and ‘outside’ the box of training data, making it creative, contextual and committed. We conclude that the use of ChatGPT is wholly incompatible with scientific responsibility and responsible management.

Tick-Borne Co-Infections: Challenges in Molecular and Serologic Diagnoses.

Co-infections are a poorly understood aspect of tick-borne diseases. In the United States alone, nineteen different tick-borne pathogens have been identified. The majority of these agents are transmitted by only two tick species, Ixodes scapularis and Amblyomma americanum. Surveillance studies have demonstrated the presence of multiple pathogens in individual ticks suggesting a risk of polymicrobial transmission to humans. However, relatively few studies have explored this relationship and its impact on human disease. One of the key factors for this deficiency are the intrinsic limitations associated with molecular and serologic assays employed for the diagnosis of tick-borne diseases. Limitations in the sensitivity, specificity and most importantly, the capacity for inclusion of multiple agents within a single assay represent the primary challenges for the accurate detection of polymicrobial tick-borne infections. This review will focus on outlining these limitations and discuss potential solutions for the enhanced diagnosis of tick-borne co-infections.

Elastodontic Appliances for the Interception of Malocclusion in Children: A Systematic Narrative Hybrid Review.

Interceptive orthodontic treatment aims to eliminate factors that prevent the harmonious development of the maxillary and mandibular arches during childhood, and elastodontic appliances (EAs) represent a group of devices with an increasingly important role. This systematic narrative hybrid review (HR) aims to provide an overview of the clinical indications for the use of EAs according to the available evidence and to identify potential research areas for unexplored applications. To assess the available literature on the subject, selective database searches were performed between July 2023 and September 2023. With the assistance of a health sciences librarian, a search strategy that utilized terms related to elastodontic therapy was developed. Embase, Scopus, PubMed, and Web of Science were the databases used. The current literature addressing the usability of EAs is scarce and mostly limited to case reports and case series. After 2168 citations were found through the searches, 13 studies were ultimately included. In this regard, information about the clinical use and effectiveness of EAs are reported in a narrative form, defining specific domains of the application that are clinically oriented, including sagittal and transversal discrepancies, atypical swallowing, teeth malposition, two-phase orthodontics and a lack of teeth retention. Within the intrinsic quality limitation of the available literature, it seems that EAs may represent a promising treatment alternative for managing mild-to-moderate malocclusion in children as an adjuvant therapy to the interruption of spoiled habits.

A novel LCC‐C(CL)2 compensation network for efficient magnetically coupled resonant wireless power transfer for EV charging

AbstractDue to the intrinsic limitations, for example, limited adjustment freedom, the traditional wireless power transfer (WPT) systems cannot maintain high efficiency under variable loading conditions. To well solve this problem, a family of general magnetically coupled resonant WPT (MCR‐WPT) compensation topologies composed of LCC‐C with an ‐stage LC structure, or LCC‐C(CL) , is proposed in this paper. Under the guideline of the generalized structure, some existing topologies are derived for verification, with new topologies established with unique advantages. From the LCC‐C(CL) structures, a suitable topology can be selected to achieve a desired design purpose, such as constant voltage transmission, constant current transmission, or high‐efficiency transmission. Prototype experiments and comparisons are conducted in this work, which demonstrate the effectiveness and applicability of the proposed LCC‐C(CL) MCR‐WPT converter family and the parameter design method. Experimental results also show that the new topology can maintain high‐efficiency power transfer for a wide range of loads. Even under light loading conditions, the power transfer efficiency is 30% higher than the traditional benchmark topology. The proposed WPT approach will have wide applications for on‐road wireless electric vehicle charging.

One future to bind them all? Modern central banking and the limits of performative governability

In this article, I analyse the case of ‘modern’ central banking’s dual failures in effectively containing financial fragilities and inflationary pressures as a cautionary tale about the intrinsic limitations and contradictions of ‘governing through quantified futures’. I construct a genealogy of central banks’ much-vaunted ‘performative art’ of governing the economy through the management of expectations in order to reveal a crucial tension between the control of expectations about the future and controlling the future through (present) expectations of it. I argue that social scientific analyses tend to operate with a truncated understanding of performativity that prevents them from developing sufficiently precise and discerning accounts of the mechanics of performative processes to reveal their (intrinsic) limitations and explain how and why they (may) fail. By dissecting the surprisingly complex mechanics of central banks’ performative agency, I thus contribute towards a more precise theorisation of how governing through quantified futures operates.

Pretreatment Process Before Heat Pyrolysis of Plant‐based Precursors Paving Way for Fabricating High‐Performance Hard Carbon for Sodium‐Ion Batteries

AbstractThe increasing demand for sodium‐ion batteries has risen due to sodium resources that are inexpensive and abundant compared with its counterpart of lithium‐ion batteries. Development of anode materials with high performance is central issue for sodium‐ion batteries. Plant‐based hard carbon material is a promising anode material for sodium ion batteries due to its green preparation process, favorable ions transport channel, and special porous structure. Previous studies have not systematically correlated the material's inherent structure limitation with its electrochemical properties, resulting in a complex classification and a lack selection of pretreatment methods. In this review, the intrinsic limitations in the structure of plant‐based precursors are divided into four parts, including impure components, single chemical structure, uncontrollable crystalline texture, and irregular porous structure. The previous pretreatment methods are reclassified, including purification component aiming at plant‐based precursors, optimized structure, and precabornized process aiming at intermediate products. Furthermore, the pretreatment strategies are discussed according to the preferred structures of plant‐based hard carbon and the relationship between structure and performance is systematically expounded. This review provides deep understanding of the mechanism of pretreatment and a guide for selecting appropriate pretreatment strategies to optimize the structure of plant‐based hard carbon for sodium ion batteries.

Assessment of a Machine Learning Algorithm Using Web Images for Flood Detection and Water Level Estimates

Improving our skills to monitor flooding events is crucial for protecting populations and infrastructures and for planning mitigation and adaptation strategies. Despite recent advancements, hydrological models and remote sensing tools are not always useful for mapping flooding at the required spatial and temporal resolutions because of intrinsic model limitations and remote sensing data. In this regard, images collected by web cameras can be used to provide estimates of water levels during flooding or the presence/absence of water within a scene. Here, we report the results of an assessment of an algorithm which uses web camera images to estimate water levels and detect the presence of water during flooding events. The core of the algorithm is based on a combination of deep convolutional neural networks (D-CNNs) and image segmentation. We assessed the outputs of the algorithm in two ways: first, we compared estimates of time series of water levels obtained from the algorithm with those measured by collocated tide gauges and second, we performed a qualitative assessment of the algorithm to detect the presence of flooding from images obtained from the web under different illumination and weather conditions and with low spatial or spectral resolutions. The comparison between measured and camera-estimated water levels pointed to a coefficient of determination R2 of 0.84–0.87, a maximum absolute bias of 2.44–3.04 cm and a slope ranging between 1.089 and 1.103 in the two cases here considered. Our analysis of the histogram of the differences between gauge-measured and camera-estimated water levels indicated mean differences of −1.18 cm and 5.35 cm for the two gauges, respectively, with standard deviations ranging between 4.94 and 12.03 cm. Our analysis of the performances of the algorithm to detect water from images obtained from the web and containing scenes of areas before and after a flooding event shows that the accuracy of the algorithm exceeded ~90%, with the Intersection over Union (IoU) and the boundary F1 score (both used to assess the output of segmentation analysis) exceeding ~80% (IoU) and 70% (BF1).

Knowledge distillation of multi-scale dense prediction transformer for self-supervised depth estimation

Depth estimation is an inverse projection problem that estimates pixel-level distances from a single image. Although, supervised methods have shown promising results, it has intrinsic limitations in requiring ground truth depth from an external sensor. On the other hand, self-supervised depth estimation relieves the burden for collecting calibrated training data, while there is still a large performance gap between supervised and self-supervised methods. The objective of this study is to reduce the performance gap between the supervised and self-supervised approaches. The loss function of previous self-supervised methods is mainly based on a photometric error, which is indirectly computed from synthesized images using depth and pose estimates. In this paper, we argue that direct depth cue is more effective to train a depth estimation network. To obtain the direct depth cue, we employed a knowledge distillation technique, which is a teacher-student learning framework. The teacher network was trained in a self-supervised manner based on a photometric error, and its predictions were utilized to train a student network. We constructed a multi-scale dense prediction transformer with Monte Carlo dropout, and multi-scale distillation loss was proposed to train the student network based on the ensemble of stochastic estimates. Experiments were conducted on the KITTI and Make3D datasets, and our proposed method achieved the state-of-the-art accuracy in self-supervised depth estimation. Our code is publicly available at https://github.com/ji-min-song/KD-of-MS-DPT.

Intrinsic surface p-wave superconductivity in layered AuSn4

The search for topological superconductivity (TSC) is currently an exciting pursuit, since non-trivial topological superconducting phases could host exotic Majorana modes. However, the difficulty in fabricating proximity-induced TSC heterostructures, the sensitivity to disorder and stringent topological restrictions of intrinsic TSC place serious limitations and formidable challenges on the materials and related applications. Here, we report a new type of intrinsic TSC, namely intrinsic surface topological superconductivity (IS-TSC) and demonstrate it in layered AuSn4 with Tc of 2.4 K. Different in-plane and out-of-plane upper critical fields reflect a two-dimensional (2D) character of superconductivity. The two-fold symmetric angular dependences of both magneto-transport and the zero-bias conductance peak (ZBCP) in point-contact spectroscopy (PCS) in the superconducting regime indicate an unconventional pairing symmetry of AuSn4. The superconducting gap and surface multi-bands with Rashba splitting at the Fermi level (EF), in conjunction with first-principle calculations, strongly suggest that 2D unconventional SC in AuSn4 originates from the mixture of p-wave surface and s-wave bulk contributions, which leads to a two-fold symmetric superconductivity. Our results provide an exciting paradigm to realize TSC via Rashba effect on surface superconducting bands in layered materials.

Macrophage Membrane-Camouflaged Aggregation-Induced EmissionNanoparticles Enhance Photodynamic-Immunotherapy to Delay Postoperative Tumor Recurrence.

Surgery is a traditional tumor treatment, and immunotherapy can reduce the postoperative recurrence of tumors. However, the intrinsic limits of low responsive rate and non-tumor specificity of immunotherapy agents are still insufficient to address therapeutic demands. Herein, the macrophages membrane camouflaged nanoparticles (NPs), named M@PFC, consisting of the aggregation-induced emission photosensitizer (PF3-PPh3 ) and immune adjuvant (CpG), are reported. As the protein on the membrane interacts with the vascular cell adhesion molecule 1 (VCAM-1) of cancer cells, M@PFC efficiently transports CpG to the tumor. Meanwhile, M@PFC can evade clearance by the immune system and prolong the circulation time in vivo; thus, enhancing their accumulation in tumors. PF3-PPh3 promotes high production of reactive oxygen species (ROS) and triggers immune cell death (ICD) in tumor cells under light exposure. Importantly, CpG enrichment in tumors can stimulate tumor cells to produce immune factors to assist in enhancing ICD effects. The synergistic effect combining the PDT properties of the aggregation-induced emission (AIE)-active photosensitizer and immunotherapy properties of CpG significantly delays tumor recurrence after surgery. In conclusion, this strategy achieves the synergistic activation of the immune system for anti-tumor activity, providing a novel paradigm for the development of therapeutic nanodrugs to delay postoperative tumor recurrence.

Development and Validation of a Risk Prediction Tool for In-hospital Mortality After Thoracic Endovascular Repair in Patients with Blunt Thoracic Aortic Injury Using the Aortic Trauma Foundation Registry

The objective of our present effort was to use an international blunt thoracic aortic injury (BTAI) registry to create a prediction model identifying important preoperative and intraoperative factors associated with postoperative mortality, and to develop and validate a simple risk prediction tool that could assist with patient selection and risk stratification in this patient population. For the purpose of the present study, all patients undergoing thoracic endovascular aortic repair (TEVAR) for BTAI and registered in the Aortic Trauma Foundation (ATF) database from January 2016 as of June 2022 were identified. Patients undergoing medical management or open repair were excluded. The primary outcome was binary in-hospital all-cause mortality. Two predictive models were generated: a preoperative model (i.e. only including variables before TEVAR or intention-to-treat) and a full model (i.e. also including variables after TEVAR or per-protocol). Out of a total of 944 cases included in the ATF registry until June 2022, 448 underwent TEVAR and were included in the study population. TEVAR for BTAI was associated with an 8.5% in-hospital all-cause mortality in the ATF dataset. These study subjects were subsequently divided using 3:1 random sampling in a derivation cohort (336; 75.0%) and a validation cohort (112; 25.0%). The median age was 38years, and the majority of patients were male (350; 78%). A total of 38 variables were included in the final analysis. Of these, 17 variables were considered in the preoperative model, 9 variables were integrated in the full model, and 12 variables were excluded owing to either extremely low variance or strong correlation with other variables. The calibration graphs showed how both models from the ATF dataset tended to underestimate risk, mainly in intermediate-risk cases. The discriminative capacity was moderate in all models; the best performing model was the full model from the ATF dataset, as evident from both the Receiver Operating Characteristic curve (Area Under the Curve 0.84; 95% CI 0.74-0.91) and from the density graph. In this study, we developed and validated a contemporary risk prediction model, which incorporates several preoperative and postoperative variables and is strongly predictive of early mortality. While this model can reasonably predict in-hospital all-cause mortality, thereby assisting physicians with risk-stratification as well as inform patients and their caregivers, its intrinsic limitations must be taken into account and it should only be considered an adjunctive tool that may complement clinical judgment and shared decision-making.

Image De-raining Transformer.

Existing deep learning based de-raining approaches have resorted to the convolutional architectures. However, the intrinsic limitations of convolution, including local receptive fields and independence of input content, hinder the model's ability to capture long-range and complicated rainy artifacts. To overcome these limitations, we propose an effective and efficient transformer-based architecture for the image de-raining. Firstly, we introduce general priors of vision tasks, i.e., locality and hierarchy, into the network architecture so that our model can achieve excellent de-raining performance without costly pre-training. Secondly, since the geometric appearance of rainy artifacts is complicated and of significant variance in space, it is essential for de-raining models to extract both local and non-local features. Therefore, we design the complementary window-based transformer and spatial transformer to enhance locality while capturing long-range dependencies. Besides, to compensate for the positional blindness of self-attention, we establish a separate representative space for modeling positional relationship, and design a new relative position enhanced multi-head self-attention. In this way, our model enjoys powerful abilities to capture dependencies from both content and position, so as to achieve better image content recovery while removing rainy artifacts. Experiments substantiate that our approach attains more appealing results than state-of-the-art methods quantitatively and qualitatively.

0D van der Waals interfacial ferroelectricity

The dimensional limit of ferroelectricity has been long explored. The critical contravention is that the downscaling of ferroelectricity leads to a loss of polarization. This work demonstrates a zero-dimensional ferroelectricity by the atomic sliding at the restrained van der Waals interface of crossed tungsten disufilde nanotubes. The developed zero-dimensional ferroelectric diode in this work presents not only non-volatile resistive memory, but also the programmable photovoltaic effect at the visible band. Benefiting from the intrinsic dimensional limitation, the zero-dimensional ferroelectric diode allows electrical operation at an ultra-low current. By breaking through the critical size of depolarization, this work demonstrates the ultimately downscaled interfacial ferroelectricity of zero-dimensional, and contributes to a branch of devices that integrates zero-dimensional ferroelectric memory, nano electro-mechanical system, and programmable photovoltaics in one.

Transformer-based hierarchical dynamic decoders for salient object detection

Global context and global contrast are crucial clues for Salient Object Detection (SOD) in images. Most advanced SOD methods exploit CNN-based architectures, achieving impressive results. However, these methods have intrinsic limitations in capturing long-range global information since a CNN extracts feature in local sliding windows. In contrast, transformers exploit a self-attention mechanism to extract features, gaining a powerful capability of learning global cues. Nonetheless, a pure transformer-based network consumes a large computational overhead and easily suffers from attention collapse, as it goes deeper. To address this issue, in this paper, we propose a Transformer-based Hierarchical Dynamic Decoder (T-HDDNet) for image salient object detection. Specifically, our T-HDDNet employs the transformer to encode each image patch into multi-level and multi-resolution features based on the long-range dependencies among pixels. To obtain an accurate saliency map of high resolution, we develop a dynamic dual upsampling mechanism to enlarge feature spatial size in a data-driven manner, together with a dynamic feature fusion unit. Ultimately, the hierarchical dynamic decoders built on the basis of these two units are used to attain the final saliency progressively. Extensive experimental results show that the proposed method achieves the best performance on all benchmarks, in comparison with state-of-the-art technologies.

To what extent do physiological tolerances determine elevational range limits of mammals?

A key question in biology concerns the extent to which distributional range limits of species are determined by intrinsic limits of physiological tolerance. Here, we use common-garden data for wild rodents to assess whether species with higher elevational range limits typically have higher thermogenic capacities in comparison to closely related lowland species. Among South American leaf-eared mice (genus Phyllotis), mean thermogenic performance is higher in species with higher elevational range limits, but there is little among-species variation in the magnitude of plasticity in this trait. In the North American rodent genus Peromyscus, highland deer mice (Peromyscus maniculatus) have greater thermogenic maximal oxygen uptake ( ) than lowland white-footed mice (Peromyscus leucopus) at a level of hypoxia that matches the upper elevational range limit of the former species. In highland deer mice, the enhanced thermogenic in hypoxia is attributable to a combination of evolved and plastic changes in physiological pathways that govern the transport and utilization of O2 and metabolic substrates. Experiments with Peromyscus mice also demonstrate that exposure to hypoxia during different stages of development elicits plastic changes in cardiorespiratory traits that improve thermogenic via distinct physiological mechanisms. Evolved differences in thermogenic capacity provide clues about why some species are able to persist in higher-elevation habitats that lie slightly beyond the tolerable limits of other species. Such differences in environmental tolerance also suggest why some species might be more vulnerable to climate change than others.

The Potential Game‐Changer: A Concept‐to‐Proof Study on D:A Heterojunction‐Free Organic Photovoltaics†

Comprehensive SummarySince 1986, the donor‐acceptor (D:A) heterojunction has been regarded a necessity for high‐efficiency organic photovoltaics (OPVs), due to its unique advantage in compensating the intrinsic limitations of organic semiconductors, such as high exciton binding energy and poor ambipolar charge mobility. While this adversely causes tremendous non‐radiative charge recombination and instability issues, which currently become the most critical limits for commercialization of OPVs. Here, we present a concept‐to‐proof study on the potential of D:A heterojunction free OPV by taking advantage of recent progress of non‐fullerene acceptors. First, we demonstrate that the “free carriers” can be spontaneously generated upon illumination in an NFA, i.e., the 6TIC‐4F single layer. Second, the 6TIC‐4F layer also exhibits good ambipolar charge transporting property. These exceptional characteristics distinguish it from the traditional organic semiconductors, and relieve it from the reliance of D:A heterojunction to independently work as active layer. As a result, the subsequent OPV by simply sandwiching the 6TIC‐4F layer between the cathode and anode yields a considerably high power conversion efficiency ~1%. Moreover, we find the D:A heterojunction free device exhibits two order of magnitude higher electroluminescence quantum efficiency and significantly reduced VOC loss by 0.16 eV compared to those of the D:A BHJ structure, validating its promise for higher efficiency in the future. Therefore, our work demonstrates the possibility of using D:A heterojunction‐free device structure for high performance, that can potentially become the next game changer of OPV.

Computational materials discovery and development for Li and non-Li advanced battery chemistries

Since the discovery of batteries in the 1800s, their fascinating physical and chemical pro­perties have led to much research on their synthesis and manufacturing. Though lithium-ion batteries have been crucial for civilization, they can still not meet all the growing demands for energy storage because of the geographical distribution of lithium resources and the intrinsic limitations in the cell energy density, performance, and reliability issues. As a result, non-Li-ion batteries are becoming increasingly popular alternatives. Designing novel materials with desired properties is crucial for a quicker transition to the green energy ecosystem. Na, K, Mg, Zn, Al ion, etc. batteries are considered the most alluring and promising. This article covers all these Li, non-Li, and metal-air cell chemistries. Recently, com­putational screening has proven to be an effective tool to accelerate the discovery of active materials for all these cell types. First-principles methods such as density functional theory, molecular dynamics, and Monte Carlo simulations have become established techni­ques for the preliminary, theoretical analysis of battery systems. These computational methods generate a wealth of data that might be immensely useful in the training and vali­dating of artificial intelligence and machine learning techniques to reduce the time and capital expenditure needed for discovering advanced materials and final product develop­ment. This review aims to summarize the application of these techniques and the recent deve­lopments in computational methods to discover and develop advanced battery chemistries.

Sixth Ligand Induced HNO/NO- Release by a Five-Coordinated Cobalt(II) Nitrosyl Complex Having a {CoNO}8 Configuration.

Nitroxyl (HNO) and nitroxide (NO-) anion, the one-electron-reduced form of nitric oxide (NO), have been shown to have distinct advantages over NO from pharmacological and therapeutic points of view. However, the role of nitroxyl in chemical biology has not yet been studied as extensively as that of NO. Consequently, only a few examples of HNO donors such as Angeli's salt, Piloty's acid, or acyl- and acyloxynitroso derivatives are known. However, the intrinsic limitations of all of these hinder their widespread utility. Metal nitrosyl complexes, although few examples, could serve as an efficient HNO donor. Here, a cobalt nitrosyl complex of the {CoNO}8 (1) configuration has been reported. This complex in the presence of a sixth ligand [BF4-, DTC- (diethyldithiocarbamate anion), or imidazole] releases/donates HNO/NO-. This has been confirmed using well-known HNO/NO- acceptors like [Fe(TPP)Cl] and [Fe(DTC)3]. The HNO release has been authenticated further by the detection and estimation of N2O using gas chromatography-mass spectroscopy as well as its reaction with PPh3.

Measuring low doping level and short carrier lifetime in indium arsenide with a contactless terahertz technique at room temperature

Non-destructive, reliable, and accurate measurements of low doping levels and carrier lifetimes in small direct-bandgap semiconductors such as indium arsenide (InAs) at room temperature are challenging due to intrinsic limitations of conventional experimental techniques. In this article, we introduce a contactless optical-terahertz (THz) pump–probe technique in a continuous-wave regime measuring low doping levels in the 1015−1016cm−3 range and lifetimes down to 8 ns at room temperature in InAs. We show that a single-parameter material model can be used to retrieve the free carrier density from THz transmission measurements of InAs layers. Additionally, we show that lifetime of photogenerated excess carriers can be measured by modulating this carrier density using an amplitude-modulated optical pump.

A Post-Secular Approach to Managing Diversity in Liberal Democracies: Exploring the Interplay of Human Rights, Religious Identity, and Inclusive Governance in Western Societies

This article delves into the pressing challenges confronting liberal democracies in Western Europe as they grapple with managing religious diversity, with a specific focus on Muslim minorities. Historically, the secularization paradigm has been at the forefront of managing such diversity; however, its intrinsic limitations become increasingly evident in the context of super-diversity, underscoring the need for a paradigmatic shift toward post-secularization. Central to this discourse are the nuanced concepts of “culturalization of religion” and “religionization of culture”, which illuminate the disparities in treatment between majority and minority religious groups. The article identifies three endogenous limitations intrinsic to liberal democracies: the contested nature of state neutrality vis-à-vis religion and belief, the implementation of this principle through non-neutral judicial tools, and the historically and culturally laden context within which the principle of neutrality is enacted. Drawing on the seminal contributions of Jürgen Habermas to post-secularization theory, the article posits that fostering genuine inclusivity and pluralism in managing religious diversity necessitates a departure from the rigid division between the religious and the secular. Instead, it calls for an acknowledgment of the dialogical relationship between the religious and secular dimensions as they manifest in the public sphere by religious minorities.