Actual Capabilities Research Articles

Half-Hearted or Pragmatic? Explaining EU Strategic Autonomy and the European Defence Fund through Institutional Dynamics

In 2016, the EU Global Strategy introduced the ambition of strategic autonomy, referring to the ability to protect the Union against external threats autonomously. To realise this ambition, the EU also launched various capability development initiatives, in particular, the European Defence Fund (EDF). Much of the available literature presents rationalist explanations of the EU’s development of strategic autonomy and the EDF. These studies attribute strategic autonomy ambition to external conditions and consider it as an act of strategic hedging or bandwagoning. However, the subsequent limited progress in actual capability development casts doubt on these explanations. By drawing on historical institutionalism, this study examines the EU’s current approach to strategic autonomy to see whether internal factors would offer an alternative explanation to the disjunction between the ambitions and actions. For this aim, the study scrutinises the evolution of the EDF as an instrument and the role of the Commission as an agent of change. Based on primary and secondary data, the analysis shows that even though external crises have created critical junctures that compel the EU to reorient its goals, the endogenous elements of institutional change have significantly influenced the EU’s choice of means and redistribution of resources. The findings reveal that the Commission’s ability to reinterpret the original rules and exploit gaps and ambiguities in their local enactment in a path-dependent manner has considerably affected the outcome of this change.

Issues of Interaction Between Operational and Strategic Management of an Enterprise in a Market Environment

Main problem: One of the priority directions for improving the management system at the enterprise is the organization of effective interaction between strategic and operational management through the formation of a mechanism for their interaction, orienting operational management to achieve strategic goals and objectives. In market conditions, issues of interaction between strategic and operational production management are particularly relevant, allowing solving complex economic problems of the enterprise related to the rational use of resources involved in production, reducing production costs, improving the quality of products, optimizing production potential in order to increase the efficiency of the entire enterprise as a whole and its structural divisions. The role of the mechanism of interaction between strategic and operational production management also lies in the possibility of coordinating those final strategic goals that the enterprise strives for with the actual internal production capabilities. The purpose of this article is to study the issues of interaction between operational and strategic management of an enterprise in a market environment. The theory and modern practice of production management in a competitive environment show that the implementation of operational production management is a natural stage in the implementation of strategic management, therefore, there is a close relationship between them, which manifests itself in their interaction. Methods: The methods of analysis and generalization, the method of systematic and comparative analysis, structural and functional method, economic and mathematical methods, graphical and tabular method were used in the work. Results and their significance: the authors consider the theoretical aspects of the relationship between strategic and operational production management at an enterprise in a competitive environment. There is a low level of compliance of operational tasks of production with the strategic goals of development of domestic enterprises and the special role of information systems designed to link operational and strategic management tasks together. Accordingly, the key aspects of information technology implementation are investigated, including the need for employee training, strategic communications planning, information flow management and the use of data analytics. Examples of successful application of information technologies in various industries and organizations are given.

Lunar In Situ Large-Scale Construction: Quantitative Evaluation of Regolith Solidification Techniques

Lunar habitat construction is crucial for successful lunar exploration missions. Due to the limitations of transportation conditions, extensive global research has been conducted on lunar in situ material processing techniques in recent years. The aim of this paper is to provide a comprehensive review, precise classification, and quantitative evaluation of these approaches, focusing specifically on four main approaches: reaction solidification (RS), sintering/melting (SM), bonding solidification (BS), and confinement formation (CF). Eight key indicators have been identified for the construction of low-cost and high-performance systems to assess the feasibility of these methods: in situ material ratio, curing temperature, curing time, implementation conditions, compressive strength, tensile strength, curing dimensions, and environmental adaptability. The scoring thresholds are determined by comparing the construction requirements with the actual capabilities. Among the evaluated methods, regolith bagging has emerged as a promising option due to its high in situ material ratio, low time requirement, lack of high-temperature requirements, and minimal shortcomings, with only the compressive strength falling below the neutral score. The compressive strength still maintains a value of 2–3 MPa. The proposed construction scheme utilizing regolith bags offers numerous advantages, including rapid and large-scale construction, ensured tensile strength, and reduced reliance on equipment and energy. In this study, guidelines for evaluating regolith solidification techniques are provided, and directions for improvement are offered. The proposed lunar habitat design based on regolith bags is a practical reference for future research.

What Should we Reasonably Expect from Artificial Intelligence?

Objective: the objective of this article is to address the misalignment between the expectations of Artificial Intelligence (or just AI) systems and what they can currently deliver. Despite being a pervasive and cutting-edge technology present in various sectors, such as agriculture, industry, commerce, education, professional services, smart cities, and cyber defense, there exists a discrepancy between the results some people anticipate from AI and its current capabilities. This misalignment leads to two undesirable outcomes: Firstly, some individuals expect AI to achieve results beyond its current developmental stage, resulting in unrealistic demands. Secondly, there is dissatisfaction with AI's existing capabilities, even though they may be sufficient in many contexts.Methods: the article employs an analytical approach to tackle the misalignment issue, analyzing various market applications of AI and unveils their diversity, demonstrating that AI is not a homogeneous, singular concept. Instead, it encompasses a wide range of sector-specific applications, each serving distinct purposes, possessing inherent risks, and aiming for specific accuracy levels.Results: the primary finding presented in this article is that the misalignment between expectations and actual AI capabilities arises from the mistaken premise that AI systems should consistently achieve accuracy rates far surpassing human standards, regardless of the context. By delving into different market applications, the author advocates for evaluating AI's potential and accepted levels of accuracy and transparency in a context-dependent manner. The results highlight that each AI application should have different accuracy and transparency targets, tailored on a case-by-case basis. Consequently, AI systems can still be valuable and welcomed in various contexts, even if they offer accuracy or transparency rates lower or much lower than human standards.Scientific novelty: the scientific novelty of this article lies in challenging the widely held misconception that AI should always operate with superhuman accuracy and transparency in all scenarios. By unraveling the diversity of AI applications and their purposes, the author introduces a fresh perspective, emphasizing that expectations and evaluations should be contextualized and adapted to the specific use case of AI.Practical significance: the practical significance of this article lies in providing valuable guidance to stakeholders within the AI field, including regulators, developers, and customers. The article's realignment of expectations based on context fosters informed decision-making and promotes responsible AI development and implementation. It seeks to enhance the overall utilization and acceptance of AI technologies by promoting a realistic understanding of AI's capabilities and limitations in different contexts. By offering more comprehensive guidance, the article aims to support the establishment of robust regulatory frameworks and promote the responsible deployment of AI systems, contributing to the improvement of AI applications in diverse sectors. The author's call for fine-tuned expectations aims to prevent dissatisfaction arising from unrealistic demands and provide solid guidance for AI development and regulation.

Axiomatic Design of a Test Artifact for PBF-LM Machine Capability Monitoring

Powder Bed Fusion Laser Melting (PBF-LM) additive manufacturing technology is expected to have a remarkable impact on the industrial setting, making possible the realization of a metallic component with very complex designs to enhance product performance. However, the industrial use of the PBF-LM system needs a capability monitoring system to ensure product quality. Among the various studies developed, the investigation of methodology for the actual machine capability determination has been faced and still represents an open point. There are multiple authors and institutes proposing different investigation methods, ranging from the realization of samples (ex situ analysis) to installing monitoring devices on the machine (in situ analysis). Compared to other approaches, sample realization allows for assessing how the machine works through specimen analysis, but it is sensitive to the sample design. In this article, we first present an analysis of a well-known test artifact from an Axiomatic Design perspective. Second, based on the customer needs analysis and adjustments with respect to the use of hypothetical additive production lines, a new test artifact with an uncoupled design matrix is introduced. The proposed design has been experimentally tested and characterized using artifact made of Inconel 718 superalloy to evaluate its performance and representativeness in machine capability assessment. The results show an accurate identification of beam offset and scaling factor considering all the building platform positions. In addition, the artifact is characterized by a reduced building time (more than 90% with respect to the reference NIST artifact) and a halved inspection time (from 16 h to 8 h).

Investigation of smart thermostat fault detection and diagnosis potential for air-conditioning systems using a Modelica/EnergyPlus co-simulation approach

Smart thermostats offer low-cost alternative for fault detection and diagnosis (FDD) in residential air-conditioning (AC) systems. However, smart thermostats do not directly measure AC performance, but measures indoor air conditions which reflect the indoor air responses to both AC operations and other factors like weather and building gains. Estimating uncontrollable building gain disturbances is essential to differentiate them from AC impacts. Since the uncontrollable disturbances are difficult to monitor using smart thermostat data, there is need to investigate the actual capability of smart thermostats for FDD. In this study, an integrated building and Vapor Compression cycle-based AC model was developed and simulated using EnergyPlus/Spawn and Dymola. The simulation was performed under ten scenarios covering relevant gains, low charge, and low indoor airflow faults. The sensitivity of the time-to-cool feature under these scenarios was studied. The results show about 10% time-to-cool increase from varied internal gains and infiltration. Meanwhile, low charge at 10%, 20% and 30% severity increased time-to-cool by about 3%, 6%, and 20%, respectively, suggesting that if these gains are neglected in an FDD method, it might not be possible to detect low charge below 30% using smart thermostat data since even without a fault, the combined impact of those gains already exceeded that for 20% low charge. For low indoor airflow at 10%, 20% and 30% severity, the increase in time-to-cool was about 3%, 9% and 12%, meaning that only low indoor airflow fault above 30% can be confidently detected if those gains are not captured. Thus, this study demonstrates the need to consider some of these gains in developing smart thermostat based FDD algorithms.

In silico bioactivity analysis of peptide fractions derived from brewer's spent grain hydrolysates

SummaryBrewer's spent grain is an agro‐industrial waste of barley malt from the manufacture of beer, which is rich in protein. Thus, this by‐product of the beer industry could be utilised to obtain bioactive peptides through enzymatic hydrolysis. The present study aimed to evaluate potential bioactivities (anti‐inflammatory, antidiabetic, antioxidant, antimicrobial) of peptide sequences identified in 5‐kDa peptide fractions derived from brewer's spent grain hydrolysates. Two experimental hydrolysates (run 5 and run 21) were selected based on their higher degree of hydrolysis (>55%), which were subjected to peptide sequence identification and used for in silico analysis. A total of fifty peptide sequences were identified between both runs. In silico analysis identified seventeen and thirteen multifunctional peptides in runs 5 and 21, respectively, which showed between two and four potential bioactivities. Interestingly, one peptide sequence (DLPPER) showed four potential bioactivities and, according to the Docking analysis, showed better Vina scores towards the protein's targets DPP‐IV (antidiabetic) and glucocorticoid receptor (anti‐inflammatory). These results demonstrate that peptides from brewer's spent grain hydrolysates may contribute to improving human health by offering potential protection against some chronic‐related diseases; however, further follow‐up in vitro and in vivo studies are needed to evaluate its actual capability.

The risks of job flexibility: an analysis of reconciling paid work and informal care across organisations using the capabilities approach

While welfare states and work organisations often provide policies to facilitate a reconciliation of paid work and informal care, the literature pays little attention to employees’ actual capabilities to engage with such policies. In this article, we apply the capabilities approach to interview data from managers and employees in the Netherlands to understand employees’ use or non-use of the policies. We found that job flexibility may help workers reconcile work and care in the short run but that respondents with greater job flexibility reported more health problems than respondents with less flexibility, which calls into question the sustainability of these policies.

Test Methods for Validating Inductive Transformer Performance during Cable Discharge: Main Principles, Parameters and Testing Procedures

High voltage connections will discharge through inductive voltage and combined transformers when they are switched off. In the past, there was a wide margin between the required capabilities and the actual capabilities of these components. Nowadays, however, this aspect is becoming more prominent due to a higher penetration by cable systems into the grid. Currently, none of the relevant international standards contain any definitions or requirements for line discharge, nor test methods which would validate the ability of the transformer to discharge a line of certain capacitance at a certain voltage. This is why the aim of this paper is to provide a foundation for how to specify, assess and verify the capacitive discharge performance of instrument transformers. This paper will present the basic theory, influencing parameters, multiple test sequences and acceptance criteria for cable discharge. It is clear that capacitive discharges present both thermal and mechanical stress to the unit, which is why this paper will detail how to guarantee both aspects. The idea is that this paper serves as a literal blueprint which can then be transferred to the relevant clauses in upcoming international standards. All conclusions within this paper are achieved through calculations and testing on actual 123 kV inductive voltage transformers and combined units.

A risk-aware reference trajectory resampling method for quadrotor tracking accuracy improvement

PurposeMany existing trajectory optimization algorithms use parameters like maximum velocity or acceleration to formulate constraints. Due to the ignoring of the quadrotor actual tracking capability, the generated trajectories may not be suitable for tracking control. The purpose of this paper is to design an online adjustment algorithm to improve the overall quadrotor trajectory tracking performance.Design/methodology/approachThe authors propose a reference trajectory resampling layer (RTRL) to dynamically adjust the reference signals according to the current tracking status and future tracking risks. First, the authors design a risk-aware tracking monitor that uses the Frenét tracking errors and the curvature and torsion of the reference trajectory to evaluate tracking risks. Then, the authors propose an online adjusting algorithm by using the time scaling method.FindingsThe proposed RTRL is shown to be effective in improving the quadrotor trajectory tracking accuracy by both simulation and experiment results.Originality/valueInfeasible reference trajectories may cause serious accidents for autonomous quadrotors. The results of this paper can improve the safety of autonomous quadrotor in application.

Physiological workload assessment for highly flexible fine-motory assembly tasks using machine learning

In assembly of small-volume products, tasks are still frequently executed manually. However, the lead times foreseen for these tasks, often do not take into account the actual capabilities of the employees, which in turn leads to increased workload and the associated stress among the employees. This paper investigates how a commercially available wearable low-cost sensor and two machine learning algorithms can be applied to measure and evaluate heart rate, heart rate variability and respiration rate to establish a relationship with workload. The investigated algorithms, namely Random Forest and K-Nearest-Neighbours are able to distinguish between tasks phases and rest phases as well as between easy and difficult tasks executed by the employee, which is the main novelty of this paper.

Композиционно-структурные, семантические и пресуппозиционально-прагматические параметры и дефекты сгенерированных коротких текстов в языковой нейросети GigaChat

Introduction. The relevance of the study lies primarily in the fact that the increasingly active appeal of the widest circles of users to the generation of texts of different genres, properties and volumes using the so-called LLM (Large Language Model) creates the need to study the actual linguistic capabilities of these generative models, including the need for linguistic research of the texts they generate. It is also important that a large number of specialists from different fields actively use the generative capabilities of linguistic neural networks for purely professional purposes, which means that the quality of generated texts has acquired the status of a production factor and directly affects success in professional activities, which also indicates the relevance of our research. Materials and Methods. The texts of short stories were chosen as the object of study, since in a certain respect, such texts can be considered as archetypal structures that underlie texts of certain functional types (scripts, advertising texts and stories). Analysis. This state of affairs requires studying the generated stories, including in the aspect of the representation of frame structures in it, since this will allow us to get an idea of how texts of this type are structured from the point of view of standard linguistic semantics, including syntactic semantics. Results. It has been established that, despite all grammatical, structural-semantic and compositional adequacy, the texts of short stories generated in GigaChatPro, created by the network on a certain topic or according to a certain frame, may contain violations in global semantic organization, generated by both presuppositional-pragmatic violations and violations in text reference. Violations in the general functional pragmatics of the generated stories were also noted: pronounced edification and the length of sentences up to 5-7 words are characteristic of stories for children of primary school age, despite the fact that the themes of these stories initially do not correspond to their age interests.

A Supply Chain Resilience Capability Framework and Process for Mitigating the COVID-19 Pandemic Disruption

This article aims to explore the supply chain (SC) resilience capabilities of firms, focusing on their ability to respond to the COVID-19 pandemic disruption. Based on two cases each in the pharmaceutical and mineral water industries, this article identifies various strategies managers mobilized to tackle SC disruptions during the COVID-19 pandemic. This article contributes to theory through a proposed double-helix framework showing the dimension of disruptions and the capabilities concept to mitigate COVID-19. In addition, an in-depth investigation of the perceived importance versus actual SC resilience capabilities deployed is discussed and validated with practitioners. The findings of this article also address a critical gap in the SC operations management literature and provide a practical approach for managers to better manage future pandemic disruptions.

Improvements of Autopilot Technology for Electric Vehicle: Case Study of Nio ES8 Crash on Highway

In recent years, the automotive industry has witnessed a significant surge in the popularity of electric and hybrid vehicles, particularly in the low to mid-level car market, attributed to their robust power output and enhanced comfort levels. Despite their current popularity, the initial introduction of these vehicles met with a tepid reception, largely owing to the radical shift in vehicle powertrain technology they represented. To bolster the market acceptance of these innovations, established and nascent automotive companies leveraged the allure of "automatic assisted driving" as a potent marketing strategy. However, this study highlights the early marketing tactics that often blurred the lines between "assisted driving" and "autonomous driving" through ambiguous linguistic representations, potentially misleading consumers regarding the actual capabilities of the technology at hand. As of now, the technology to facilitate fully autonomous vehicles remains undeveloped. This paper critically examines the repercussions of such marketing strategies, underscored by a significant traffic incident on China's Putian Expressway on August 12, 2021, which catalyzed a societal reevaluation of the reliability and safety of prevalent assisted driving systems. This research seeks to foster a nuanced understanding of the evolving dynamics in the automotive industry, emphasizing the imperative for clear, accurate marketing and the continuous development of reliable and safe assisted driving technologies. It serves as a clarion call for responsible innovation and marketing in the pursuit of a safer and more sustainable automotive future.

Exploring the Potential of ChatGPT-4 in Predicting Refractive Surgery Categorizations: Comparative Study.

Refractive surgery research aims to optimally precategorize patients by their suitability for various types of surgery. Recent advances have led to the development of artificial intelligence-powered algorithms, including machine learning approaches, to assess risks and enhance workflow. Large language models (LLMs) like ChatGPT-4 (OpenAI LP) have emerged as potential general artificial intelligence tools that can assist across various disciplines, possibly including refractive surgery decision-making. However, their actual capabilities in precategorizing refractive surgery patients based on real-world parameters remain unexplored. This exploratory study aimed to validate ChatGPT-4's capabilities in precategorizing refractive surgery patients based on commonly used clinical parameters. The goal was to assess whether ChatGPT-4's performance when categorizing batch inputs is comparable to those made by a refractive surgeon. A simple binary set of categories (patient suitable for laser refractive surgery or not) as well as a more detailed set were compared. Data from 100 consecutive patients from a refractive clinic were anonymized and analyzed. Parameters included age, sex, manifest refraction, visual acuity, and various corneal measurements and indices from Scheimpflug imaging. This study compared ChatGPT-4's performance with a clinician's categorizations using Cohen κ coefficient, a chi-square test, a confusion matrix, accuracy, precision, recall, F1-score, and receiver operating characteristic area under the curve. A statistically significant noncoincidental accordance was found between ChatGPT-4 and the clinician's categorizations with a Cohen κ coefficient of 0.399 for 6 categories (95% CI 0.256-0.537) and 0.610 for binary categorization (95% CI 0.372-0.792). The model showed temporal instability and response variability, however. The chi-square test on 6 categories indicated an association between the 2 raters' distributions (χ²5=94.7, P<.001). Here, the accuracy was 0.68, precision 0.75, recall 0.68, and F1-score 0.70. For 2 categories, the accuracy was 0.88, precision 0.88, recall 0.88, F1-score 0.88, and area under the curve 0.79. This study revealed that ChatGPT-4 exhibits potential as a precategorization tool in refractive surgery, showing promising agreement with clinician categorizations. However, its main limitations include, among others, dependency on solely one human rater, small sample size, the instability and variability of ChatGPT's (OpenAI LP) output between iterations and nontransparency of the underlying models. The results encourage further exploration into the application of LLMs like ChatGPT-4 in health care, particularly in decision-making processes that require understanding vast clinical data. Future research should focus on defining the model's accuracy with prompt and vignette standardization, detecting confounding factors, and comparing to other versions of ChatGPT-4 and other LLMs to pave the way for larger-scale validation and real-world implementation.

Development and Testing of an Active Noise Control System for Urban Road Traffic Noise

As urbanization accelerates, the increasing number of vehicles and travel demands contribute to escalating road traffic noise pollution. Although passive noise control techniques such as noise barriers and green belts effectively mitigate noise, they occupy urban space, exacerbating the scarcity and high cost of already congested city areas. Emerging as a novel noise reduction strategy, active noise control (ANC) eliminates the need for physical isolation structures and addresses the noise within specific frequency ranges more effectively. This paper investigates the characteristics of urban road traffic noise and develops an ANC prototype. Utilizing the Least Mean Squares (LMS) algorithm, we conduct active noise control tests for various types of single- and dual-frequency noise within the prototype’s universal platform to validate its actual noise reduction capabilities. The study demonstrates that urban road traffic noise is mostly in the mid- to low-frequency range (below 2000 Hz). The developed ANC prototype significantly reduces single- or dual-frequency noise within this range, achieving a maximum noise reduction of nearly 30 dB(A). Future research should expand noise reduction tests across more frequency bands and assess the noise reduction effectiveness against real road traffic noise.

Force-based organization and control scheme for the non-prehensile cooperative transportation of objects

AbstractOver decades of robotics research, cooperative object transportation has been studied as a meaningful model problem for robotic networks because it possesses a variety of crucial challenges. Although these challenges are demanding, the cooperation of multiple robots has the potential to solve automation problems that are beyond the scope of an individual robot. So far, the model problem has mostly been addressed by explicitly controlling the robots’ positions. However, the position-based approach suffers from some intrinsic detriments, for example, the lack of explicit feedback between robots and object. Moreover, it remains an open question how many robots shall be employed to ensure a successful transportation. This paper’s purpose is to overcome these challenges using a novel force-based approach taking into account the robots’ actual manipulation capabilities, that is, the exerted forces. Using cost-efficient hardware, the interaction forces are measured and, what is more, explicitly controlled by a highly responsive onboard controller. Employing a tailored software architecture, the novel force-based scheme, useful for robotic manipulation beyond the benchmark problem, is probably the most flexible of its kind regarding the number of robots and the object’s shape. The controller’s functionality and performance as well as the scheme’s versatility are demonstrated by several hardware experiments.

MSDSE: Predicting drug-side effects based on multi-scale features and deep multi-structure neural network

Unexpected side effects may accompany the research stage and post-marketing of drugs. These accidents lead to drug development failure and even endanger patients' health. Thus, it is essential to recognize the unknown drug-side effects. Most existing methods in silico find the answer from the association network or similarity network of drugs while ignoring the drug-intrinsic attributes. The limitation is that they can only handle drugs in the maturation stage.To be suitable for early drug-side effect screening, we conceive a multi-structural deep learning framework, MSDSE, which synthetically considers the multi-scale features derived from the drug. MSDSE can jointly learn SMILES sequence-based word embedding, substructure-based molecular fingerprint, and chemical structure-based graph embedding. In the preprocessing stage of MSDSE, we project all features to the abstract space with the same dimension. MSDSE builds a bi-level channel strategy, including a convolutional neural network module with an Inception structure and a multi-head Self-Attention module, to learn and integrate multi-modal features from local to global perspectives. Finally, MSDSE regards the prediction of drug-side effects as pair-wise learning and outputs the pair-wise probability of drug-side effects through the inner product operation. MSDSE is evaluated and analyzed on benchmark datasets and performs optimally compared to other baseline models. We also set up the ablation study to explain the rationality of the feature approach and model structure. Moreover, we select model partial prediction results for the case study to reveal actual capability. The original data are available at http://github.com/yuliyi/MSDSE.

Livestock Practices: Traditional Animal Holdings Classification in Qatar 2020 Towards Sustainable Food Security

Background: Traditional animal holdings (TAH) in Qatar face many managerial challenges, such as inadequate usage of land capacity, low levels of animal productivity and low economic returns. The top priority of the Ministry of Municipality strategy is to take care of TAH to ensure the sustainability of this activity and to maximize its role in national food security. To support future policy choices and services provision, the ministry initiated a TAH classification system. In 2020, the Social and Economic Survey Research Institute (SESRI) of Qatar University conducted a comprehensive agriculture census that followed a well-known methodology. The census form consisted of questions that guided the classification of TAH. The aim of this study is to help assess TAH performance using data from the census. Results: The Animals Holdings Classification Index (AHCI) divided the current holdings into one of five categories (A, B, C, D or E) in accordance with seven factors as classification criteria. These factors were levels of land and barn capacity utilization, livestock productivity, economic return, biosecurity measures, husbandry system and usage of technology for animal production. The results showed that most of the holdings fell into categories C and D. The lowest-scoring criteria were commitment to biosecurity measures and economic benefit. We recommended intensifying extension and enacting legislation to organize holdings to comply with biosecurity measures and initiating marketing programs and market outlets for TAH. According to Qatar’s 2021 agriculture census, there are three different types of holdings: roving holdings (mobile), 33.6%; holdings in compounds, 57.6; and holdings outside rural houses, 8.8%. Conclusions: The AHCI not only determines a holding’s actual productivity capability but also encourages holders to develop and upgrade their holdings. Furthermore, it helps the government fill gaps and provide services based on information and evidence.

Triple-Mode Sensor Coupled by Photoelectrochemical, Electrochromic, and Spectral Signals for Sensitive Visualized Detection of Nonylphenol.

Conventional photoelectrochemical (PEC) biosensors suffer from the difficulty of visualizing rapid detection and limited accuracy due to a single-signal output. Here, we develop a PEC, electrochromic (EC), and spectral (ST) triple-mode platform for the sensitive visualized detection of nonylphenol (NP). First, the reasonably stepped Fermi energy level arrangement between the defective TiO2 anode and MoO3 cathode enables a remarkable photocurrent response (Mode 1). Then, MoO3 itself is a widely used EC candidate, which can react with free Li-ions to form a LixMoO3 intermediate, and its color will change from white to blue accordingly (Mode 2). More importantly, MoO3 is also a Li-ion host and the potential of LixMoO3 depends on the inserted Li-ion quantity deduced by spectral analysis on residual Li-ions in the electrolyte (Mode 3). The EC signal endows fast visual detection, and triple-mode cross-validation improves reliability and accuracy. As a result, this PEC-EC-ST triple-mode molecularly imprinted sensor has a wide linear range (1-5000 μg L-1), a low detection limit (0.18 μg L-1), selectivity, stability, reproducibility, and actual sample detection capability. This innovative multimode platform not only improves detection reliability but also broadens applications of electrochromic/energy storage materials in biosensors.