Practical Limitations Research Articles

The Role of Artificial Intelligence in Contemporary Journalism Practice in Two African Countries

Contemporary discussions about the application of artificial intelligence in newsrooms are commonplace because of the unique opportunities it presents for news media. This study investigated the intricate relationship between journalism and AI with the broad research question: How are journalists adopting AI technologies and what challenges and opportunities do such technologies present to them? Eighteen journalists practising in Ghana and South Africa were interviewed through qualitative research techniques. Transcribed interview data were analysed thematically using the data analysis method proposed by Charmaz. The findings were that most newsrooms in the two countries have not formally incorporated AI tools into newsroom practices. However, journalists use AI tools at their discretion in a non-complex manner, such as transcription, research, generating story ideas, and fact-checking. Practical limitations to the formal integration of AI technology into newsroom operations include cost, language barrier, and aversion to change. Although participants recognised the advantages of employing AI for newsroom tasks, they were also concerned about the ethical quandaries of misinformation, improper attribution, and intellectual property. Participants also thought that fact-checking and mindfulness regarding ethical usage might increase ethical AI usage in newsrooms. This study adds an important perspective on AI’s role in African journalism, addressing the obstacles and ethics concerns.

Adaptive-neural command filtered synchronization control of tele-robotic systems using disturbance observer with safety enhancement

Due to the nature of the tele-robotic systems, some main problems are imposed, such as external disturbances, time-varying delays and external torques. However, some applications such as tele-surgery systems, require high precision, safety and accurate transmission of information between the leader and follower robots. In order to facilitate the imposed restrictions and to guarantee transient-state, and even steady-state performances in the presence of external disturbances and system uncertainties, the time-varying full state constrained control is employed by applying the Barrier Lyapunov function (BLF). In this regard, a new adaptive neural network torque observer is proposed to make the system independent from the force sensors. Moreover, the independence of the proposed algorithm from perfect knowledge of the manipulator dynamics and time-delay’s derivative of communication channels are advantages of this paper. Furthermore, the key idea of this paper is to lessen the computational complexity in the backstepping-based adaptive neural controller by means of the command filter strategy and the BLF approach is combined to converge the synchronization error signals into a predefined constraint. In order to consider practical limitations, time-varying delays in the communication channel and input saturation constraint are also considered in the design mechanism. Finally, the stability analysis of the observer and controller together is conducted and the evaluation of the performance of the proposed method is performed through a series of various scenarios and comparisons.

Benefits and limitations of using AI in German language lessons on the example of ChatGPT

Today’s artificial intelligence systems, using the ever-increasing textual resources available in virtual space, are changing our perception of language and influencing the way we communicate and learn. The recently launched ChatGPT platform, based on an autoregressive algorithm, naturally imitates an omniscient communication partner. The high popularity achieved by the system in a very short time, especially among young users, should be a reason to consider the impact of this type of tool on modern education, including the teaching of foreign languages and the role of the teacher in the teaching process. The following article will present, based on practical examples, the benefits and limitations of using artificial intelligence in German language lessons, both from the teacher and student perspective.

A parallel power system linear model reduction method based on extended Krylov subspace

With the ever-increasing scale of power systems, stability analysis and control usually bear heavy storage and massive calculation burdens. In view of this, the model order reduction technique proves valuable by constructing a low-dimensional approximate model of the original system, which is crucial for efficiently handling large-scale systems. Balanced truncation (BT), a famous model reduction method, confronts practical limitations as it requires the systems to be stable and cannot deal with unstable models. Therefore, a parallel linear balanced truncation method for power systems based on extended Krylov subspace (EKS) is proposed in this work. Besides extending the BT method to unstable systems by α-shift, the key contribution also lies in strategies to enhance the convergence of the EKS method, whereupon the algorithm improvements include effective and efficient techniques for solving dual Lyapunov equations, and parallel acceleration of the singular value decomposition. The results of the simulation case verify that the proposed method can effectively improve the convergence of the EKS method by increasing α-shift, and the improvement work in this paper reduces the total time consumption of the BT method by about 26 %–33 % of the original, exhibiting better calculation efficiency. In addition, the case studies show that the simplified model still retains the time-domain and frequency-domain response characteristics of the original high-dimensional model.

International students' intercultural sensitivity and academic adaptation in Chinese universities: The mediating role of general health

AbstractThe quest to flourish academically and acquire skills needed for job opportunities has brought people from different cultures and countries to closely interact. Using Vygotsky's sociocultural theory, this study examines the relationship between international students' intercultural sensitivity and academic adaptation and the mediating role of general health. A one‐way analysis of variance and structural equation model were conducted to assess the differences and relationships between the constructs, utilizing 338 respondents. The findings showed that male, doctoral, and English‐taught students reported significantly higher academic adaptation. Students who have no or less COVID‐19 related issues reported higher general health. Further, the regression results revealed that intercultural sensitivity is related to academic adaptation, and general health partially mediated the intercultural sensitivity and academic adaptation relationship. These findings demonstrate that intercultural sensitivity nurtures students' self‐esteem and mental toughness, which allow them to reap good health outcomes that impact academic adaptation. The practical implications and limitations of the study have been discussed.

Synergistic enhancement of electric double layers and faradaic reactions in capacitive deionization: The role of NTP@C composite

Capacitive Deionization (CDI) has received attention for its energy efficiency and minimal environmental impact, but it faces practical limitations due to the constrained desalination capacity of conventional systems. Our study addresses this challenge by introducing a novel electrode material, NaTi2(PO4)3 (NTP) coated with interconnected mesoporous carbon (NTP@C). This innovative material leverages the synergistic effects of electric double layers and faradic reaction conversion faradic. Unlike traditional carbon coating, our new strategy significantly changes the shape of NTP from coral to ellipse, significantly shortening the diffusion path of sodium ions (Na+), thus accelerating the mass transfer kinetics. Comprehensive morphological, structural, and electrochemical analyses reveal that NTP@C outperforms traditional electrodes in terms of electrochemical stability, ion transmission rates, and desalination capacity. At 1.8 V and 3 g/L NaCl concentration, NTP@C achieves a remarkable salt adsorption capacity of 233.07 mg g−1, with a desalination capacity growth rate of 188 % between 1.4 V and 1.6 V—surpassing the 159 % growth rate of NTP. Additionally, NTP@C exhibits higher cycle performance, maintaining 89.42 % stability over 50 cycles compared to NTP’s 74.39 %. The innovative carbon coating technique prevents NTP particle agglomeration, enhances conductivity, and increases the specific surface area, culminating in a unique interconnected and wrapped carbon structure. This results in an exceptional synergy between the electric double layer and faradic reaction conversion faradic, propelling NTP@C to outstanding desalination performance and cycle stability.

An Extended Theory of Planned Behaviour for Explaining Chinese Domestic Electric Vehicle Purchasing Behaviour

Compared to traditional fuel-powered cars, electric vehicles (EVs) are widely acknowledged as a viable form of transportation and an effective way to address the issues of energy scarcity and air pollution. However, the majority of countries, including China, have recently lowered their preferential treatment and will instead adopt a market-driven strategy to promote EVs. Thus, it is critical to identify the particular elements that support and undermine consumer decisions to choose EVs. This study investigates the impact of perceived value (PV) and consumer ethnocentrism (CE), along with components of TPB, on consumers’ intentions towards the adoption of domestic EVs by using a convenience sample method to collect data. The results showed that attitude, subjective norm (SN), and perceived behavioural control (PBC) positively influence intention. SN positively influences attitude, and attitude plays a mediational role between SN and intention. In addition, PV and CE positively influence attitude towards purchasing domestic EVs. Last, the theoretical and practical implications and limitations are discussed accordingly.

Analysis of Innovation and Technology in Banking

As proven by both private and public banks, IT-enabled innovation has had a substantial influence on a range of industries, including banking. These financial institutions value innovation and have achieved success as a consequence of technical breakthroughs. The increased competitiveness has compelled all players to act more rapidly. As a result, public sector banks have experienced substantial transformations, and consumers are now confronted with a slew of new options. These banks, which previously depended on traditional methods, are now proactively getting out to the public through billboards, FM radio, and other media outlets. In addition, endorsements from famous people are becoming more common in government entities. The widespread usage of basic banking processes has increased banks' need to innovate. Client satisfaction increases as a consequence in both public and private banks. The study's purpose is to investigate how new technology affects customer satisfaction in Bhopal's public and commercial banks. Customer data obtained from these banks was reviewed, with substantial results. In terms of innovative successes, private banks outscored state banks, according to the poll. Rising banking rivalry is widely considered to have had an immediate influence on how financial institutions prioritize customer-oriented operations and define company goals. Considering the significance of technology and innovation in the modern economy for strategic management, business development, and economic success, new approaches to understanding the relationship among technological innovation and financial services are critical. These unique strategies are required for gaining an advantage over the competition. With this in mind, the goal of this study is to investigate the basic implications of technological innovation on financial services at the bank's location level, specifically through an examination of customer-facing staff input. According to the findings, information and communication technology is critical to the sector's performance. The paper also examines the empirical study's practical implications, strengths, and limitations.

Robot-Mediated Intergenerational Childcare: Experimental Study Based on Health-Screening Task in Nursery School

Intergenerational interactions between children and older adults are gaining broader recognition because of their mutual benefits. However, such interactions face practical limitations owing to potential disease transmission and the poor health of older adults for face-to-face interactions. This study explores robot-mediated interactions as a potential solution to address these issues. In this study, older adults remotely controlled a social robot to perform a health-screening task for nursery school children, thereby overcoming the problems associated with face-to-face interactions while engaging in physical interactions. The results of this study suggested that the children responded favorably to the robot, and the rate of positive response increased over time. Older adults also found the task generally manageable and experienced a significant positive shift in their attitude toward children. These findings suggest that robot-mediated interactions can effectively facilitate intergenerational engagement and provide psychosocial benefits to both the parties to the engagement. This study provides valuable insights into the potential of robot-mediated interactions in childcare and other similar settings.

Mediation Roles of Transformational Leadership and Psychological Empowerment on Work-Life Balance and In-role Performance of Vocational Educators in Nigeria

This study is necessitated by the dire need to determine the mechanisms of leadership transformation approaches that explain why 360-degree feedback is effective in helping supervisors and subordinates identify weaknesses and strengths and take positive actions for institutional health, growth, and productivity. The study tested five hypotheses based on survey responses from 754 TVET stakeholders (Deans, HODs, educators, undergraduate and postgraduate students). Data was elicited from the participants usingfour Scales. The reliability of the instruments was determined using the Cronbach alpha technique. Data collected was analysed using descriptive, inferential statistics and hierarchical Multiple Regression method to determine the total, direct and indirect effects of the variables studied. The result indicated that psychological empowerment and work-life balance jointly and significantly mediated the relationship between transformational leadership and the in-role performance (IRP), and also psychologicalempowerment and educators’ IRP. This study contributes to the extant literature that promote supervisors’ transformational leadership styles, vocational educators’ in-role performance, and work-life balance. The practical implications, limitations, and suggestions for future research were discussed.

Bayesian estimation of the mean time between failures of subsystems with different causes using interval‐censored system maintenance data

AbstractEnsuring an acceptable level of reliability stands as a primary imperative for any mission‐focused operation since it serves as a critical determinant of success. Inadequate reliability can lead to severe repercussions, including substantial expenses for repairs and replacements, missed opportunities, service disruptions, and in the worst cases, safety violations and human casualties. Within national defense organizations such as the USAF, the precise assessment and maintenance of system reliability play a pivotal role in ensuring the success of mission‐critical operations. In this research, our primary objective is to model the reliability of repairable subsystems within the framework of competing and complementary risks. Subsequently, we construct the overall reliability of the entire repairable system, utilizing day‐to‐day group‐censored maintenance data from two identical aircraft systems. Assuming that the lifetimes of subsystems follow non‐identical exponential distributions, it is theoretically justified that the system reliability can be modeled by homogeneous Poisson processes even though the number of subsystems of any particular type is unknown and the temporal order of multiple subsystem failures within a given time interval is uncertain due to interval censoring. Using the proposed model, we formulate the likelihood function for the mean time between failures of subsystems with different causes, and subsequently establish an inferential procedure for the model parameters. Given a considerable number of parameters to estimate, we explore the efficacy of a Bayesian approach, treating the contractor‐supplied estimates as the hyperparameters of prior distributions. This approach mitigates potential model uncertainty as well as the practical limitation of a frequentist‐based approach. It also facilitates continuous updates of the estimates as new maintenance data become available. Finally, the entire inferential procedures were implemented in Microsoft Excel so that it is easy for any reliability practitioner to use without the need to learn sophisticated programming languages. Thus, this research supports an ongoing, real‐time assessment of the overall mission reliability and helps early detection of any subsystem whose reliability is below the threshold level.

Using Graphs to Perform Effective Sensor-Based Human Activity Recognition in Smart Homes.

There has been a resurgence of applications focused on human activity recognition (HAR) in smart homes, especially in the field of ambient intelligence and assisted-living technologies. However, such applications present numerous significant challenges to any automated analysis system operating in the real world, such as variability, sparsity, and noise in sensor measurements. Although state-of-the-art HAR systems have made considerable strides in addressing some of these challenges, they suffer from a practical limitation: they require successful pre-segmentation of continuous sensor data streams prior to automated recognition, i.e., they assume that an oracle is present during deployment, and that it is capable of identifying time windows of interest across discrete sensor events. To overcome this limitation, we propose a novel graph-guided neural network approach that performs activity recognition by learning explicit co-firing relationships between sensors. We accomplish this by learning a more expressive graph structure representing the sensor network in a smart home in a data-driven manner. Our approach maps discrete input sensor measurements to a feature space through the application of attention mechanisms and hierarchical pooling of node embeddings. We demonstrate the effectiveness of our proposed approach by conducting several experiments on CASAS datasets, showing that the resulting graph-guided neural network outperforms the state-of-the-art method for HAR in smart homes across multiple datasets and by large margins. These results are promising because they push HAR for smart homes closer to real-world applications.

Optimal transport for automatic alignment of untargeted metabolomic data.

Untargeted metabolomic profiling through liquid chromatography-mass spectrometry (LC-MS) measures a vast array of metabolites within biospecimens, advancing drug development, disease diagnosis, and risk prediction. However, the low throughput of LC-MS poses a major challenge for biomarker discovery, annotation, and experimental comparison, necessitating the merging of multiple datasets. Current data pooling methods encounter practical limitations due to their vulnerability to data variations and hyperparameter dependence. Here, we introduce GromovMatcher, a flexible and user-friendly algorithm that automatically combines LC-MS datasets using optimal transport. By capitalizing on feature intensity correlation structures, GromovMatcher delivers superior alignment accuracy and robustness compared to existing approaches. This algorithm scales to thousands of features requiring minimal hyperparameter tuning. Manually curated datasets for validating alignment algorithms are limited in the field of untargeted metabolomics, and hence we develop a dataset split procedure to generate pairs of validation datasets to test the alignments produced by GromovMatcher and other methods. Applying our method to experimental patient studies of liver and pancreatic cancer, we discover shared metabolic features related to patient alcohol intake, demonstrating how GromovMatcher facilitates the search for biomarkers associated with lifestyle risk factors linked to several cancer types.

Optimal transport for automatic alignment of untargeted metabolomic data

Untargeted metabolomic profiling through liquid chromatography-mass spectrometry (LC-MS) measures a vast array of metabolites within biospecimens, advancing drug development, disease diagnosis, and risk prediction. However, the low throughput of LC-MS poses a major challenge for biomarker discovery, annotation, and experimental comparison, necessitating the merging of multiple datasets. Current data pooling methods encounter practical limitations due to their vulnerability to data variations and hyperparameter dependence. Here, we introduce GromovMatcher, a flexible and user-friendly algorithm that automatically combines LC-MS datasets using optimal transport. By capitalizing on feature intensity correlation structures, GromovMatcher delivers superior alignment accuracy and robustness compared to existing approaches. This algorithm scales to thousands of features requiring minimal hyperparameter tuning. Manually curated datasets for validating alignment algorithms are limited in the field of untargeted metabolomics, and hence we develop a dataset split procedure to generate pairs of validation datasets to test the alignments produced by GromovMatcher and other methods. Applying our method to experimental patient studies of liver and pancreatic cancer, we discover shared metabolic features related to patient alcohol intake, demonstrating how GromovMatcher facilitates the search for biomarkers associated with lifestyle risk factors linked to several cancer types.

From our ivory towers: Enacting our collective daily resistance through ungrading in a feminist co-mentoring group

Grading has been central to schooling of all levels. Aligned with many critical educators, we argue that conventional grading systems in higher education contexts perpetuate a transactional relationship between student and professor and student and university. In this collaborative self-study, we, three pre-­tenured teacher educators at the same midsize, midwestern university, formed a feminist co-mentoring group and explored ungrading in our own teacher preparation classrooms. Through narrative inquiry, our data uncovered themes of (1) traditional grading practices limit; (2) renewed resolve in ungrading despite obstacles; (3) ungrading as an act of resistance.

Spectrum-based deep learning framework for dermatological pigment analysis and simulation

Background:Deep learning in dermatology presents promising tools for automated diagnosis but faces challenges, including labor-intensive ground truth preparation and a primary focus on visually identifiable features. Spectrum-based approaches offer professional-level information like pigment distribution maps, but encounter practical limitations such as complex system requirements. Methods:This study introduces a spectrum-based framework for training a deep learning model to generate melanin and hemoglobin distribution maps from skin images. This approach eliminates the need for manually prepared ground truth by synthesizing output maps into skin images for regression analysis. The framework is applied to acquire spectral data, create pigment distribution maps, and simulate pigment variations. Results:Our model generated reflectance spectra and spectral images that accurately reflect pigment absorption properties, outperforming spectral upsampling methods. It produced pigment distribution maps with correlation coefficients of 0.913 for melanin and 0.941 for hemoglobin compared to the VISIA system. Additionally, the model’s simulated images of pigment variations exhibited a proportional correlation with adjustments made to pigment levels. These evaluations are based on pigment absorption properties, the Individual Typology Angle (ITA), and pigment indices. Conclusion:The model produces pigment distribution maps comparable to those from specialized clinical equipment and simulated images with numerically adjusted pigment variations. This approach demonstrates significant promise for developing professional-level diagnostic tools for future clinical applications.

Functional Connectivity of Moral Cognition and Traits of Psychopathy in Youth: An Analysis of the Scientific Literature

The aim of the study is to analyze the scientific literature and evidence on the mechanisms of functional connectivity involved in moral cognition processes in youth with psychopathy traits. Scientometric methods of citation networks based on graph theory were used. Bibliometrix, Sci2 Tool, and Gephi were used for the analysis. It was found that the dorsal and ventral regions of the prefrontal cortex, orbitofrontal cortex, amygdala, hippocampus, angular gyrus, anterior cingulate and temporal cortex evidence functional alterations in young people with psychopathic traits. It was also reported that graymatter concentration in the left posterior and right anterior cingulate are possible endophenotypes of psychopathic traits. The theoretical, empirical, and legal implications of a moral neural perspective of psychopathy andantisocial behavior are discussed. The results demonstrated that psychopathic traits, which occasionally lead to criminal and violent behaviors, have a neurobiological basis related to moral cognition processes. However, a neuronal moral model has theoretical and practical limitations, as well as legal implications for free will and moral agency capacity.

Si, O-Codoped Carbonized Polymer Dots with High Chemiresistive Gas Sensing Performance at Room Temperature.

A new type of carbonized polymer dot was prepared by the one-step hydrothermal method of triethoxylsilane (TEOS) and citric acid (CA). The sensor made from carbonized polymer dots (CPDs) showed superior gas sensing performance toward ammonia at room temperature. The Si, O-codoped CPDs exhibited superior ammonia sensing performance at room temperature, including a low practical limit of detection (pLOD) of 1 ppm (Ra/Rg: 1.10, 1 ppm), short response/recovery time (30/36 s, 1 ppm), high humidity resistance (less than 5% undulation when changing relative humidity to 80 from 30%), high stability (less than 5% initial response undulation after 120 days), reliable repeatability, and high selectivity against other interferential gases. The gas sensing mechanism was investigated through control experiments and in situ FTIR, indicating that Si, O-codoping essentially improves the electron transfer capability of CPDs and synergistically dominates the superior ammonia sensing properties of the CPDs. This work presents a facile strategy for constructing novel high-performance, single-component carbonized polymer dots for gas sensing.

Metal organic framework-derived porous Ni-doped In2O3 for highly sensitive and selective detection to hydrogen at low temperature

Achieving high sensitivity and selective detection at low temperature is of great significance for hydrogen leakage monitoring and early warning. As a very promising hydrogen sensitive material, In2O3 still faces great challenges in achieving high-performance hydrogen sensing at low temperatures due to inherent defects such as low specific surface area and limited active sites. Here, we prepared Ni-doped In2O3 mesoporous nanomaterials using metal organic framework (MOF) as sacrificial templates. The prepared Ni-doped In2O3 exhibited mesoporous structures with small diameters (2–3 nm), which could be used as effective molecular sieves for the selective detection of hydrogen. The high specific surface area (72.0 m²/g) provided numerous active sites for surface redox reactions, resulting in a remarkable hydrogen sensing response. Notably, gas sensitivity tests demonstrated that the Ni-doped In2O3 gas sensor achieved optimal hydrogen detection performance at 100°C. Particularly, the 3 at% Ni-doped In2O3 nanomaterials exhibited a high response of 12.5–100 ppm hydrogen, along with favorable response/recovery time and a practical limit of detection down to 5 ppm. Furthermore, the Ni-doped In2O3 sensor exhibited excellent reversibility, reproducibility, selectivity and long-term stability. In addition, simulation calculations based on molecular dynamics (MD) and density functional theory (DFT) show that molecular sieve effect is the main reason for the enhanced selectivity, and Ni doping induced oxygen vacancy (OV) defects could enhance gas adsorption. This work could provide technical support for leak detection and warning during hydrogen use, storage and transportation in the future.

Dual-Mode Comb Plasmonic Optical Fiber Sensing.

Surface plasmon (SP) excitation in metal-coated tilted fiber Bragg gratings (TFBGs) has been a focal point for highly sensitive surface biosensing. Previous efforts focused on uniform metal layer deposition around the TFBG cross section and temperature self-compensation with the Bragg mode, requiring both careful control of the core-guided light polarization and interrogation over most of the C + L bands. To circumvent these two important practical limitations, we studied and developed an original platform based on partially coated TFBGs. The partial metal layer enables the generation of dual-comb resonances, encompassing highly sensitive (TM/EH mode families) and highly insensitive (TE/HE mode families) components in unpolarized transmission spectra. The interleaved comb of insensitive modes acts as wavelength and power references within the same spectral region as the SP-active modes. Despite reduced fabrication and measurement complexity, refractometric accuracy is not compromised through statistical averaging over seven individual resonances within a narrowband window of 10 nm. Consequently, measuring spectra over 60 nm is no longer needed to compensate for small temperature or power fluctuations. This sensing platform brings the following important practical assets: (1) a simpler fabrication process, (2) no need for polarization control, (3) limited bandwidth interrogation, and (4) maintained refractometric accuracy, which makes it a true game changer in the ever-growing plasmonic sensing domain.