Miracle-dz/UniFAR-FTUs huggingface dataset:
A Functional Architecture for 6G Special-Purpose Industrial IoT Networks

Future industrial applications will encompass compelling new use cases requiring stringent performance guarantees over multiple key performance indicators, such as reliability, dependability, latency, time synchronization, security, etc. Achieving such stringent and diverse service requirements necessitates the design of a <italic>special-purpose Industrial-Internet-of-Things (IIoT) network</italic> comprising a multitude of specialized functionalities and technological enablers. This article proposes an innovative architecture for such a special-purpose sixth generation (6G) IIoT network incorporating seven functional building blocks categorized into <italic>special-purpose functionalities</italic> and <italic>enabling technologies</italic>. The former consists of <italic>Wireless Environment Control</italic>, <italic>Traffic/Channel Prediction</italic>, <italic>Proactive Resource Management,</italic> and <italic>End-to-End Optimization</italic> functions, whereas the latter includes <italic>Synchronization and Coordination</italic>, <italic>Machine Learning and Artificial Intelligence Algorithms</italic>, and <italic>Auxiliary Functions</italic>. The proposed architecture aims at providing a resource-efficient and holistic solution for the complex and dynamically challenging requirements imposed by future 6G industrial use cases. Selected test scenarios are provided and assessed to illustrate cross-functional collaboration and demonstrate the applicability of the proposed architecture in a wireless IIoT network.
An Embedded System for Acoustic Data Processing and AI-based Real-Time Classification for Road Surface Analysis

The current roadway monitoring is expensive and not systematic. This paper proposes a new system able to evaluate the pavement quality of road infrastructure. The embedded system records and processes the acoustic data of the wheel-road interaction and classifies in real-time roadways’ health thanks to integrated AI solutions. The measurements to produce the dataset to train a convolutional neural network (CNN) were collected using a vehicle operating at different cruise speeds in the area of Pisa. The dataset is composed by acoustic data belonging to several typologies of roads: dirty or grass roads, high roughness surfaces and roads with cracks or potholes. The raw audio signals were split, labelled, and converted into images by calculating the Mel spectrogram. Finally, the authors designed a tiny CNN with a size equal to 18 kB able to classify between four different classes: good quality road, ruined road, silence and unknown. The CNN architecture achieves an accuracy of about 93% on the original model and 90% on the quantized one. Quantization permits to convert the final architecture into a suitable form to be deployed on a low-complex embedded system integrated in the tyre cavity. In addition, a custom board was designed to act as IoT node thanks to a Bluetooth Low Energy communication towards smartphones and/or car infotainment systems. These systems, featured with GPS, guarantee to obtain real-time maps service of road quality. At authors’ knowledge, this is the first real-time and fully integrated solution at the state of the art for road pavement quality analysis and classification on acoustic data.
Stemming the Great Resignation through Leadership Development

The recent rise in voluntary turnover has sparked a renewed focus on attracting and retaining talent. In their attempts to stem the tide of the Great Resignation, organizations are augmenting traditional retention strategies – e.g., higher pay, enhanced benefits, more opportunities for career advancement etc. – with remote and hybrid work schedules brought about by the COVID pandemic. Given its inherent appeal to both employees and organizations, leadership development (LD) opportunities have long been believed to play a crucial role in helping to attract and retain employees. While the body of existing correlational research does point to a positive relationship between LD opportunities and retention, there is scant research that elucidates the mechanism(s) that may help to bridge the two. Guided by existing research, this paper examined Center for Creative Leadership’s large database of program evaluation data to uncover those potential mechanisms. We found preliminary support for three specific outcomes of leadership development that may serve as potential mediators linking LD with retention; they include enhanced self-efficacy, meaningful connections, and capacity to engage followers. We conclude with implications of these findings for future research as well as some caveats related to our investigation.
Mixed-Input Bayesian Optimization Method for Structural Damage Diagnosis

Structural health monitoring (SHM) is of significant importance in the operation of engineering systems to ensure the durability and reliability. In this article, we introduce a Bayesian optimization method using a multioutput Gaussian process to solve the structural fault diagnosis problem. This method utilizes a high fidelity finite element model (FE) of the structure and the impedance/admittance measurements from the structure to identify the location and severity of the damage. The method improves the accuracy of the damage diagnosis by adopting a multioutput Gaussian process as the surrogate model for the full FE model and Thompson sampling approach is used to guide the search for the structural damage in the Bayesian optimization. The detailed algorithms are presented, and the convergence analysis of the method is conducted. We apply our proposed method on simulated synthetic functions and it achieves better performance and higher convergence speed than the traditional mixed input optimization methods. We then apply our method on a real world structural damage identification problem using measured piezoelectric admittance data and illustrate the effectiveness of the proposed method.
Can Interpretable Reinforcement Learning Manage Prosperity Your Way?

Personalisation of products and services is fast becoming the driver of success in banking and commerce. Machine learning holds the promise of gaining a deeper understanding of and tailoring to customers’ needs and preferences. Whereas traditional solutions to financial decision problems frequently rely on model assumptions, reinforcement learning is able to exploit large amounts of data to improve customer modelling and decision-making in complex financial environments with fewer assumptions. Model explainability and interpretability present challenges from a regulatory perspective which demands transparency for acceptance; they also offer the opportunity for improved insight into and understanding of customers. Post-hoc approaches are typically used for explaining pretrained reinforcement learning models. Based on our previous modeling of customer spending behaviour, we adapt our recent reinforcement learning algorithm that intrinsically characterizes desirable behaviours and we transition to the problem of prosperity management. We train inherently interpretable reinforcement learning agents to give investment advice that is aligned with prototype financial personality traits which are combined to make a final recommendation. We observe that the trained agents’ advice adheres to their intended characteristics, they learn the value of compound growth, and, without any explicit reference, the notion of risk as well as improved policy convergence.
Development of seismic fragility curves for mainshock-damaged reinforced-concrete structures

This communication presents an original procedure to derive fragility curves for previously damaged structural systems. These damage state-dependent functions constitute an important component in the framework of time-dependent risk assessment. For instance, they are used to estimate the updated vulnerability of structures exposed to potential aftershocks, based on the knowledge of their damage state after the main seismic event. While the state-of-the-art method for deriving such functions relies on incremental dynamic analysis, the present work proposes an alternative approach based on (i) the application of sequences of ground-motion records to the undamaged structure in order to have a representative panel of structures in each damage state, and (ii) an innovative statistical treatment of the permanent residual drift of damaged structures Δ_(p,t0) to derive the fragility functions: the maximum transient additional drift (Δ_(t,max)-Δ_(p,t0)) is correlated to the intensity measure of the ground motion, and the influence of the initial permanent drift is then eliminated by estimating its distribution for a given initial damage state. The developed procedure is applied to a single story reinforced-concrete frame with a concentrated plasticity model and fragility curves are proposed for various initial damage states.
ANALISIS FAKTOR INTERNAL PERUBAHAN KURS RUPIAH TERHADAP DOLLAR AMERIKA DENGAN MENGGUNAKAN MULTIPLE REGRESSION ANALYSIS INSTRUMENT WITH ERROR CORRECTION MODEL (ECM)

Tujuan dari penelitian ini adalah untuk menganalisis pengaruh jumlah uang beredar, inflasi, suku bunga SBI, dan impor untuk nilai tukar Indonesia Rupiah terhadap Dolar AS. Dalam analisis, digunakan beberapa analisis regresi instrumen dengan Model Error Correction Model (ECM). Dengan persamaan metode diperoleh ini regresi di jangka panjang dan jangka pendek keseimbangan. Dalam model ekuilibrium jangka panjang, tertutup serangkaian proses penyesuaian yang membawa setiap kejutan bagi keseimbangan. Dengan kata lain, dalam jangka panjang sangat mungkin dilakukan penyesuaian penuh untuk setiap perubahan yang timbul. Hasil estimasi dari ECM regresi dan analisis jangka panjang menunjukkan bahwa variabel inflasi, suku bunga SBI, dan impor memiliki pengaruh yang signifikan dengan arah positif untuk bertukar tingkat. Sedangkan variabel Jumlah Uang yang Beredar (JUB) memiliki pengaruh dengan arah negatif terhadap nilai tukar.
Clustering : it's impact on the competitiveness of small manufacturers in the clothing industry in Port Elizabeth

This research has aimed to explore the impact of clustering on the ability of small manufacturers in the clothing industry in Port Elizabeth to enhance their competitiveness. The research study was conducted in two stages, which included both qualitative and quantitative research methods. Firstly, an exploratory qualitative study using the Delphi method was employed to identify the key problems currently being experienced by small manufacturers in the clothing industry. Finally, the application of a structured questionnaire to small manufacturers in the clothing industry followed, exploring the awareness of and interest in clustering as a strategy for enhancing competitiveness. A key observation to emerge from the qualitative research is that significant differences were evident between the responses of academics, professionals in small business development and small business owners. An analysis of the problems experienced by small manufacturers in the clothing industry revealed that high labour costs and low I productivity were the frontrunners. The lack of knowledge was cited as the most important problem preventing the wide scale implementation of horizontal clustering. Another key observance to emerge from the quantitative study is the fact that very few significant differences are evident between the two groups viz. small business owners and experts. The research indicated that although in the minority, horizontal linkages exist between small clothing manufacturers in Port Elizabeth. The results indicated furthermore that the majority of small clothing manufacturers in Port Elizabeth are
Why Do We Find These Effects? An Examination of Mediating Pathways Explaining the Effects of School Turnaround

Abstract Mixed results from evaluations of school reform suggest a need for evidence to explain why some models succeed while others fail. Addressing that need, this study uses structural equation modeling to estimate difference-in-differences models that examine mediating mechanisms for positive effects produced by Innovation Zone (iZone) reforms in Memphis, Tennessee. I find that iZone schools increased peer collaboration between teachers, which resulted in improved student achievement. Also, recruiting effective teachers led to a more positive learning environment and ultimately to improved student achievement. These results highlight peer collaboration, a positive learning environment, and the recruitment of effective educators as important practices that will likely facilitate improved school performance under future school reform plans.
Model‐based robust control design and experimental validation of collaborative industrial robot system with uncertainty

Based on the traditional PID control and robust control algorithm, a novel practical robust control method is designed for the 6‐DOF collaborative industrial robot with uncertainty. The proposed algorithm consists of a robust term and a model‐based PD control term, which we call MPDP controller. It is demonstrated by Lyapunov theoretical analysis that the algorithm is able to guarantee uniform boundedness and uniform ultimate boundedness of the system. Simulations and experiments show the good performance of MPDP control in a robot with smaller steady‐state tracking errors and better robustness compared to PID controllers.
Enterprise Information Security Management Using Internet of Things Combined with Artificial Intelligence Technology

This work is conducted to deal with the information security of enterprise management under the background of current global informatization, popularize the modern Internet of Things (IoT) management technology of enterprises, and maintain the information security of enterprises and provide modern upgrading means for enterprise management. In this work, it firstly introduces the application scenarios of current Internet and Artificial Intelligence (AI) technology and expounds the IoT technology. Secondly, the enterprise management platform is designed, the requirements of enterprise modern management are analyzed, and then the design requirements of system functions and the design of the information security architecture of the IoT are proposed. Furthermore, an enterprise information security management platform is designed, which covers four parts: IoT data mining management, equipment management, key management, and database management. In addition, the performance of the security management platform is tested from four parts: concurrency testing, stress testing, large data volume testing, and security testing. The research results show that the IoT-based enterprise information security management platform designed in this work under the background of AI has perfect functions and stable performance of each module. Concurrency testing, stress testing, large data volume testing, and stability testing are performed on it, and the success rate of the platform in each task reaches 100%. The average response time of concurrent testing and stress testing is about 0.13 seconds, and that of the event entry events is 0.25 seconds. The central processing unit (CPU) occupancy rate in each monitoring task is always lower than 20%. Therefore, it is determined that the performance of the IoT-based enterprise information security management platform designed in this work is sufficient to meet the daily management of enterprises. This work can provide a guarantee for enterprise information security management using AI technology, setting an example for future related research.
Export Enhancing Effects of Information and Communication Technologies: Evidence from Bangladesh

The study examines the impact of information and communications technology (ICT) on bilateral export flows from Bangladesh to its trading partners based on an augmented panel gravity model. It includes 108 importers of Bangladeshi goods. The sample period extends from 2000 to 2018. Primarily, we employ Pooled Ordinary Least Square (POLS) model, and we utilize Poisson Pseudo Maximum Likelihood (PPML) as an alternative estimation technique to check robustness. The findings from the OLS model indicate that ICT has positive and significant effects on Bangladesh’s bilateral export. The results from PPML also support the findings from the OLS model. Therefore, the results come out to be robust. These findings imply that by reducing trade-related costs e. g. , shipping costs, market access costs, and communication and information costs, ICT growth in Bangladesh as well as in its trading partner countries enhances trade flows and therefore has a trade-boosting effect.
Peroxynitrite Mediated Oxidations: Spin Trap Studies; Nitration and Hydroxylation of Phenol Result From Different Intermediates; CO(2) Catalyzes the Decomposition of Peroxynitrite.

In the firs t project the spin trap DMPO was used to probe for the production o f hydroxyl radicals from peroxynitrite. Peroxynitrite reacts w ith DMPO to give ‘DMPO-OH, suggesting that 'DMPO-OH results from a reaction between DMPO and HOONO or between DMPO and an activated intermediate of HOONO. Thiols produce a large increase in the ‘DMPOOH signal intensity. Experiments employing SOD suggest that this increase results from the decomposition o f ‘DMPO-OOH. These results are incompatible with the decomposition o f peroxynitrite to form ‘OH but suggest that an intermediate of HOONO is produced that Is less reactive and more selective than the hydroxyl radical. In the second project, we have examined the formation of hydroxyphenols, nitrophenols, and the minor products 4-nitrosophenol, benzoquinone, 2,2'-biphenol, and 4,4'-biphenol from the reaction of peroxynitrite w ith phenol w ith and w ithou t added carbonate. W ithout added carbonate, both the yields and rates of formation of nitrophenols and hydroxyphenols have different pH profiles. The sum of the rate constants for nitration and hydroxylation is identical to the rate constant for the spontaneous decomposition of peroxynitrite. When carbonate is added, hydroxylation is inhibited, whereas the rates of formation and
Research on low illumination coal gangue image enhancement based on improved Retinex algorithm

ABSTRACT Aiming at the problems of coal and gangue image recognition in low illumination or high dust concentration environment, an adaptive enhancement method based on improved Retinex algorithm, is proposed: H-GF-MSR algorithm. The algorithm is processed in HSV color space, uses guided filter for multi-scale Retinex algorithm in brightness component, and carries out adaptive saturation stretching in saturation component. Then the images are processed by histogram equalization, and the two algorithms are fused to improve the accuracy. Compared with SSR algorithm, MSRCR algorithm and other classical algorithms, this algorithm is improved in suppressing the generation of halo and image edge enhancement. The experimental results show that in terms of image evaluation indexes such as information entropy, image mean, image average gradient and peak signal-to-noise ratio, compared with SSR algorithm, the algorithm improves by 5.4%, 41.1%, 33.2% and 0.4% respectively; compared with MSRCR algorithm, the average improvement is 2.9%, 32.4%, 31.7% and 0.7% respectively. Through the enhancement experiments of coal gangue image in the illumination range of 10 lux ~ 200 lux, the algorithm has better enhancement effect. It can effectively suppress the bright light and restore the original details of the object.
Informal Caregivers’ Attitude Toward Dementia: The Impact of Dementia Knowledge, Confidence in Dementia Care, and the Behavioral and Psychological Symptoms of the Person with Dementia. A Cross-Sectional Study

Background: Dementia is rapidly increasing worldwide due to demographic aging. More than two-thirds of patients are cared by family members. The quality of care depends on the caregivers’ attitude toward dementia influencing patient care decisions. Objective: The aim of this study is to examine the factors that influence the caregivers’ attitude and whether there is an association between participation in a psycho-educational program and attitude. Methods: We performed a cross-sectional study using a structured closed-ended questionnaire to retrieve socio-demographic information from caregivers and the persons with dementia (N = 86). The study included validated scales such as the Dementia Attitude Scale, the Dementia Knowledge Assessment Tool 2, the Positive Aspects of Caregiving, the Zarit Burden Interview, the Confidence in Dementia Scale, and Spielberger’s State-Trait Anxiety Inventory, as well as a neuropsychological battery to assess the condition of people with dementia. Results: Our final model explains 55.6% of the total variance and shows a significant correlation of five factors with attitude toward dementia: confidence, behavioral and psychological symptoms of dementia, anxiety as a trait, positive aspects of caregiving, and dementia knowledge. The caregivers who participated in a psycho-educational program showed a significantly more positive attitude toward dementia, better dementia knowledge, higher confidence in dementia care, and lower anxiety as a state. Conclusion: The strong correlation of attitude and knowledge, as well as confidence in dementia care, supports the tripartite model of attitude, which hypothesizes the interrelation of affect, cognition, and behavior.
Medical rescuers’ occupational health during COVID-19: Contribution of coping and emotion regulation on burnout, trauma and post-traumatic growth

The COVID-19 pandemic places unique challenges to medical rescuers’ occupational health. Thus, it is crucial to assess its direct and indirect impacts on key psychological outcomes and adaptation strategies. This study aims to analyse the impact of this pandemic on medical rescuers’ coping and emotion regulation strategies, and their levels of work-related psychological outcomes, such as burnout, trauma and post-traumatic growth. Additionally, it aims to analyse the contribution of coping and emotion regulation strategies, employed to manage the COVID-19 pandemic, on burnout, trauma and post-traumatic growth. A sample of 111 medical rescuers answered the Brief Cope, Emotion Regulation Questionnaire, Oldenburg Burnout Inventory, Impact of Event Scale-Revised and Post-Traumatic Growth Inventory. Medical rescuers resorted moderately to coping and emotion regulation strategies, since the beginning of COVID-19. They presented moderate burnout and post-traumatic growth and low trauma. Coping presented a higher weight on burnout, trauma and post-traumatic growth, than emotion regulation. Expressive suppression and dysfunctional coping predicted burnout and trauma, and problem and emotion-focused coping predicted post-traumatic growth. Dysfunctional coping mediated and, thus, exacerbated the effect of expressive suppression on burnout and on trauma. Practitioners should pay closer attention to professionals with higher burnout and trauma. Occupational practices should focus on reducing dysfunctional coping and expressive suppression and promoting problem-focused coping.
Maternal and Neonatal Outcomes of Adolescent Pregnancy: A Narrative Review

Adolescent pregnancy is the pregnancy of girls aged 10-19 years, leading to many maternal and neonatal adverse effects. These pregnancies have been a global concern for many decades and yet are still prevailing. This article has reviewed the significant determinants of adolescent pregnancy and various maternal adverse effects, including preeclampsia, preterm premature rupture of the membrane (PPROM), maternal anemia, sexually transmitted diseases, postpartum depression, and maternal deaths, and adverse neonatal outcomes, including low birth weight (LBW), prematurity, stillbirths, early neonatal demise, and low Apgar score. Various pathophysiologic events that lead to such adverse consequences have been briefly discussed in the article and how such occurrences can be overcome. This article has also emphasized the need to implement various modalities such as sex education, availability of contraceptives, and bringing community-level awareness to lower the prevalence of adolescent pregnancy.