Miracle-dz/UniFAR-FTUs huggingface dataset:
Indoor Volatile Organic Compounds: Concentration Characteristics and Health Risk Analysis on a University Campus

Volatile organic compounds (VOCs) are major indoor air pollutants that contain several toxic substances. However, there are few studies on health risk assessments of indoor VOCs in China. This study aimed to determine the concentration characteristics of VOCs on college campuses by collecting VOC samples from different locations on campus during different seasons combined with the exposure times of college students in each location obtained from a questionnaire survey to assess the possible health risks. The highest total VOC concentration (254 ± 101 µg/m3) was in the dormitory. The seasonal variation of TVOC concentrations was related to the variation of emission sources in addition to temperature. Health risk assessments of VOCs were evaluated using non-carcinogenic and carcinogenic risk values, represented by hazard quotient (HQ) and lifetime cancer risk (LCR), respectively. The non-carcinogenic risks at all sampling sites were within the safe range (HQ < 1). Dormitories had the highest carcinogenic risk, whereas the carcinogenic risk in the other three places was low (with LCR < 1.0 × 10−6). Moreover, 1,2-dichloroethane was identified as a possible carcinogenic risk substance in the dormitory due to its high LCR (1.95 × 10−6). This study provides basic data on health risks in different locations on campus and a basis for formulating measures to improve people’s living environments.
The Influence Of Talent Acquisition And Talent Pool On The Leadership Readiness Mediated By Perceived Organization Support

Human resource management plays a great role within a company. The main role is to prepare capable and relevant people to run and operate the company's business activities. Gaps that often exist in human resources management and expected leaders in the future trigger a serious problem in the company and may cause momentum loss, so there should be more preparations needed for the regeneration of leaders in the future. This research is conducted to identify the infuence of talent acquisition and talent pool on leadership readiness, as well as to examine perceived organization support as a mediation of the relationship between talent acquisition and talent pool on leadership readiness. This study takes the data from 57 branch offices of state-owned insurance companies using stratified sampling. The results showed that talent acquisition had a significant effect on leadership readiness, with a coefficient value of 0.539 and a statistical t-value of 9.926 (>t critical 1.96). The influence of talent pool on leadership readiness produces a coefficient value of 0.566 and a statistical t value of 10,567 which means it has a significant effect. Furthermore, the influence of organizational support on leadership readiness produces a coefficient value of 0.482 and a statistical t value of 8.545 which means it has a significant effect. Meanwhile, perceived organizational support significantly mediates the relationship between talent and talent pool on leadership readiness, with Z values on the Sobel test of 6.77 and 7.931 (>z table 1.960). These factors can help the company to conduct a mapping to prepare cadres to fill positions as leaders in the future.
Global boundedness and decay for a multi-dimensional chemotaxis-haptotaxis system with nonlinear diffusion

This paper deals with a parabolic-elliptic-ODE chemotaxis-haptotaxis system with nonlinear diffusion \begin{eqnarray*}\label{1a} \left\{ \begin{split}{} &u_t=\nabla\cdot(\varphi(u)\nabla u)-\chi\nabla\cdot(u\nabla v)-\xi\nabla\cdot(u\nabla w)+\mu u(1-u-w), \\ &0=\Delta v-v+u, \\ &w_{t}=-vw, \end{split} \right. \end{eqnarray*} under Neumann boundary conditions in a smooth bounded domain $\Omega\subset \mathbb{R}^{n}$ $(n\geq1)$, where $\chi$, $\xi$ and $\mu$ are positive parameters and $\varphi(u)$ is a nonlinear diffusion. Under the non-degenerate diffusion and some suitable assumptions on positive parameters $\chi,\xi,\mu$, it is shown that the corresponding initial boundary value problem possesses a unique global classical solution that is uniformly bounded in $\Omega\times(0,\infty)$. Moreover, under the degenerate diffusion, it is proved that the corresponding problem admits at least one nonnegative global bounded-in-time weak solution. Finally, for the suitably small initial data $w_{0}$, we give the decay estimate of $w$.
Interval Load Forecasting for Individual Households in the Presence of Electric Vehicle Charging

The transition to Electric Vehicles (EV) in place of traditional internal combustion engines is increasing societal demand for electricity. The ability to integrate the additional demand from EV charging into forecasting electricity demand is critical for maintaining the reliability of electricity generation and distribution. Load forecasting studies typically exclude households with home EV charging, focusing on offices, schools, and public charging stations. Moreover, they provide point forecasts which do not offer information about prediction uncertainty. Consequently, this paper proposes the Long Short-Term Memory Bayesian Neural Networks (LSTM-BNNs) for household load forecasting in presence of EV charging. The approach takes advantage of the LSTM model to capture the time dependencies and uses the dropout layer with Bayesian inference to generate prediction intervals. Results show that the proposed LSTM-BNNs achieve accuracy similar to point forecasts with the advantage of prediction intervals. Moreover, the impact of lockdowns related to the COVID-19 pandemic on the load forecasting model is examined, and the analysis shows that there is no major change in the model performance as, for the considered households, the randomness of the EV charging outweighs the change due to pandemic.
Reducing Weaning Stress in Piglets by Pre-Weaning Socialization and Gradual Separation from the Sow: A Review

Simple Summary In commercial pork production, piglets are weaned at a fairly young age. Piglets are removed from the sow and are subject to sudden changes in their diet, environmental conditions and social grouping. For the piglet, this causes major upheaval and disruption to eating and behavioral patterns, leading to distress, gastrointestinal tract dysfunction and behavioral disorders after weaning. This contrasts with natural conditions where nest-leaving of the sow and her offspring and joining the herd with increasing interactions with non-littermate piglets occurs between approximately 6.5 and 15 days after birth, with weaning typically completed between 14 and 18 weeks of age. Strategies that could be adapted to a commercial setting and that allow socialization of non-littermate piglets before weaning and gradual separation of the litter from the sow in the period leading up to weaning may reduce the stress occurring after weaning and improve performance, welfare and gastrointestinal function. This review summarizes current knowledge concerning such strategies. Abstract The weaning of pigs in most commercial pork production systems is an abrupt event performed at a fairly young age, i.e., mostly between 2.5 and 5 weeks of age. This practice induces a stress response, and its impact on behavior, performance and the gastrointestinal tract has been well described. Historically, there has been a focus on pre- and post-weaning nutritional strategies and post-weaning housing conditions and medication to improve production and reduce mortality after weaning. However, alternative pre-weaning housing and management systems that promote the development of natural social behaviors of piglets before weaning have recently received more attention. Co-mingling of non-littermates before weaning is a strategy that aims to initiate social interactions prior to weaning. The separation of the litter from the sow in the period leading up to weaning, termed intermittent suckling, aims to enhance the gradual separation from the sow. In addition, these practices encourage the young pig to learn explorative nutrient sourcing. Altogether, they may reduce weaning-associated stress. In this review, these strategies are defined, and their effects on behavior, performance, mortality, gastrointestinal function and immunocompetence are described. Though these strategies may be adapted to a commercial setting, it also becomes clear that many factors can contribute to the success of these strategies.
Development in Shortwave Infrared Focal Plane Array and Application

Some conceptions and definitions of Short Wavelength Infrared(SWIR) Focal Plane Array(FPA) are presented briefly.The internal and international development in SWIRFPA field are described in detail in materials and technics.Some HgCdTe and InGaAs SWIRFPA products are shown.The directions of internal and international research are described.Finally,several typical applications of SWIRFPA are introduced.
CFMA (Compute-Forward Multiple Access) and its Applications in Network Information Theory

While both fundamental limits and system implementations are well understood for the point-to-point communication system, much less is developed for general communication networks. This thesis contributes towards the design and analysis of advanced coding schemes for multi-user communication networks with structured codes. The first part of the thesis investigates the usefulness of lattice codes in Gaussian networks with a generalized compute-and-forward scheme. As an application, we introduce a novel multiple access technique --- Compute-Forward Multiple Access (CFMA), and show that it achieves the capacity region of the Gaussian multiple access channel (MAC) with low receiver complexities. Similar coding schemes are also devised for other multi-user networks, including the Gaussian MAC with states, the two-way relay channel, the many-to-one interference channel, etc., demonstrating improvements of system performance because of the good interference mitigation property of lattice codes. As a common theme in the thesis, computing the sum of codewords over a Gaussian MAC is of particular theoretical importance. We study this problem with nested linear codes, and improve upon the currently best known results obtained by nested lattice codes. Inspired by the advantages of linear and lattice codes in Gaussian networks, we make a further step towards understanding intrinsic properties of the sum of linear codes. The final part of the thesis introduces the notion of typical sumset and presents asymptotic results on the typical sumset size of linear codes. The results offer new insight to coding schemes with structured codes.
Optimizing Research on K-means Based on Data Partition

The K-means clustering algorithm is one kind of mainstream iterative drop clustering algorithm,which restrains in the partial optimized state.Because K-means randomly selects initial clustering center,which result in the result of clustering is obviously fluctuate along with the initial input.Thus this paper adopts the pretreatment way to select the initial clustering center.First under one kind of norm,calculate out the farthest distance,then use the method of data partition to divide the data set into k section and select a center in each section.The experiment demonstrates the optimizing K-means eliminate the initial input influence,effectively reduced the iteration number of times and clustering error.
Inflammation: The Link between Neural and Vascular Impairment in the Diabetic Retina and Therapeutic Implications

The etiology of diabetic retinopathy (DR) is complex, multifactorial and compromises all the elements of the retinal neurovascular unit (NVU). This diabetic complication has a chronic low-grade inflammatory component involving multiple inflammatory mediators and adhesion molecules. The diabetic milieu promotes reactive gliosis, pro-inflammatory cytokine production and leukocyte recruitment, which contribute to the disruption of the blood retinal barrier. The understanding and the continuous research of the mechanisms behind the strong inflammatory component of the disease allows the design of new therapeutic strategies to address this unmet medical need. In this context, the aim of this review article is to recapitulate the latest research on the role of inflammation in DR and to discuss the efficacy of currently administered anti-inflammatory treatments and those still under development.
Minimal Fat Content in Papillary Renal Cell Carcinoma Diagnosed with Dual-Layer Dual-Energy CT

A 56-year-old man with a previous right nephrectomy for multiple papillary renal cell carcinomas (pRCC) underwent a follow-up CT scan. Using a dual-layer dual-energy CT (dlDECT), we demonstrated the presence of a small amount of fat in a 2.5 cm pRCC that mimicked the diagnosis of angiomyolipoma (AML). Histological examination demonstrated the absence of macroscopic intratumoral adipose tissue, showing a fair amount of enlarged foam macrophages loaded with intracytoplasmic lipids. The presence of fat density in an RCC is an extremely rare occurrence in the literature. To our knowledge, this is the first description using dlDECT of a minimal amount of fat tissue in a small RCC due to the presence of tumor-associated foam macrophages. Radiologists should be aware of this possibility when characterizing a renal mass with DECT. The option of RCCs must be considered, especially in the case of masses with an aggressive character or a positive history of RCC.
Ultrasonic array imaging of multilayer structures using full matrix capture and extended phase shift migration

Multilayer structures have been widely used in industrial fields, and non-destructive evaluation of these structures is of great importance to assure their quality and performance. Recently, ultrasonic array imaging using full matrix capture, e.g. the total focusing method (TFM), has been shown to increase sensitivity to small defects and improve imaging resolution in homogeneous media. However, it cannot be applied to multilayer structures directly, due to the sound velocity variation in different layers and because refraction occurs at layer interfaces, which gives rise to difficulties in determining the propagation path and time. To overcome these problems, an extended phase shift migration (EPSM) is proposed for the full matrix imaging of multilayer structures in this paper. Based on the theory of phase shift migration for monostatic pulse-echo imaging, full matrix imaging using EPSM is derived by extrapolating the wavefields in both transmission and reception, and extended to the multilayer case. The performance of the proposed algorithm is evaluated by full matrix imaging of a two-layer structure with side-drilled holes conducted both in the simulation and the experiment. The results verify that the proposed algorithm is capable of full matrix imaging of a layered structure with a high resolution and signal-to-noise ratio. For comparison, full matrix imaging using the TFM with root-mean-squared velocity is also performed, and the results demonstrate that the proposed algorithm is superior to the TFM in improving both the image quality and resolution.
Distributed torsion sensor based on cascaded coaxial cable Fabry–Perot interferometers

Cascaded coaxial cable Fabry–Perot interferometers (FPI) are studied and demonstrated for distributed torsion measurement. Multiple weak reflectors are implemented on a coaxial cable so that any two consecutive reflectors can form a Fabry–Perot cavity. By fixing the cable sensor in a helical form on a shaft, the distributed torsion of the shaft can be measured by the cascaded Fabry–Perot cavities. A test on a single section shows that the sensor has a linear response with a sensitivity of 1.834 MHz (rad/m)−1 in the range of twisted rate from 0 to 8.726 rad m−1. The distributed torsion sensing capability is useful in drilling process monitoring, structure health monitoring and machine failure detection.
PERAN DIGITAL SKILL DAN WORKFORCE TRANSFORMASTION TERHADAP KINERJA UMKM

Business revival in the new normal era is currently the main focus for Micro, Small and Medium Enterprises (MSMEs) after nearly a year of experiencing a downturn during the pandemic. Maintaining stable performance in the new normal era is currently the main strategy for MSMEs which have a strategic role in improving the national economy. MSME actors face many problems including limited business capital, business licensing, lack of innovation, and not a few MSMEs are still stuttering about digital technology today. Therefore, the contribution of various parties is needed in an effort to improve its performance. This study aims to develop a model for improving the performance of MSMEs through digital skills, workforce transformation and digital transformation as interventions. This digital change is urgently needed for MSMEs in increasing their readiness to face the current pandemic due to the limited space for MSMEs to move. Increasing digital transformation in MSMEs needs support from digital skills and workforce transformation from MSME human resources so that it will trigger an increase in MSME performance. This research was conducted using a survey method by distributing questionnaires to 250 MSMEs in Central Java. Analysis of research data was carried out by testing structural equation modeling (SEM) and using the variance approach (partial least squares), then data processing was supported by the SmartPLS application. The results show that increasing the performance of MSMEs can be improved through digital transformation. And in improving digital transformation through digital skill readiness and carrying out HR workforce transformation, so that MSMEs are more able to compete and adapt during a pandemic.
Robust H ∞ controller design for polynomial fuzzy control systems by sum-of-squares approach

This study presents the robust H ∞ control of polynomial fuzzy systems when model uncertainties and external disturbances are simultaneously taken into account. Using a non-quadratic Lyapunov function, two stability conditions were derived in the form of sum-of-squares (SOS). The proposed polynomial fuzzy controller possesses H ∞ robust performance against norm-bounded disturbances and uncertainties. Simulation results demonstrated the effectiveness of the proposed SOS-based stability conditions, which are more general than those of existing approaches.
FDTD analysis of nonlinear magnetic diffusion by reduced c

The finite-difference time-domain (FDTD) approach is a particularly useful method for investigating nonlinear electromagnetics because the nonlinear relationships may be directly incorporated into the equation for advancing the electromagnetic fields. This approach is applicable to nonlinear magnetic materials with or without hysteresis (i.e. time dependence). Problems with geometric details that are very small compared to signal wavelengths, however, stress FDTD techniques. This occurs because the Courant-limited /spl Delta/t can become small enough to require the use of >10/sup 11/ time steps unless one is tricky. Tricky means observing that this sort of problem has a solution which seldom depends on the speed of light c. Here, we demonstrate a procedure to elude the Courant stability condition by defining c to be 100 m/s. The present problem arose out of a magnetic diffusion FDTD application. The "reduced-c" technique should work, however, for any problem where /spl Delta/t is limited by /spl Delta/x/c which, in turn, is limited by the smallness of the problem geometry, not by a need for extremely fine temporal electromagnetic resolution. >
False Data Injection Attack and Corresponding Countermeasure in Multienergy Systems

Worldwide ambitions to combat climate change have expedited the penetration of renewables and decarbonization. Multienergy systems (MES), where different energy systems are optimally coordinated, have been recognized as a key element in future low-carbon energy operations. However, MES operations involve intense exchanges of information and control signals, thereby intensify the risk of cyberattacks. Existing cybersecurity research mainly focuses on a single-energy system, with only a few pioneering cybersecurity analyses for MES focusing on uncoordinated cyberattacks. A lack of detailed discussion and analysis on the optimally coordinated cyberattack targeting MES prevents operators from accurately evaluating the potential damages of cyberattack in MES operations. Therefore, this article first proposes an optimally coordinated false data injection attack (OC-FDIA) against MES, where attacks from different energy systems are coordinated to disturb the MES operation. Then, we show that the OC-FDIA can cause synergetic effects leading to much more severe damage than single-system FDIAs and uncoordinated FDIAs. Further, an effective countermeasure is developed to mitigate the OC-FDIA based on deep learning (DL). Eventually, the proposed OC-FDIA and its countermeasures are demonstrated through integrated electricity and gas test systems.
The somatomotor profile of U14 basketball players

Basketball is a team sport spread all over the world. In recent years, a lot of scientific research has been conducted and highlighted aspects regarding the improvement of physical condition as well as technical and tactical issues specific to the game of basketball. Physical training has become increasingly important in basketball, and players are stronger and more agile, even if their body dimensions are impressive. On the other hand, the game of basketball requires some rigour in motor but especially somatic terms. At international level, there are a number of somatic and motor standards according to which basketball players are selected, but no such scientific approach has been made in our country so far. This study aims to identify the somatic and motor profile of U14 basketball players according to the playing position and to compare the results with those of regional teams from Austria and Greece. The investigated sample consisted of 31 young basketball players, members of the extended team of the Romanian Basketball Federation. The methods used were: literature review, observation, tabular method, mathematical statistics and graphical method. The results show that both the anthropometric and physical test values analysed for each playing position are much weaker for Romanian players than for those from Greece and Austria.
Asymmetric effects of inflation rate changes on the stock market index: The case of Indonesia

Over the years, macroeconomic fundamentals and the stock market were found to have symmetrical relationship in numerous scientific investigations. These fundamentals provide crucial knowledge regarding stock price indices by providing forecasts for the future and information on the current status of the economy. This study employs a Nonlinear Autoregressive Distributed Lags (NARDL) model to fill in the research gap by estimating the asymmetric relationship between inflation and stock market from 1996 to 2020. The study suggests that inflation has a long-run and short-run asymmetric affect on the stock price, while both positive and negative inflation changes harm stock prices. As it reveals, the asymmetric impact of inflation on the stock market, this study can assist investors and businesses in making well-informed decisions that result in a more efficient allocation of resources, ultimately benefiting the economy. Additionally, policymakers can utilize these findings to design effective strategies for managing inflation, stabilizing prices, promoting economic growth, and ensuring financial market stability.