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In Paper I [H. Francisco, A. C. Cancio, and S. B. Trickey, J. Chem. Phys. 159, 214102 (2023)], we gave a regularization of the Tao–Mo exchange functional that removes the order-of-limits problem in the original Tao–Mo form and also eliminates the unphysical behavior introduced by an earlier regularization while essentially preserving compliance with the second-order gradient expansion. The resulting simplified, regularized (sregTM) functional delivers performance on standard molecular and solid state test sets equal to that of the earlier revised, regularized Tao–Mo functional. Here, we address de-orbitalization of that new sregTM into a pure density functional. We summarize the failures of the Mejía-Rodríguez and Trickey de-orbitalization strategy [Phys. Rev. A 96, 052512 (2017)] when used with both versions. We discuss how those failures apparently arise in the so-called z′ indicator function and in substitutes for the reduced density Laplacian in the parent functionals. Then, we show that the sregTM functional can be de-orbitalized somewhat well with a rather peculiarly parameterized version of the previously used deorbitalizer. We discuss, briefly, a de-orbitalization that works in the sense of reproducing error patterns but that apparently succeeds by cancelation of major qualitative errors associated with the de-orbitalized indicator functions α and z, hence, is not recommended. We suggest that the same issue underlies the earlier finding of comparatively mediocre performance of the de-orbitalized Tao–Perdew–Staroverov–Scuseri functional. Our work demonstrates that the intricacy of such two-indicator functionals magnifies the errors introduced by the Mejía-Rodríguez and Trickey de-orbitalization approach in ways that are extremely difficult to analyze and correct. [ABSTRACT FROM AUTHOR]

The revised, regularized Tao–Mo (rregTM) exchange-correlation density functional approximation (DFA) [A. Patra, S. Jana, and P. Samal, J. Chem. Phys. 153, 184112 (2020) and Jana et al., J. Chem. Phys. 155, 024103 (2021)] resolves the order-of-limits problem in the original TM formulation while preserving its valuable essential behaviors. Those include performance on standard thermochemistry and solid data sets that is competitive with that of the most widely explored meta-generalized-gradient-approximation DFAs (SCAN and r2SCAN) while also providing superior performance on elemental solid magnetization. Puzzlingly however, rregTM proved to be intractable for de-orbitalization via the approach of Mejía-Rodríguez and Trickey [Phys. Rev. A 96, 052512 (2017)]. We report investigation that leads to diagnosis of how the regularization in rregTM of the z indicator functions (z = the ratio of the von-Weizsäcker and Kohn–Sham kinetic energy densities) leads to non-physical behavior. We propose a simpler regularization that eliminates those oddities and that can be calibrated to reproduce the good error patterns of rregTM. We denote this version as simplified, regularized Tao–Mo, sregTM. We also show that it is unnecessary to use rregTM correlation with sregTM exchange: Perdew–Burke–Ernzerhof correlation is sufficient. The subsequent paper shows how sregTM enables some progress on de-orbitalization. [ABSTRACT FROM AUTHOR]

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Background and Study Aim. Biomechanics is a fundamental component of Kinesiology, offering critical insights into the mechanics of human movement. As the demand for comprehensive educational programs in Kinesiology grows, understanding the role and effectiveness of biomechanics within these programs becomes increasingly important. The aim of the study is to investigate and highlight the role and importance of biomechanics in the context of higher education, with an emphasis on the discipline of Kinesiology. Material and Methods. This study involved 71 students from the Department of Physical Education and Sport at Pitești University Center. It included 43 students specializing in Physical Education and Sport (PES) and 28 in Sports and Motor Performance (SMP). The research utilized the university's e-learning platform to monitor teaching and evaluation activities within the Kinesiology discipline. The evaluation of fundamental Kinesiology knowledge was assessed through various indicators. Google Forms-Questionnaire sought to gauge student opinions on the thematic content of Biomechanics as applied to Kinesiology and to evaluate the teaching staff. The nonparametric Kruskal-Wallis Test was employed to analyze differences in means across the groups. Results. The study revealed a diverse interest in sports across the Physical Education and Sport (PES) and Sports and Motor Performance (SMP) programs. Soccer, basketball, and handball were the most popular among students. Comparative analysis of academic performance showed no significant differences between the groups in various assessments (p>0.05). The overall student feedback on the "Fundamentals of Biomechanics of Physical Activities and Sports" course was predominantly positive. Over 50% of the students rated it as very good. Areas for improvement in teaching methods were also identified to enhance the academic experience. Conclusions. This study highlights the effective integration of biomechanics within the Kinesiology discipline at the higher education level. Findings suggest that well-structured educational programs can enhance students' understanding and application of biomechanics. This contributes positively to their academic and professional development in sports sciences. Continuous curriculum assessment and refinement are crucial for maintaining the relevance and effectiveness of such programs.

Purpose: [18F]MK-6240, a second-generation tau PET tracer, is increasingly used for the detection and the quantification of in vivo cerebral tauopathy in Alzheimer's disease (AD). Given that neurological symptoms are better explained by the topography rather than by the nature of brain lesions, our study aimed to evaluate whether cognitive impairment would be more closely associated with the spatial extent than with the intensity of tau-PET signal, as measured by the standard uptake value ratio (SUVr). Methods: [18F]MK6240 tau-PET data from 82 participants in the AD spectrum were quantified in three different brain regions (Braak ≤ 2, Braak ≤ 4, and Braak ≤ 6) using SUVr and the extent of tauopathy (EOT, percentage of voxels with SUVr ≥ 1.3). PET data were first compared between diagnostic categories, and ROC curves were computed to evaluate sensitivity and specificity. PET data were then correlated to cognitive performances and cerebrospinal fluid (CSF) tau values. Results: The EOT in the Braak ≤ 2 region provided the highest diagnostic accuracies, distinguishing between amyloid-negative and positive clinically unimpaired individuals (threshold = 9%, sensitivity = 79%, specificity = 82%) as well as between prodromal AD and preclinical AD (threshold = 38%, sensitivity = 81%, specificity = 93%). The EOT better correlated with cognition than SUVr (∆R2 + 0.08–0.09) with the best correlation observed for EOT in the Braak ≤ 4 region (R2 = 0.64). Cognitive performances were more closely associated with PET metrics than with CSF values. Conclusions: Quantifying [18F]MK-6240 tau PET in terms of EOT rather than SUVr significantly increases the correlation with cognitive performances. Quantification in the mesiotemporal lobe is the most useful to diagnose preclinical AD or prodromal AD. [ABSTRACT FROM AUTHOR]

The success and sustainability of U.S. EPA efforts to reduce, refine, and replace in vivo animal testing depends on the ability to translate toxicokinetic and toxicodynamic data from in vitro and in silico new approach methods (NAMs) to human-relevant exposures and health outcomes. Organotypic culture models employing primary human cells enable consideration of human health effects and inter-individual variability but present significant challenges for test method standardization, transferability, and validation. Increasing confidence in the information provided by these in vitro NAMs requires setting appropriate performance standards and benchmarks, defined by the context of use, to consider human biology and mechanistic relevance without animal data. The human thyroid microtissue (hTMT) assay utilizes primary human thyrocytes to reproduce structural and functional features of the thyroid gland that enable testing for potential thyroid-disrupting chemicals. As a variable-donor assay platform, conventional principles for assay performance standardization need to be balanced with the ability to predict a range of human responses. The objectives of this study were to (1) define the technical parameters for optimal donor procurement, primary thyrocyte qualification, and performance in the hTMT assay, and (2) set benchmark ranges for reference chemical responses. Thyrocytes derived from a cohort of 32 demographically diverse euthyroid donors were characterized across a battery of endpoints to evaluate morphological and functional variability. Reference chemical responses were profiled to evaluate the range and chemical-specific variability of donor-dependent effects within the cohort. The data-informed minimum acceptance criteria for donor qualification and set benchmark parameters for method transfer proficiency testing and validation of assay performance. [ABSTRACT FROM AUTHOR]

Ensuring safety for vehicle overtaking systems is one of the most fundamental and challenging tasks in autonomous driving. This task is particularly intricate when the vehicle must not only overtake its front vehicle safely but also consider the presence of potential opposing vehicles in the opposite lane that it will temporarily occupy. In order to tackle the overtaking task in such challenging scenarios, we introduce a novel integrated framework tailored for vehicle overtaking manoeuvres. Our approach integrates the theories of varying-level control barrier functions (CBF) and time-optimal model predictive control (MPC). The main feature of our proposed overtaking strategy is that it is safe-by-construction, which enables rigorous mathematical proof and validation of the safety guarantees. We show that the proposed framework is applicable when the opposing vehicle is either fully autonomous or driven by human drivers. To demonstrate our framework, we perform a set of simulations for overtaking scenarios under different settings. The simulation results show the superiority of our framework in the sense that it ensures collision-free and achieves better safety performance compared with the standard MPC-based approach without safety guarantees. [ABSTRACT FROM AUTHOR]

In recent years, the proliferation of vehicles on roadways, accompanied by an escalating demand for augmented safety and refined traffic management, has fueled substantial progress in the domains of computer vision and deep learning methodologies. This technological surge has precipitated ground-breaking applications, with real-time vehicle detection emerging as a linchpin. Capability to accurately detect vehicles in a real-time holds profound implications across diverse domains. In the context of autonomous driving, it underpins essential functions such as path planning, collision avoidance, and decision-making, essential for the safe operation of autonomous vehicles. In the arena of traffic monitoring, it empowers transportation authorities to acquire real-time insights into traffic dynamics, congestion patterns, and incident detection, thereby optimizing traffic flow and resource allocation strategies. Furthermore, within surveillance systems, real-time vehicle detection equips security personnel with the means to swiftly identify potential security threats, enhancing overall situational awareness and safety. This paper contributes to this dynamic landscape by introducing an innovative architecture expressly crafted for vehicle detection. Leveraging state-of-the-art computer vision techniques and deep learning methodologies, this architecture addresses the evolving challenges posed by contemporary traffic scenarios. Through the enhancement of real-time vehicle detection capabilities, our aim is to fortify the foundations of safety, efficiency, and security within vehicular contexts. The paper includes a fusion of AlexNet and Darknet accompanied by a comparative analysis. It is expected to deliver unmatched dependability and accuracy in real-time traffic monitoring, setting a new standard for performance in this application. [ABSTRACT FROM AUTHOR]

We provide new algorithms for two tasks relating to heterogeneous tabular datasets: clustering, and synthetic data generation. Tabular datasets typically consist of heterogeneous data types (numerical, ordinal, categorical) in columns, but may also have hidden cluster structure in their rows: for example, they may be drawn from heterogeneous (geographical, socioeconomic, methodological) sources, such that the outcome variable they describe (such as the presence of a disease) may depend not only on the other variables but on the cluster context. Moreover, sharing of biomedical data is often hindered by patient confidentiality laws, and there is current interest in algorithms to generate synthetic tabular data from real data, for example via deep learning. We demonstrate a novel EM-based clustering algorithm, MMM ("Madras Mixture Model"), that outperforms standard algorithms in determining clusters in synthetic heterogeneous data, and recovers structure in real data. Based on this, we demonstrate a synthetic tabular data generation algorithm, MMMsynth, that pre-clusters the input data, and generates cluster-wise synthetic data assuming cluster-specific data distributions for the input columns. We benchmark this algorithm by testing the performance of standard ML algorithms when they are trained on synthetic data and tested on real published datasets. Our synthetic data generation algorithm outperforms other literature tabular-data generators, and approaches the performance of training purely with real data. [ABSTRACT FROM AUTHOR]

Brain segmentation from neonatal MRI images is a very challenging task due to large changes in the shape of cerebral structures and variations in signal intensities reflecting the gestational process. In this context, there is a clear need for segmentation techniques that are robust to variations in image contrast and to the spatial configuration of anatomical structures. In this work, we evaluate the potential of synthetic learning, a contrast‐independent model trained using synthetic images generated from the ground truth labels of very few subjects. We base our experiments on the dataset released by the developmental Human Connectome Project, for which high‐quality images are available for more than 700 babies aged between 26 and 45 weeks postconception. First, we confirm the impressive performance of a standard UNet trained on a few volumes, but also confirm that such models learn intensity‐related features specific to the training domain. We then confirm the robustness of the synthetic learning approach to variations in image contrast. However, we observe a clear influence of the age of the baby on the predictions. We improve the performance of this model by enriching the synthetic training set with realistic motion artifacts and over‐segmentation of the white matter. Based on extensive visual assessment, we argue that the better performance of the model trained on real T2w data may be due to systematic errors in the ground truth. We propose an original experiment allowing us to show that learning from real data will reproduce any systematic bias affecting the training set, while synthetic models can avoid this limitation. Overall, our experiments confirm that synthetic learning is an effective solution for segmenting neonatal brain MRI. Our adapted synthetic learning approach combines key features that will be instrumental for large multisite studies and clinical applications. [ABSTRACT FROM AUTHOR]

Recently, with the emergence of many image editing tools (photoshop, Topaz studio, etc.), the authenticity of images has been severely challenged. However, the performance of some existing traditional feature extraction methods and detection methods based on convolutional neural network (CNN) is poor, and the information provided by the features extracted from the network is limited and single. In this paper, an end-to-end ringed residual U-Net is proposed to detect image splicing forgery by blending features of non-natural regions. Some regions with significant differences from the image background are defined as non-natural regions(such as the irregular border at the splicing of images). In this paper, a feature enhancement module for non-natural regions is constructed, which the image through the pooling of four different scales, and these features are then combined with the original image and input to the backbone network for processing, aiming to highlight regions of the image that differ significantly from the background. Therefore, after adding the feature enhancement module for non-natural regions to the end-to-end ring residual U-Net, more attention will be paid to the tampering regions in the feature extraction stage, image manipulation detection and localization will also become more accurate. Compared with some mainstream methods, this method achieves better performance on the three standard datasets(CASIA2.0, NIST2016, COLUMBIA). In addition, it has excellent robustness under JPEG compression attack and noise corruption attack. [ABSTRACT FROM AUTHOR]

Simple Summary: Vegetation communities are impacted by invasive plants in predictable ways, namely, reductions in species richness, diversity, and floristic quality (i.e., the "nativeness" of the plant community). In ecological restoration, these plant community properties are important in establishing performance standards, especially for restored wetlands and streams. Performance standards can be thought of as the "report card" for ecological restoration. In the United States, much of this ecosystem restoration occurs as compensatory mitigation (i.e., to compensate for impacts to wetlands and streams elsewhere). The presence of invasive plant species is an important performance standard used in compensatory mitigation to determine whether or not wetland and stream mitigation sites comply with environmental laws. Invasive plants detract from restoration performance, but it is unclear what level of invasion should trigger a legal requirement for invasive plant removal. This study found that lower levels of invasion (i.e., 5–10%) do not diminish native vegetation community properties on wetland and stream mitigation sites, so low invasive performance standards could be causing more harm than good via the loss of native species from treatments like broad-spectrum chemical herbicides. Our research points to a more moderate standard of 10%, along with annual invasive mapping. We sampled vegetation communities across plant invasion gradients at multiple wetland and stream mitigation sites in the Coastal Plain and Piedmont physiographic provinces of Virginia, USA. Impacts of invasion were evaluated by tracking changes in species composition and native vegetation community properties along the abundance gradients of multiple plant invaders. We found that native species richness, diversity, and floristic quality were consistently highest at moderate levels of invasion (ca. 5–10% relative abundance of invader), regardless of the identity of the invasive species or the type of mitigation (wetland or stream). Likewise, native species composition was similar between uninvaded and moderately invaded areas, and only diminished when invaders were present at higher abundance values. Currently, low thresholds for invasive species performance standards (e.g., below 5% relative abundance of invader) compel mitigation managers to use non-selective control methods such as herbicides to reduce invasive plant cover. Our results suggest that this could cause indiscriminate mortality of desirable native species at much higher levels of richness, diversity, and floristic quality than previously thought. From our data, we recommend an invasive species performance standard of 10% relative invader(s) abundance on wetland and stream mitigation sites, in combination with vigilant invasive plant mapping strategies. Based on our results, this slightly higher standard would strike a balance between proactive management and unnecessary loss of plant community functions at the hands of compulsory invasive species management. [ABSTRACT FROM AUTHOR]

Present study focused on to investigate the impact of end friction and lateral inertia on the dynamic properties of concrete obtained by performing the dynamic compression test on developed split Hopkinson pressure bar (SHPB) setup. The standard (SC) and high-strength concrete (HSC) exhibiting characteristic compressive strength of 35 and 60 MPa, respectively used in the present investigation. The concrete specimens with 29.5 and 45 mm diameter describing the aspect ratio about of 1 and 0.67, respectively, has been subjected to strain rates ranging from 78 to 336 s−1. The systematic experimental analysis of the influence of strain rate, end friction, and lateral inertia has been performed on the dynamic compressive properties. The influences of these factors on the dynamic compressive strength (DCS) and dynamic increase factor (DIF) have been discussed thoroughly. The results findings confirmed that as the strain rate rises, the dynamic characteristics of concrete also escalate. This is explained by the observed variations in DIF, which fall within the ranges of 1.76–2.65 for standard concrete (SC) and 1.28–1.70 for high-strength concrete (HSC). It was also confirming that the sensitivity of lateral inertia and end friction confinement is influenced by both the specimen diameter and strain rate. The lateral inertia confinement in larger diameter specimens significantly increases the DIF. In addition, the presence of end friction at the specimen bar interface also constrains the lateral deformation and significantly increases the DIF μ ≠ 0 , but there was no visible effect of specimen diameter was observed on the DIF μ = 0 of the concrete. [ABSTRACT FROM AUTHOR]

We propose a segmentation-based bounding box generation method for omnidirectional pedestrian detection that enables detectors to tightly fit bounding boxes to pedestrians without omnidirectional images for training. Due to the wide angle of view, omnidirectional cameras are more cost-effective than standard cameras and hence, suitable for large-scale monitoring. However, state-of-the-art pedestrian detectors cannot directly be applied to omnidirectional pedestrian detection because pedestrians' appearance in omnidirectional images may be rotated to any angle, which substantially degrades the performance of standard pedestrian detectors. Existing methods mitigate this issue by transforming images during inference, though the methods have limited detection accuracy and slow detection speed. A recently proposed method obviates the transformation at the cost of laborious annotation works and trains detectors with omnidirectional pedestrian detection datasets. We propose instead leveraging an existing large-scale object detection dataset to obviate both the transformation and annotation works. We train a detector with rotated images and tightly fitted bounding box annotations generated from the segmentation annotations in the object detection dataset, which enable detectors to detect pedestrians in omnidirectional images with tightly fitted bounding boxes. We also develop pseudo-fisheye distortion augmentation, which deforms images of perspective view to imitate the fisheye distortion and further enhances the performance. Extensive analysis shows that our detector successfully fits bounding boxes to pedestrians and demonstrates substantial performance improvement over existing methods. [ABSTRACT FROM AUTHOR]

This research is motivated by the low performance results shown by permanent lecturers at "B" Accredited Private Universities in Bandung City, especially in the field of research with the obligation for each lecturer to publish their scientific work in national or international journals. The aim of this research is to analyze the influence of Emotional Intelligence and Career Development on Employee Performance. This research is included in quantitative research which is used to test hypotheses with the results in the form of the significance of the relationship between the variables studied. The population of this study was all permanent lecturers at a "B" accredited private university in the city of Bandung, totaling 1009 people and with a sample size of 286 respondents. And the sampling technique used in this research is cluster sampling. Meanwhile, the data analysis used in this research is in the form of multiple regression analysis calculated using the SPSS 23.00 program. The research results show that Emotional Intelligence and Career Development, both partially and simultaneously, show a significant influence on Employee Performance. [ABSTRACT FROM AUTHOR]

Transformer-based language models have been shown to be highly effective for several NLP tasks. In this article, we consider three transformer models, BERT, RoBERTa, and XLNet, in both small and large versions, and investigate how faithful their representations are with respect to the semantic content of texts. We formalize a notion of semantic faithfulness, in which the semantic content of a text should causally figure in a model's inferences in question answering. We then test this notion by observing a model's behavior on answering questions about a story after performing two novel semantic interventions—deletion intervention and negation intervention. While transformer models achieve high performance on standard question answering tasks, we show that they fail to be semantically faithful once we perform these interventions for a significant number of cases (∼ 50% for deletion intervention, and ∼ 20% drop in accuracy for negation intervention). We then propose an intervention-based training regime that can mitigate the undesirable effects for deletion intervention by a significant margin (from ∼ 50% to ∼ 6%). We analyze the inner-workings of the models to better understand the effectiveness of intervention-based training for deletion intervention. But we show that this training does not attenuate other aspects of semantic unfaithfulness such as the models' inability to deal with negation intervention or to capture the predicate–argument structure of texts. We also test InstructGPT, via prompting, for its ability to handle the two interventions and to capture predicate–argument structure. While InstructGPT models do achieve very high performance on predicate–argument structure task, they fail to respond adequately to our deletion and negation interventions. [ABSTRACT FROM AUTHOR]

The role of leadership is very strategic and important for the achievement of the mission, vision and goals of an organization, is one of the motives that encourage humans to always investigate the intricacies associated with leadership. Transformational leadership basically encourages subordinates to do better Behavior demanded by today's organizations is not only in-role behavior but also extra- role behavior. Extra-role behavior is also called the Organizational itizenship Behavior.OCB is a term used to identify the employee behaviors that can be called a "good employee". If employees in the organization have OCB, efforts to control employee declined, since employees can control their own behavior or being able to choose the best behavior for the benefit of the organization. OCB will enhance transformational leadership because the good nature of OCB will influence the behavior of the leader. There is a significant influence ith the value of P = 0.00 between OCB and transformational leadership. This shows there is an increase in transformational leadership due to OCB behavior. [ABSTRACT FROM AUTHOR]

This research aims to determine the effect of leadership style, motivation, and work discipline on the performance of employees. The method study uses descriptive analysis with a correlation approach. T-test results It is obtained that the calculated t-value of the motivation variable (X2) has a mark p-value as big as 0,000 <0.05. It means significant, while the t count is 7.287 > from the t table 1.987, meaning it is substantial. The means of 7,287 (X2) motivation are in a way that is partially influential to Performance Employee (Y). The size that influences motivation in performance employees is as big as 37.6%. 3) Discipline influences employee performance. The t-test results showed that the t value was calculated variable motivation (X3) own mark p-value as big as 0,000<0.05 means significant, while the t count is 4.794 > from the t table 1,987 It means substantial. The means 4,794 (X3) discipline is in a way that is partially influential to Performance Employee (Y). The influence of discipline on the performance of employees is as significant as 20.7%. 4) The influence between Style Leadership (X 1), Motivation (X2), and discipline (X3) on Performance Employees (Y) is 0.739; this shows that these four variables influence the strong one at limit interpretation intermediate R-value 0.60 – 0.799. [ABSTRACT FROM AUTHOR]

In this study, movable steel barrier with grade SB light composite corrugated beam is designed, which addresses the problems of the prior central partition belt portable guardrail in terms of easy mobility, local safety, easy construction, and other indications. This guardrail employs explicit algorithms to conduct a dynamic finite element simulation analysis and a real vehicle crash test, and verifies the guardrails' blocking, guiding, and buffering functions in accordance with the SB level collision conditions listed in the Standard for Safety Performance Evaluation of Highway Barriers (JTG B05-01-2013). According to the results, the safety performance of SB grade lightweight composite corrugated beam movable steel guardrail meets the requirements of the Standard for Safety Performance Evaluation of Highway Barriers (JTG B05-01-2013). In addition, the guardrail can be opened for 12 meters in 1 minute and returned to close in 2 minutes. The opening and restoration of the movable guardrail is superior to the previous central divider movable guardrail. This guardrail has been tried for some high-speed and its safety performance has been verified again in actual high-speed vehicle collisions. [ABSTRACT FROM AUTHOR]

To meet European carbon neutrality targets and comply with building energy performance regulations, renovating historic buildings is considered one of the most challenging tasks for the construction sector. On one hand, commonly employed renovation solutions are often more difficult to implement on these structures. On the other hand, renovation work must be carried out while preserving their heritage value and integrity. The European standard EN 16883 on conservation and energy renovation performance of cultural heritage was developed in 2017 with the aim of facilitating energy performance improvements in historic buildings while respecting their cultural significance. In pursuit of the same objective, the "P-Renewal" project focuses on the energy retrofit of pre-war Walloon housing with heritage value, providing valuable support through a reflective process and decision-making tools. These tools enable the selection of renovation strategies that effectively combine the preservation of heritage value with improvements in internal comfort, energy efficiency, and environmental performance. This study compares the reflective process of the research project with the guidelines of the standard EN 16883 and discusses the transferability of this contribution to other European contexts. This will significantly contribute to the evolution of energy performance standards for the renovation of historic buildings. [ABSTRACT FROM AUTHOR]

The VEMoS-AXL system is a new optical biometer based on spectral domain optical coherence tomography (SD-OCT) that has been tested in terms of intrasession repeatability and compared with a swept-source optical coherence tomography biometer (SS-OCT), which is recognized as the gold standard for the performance of an agreement analysis. A biometric analysis was performed three consecutive times in 120 healthy eyes of 120 patients aged between 18 and 40 years with the SD-OCT system, and afterwards, a single measurement was obtained with the SS-OCT system. Within-subject standard deviations were 0.004 mm, 4.394 µm, and 0.017 mm for axial length (AL), central corneal thickness (CCT), and anterior chamber depth (ACD) measures obtained with the SD-OCT biometer, respectively. The agreement between devices was good for AL (limits of agreement, LoA: −0.04 to 0.03 mm) and CCT (LoA: −4.36 to 14.38 µm), whereas differences between devices were clinically relevant for ACD (LoA: 0.03 to 0.21 mm). In conclusion, the VEMoS-AXL system provides consistent measures of anatomical parameters, being most of them interchangeable with those provided by the SS-OCT-based gold standard. [ABSTRACT FROM AUTHOR]

There appears to be a mismatch between apparent incompetence in the world and the amount of sociological research it attracts. The aim of this article is to outline a sociology of incompetence and justify its value. I begin by defining incompetence as unsatisfactory performance relative to standards. Incompetence is thus intrinsically sociological in being negotiated and socially (re)constituted. The next section foregrounds how widespread and serious incompetence is. This renders effective sociological understanding crucial to welfare. The article then systematically analyses uses of the term in the British Journal of Sociology (a good quality general journal) to assess the current state of research. This analysis fully confirms the neglect of incompetence as a research topic. The next section proposes suitable methods for preliminary incompetence research addressing distinctive challenges like the stigma of being incompetent. These sections then allow incompetence to be better contextualised by other contributing concepts like power, bureaucracy and meritocracy. The final section justifies suggestions about directions for future research. [ABSTRACT FROM AUTHOR]

Background The LactoSens® R method was previously shown to have acceptable accuracy and repeatability precision as required by AOAC Standard Method Performance Requirements (SMPR®) 2018.009 for determination of lactose in low-lactose or lactose-free milk, milk products, and products containing dairy ingredients and was awarded Official Method of Analysis SM (OMA) First Action status in 2020. Objective The method was subjected to a multilaboratory validation (MLV) study to evaluate the reproducibility precision of the method. Methods Fourteen validation materials were provided to 15 laboratories in seven countries as blind duplicates. The materials ranged from 0 to 173 mg/100 g lactose. Each laboratory analyzed the blind duplicates according to OMA 2020.01. The data were analyzed for repeatability and reproducibility precision. Results RSDr values varied from 2.81 to 8.76%, and RSDR values varied from 4.25 to 12.5%. When sorted by category and concentration range, these results met the repeatability and reproducibility criteria required by SMPR 2018.009. Conclusions The data generated in the MLV support the adoption of OMA 2020.01 as Final Action status. Highlights The LactoSens R method, as described by OMA 2020.01 , provides an accurate and precise determination of lactose in a variety of low-lactose and lactose-free milk, milk products, and products containing dairy ingredients in minutes. [ABSTRACT FROM AUTHOR]

A wide range of issues including missing values, class noise, class imbalance, outliers, correlation and irrelevant variables have the potential to negatively affect the overall performance of disease diagnosis classification algorithms. This study proposes a new technique, alternative to the t-Score method, to increase the performance of ensemble learning classification algorithms by removing irrelevant variables. Therefore, three publicly available datasets from medical domain varying in their sample sizes, number of variables, and data preprocessing problems were selected and processed with our newly proposed feature selection method called Outliers and Correlation t-Score (OCtS). Afterwards, six widely used ensemble learning algorithms including Random Forest, Gradient Boosting Machine, Extreme Gradient Boosting Machine, Light Gradient Boosting Machine, CatBoost, and Bagging were employed for disease diagnosis classification, and performance metrics were measured. Our results indicate that the classification performance of six ensemble learning algorithms significantly increased when the OCtS method was employed, and our feature selection method, OCtS, exhibited higher performance compared to the standard t-score method across all datasets (p = 0.0001). We conclude that, using data preprocessing methods with OCtS offers better algorithm performance when employing ensemble learning algorithms in disease diagnosis classification. [ABSTRACT FROM AUTHOR]

To enhance the performance of the diffusion robust adaptive filtering algorithm, this paper presents a novel loss function framework named the "Tanh cost function framework." By utilizing this framework, we are able to incorporate standard cost functions, devise novel cost functions, and introduce the associated diffusion algorithm. Moreover, the convergence of the algorithm is thoroughly analyzed. Furthermore, the proposed diffusion Tanh algorithm family is simulated in a system identification model to assess its performance, demonstrating superior performance compared to the standard diffusive algorithm. Notably, the DTHLMS within the diffusion tanh algorithm family exhibits superior performance over the mainstream robust diffusion algorithms. [ABSTRACT FROM AUTHOR]

This study aimed to analyze the effects of job stress work conflict and work discipline on the performance of employees of PT. Pelindo II-IPC Jakarta in 2016. The results of research using primary data in the form of a questionnaire to 100 respondents in PT. Pelindo II-IPC Jakarta in 2016 showed that there is influence between Conflict Work, Work Stress and Work Discipline on employee performance PT. Pelindo II-IPC Jakarta. This is evidenced by F count larger than F table and t greater than t table. The results of calculations based on t test obtained t for Conflict Work (X1) of 10.386 and thitung for Work Stress (X2) is 5,918 bigger than t table 1.660, meaning that the work conflict and work stress and significant positive effect on employee performance of PT Pelindo II. While thitung for Work Discipline (X3) of 1.178 is smaller than 1,660, which means ttabel Discipline had no effect on employee performance of PT Pelindo II. While the regression model as follows Y = 0,571X1 + 0,383X2 + 0,080X3 This suggests that the three independent variables X1, X2, and X3 affect the Y. [ABSTRACT FROM AUTHOR]

This research aims to analyze the influence of democratic leadership style, work discipline, intrinsic motivation and physical work environment on the employees' performance of PT. Straw Plus East Jakarta. This research data uses primary data in the form of a questionnaire to 100 respondents using the multiple linear regression method which was processed using SPSS 17.0 software. From data that has met the validity test, reliability test and classical assumption test. the variables are democratic leadership style, work discipline, intrinsic motivation and physical work environment on employee performance. From the model feasibility test, it shows that the four independent variables studied are proven to have a positive and real effect on the dependent variable together. Through testing the coefficient of determination (R2), it can be seen that democratic leadership style, work discipline, intrinsic motivation and physical work environment, each have an influence on employee performance, namely 93.9%, meaning that 93.9% of the dependent variable of employee performance can be explained by the independent variables are democratic leadership style, work discipline, intrinsic motivation and physical work environment. Meanwhile, the remaining 6.1% is explained by other variables that were not included in this research. [ABSTRACT FROM AUTHOR]

This research aims to analyze work stress, work ethic and supervision of employees at PT. Bank Central Asia, Tbk, especially the Bidakara Tower Main Branch Office, with a total of 100 research samples taken from permanent employees and interns. Research results using Multiple Linear Regression Analysis show that work supervision is more dominant in influencing employee performance than work stress and work ethic. Work stress has a positive and significant effect on employee performance. Bank BCA strives to be better at managing work stress to meet employee needs and produce good performance. Work ethic also has a positive but not too big effect on employee performance. BCA Bank pays attention to a high work ethic because it is rooted in strong cooperation and strong commitment from employees. Work ethic is very important to motivate employees to improve their performance. Supervision influences employee performance more dominantly than work stress and work ethic. Good supervision will reduce the rate of work errors. Work supervision also influences employee work motivation to provide and improve their own good performance. [ABSTRACT FROM AUTHOR]

This study aims to analyze how strong the influence of Compensation, Communication Effectiveness, and Promotion on Employee Performance partially, this research was conducted at PT. Globalindo Artistic Creations, Bekasi. This study was conducted using primary data obtained from the distribution of questionnaires, a population of 140 employees with a sample of 104 respondents. Management of analytical data using validity and reliability tests as well as regression and correlation tests using SPSS 20. Based on the results of the study, multiple regression equations Y = 1.034 + 0.233 X1 + 0.250 X2 + 0.492 X3 + e The results showed that Compensation (X1) has a positive influence on employee performance (Y), Communication Effectiveness (X2) has a positive influence on employee performance (Y) and Promotion Against (X3) has a positive influence on employee performance (Y). The result of the correlation between Compensation, Communication Effectiveness, and Promotion on employee performance (Y). And the results of the F Test of Compensation, Communication Effectiveness, and Promotion together have a positive and significant effect on Employee Performance. [ABSTRACT FROM AUTHOR]

The purpose of this study was to determine the effect of work environment, individual characteristics, communication and work discipline on employee performance in PT. Bank Negara Indonesia (Persero) Tbk. Jakarta City Region. With a sample of 120 employees/respondents. Seals are obtained using the Probability Sampling technique, that is, the required information obtained from all elements has the opportunity to be selected as a sample. Hypothesis testing uses multiple linear regression analysis using SPSS version 24.0. The results showed that work environment, individual characteristics, communication and work discipline on employee performance. [ABSTRACT FROM AUTHOR]

This study aims to analyze how much influence incentives, work environment and work discipline have on employee job satisfaction. In this study using primary data obtained from the distribution of questionnaires to 142 employees as respondents at PT, XYZ cakung branch. This study used descriptive analysis methods and inferential analysis. Data were analyzed using multiple linear regression correlation analysis, instrument test, model feasibility test, and t test. The results of this study show a positive and significant influence on employee job satisfaction, resulting from the results of the regression equation model in the Coefficients table Y = 4.307 + 0.246 + 0.241 + 0.202. In the feasibility test of the model, a significant value of 0.000 was obtained, which means that the model is said to be significant and the model is said to be feasible to be used in this study. In the results of the t-test hypothesis value, the sig value on the incentive variable is 0.000, the sig value on the work environment variable is 0.008, and the sig value on work discipline is 0.000. So it can be concluded that partially these three variables have a positive and significant influence on the dependent variable of employee performance. If the management provides good incentives and creates a comfortable and conducive work environment as well as implementing good discipline in the work environment to stimulate employees to do their jobs better, it will be able to influence employees to do their jobs better. Which will have an impact on achieving common goals. [ABSTRACT FROM AUTHOR]

This research aims to analyze how much influence Financial Compensation, Work Motivation and Work Discipline on employee performance. In this research using primary data obtained from questionnaires to 116 employees as respondents in companies jabbek. This research uses descriptive analysis and inferential analysis methods. Data were analyzed using multiple linear regression correlation analysis, test instruments, test the feasibility of the model, and the t test. The results of this study indicate a positive and significant effect on Employee Performance, which is generated from the results of the regression equation model in the Coefficients Y table = 5.653 + 0.508 + 0.082+ 0.234. In the model feasibility test results obtained significant value of 0,000 which means the model is said to be significant and the model is said to be feasible to use in this study. On the results of the t-test hypotheses obtained sig value on the variable Financial Compensation of 0,000, the sig value on the Work Motivation variable is 0.046, and the sig value in the Work Discipline is 0,000. Then it can be concluded that the partial meaning of the three variables has a positive and significant influence on the dependent variable of employee performance. [ABSTRACT FROM AUTHOR]

This research aims to review the influence of non-financial compensation, organization culture and work stress on the performances of employee at Directorate of Drug Investigation Polda Mtero Jaya, South Jakarta. Research method used in this research that is quantitative descriptive with a population in this research is the employee of Directorate of Drug Investigation Polda Mtero Jaya, South Jakarta. There are four variables investigated in this study that is the nonfinancial compensation (X1), organization culture (X2), work stress (X3) as independent variable and employee performance (Y) as dependent variable. Sample used in this research as many as 100 respondents. This research data using primary data in the form of a questionnaire the method of linear regression double processed using the software SPSS 17.0. The results of research based on linear regression shows that non-financial compensation, organization culture, work stress positive influential and significant to employee performance. [ABSTRACT FROM AUTHOR]

The purpose of this study was to analyze the effect of work discipline, compensation, career development and work environment on employee performance at the Ministry of Religion's Office in South Jakarta. In this study using primary data obtained from distributing questionnaires to 100 employees at the Ministry of Religion in South Jakarta City. This study uses the method of Multiple Linear Regression, the results of this study indicate that there is a positive and significant effect on employee performance. The results showed that each variable, namely Work Discipline, Compensation, Career Development and Work Environment had a positive and significant effect on Employee Performance at the Ministry of Religion in South Jakarta. [ABSTRACT FROM AUTHOR]

This study aims to determine and analyze employee motivation, discipline, and work environment and its effects on employee performance. This research was conducted at PT. Rekayasa Aplikasi Digital. In addition, this study aims to provide information and advice to the Human Resources Management of PT. Rekayasa Aplikasi Digital. Data was collected using a questionnaire with a Likert scale. The respondents in this study were 100 employees at PT. Rekayasa Aplikasi Digital itself. The analyses used in this study are quantitative descriptive analysis and qualitative analysis. The results showed that motivation, work discipline and work environment had a positive and significant effect on employee performance. This is shown from the coefficients table in the regression equation model KK = 0,261 M + 0,284 DK + 0,221 LK. The results of the conclusion of the hypothesis on the t-test partially obtained a significant value on the motivation variable of 0.05, while on the work discipline variable of 0.02 and on the work environment variable of 0.017. So it can be partially concluded that the three independent variables have a significant influence on the dependent variable. [ABSTRACT FROM AUTHOR]

This study aims to determine the effect of financial compensation, intrinsic motivation and physical work environment on the performance of employees of the Human Resources Development Agency of the Republic of Indonesia. The type of research used is quantitative research. This study used primary data by distributing questionnaires to as many as 138 respondents. Data analysis uses descriptive and inferential analysis. In the implementation of the study using primary data obtained through the distribution of questionnaires and processed with SPSS 25. The results of the study showed that the financial compensation variable (X1) had a positive and significant effect on the performance of employees of the Human Resources Development Agency of the Republic of Indonesia. The intrinsic motivation variable (X2) has a positive and significant effect on the performance of employees of the Human Resources Development Agency of the Republic of Indonesia. Physical work environment variables (X3) have a positive and significant effect on the performance of employees of the Human Resources Development Agency of the Republic of Indonesia. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study is to understand the levels (i.e. mild vs intense) of task conflict (TC) expressions between angel investors and entrepreneurs at the post-investment stage and how it affect angel investors' follow-on investment intentions with the same entrepreneur. Design/methodology/approach: Survey data was gathered from 71 angel investors in China. Mplus was used to test the proposed research model. Findings: This study found that angels perceive affective conflict (AC) when engaged in intense TC, unlike the case for mild TC expressions. Furthermore, the analysis shows that, unlike mild TC expressions, intense TC expressions impede angels' reinvestment intentions when they perceive ACs. Other results indicate that when angels perceive that entrepreneurs are not open to coaching, the prominence of mild TC expression is sharply mitigated and becomes as detrimental as intense TC expressions. Research limitations/implications: This study only focused on one specific aspect of the angel–entrepreneur post-investment relationship: The effect of their TC expressions on angels' reinvestment intentions. By no means do the authors imply that TC expression in the angel–entrepreneur post-investment relationship is the only factor that matters to angel investors in their follow-on investment intentions with the same entrepreneur. Practical implications: The findings suggest that entrepreneurs should pay careful attention to TC that may arise between them and their financiers. TCs are not entirely detrimental, but their negative effect might depend on how they are expressed. An appropriate level of TC may also improve enterprise performance and collaboration. Thus, angels and entrepreneurs should set clear goals and performance standards, where task interactions mainly focus on the goals and expected outcomes. Originality/value: Prior to this study, little was known about whether all TCs potentially lead to ACs. By distinguishing between levels (i.e. mild vs intense) of TC expressions between angels and entrepreneurs, this study adds a novel aspect to it by showing that TC, in and of itself, does not necessarily lead to AC but can lead to AC once its intensity grows. [ABSTRACT FROM AUTHOR]

Background and Objective: Indoor air pollutants are among the five most important environmental health risks. In recent years, concerns regarding indoor air quality in buildings have increased. This review has investigated studies conducted on portable air cleaners. Materials and Methods: After determining the keywords, conducting comprehensive searches in the internet databases, and taking into account the inclusion and exclusion criteria, a complete review was conducted to obtain the latest findings and the general trend of the studies. Results: Portable air cleaner devices are very diverse and are divided based on the type of technology used. Many standards have been developed to evaluate the performance of these devices in different countries, but none of them cover all aspects of performance. The effectiveness of these devices in removing VOCs is between 20% and 80%, depending on the type of technology and other influencing factors, and their effectiveness in removing suspended particles is between 70% and 95% on average. Furthermore, their efficiency in controlling COVID-19 is reported to be above 80% in cases where HEPA filters with grades 13 and above are used. Conclusion: Devices equipped with the HEPA filtration process have been found to be very effective in controlling indoor suspended particles. The use of devices equipped with UVGI, plasma, and ozone technologies should be accompanied by caution due to dangerous by-products.

Abstract Background Standards of early childhood development (ECD) are needed to determine whether children living in different contexts are developmentally on track. The Early Childhood Development Index 2030 (ECDI2030) is a population-level measure intended to be used in household surveys to collect globally comparable data on one of the indicators chosen to monitor progress toward target 4.2 of the Sustainable Development Goals: The proportion of children aged 24–59 months who are developmentally on track in health, learning and psychosocial well-being. Methods To define performance cut-scores for the ECDI2030 we followed a criterion-referenced standard setting exercise using the modified Angoff method. The exercise gauged the expectations from 15 global experts in ECD and was informed by representative population data collected in Mexico and the State of Palestine. The final calibrated age-specific performance cut-scores were applied to these data to estimate the proportion of children developmentally on track, disaggregated by background characteristics, including the child's sex and attendance to early childhood education. Results Through a process of standard setting, we generated robust performance standards for the ECDI2030 by establishing five age-specific cut-scores to identify children as developmentally on track. Conclusions This paper demonstrated how the standard setting methodology, typically applied to measures in the health and education fields, could be applied to a measure of child development. By creating robust criterion-referenced standards, we have been able to ensure that the cut-scores related to age for the ECDI2030 are based on performance standards set by global experts in the ECD field for defining on and off track development.

Litigation and courts have been important arbiters of labour policy reforms aimed at redressing Japan's growing social inequality. This article considers seven lawsuits brought by non-regular workers, who sought to use Article 20 of the 2012 Labour Contracts Act to gain equal pay for equal work. All seven cases reached Japan's Supreme Court amid Abe Shinzō's 2018 labour reforms. The Court's interpretations reaffirmed employer discretion in categorising employees as a reasonable principle, albeit one which limits the law's power to redress wage inequality. However, the Court also found the non-provision of various minor benefits to be unreasonable. These outcomes evince a pattern in which litigation marginally transforms reformed labour policy into institutional support for greater equality of compensation and benefits between workers of different statuses. More than advancing equal pay for equal work, the outcomes of these cases support recent government-industry campaigns to boost productivity by encouraging increased use of job-based treatment and diversifying Japan's employment system. [ABSTRACT FROM AUTHOR]

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A wide variety of bridge barriers are used on highways. The bearing capacities of different types of deck slabs are measured in this study by applying the yield line theory to determine the worst-case scenario. An improved configuration for the worst-case barrier base and deck slab is developed, namely the universal double-beam assembled bridge barrier, which can enhance the safety performance of exiting concrete-base bridge barriers. According to the simulated impact test results, the new barrier meets SS-level requirements in terms of containment, redirective, and buffering performance as specified in the Standard for Safety Performance Evaluation of Highway Barriers (JTG B05-01-2013). The barrier structure's compatibility with various bases is also analyzed. SS-level impact tests are conducted on real vehicles, including cars, buss, and trucks. The results show that the safety performance of the new barrier configuration reaches SS-level, and the barrier is universally compatible with concrete bases with a height of 63 cm and above. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) pandemic has created a public health crisis. This study aimed to evaluate the diagnostic performance of the Panbio and STANDARD Q COVID-19 antigen rapid diagnostic tests (RDTs) against the real-time polymerase chain reaction (RT-PCR) at one of the largest hospitals in southern Ethiopia. Nasopharyngeal samples, which were collected during the pandemic from individuals suspected of COVID-19 and stored at − 70 °C, were analyzed in June and July 2022. The performance of the Panbio COVID-19 antigen tests was evaluated in 200 randomly selected nasopharyngeal samples (100 positives and 100 negatives for severe acute respiratory syndrome 2 by RT-PCR). The STANDARD Q test was evaluated using 100 positive and 50 negative samples. The respective sensitivity, specificity, positive predictive value and negative predictive values were 88%, 99%, 98.9% and 89.2% for the Panbio test and 91%, 98%, 98.9% and 84.5%, for the STANDARD Q test. The kappa values were 0.87 for the Panbio and 0.86 for the STANDARD Q test. Based on the findings presented here, the RDTs could be utilized as an alternative to conventional RT-PCR when it is challenging to diagnose COVID-19 owing to a lack of time, skilled lab personnel, or suitable equipment or electricity. [ABSTRACT FROM AUTHOR]

Electrowetting with a dielectric layer is commonly preferred in practical applications. However, its potential is often limited by factors like the properties of the dielectric layer and its breakdown, along with the complexity of the deposition method. Fortunately, advancements in 3D inkjet printing offer a more adaptable solution for making patterned functional layers. In this study, we used a negative photoresist (HN-1901) to create a new dielectric layer for an electrowetting display on a 3-inch ITO glass using a Dimatix DMP-2580 inkjet printer. The resulting devices performed better due to their enhanced resistance to dielectric breakdown. We meticulously investigated the physical properties of the photoresist material and printer settings to achieve optimal printing. We also controlled the uniformity of the dielectric layer by adjusting ink drop spacing. Compared to traditional electrowetting display devices, those with inkjet-printed dielectric layers showed significantly fewer defects like bubbles and electrode corrosion. They maintained an outstanding response time and breakdown resistance, operating at an open voltage of 20 V. Remarkably, these devices achieved faster response times of ton 22.3 ms and toff 14.2 ms, surpassing the performance of the standard device. [ABSTRACT FROM AUTHOR]

Background: When studying the association between treatment and a clinical outcome, a parametric multivariable model of the conditional outcome expectation is often used to adjust for covariates. The treatment coefficient of the outcome model targets a conditional treatment effect. Model-based standardization is typically applied to average the model predictions over the target covariate distribution, and generate a covariate-adjusted estimate of the marginal treatment effect. Methods: The standard approach to model-based standardization involves maximum-likelihood estimation and use of the non-parametric bootstrap. We introduce a novel, general-purpose, model-based standardization method based on multiple imputation that is easily applicable when the outcome model is a generalized linear model. We term our proposed approach multiple imputation marginalization (MIM). MIM consists of two main stages: the generation of synthetic datasets and their analysis. MIM accommodates a Bayesian statistical framework, which naturally allows for the principled propagation of uncertainty, integrates the analysis into a probabilistic framework, and allows for the incorporation of prior evidence. Results: We conduct a simulation study to benchmark the finite-sample performance of MIM in conjunction with a parametric outcome model. The simulations provide proof-of-principle in scenarios with binary outcomes, continuous-valued covariates, a logistic outcome model and the marginal log odds ratio as the target effect measure. When parametric modeling assumptions hold, MIM yields unbiased estimation in the target covariate distribution, valid coverage rates, and similar precision and efficiency than the standard approach to model-based standardization. Conclusion: We demonstrate that multiple imputation can be used to marginalize over a target covariate distribution, providing appropriate inference with a correctly specified parametric outcome model and offering statistical performance comparable to that of the standard approach to model-based standardization. [ABSTRACT FROM AUTHOR]

The stochastic synthesis of extreme, rare climate scenarios is vital for risk and resilience models aware of climate change, directly impacting society in different sectors. However, creating high-quality variations of under-represented samples remains a challenge for several generative models. This paper investigates quantizing reconstruction losses for helping variational autoencoders (VAE) better synthesize extreme weather fields from conventional historical training sets. Building on the classical VAE formulation using reconstruction and latent space regularization losses, we propose various histogram-based penalties to the reconstruction loss that explicitly reinforces the model to synthesize under-represented values better. We evaluate our work using precipitation weather fields, where models usually strive to synthesize well extreme precipitation samples. We demonstrate that bringing histogram awareness to the reconstruction loss improves standard VAE performance substantially, especially for extreme weather events. [ABSTRACT FROM AUTHOR]