Search

The precision of process monitoring often encounters challenges in determining the exact shift size. Therefore, combined control charts have gained considerable attention because of their excellent speed to detect simultaneously small-to-moderate and large-size shifts. The effectiveness of the applied quality control methods strongly depends on the performance of the measurement system. Measurement error presence contributes significantly negatively toward the performance of the usual control charting schemes. This article proposes novel two-sided combined Shewhart-Cumulative EWMA-sum (Shewhart-CUESUM) control charts designed to efficiently monitor the mean of normally distributed processes. In addition, to address measurement errors, the M-Shewhart-CUESUM chart is proposed, incorporating an additive measurement error model. Evaluation of the charts through Monte-Carlo simulations, considering metrics such as average run length (ARL), extra quadratic loss, relative ARL, and performance comparison index. It is found that the combined Shewhart-CUESUM outperforms than CUESUM chart. The results show that the presence of measurement errors can significantly diminish the charts' performance, which can be mitigated by utilizing a multiple measurements scheme. Among the different well-established combined charts examined, the M-Shewhart-CUESUM chart shows considerably more sensitive to detecting simultaneously detect small and large size shifts. To employ simulated datasets to illustrate the impact of measurement errors and demonstrate the implications of the proposed charts on process mean shifts. [ABSTRACT FROM AUTHOR]

Introduction: The complete bilateral cleft lip and palate (BCLP) divides the maxillary arch into three segments, separated from each other, resulting in abnormal growth of the alveolar arch. This study evaluated the maxillary arch dimensions in BCLP and compared them with neonates without craniofacial anomalies. Methods: This retrospective study was conducted in a tertiary cleft centre. Sixty‐six neonates aged 0–5 months were divided into two groups: cleft group—children with BCLP (23 boys and 18 girls) and control group—children without craniofacial deformities (15 boys and 10 girls). The dental models were processed by a 3D scanner. Landmarks were marked to achieve inter‐canine distance, inter‐tuberosity distance and arch length measurements. t‐Tests were used for intergroup comparisons (p <.05). Results: The maxillary cleft arch was demonstrated to be wider and longer in the posterior region compared to the control group. The inter‐canine distance did not present differences between the cleft and controls. The inter‐canine distance of the control group was the only measurement influenced by the variable sex. Conclusions: The cleft significantly interfered with the arch posterior width and arch sagittal length, making them larger. There was no statistical difference in the measurements between sex in the cleft group. [ABSTRACT FROM AUTHOR]

In recent years, the study of air pollution has received increasing attention from researchers, but a summary of Alzheimer's disease (AD) and air pollution is missed. Through combing the documents in the core dataset of Web of Science, this study analyzes current research based on specific keywords. CiteSpace and VOSviewer perform statistical analysis of measurement metrics to visualize a network of relevant content elements. The research devotes discussion to the relationship between air pollution and AD. Keyword hotspots include AD, children, oxidative stress, and system inflammation. Overall, 304 documents on air pollution and AD from 2013 to 2023 were retrieved from Web of Science. One hundred twenty-two journals published relevant articles, and the number of articles has increased gradually since the past decade. Research and development in AD and air pollution are progressing rapidly, but there is still a need for more connections with multidisciplinary technologies to explore cutting-edge hotspots. [ABSTRACT FROM AUTHOR]

Aims. We used the Javalambre Photometric Local Universe Survey (J-PLUS) second data release (DR2) photometry in 12 optical bands over 2176 deg2 to estimate the fraction of white dwarfs with the presence of Ca II H+K absorption along the cooling sequence. Methods. We compared the J-PLUS photometry against metal-free theoretical models to estimate the equivalent width in the J0395 passband of 10 nm centered at 395 nm (EWJ0395), a proxy to detect calcium absorption. A total of 4399 white dwarfs with effective temperatures within 30 000 > Teff > 5500 K and masses of M > 0.45 M⊙ were analyzed. Their EWJ0395 distribution was modeled using two populations, corresponding to polluted and non-polluted systems, to estimate the fraction of calcium white dwarfs (fCa) as a function of Teff. The probability of each individual white dwarf presenting calcium absorption, pCa, was also computed. Results. The comparison of EWJ0395 with both the measured Ca/He abundance and the identification of metal pollution from spectroscopy shows that EWJ0395 correlates with the presence of Ca II H+K absorption. The fraction of calcium white dwarfs changes along the cooling sequence, increasing from fCa ≈ 0 at Teff = 13 500 K to fCa ≈ 0.15 at Teff = 5500 K. This trend reflects the selection function of calcium white dwarfs in the optical. We compare our results with the fractions derived from the 40 pc spectroscopic sample and from Sloan Digital Sky Survey (SDSS) spectra. The trend found in J-PLUS observations is also present in the 40 pc sample; however, SDSS shows a deficit of metal-polluted objects at Teff < 12 000 K. Finally, we found 39 white dwarfs with pCa > 0.99. Twenty of them have spectra presented in previous studies, whereas we obtained follow-up spectroscopic observations for six additional targets. These 26 objects were all confirmed as metal-polluted systems. Conclusions. The J-PLUS optical data provide a robust statistical measurement for the presence of Ca II H+K absorption in white dwarfs. We find a 15 ± 3% increase in the fraction of calcium white dwarfs from Teff = 13 500 K to 5500 K, which reflects their selection function in the optical from the total population of metal-polluted systems. [ABSTRACT FROM AUTHOR]

Introduction: Metacognition, or the ability to monitor and control one's cognitive processes, is critical for learning in self-regulated contexts, particularly in introductory STEM courses. The ability to accurately make predictions about one's ability and performance can determine the effectiveness in which students effectively prepare for exams and employ good study strategies. The Dunning-Kruger pattern, where low-performing individuals are more overconfident and less accurate at the ability to predict their performance than high-performing individuals, is robustly found in studies examiningmetacognitivemonitoring. The extent to which the Dunning-Kruger pattern can be explained by the lack of metacognitive awareness is not yet established in the literature. In other words, it is unclear from prior work whether low-performing students are "unskilled and unaware" or simply "unskilled but subjectively aware." In addition, arguments about whether this pattern is a psychological phenomenon or a statistical artifact of the measurement of metacognition can be found in the literature. Methods: Students enrolled in three different physics courses made predictions about their exam scores immediately before and after taking each of the three exams in the course. Student predictions were compared to their exam scores to exam metacognitive accuracy. A new method for examining the cause of the Dunning-Kruger effect was tested by examining how students adjust their metacognitive predictions after taking exams. Results: In all contexts low-performing students were more overconfident and less accurate at making metacognitive predictions than high-performing students. In addition, these students were less able to efficiently adjust their metacognitive predictions after taking an exam. Discussion: The results of the study provide evidence for the Dunning-Kruger effect being a psychological phenomenon. In addition, findings from this study align with the position that the skills needed to accurately monitor one's performance are the same as those needed for accurate performance in the first place, thus providing support for the "unskilled and unaware" hypothesis. [ABSTRACT FROM AUTHOR]

Satellite altimetry water level measurements are valuable in episodic and climate change related hydrodynamic impact studies, despite their sparse temporal distribution over the global ocean. This study presents the spatiotemporal characteristics of the open-ocean satellite derived water level measurements globally for the period 31/12/1992-15/10/2019 and evaluates their efficacy to represent the water level even during intense atmospheric conditions. Water level measurements from 23 different satellite missions are compared with tide gauge records and hydrodynamic simulations. The satellite measurements reproduce the water-level variations with good to excellent skill for ~60% of the areas considered. Additionally, satellite measurements and local atmospheric conditions are utilized in order to examine whether statistical data driven models can contribute to decreasing the temporal sparseness of the water level data over the global ocean. The suitability of this low computational-cost method is demonstrated by deriving a 63-year hindcast of the daily maximum water level for the global ocean, and for a medium-term 15-day ensemble forecast. The publicly available long-term water-level hindcast and the parameters of the datadriven statistical model derived can serve as a tool for designing and facilitating local and global coastal risk-assessment studies. [ABSTRACT FROM AUTHOR]

Progress in the semiconductor industry relies on the development of increasingly compact devices consisting of complex geometries made from diverse materials. Precise, label‐free, and real‐time metrology is needed for the characterization and quality control of such structures in both scientific research and industry. However, optical metrology of 2D sub‐wavelength structures with nanometer resolution remains a major challenge. Here, a single‐shot and label‐free optical metrology approach that determines 2D features of nanostructures, is introduced. Accurate experimental measurements with a random statistical error of 18 nm (λ/27) are demonstrated, while simulations suggest that 6 nm (λ/81) may be possible. This is far beyond the diffraction limit that affects conventional metrology. This metrology employs neural network processing of images of the 2D nano‐objects interacting with a phase singularity of the incident topologically structured superoscillatory light. A comparison between conventional and topologically structured illuminations shows that the presence of a singularity with a giant phase gradient substantially improves the retrieval of object information in such an optical metrology. This non‐invasive nano‐metrology opens a range of application opportunities for smart manufacturing processes, quality control, and advanced materials characterization. [ABSTRACT FROM AUTHOR]

Italy legalized cannabis oil for specific medical conditions (neuropathic pain, refractory epilepsy and other established pathologies) in 2015, but mandates titration of principal cannabinoids before marketing each batch using iphenated techniques coupled with mass spectrometry. To assess reliability of laboratories from the Italian National Health Service in charge of titrating the batches, the Italian National Institute of Health set up an quality control program on determination of Δ9-tetrahydrocannabinol l (THC), cannabidiol (CBD), Δ9-tetrahydrocannabinolic acid A (THCA-A) and cannabidiolic acid (CBDA) in cannabis oil preparations. Two rounds of exercises have been carried out since 2019, involving sixteen Italian laboratories. Five different cannabis oil samples (19-1A and 19-1B for the first round and 22-1A, 22-1B and 22-1C for the second one were prepared and 1 mL amount of each sample was sent to the laboratories. The quantitative performance of each laboratory was assessed calculating the Z-score value, a statistical measurement for value's relationship to the mean of a group of values. In the first round, eight out of fourteen laboratories employed an LC-MS while the remaining six used GC-MS. Differently, in the second round, six out of eleven laboratories employed a GC-MS while the remaining five used LC-MS. In the first round, only 28.6 % laboratories achieved an acceptable performance (Z-score±2), and all of them used LC-MS as analytical method. In the second round, none of the laboratories achieved an acceptable performance. Satisfactory results, based on Z-scores, were generally low (0.0–75.0 %), with only one exception of 100 % for THCA-A determination in sample 22-1B. In the second round, three false negatives (two THC and one CBD by GC-MS determination) were reported while no false positives were described in the blank sample. The two rounds yielded a mean ERR% of 42 % approximately and a mean CV% around 70 % in GC-MS determination. When applying LC-MS determination, the two rounds yielded a mean ERR% of 36 % approximately and a mean CV% around 33 %. The obtained results underline the need for a clear and consistent protocol to be adopted by all laboratories intending to include the titration of oily cannabis-based products into their routinely analytical techniques. This emphasis on methodology standardization and participation to quality control schemes is essential for ensuring reliable and accurate measurements, ultimately enhancing the overall effectiveness and reliability of medical cannabis treatments. [ABSTRACT FROM AUTHOR]

Taxpayer compliance is vital in increasing state revenue because it will encourage an increase in state revenue, which will be used to finance all state expenditures and improve the welfare of the entire community. This study uses a quantitative method that emphasizes measurement accuracy and statistical analysis to test the hypothesis on the variables studied: tax audits, fiscal services, the role of account representatives, tax sanctions, and tax compliance. The research Population includes 1,025 individual taxpayers registered at the Big Four Taxpayer Office. The results showed that tax audits positively affect taxpayer compliance because routine audits encourage taxpayers to carry out their obligations properly. Good fiscal services also increase compliance by creating comfort for taxpayers. The role of Account Representatives has a significant effect because they provide supervision, services, and consultations that encourage taxpayers to utilize these services optimally. In addition, tax sanctions also play a positive role in increasing compliance with Law No. 28 of 2007, which requires taxes as a compelling contribution. This study shows that tax audits, taxpayer services, and Account Representatives (A.R.) role significantly influence compliance. Therefore, policies that strengthen the audit function, improve the quality of services, and clarify the duties and responsibilities of A.R.s help create a more transparent and credible tax system, thus improving tax compliance [ABSTRACT FROM AUTHOR]

The fourth-generation (4 G) cellular network is currently the dominant mobile technology used worldwide. The analysis of the network behavior can help forecast the traffic and thus improve the network. The characterization of mobile user behavior can be useful in evaluating the emerging concepts. We collected traffic traces on a commercial multi-band 4 G cell to better understand and model the network and user behavior. We evaluate the network utilization, the number of connections, and the uplink and downlink cell achieved throughput. In addition, we analyze and identify statistical models that describe the connection inter-arrival time, connection duration, and connection size. The results show daily and weekly patterns that depend not only on the time of day but also on the frequency band. We find that the frequency band and time of day have a limited impact on user behavior. On the contrary, the connection inter-arrival time strongly depends on the frequency band. [ABSTRACT FROM AUTHOR]

Background: The accurate and effective evaluation of parameters such as critical shoulder angle, lateral acromion angle, and acromion index from shoulder X-ray images is crucial for identifying pathological changes and assessing disease risk in the shoulder joint. Methods: In this study, a YOLOv8-based model was developed to automatically measure these three parameters together, contributing to the existing literature. Initially, YOLOv8 was used to segment the acromion, glenoid, and humerus regions, after which the CSA, LAA angles, and AI between these regions were calculated. The MURA dataset was employed in this study. Results: Segmentation performance was evaluated with the Dice and Jaccard similarity indices, both exceeding 0.9. Statistical analyses of the measurement performance, including Pearson correlation coefficient, RMSE, and ICC values demonstrated that the proposed model exhibits high consistency and similarity with manual measurements. Conclusions: The results indicate that automatic measurement methods align with manual measurements with high accuracy and offer an effective alternative for clinical applications. This study provides valuable insights for the early diagnosis and management of shoulder diseases and makes a significant contribution to existing measurement methods. [ABSTRACT FROM AUTHOR]

Identifying protective factors that promote academic resilience is vital. Nevertheless, due to the variations in the operationalizations of academic resilience, timeframes, data sources, and employed research methods, it remains unclear whether the impact of protective factors identified across studies can be attributed to the factors themselves or to these variations. By addressing these uncertainties, this study aims to provide an overview of the protective factors that have been extensively investigated in academic resilience and their degree of influence. A literature search found 119 empirical studies on protective factors in education settings for children and adolescents. The review analyzed five protective factors groups (individual, family, school, peer, community), three operationalizations of academic resilience (simultaneous, progressive, instrumental), two timeframes (longitudinal, non-longitudinal), three data sources (self-collected, national/local assessments, international large-scale assessments), and commonly employed research methods. The studies analyzed in this review yielded mixed results regarding the impact of the examined protective factors, with measurement instruments and statistical power playing a significant role in explaining the variations. Individual and school-level characteristics emerged as the most well-studied protective factors; individual characteristics were often investigated through "instrumental" operationalization and structural equational models, whereas school-level characteristics were typically explored through "simultaneous" or "progressive" operationalizations and multilevel modeling. Approximately 31 and 16% of the studies utilized national assessments and international large-scale assessment data, respectively. Both data sources promoted the exploration of school-level factors, with the former facilitating the exploration of protective factors across time and the latter contributing to the investigation of teaching-related factors. [ABSTRACT FROM AUTHOR]

Among patients with epilepsy, 30–40% experience recurrent seizures even after adequate antiseizure medications therapies, making them refractory. The early identification of refractory epilepsy is important to provide timely surgical treatment for these patients. In this study, we analyze interictal electroencephalography (EEG) data to predict drug refractoriness in patients with temporal lobe epilepsy (TLE) who were treated with monotherapy at the time of the first EEG acquisition. Various EEG features were extracted, including statistical measurements and interchannel coherence. Feature selection was performed to identify the optimal features, and classification was conducted using different classifiers. Functional connectivity and graph theory measurements were calculated to identify characteristics of refractory TLE. Among the 48 participants, 34 (70.8%) were responsive, while 14 (29.2%) were refractory over a mean follow-up duration of 38.5 months. Coherence feature within the gamma frequency band exhibited the most favorable performance. The light gradient boosting model, employing the mutual information filter-based feature selection method, demonstrated the highest performance (AUROC = 0.821). Compared to the responsive group, interchannel coherence displayed higher values in the refractory group. Interestingly, graph theory measurements using EEG coherence exhibited higher values in the refractory group than in the responsive group. Our study has demonstrated a promising method for the early identification of refractory TLE utilizing machine learning algorithms. [ABSTRACT FROM AUTHOR]

Free speech is essential, but it can conflict with protecting marginalized groups from harm caused by hate speech. Social media platforms have become breeding grounds for this harmful content. While studies exist to detect hate speech, there are significant research gaps. First, most studies used text data instead of other modalities such as videos or audio. Second, most studies explored traditional machine learning algorithms. However, due to the increase in complexities of computational tasks, there is need to employ complex techniques and methodologies. Third, majority of the research studies have either been evaluated using very few evaluation metrics or not statistically evaluated at all. Lastly, due to the opaque, black-box nature of the complex classifiers, there is need to use explainability techniques. This research aims to address these gaps by detecting hate speech in English and Kiswahili languages using videos manually collected from YouTube. The videos were converted to text and used to train various classifiers. The performance of these classifiers was evaluated using various evaluation and statistical measurements. The experimental results suggest that the random forest classifier achieved the highest results for both languages across all evaluation measurements compared to all classifiers used. The results for English language were: accuracy 98%, AUC 96%, precision 99%, recall 97%, F1 98%, specificity 98% and MCC 96% while the results for Kiswahili language were: accuracy 90%, AUC 94%, precision 93%, recall 92%, F1 94%, specificity 87% and MCC 75%. These results suggest that the random forest classifier is robust, effective and efficient in detecting hate speech in any language. This also implies that the classifier is reliable in detecting hate speech and other related problems in social media. However, to understand the classifiers' decision-making process, we used the Local Interpretable Model-agnostic Explanations (LIME) technique to explain the predictions achieved by the random forest classifier. [ABSTRACT FROM AUTHOR]

Background: Isotretinoin is an effective treatment for acne but can cause side effects such as changes in blood lipids and liver enzymes. Laboratory monitoring is essential during treatment, but there is variation in monitoring practices. Aim: This study aims to investigate the relationship between isotretinoin therapy and its effects on complete blood count in Saudi Arabia to improve patient outcomes. Methods: The study was a retrospective cohort study conducted at King Khalid University Hospital in Riyadh, Saudi Arabia, between January 2016 and December 2020. Following the inclusion and exclusion criteria, 515 patients were randomly selected for the study. The data was analyzed using SPSS, and descriptive statistics and paired samples t-tests were employed to analyze the data. Results: In this study, 515 patients were enrolled. Of these participants, 76.7% (n=395) were females and 23.3% (n=120) were males. The mean age of the study participants was 23.98± 7.4 years and ranged between 16 and 65 years. The mean dose of Isotretinoin administered was 27.65± 9.6 mg/day, with a range of 10– 60 mg/day. The mean BMI of the study participants was 24.3± 4.1 kg/m2, ranging from 14.3 to 44.8 kg/m2. Regarding the effect of Isotretinoin on laboratory measures, significant statistical differences were found in hemoglobin measurements (t=− 3.379, p=0.001), platelets (t=− 3.169, p=0.002), neutrophils (%) (t=3.107, p=0.002), total cholesterol (t=− 13.017, p=0.000), AST (t=− 6.353, p=0.000), ALT (t=− 4.352, p=0.000), HDL (t=2.446, p=0.015), and LDL (t=− 12.943, p=0.000). However, there were no significant statistical differences in the measurements of WBC, neutrophils (count), or triglycerides. In the Chi-square analysis and Fisher's Exact test to identify the interaction between BMI, dose, and gender on abnormal lab results, significant interaction was found between participants' BMI and abnormal HDL measurements (p=0.006). Furthermore, there were significant interactions between Isotretinoin dose (either less than 30 mg/day or 30 mg/day or more) and abnormal neutrophil count (p=0.04), abnormal HDL measurements (p=0.010), and abnormal triglycerides measurements (p=0.020). Moreover, a statistically significant interaction was found between participants' gender and abnormal hemoglobin measurements (p=0.006), abnormal total cholesterol (p=0.016), abnormal AST measurements (p=0.001), abnormal ALT measurements (p=0.000), abnormal HDL measurements (p=0.000), and abnormal triglycerides measurements (p=0.007). Conclusion: In conclusion, the study found that isotretinoin therapy has significant effects on several laboratory measures, including hemoglobin, platelets, neutrophils, total cholesterol, AST, ALT, HDL, and LDL. The study also revealed significant interactions between BMI, dose, gender, and abnormal lab results. [ABSTRACT FROM AUTHOR]

From the perspective of factor input in the production process, this paper puts forward the issue of accounting and capitalization of generalized data output, brings the output of data reprocessing into the scope of statistical accounting, and proposes a systematic evolution chain of data factor forms and the division method of data factor value composition and sources. Then, based on the GDP accounting platform, a three-in-one theoretical framework covering "output-investment-assets" is built for capitalization accounting of data factor, and an accounting path covering "cost → input → output → capital formation → data assets" is designed to improve and highlight the cost accounting method with "value appreciation" as the core. Taking China as an example, its data capital formation and asset size are measured by matching data-intensive industries with data professionals, and by synthesizing data from multiple sources. The rationality and self-consistency of the theoretical and methodological research is verified by the empirical results. This study can provide theoretical reference for bringing the value accounting of data factor into the basic framework of the Systems of National Accounts (SNA). Moreover, its empirical research paradigm can also provide reference for relevant countries to carry out capitalization accounting for data factor (CADF). [ABSTRACT FROM AUTHOR]

In situ spacecraft observations are critical to our study and understanding of the various phenomena that couple mass, momentum, and energy throughout near‐Earth space and beyond. However, on‐orbit telemetry constraints can severely limit the capability of spacecraft to transmit high‐cadence data, and missions are often only able to telemeter a small percentage of their captured data at full rate. This presents a programmatic need to prioritize intervals with the highest probability of enabling the mission's science goals. Larger missions such as the Magnetospheric Multiscale mission (MMS) aim to solve this problem with a Scientist‐In‐The‐Loop (SITL), where a domain expert flags intervals of time with potentially interesting data for high‐cadence data downlink and subsequent study. Although suitable for some missions, the SITL solution is not always feasible, especially for low‐cost missions such as CubeSats and NanoSats. This manuscript presents a generalizable method for the detection of anomalous data points in spacecraft observations, enabling rapid data prioritization without substantial computational overhead or the need for additional infrastructure on the ground. Specifically, Principal Components Analysis and One‐Class Support Vector Machines are used to generate an alternative representation of the data and provide an indication, for each point, of the data's potential for scientific utility. The technique's performance and generalizability is demonstrated through application to intervals of observations, including magnetic field data and plasma moments, from the CASSIOPE e‐POP/Swarm‐Echo and MMS missions. Plain Language Summary: Measurements captured by spacecraft are necessary to our understanding the space environment near Earth and throughout our solar system. However, spacecraft can often only transmit a small portion of the data they capture back to Earth. This means that many spacecraft must prioritize intervals of data that have the highest probability of helping to further our understanding of these environments. Some missions utilize humans, on Earth, to help select these scientifically important intervals. This solution, called the Scientist‐In‐The‐Loop, can be too expensive or programmatically complex for many small missions to implement. This manuscript presents a technique for the detection of anomalous events in spaceflight measurements using statistical analysis and machine learning. These detected anomalies can be used to prioritize data that has a high probability of scientific relevance. Further, the proposed technique is highly generalizable and computationally lightweight, making it suitable for a variety of missions. Several case studies from multiple existing missions will be analyzed throughout this paper. Key Points: Spacecraft often cannot transmit all measurements to Earth at full cadence due to telemetry bandwidth limitationsMany missions must implement complex data prioritization schemes to ensure only the highest‐priority data is transmitted at high cadenceThe proposed data prioritization technique is highly generic, compatible with inexpensive hardware, and suitable for low‐cost missions [ABSTRACT FROM AUTHOR]

This research aims to measure the nitrogen balance index (NBI) used in the evaluation of nitrogen (N) and chlorophyll (CHL), anthocyanin (ANTS), and flavonol (FLAV) contents of some cereal and legume microgreens. The experiment was established in the Fully Controlled Climate Room of Van Yuzuncu Yil University Faculty of Agriculture Department of Field Crops in OctoberNovember 2020. The plant materials used in the research were Bezostaja-1, Dogu-88, Palandoken and Ayyildiz wheat, Tarm-92, Cetin-2000, Larende and Kral-97 barley, Haskara, Dirilis, Kirklar, Kahraman and Faikbey oats, Arifiye maize, Uzbek and Sazak lentils. Arda chickpea, Goynuk bean, Mung bean, black chickpea, Amazon cowpea, Lutfibey sainfoin, Bilensoy-80 alfalfa, and Dadas red clover including 14 cereal and 10 legume varieties. The seeds planted in the planting medium consisting of the mixture prepared in plastic chalets with a 1331olüme of 500 cc were taken to a fully controlled climate room and microgreens were obtained by applying 50-60% humidity, 21 ± 2/17 ± 2 °C and 16/8 light/dark period, and microgreens were obtained in cereals and legumes. Polyphenolic measurements were taken when the microgreens were 7-9 days old and when sprouts of the forage legumes group were 20-21 days old. Measurements were performed in triplicate with the Dualex+ 4 Scientific (FORCE-A, Orsay, France) device. Statistical analysis of the measurements was carried out according to the Randomized Parcels Trial Design. According to the measurement results, the highest contents based on varieties were determined in Lutfibey sainfoin for NBI (106.533±10.68 mg/g), for CHL in Arda chickpea (41.3 ±2.63 µg/cm²), for ANTS in Dadas red clover (0.08 ±0.01 dualex index) and FLAV in Amazon cowpea (0.746 ±0.03 dualex index). According to group-based averages, the highest values were determined for NBI in forage legumes (86.844 mg/g), for CHL in barley (31.14 µg/cm²) and oats, edible cereal legumes and forage legumes in the same group, for ANTS in forage legumes (0.053 dualex index) and wheat and oats in the same group, for FLAV in edible cereal legumes (0.56 dualex index) and oat in the same group. The study concluded that cereal and legume microgreens have polyphenolic-rich contents. Similar research to be carried out under controlled and field conditions would be useful in evaluating bioactive ingredients and N. [ABSTRACT FROM AUTHOR]

The simulation of rainfall interception by vegetation is essential to water resource management, considering both changing land use and climate change effects. In the rainfall interception models, the evaporation rate is frequently estimated by means of the Penman-Monteith method, but the Priestley-Taylor equation appears as a promising approach with fewer input requirements. In this study these both formulations were evaluated with the sparse Gash model with variable parametrization for estimating rainfall interception by four tree species in a Brazilian dry tropical forest. The Penman-Monteith equation was used with the canopy resistance set to zero, and the momentum method was applied for estimating the aerodynamic resistance. The Priestley-Taylor formulation was tested with the proportional coefficients (α) of 1.26 and 1.34. The results of rainfall predictions were compared with the measurements by statistical indicators, which pointed slightly favorable to Penman-Monteith method. The Priestley-Taylor with α = 1.26 resulted in predictions better than with α = 1.34. Most of the simulations were classified as good (CMRE varying from 5.5 − 9.3%). The Priestley-Taylor method can be used for estimating the evaporation rate in simulations based on the sparse Gash model with variable parametrization in the studied dry tropical forest, under situations with restrictions of micrometeorological measurements or minimal processing time requirement. [ABSTRACT FROM AUTHOR]

This article discusses the importance of adhering to the Fundamental Principles of Official Statistics (FPOS) in the production and dissemination of statistical information. It explores the challenges faced by Eurostat, the statistical office of the EU, including data quality and accuracy, resource constraints, and maintaining public trust. The article also highlights the challenges and problems in statistical analysis, such as model selection, missing data, and interpretation and communication. It emphasizes the interdisciplinary nature of quantitative methods in social sciences and their role in informing policy decisions. The text discusses the evolution and limitations of quantitative methods, econometrics, and statistics in social sciences and economic analysis, and concludes that while these methods have limitations, they can be valuable tools when used correctly and transparently. It also discusses the challenges and limitations of quantitative methods, emphasizing the importance of employing appropriate methodologies and involving statisticians in data analysis and study design. The text concludes by advocating for innovative approaches to skills dissemination and expanding the reach of statistical knowledge. [Extracted from the article]

ECG signals play a vital role in the diagnosis of cardiovascular conditions. However, they often suffer from the effects of various noise sources, including baseline wandering, respiratory artifact noise, power line interference and electrode motion artifacts. To overcome these challenges, it is imperative to implement low-frequency signal noise reduction strategies. Such strategies aim to significantly improve the quality of ECG signals, thus promoting more accurate and reliable diagnosis of cardiovascular disorders. This paper conducts a comparative analysis to assess the effectiveness of commonly used filtering and wavelet techniques in reducing Baseline Wander (BW) noise within ECG signals generated by the influence of breathing or electrode movements. It is common to observe the selection and evaluation of only one particular technique in the existing literature. In contrast, this study aims to provide a comprehensive comparative analysis, providing insight into the performance and relative merits of different techniques. Our research uses both filtering and Discrete Wavelet Transform (DWT) techniques in baseline noise removal. In this context, a reference point is established utilizing noise-free signals and a meticulous investigation of the wavelet-based approach that most effectively eliminates the resulting noise is provided. Subsequently, we assess the reference input and output signal via Signal-to-Noise Ratio (SNR) and Kolmogorov–Smirnov statistical test measurements. The most important contribution of this work to the scientific community resides in the comprehensive examination of IIR/FIR-based and wavelet method-based filtering methods capable of yielding the highest SNR levels across various ECG signals with various types of BW noise. Additionally, the effectiveness of the Chebychev-II filter in BW noise removal is highlighted. Our study was conducted using the MATLAB platform and code command lines were shared to facilitate the reproduction of our study by other researchers. It is considered that this study will be an important reference in the selection of effective techniques for removing BW noise within ECG signals. [ABSTRACT FROM AUTHOR]

Background: Insertion of an external ventricular drain (EVD) is a first-line treatment of acute hydrocephalus caused by aneurysmal subarachnoid haemorrhage (aSAH). Once the patient is clinically stable, the EVD is either removed or replaced by a permanent internal shunt. The optimal strategy for cessation of the EVD is unknown. Prompt closure carries a risk of acute hydrocephalus or redundant shunt implantations, whereas gradual weaning may increase the risk of EVD-related infections. Methods: DRAIN (Danish RAndomised Trial of External Ventricular Drainage Cessation IN Aneurysmal Subarachnoid Haemorrhage) is an international multicentre randomised clinical trial comparing prompt closure versus gradual weaning of the EVD after aSAH. The primary outcome is a composite of VP-shunt implantation, all-cause mortality, or EVD-related infection. Secondary outcomes are serious adverse events excluding mortality and health-related quality of life (EQ-5D-5L). Exploratory outcomes are modified Rankin Scale, Fatigue Severity Scale, Glasgow Outcome Scale Extended, and length of stay in the neurointensive care unit and hospital. Outcome assessment will be performed 6 months after ictus. Based on the sample size calculation (event proportion 80% in the gradual weaning group, relative risk reduction 20%, alpha 5%, power 80%), 122 participants are required in each intervention group. Outcome assessment for the primary outcome, statistical analyses, and conclusion drawing will be blinded. Two independent statistical analyses and reports will be tracked using a version control system, and both will be published. Based on the final statistical report, the blinded steering group will formulate two abstracts. Conclusion: We present a pre-defined statistical analysis plan for the randomised DRAIN trial, which limits bias, p-hacking, and data-driven interpretations. This statistical analysis plan is accompanied by tables with simulated data, which increases transparency and reproducibility. Trial registration: ClinicalTrials.gov identifier: NCT03948256. Registered on May 13, 2019. [ABSTRACT FROM AUTHOR]

Malaria is a deadly disease that can take a person's life if not predicted or cured correctly. Numerous factors like temperature, humidity, precipitation, etc., impact India's increasing cases of malaria diseases. This research presents an advanced machine learning regression technique recently developed to anticipate the prevalence of malaria in Tripura using a real-world data. The proposed structure uses nine different regression methods, such as multilayer perceptron (MLP), random forest, support vector regressor, gradient boosting regressor, Bayesian ridge, kernel ridge, extreme gradient boost regressor (XGB), light gradient boosting machine (LGBM regressor and linear regression, to predict malaria using the most affecting factors of malaria diseases, namely temperature, humidity, precipitation, month, and years as input. Furthermore, to opt out the best suited technology for malaria cases prediction, the range of value method (ROVM)–multi-criteria decision methods (MCDM) technique has been applied, considering various statistical measurements as criteria. Ultimately, a comparison of various MCDM techniques reveals that MLP, XGB, and RF emerge as the top three choices. MLP regression, with root-mean-square error (RMSE) value of 0.03357, yields the lowest RMSE value, and the coefficient of determination (R2) is 0.97616, yielding the maximum among other regressions. To effectively battle the illness in Tripura, it could be useful for continuing intervention tactics by governmental, profit and nonprofit organizations. [ABSTRACT FROM AUTHOR]

Copyright of Psychological Science is the property of Psychological Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With our increasing reliance on technology, there is a growing demand for efficient and seamless access control systems. Smartphone-centric biometric methods offer a diverse range of potential solutions capable of verifying users and providing an additional layer of security to prevent unauthorized access. To ensure the security and accuracy of smartphone-centric biometric identification, it is crucial that the phone reliably identifies its legitimate owner. Once the legitimate holder has been successfully determined, the phone can effortlessly provide real-time identity verification for various applications. To achieve this, we introduce a novel smartphone-integrated detection and control system called Identification: Legitimate or Counterfeit (ILC), which utilizes gait cycle analysis. The ILC system employs the smartphone's accelerometer sensor, along with advanced statistical methods, to detect the user's gait pattern, enabling real-time identification of the smartphone owner. This approach relies on statistical analysis of measurements obtained from the accelerometer sensor, specifically, peaks extracted from the X-axis data. Subsequently, the derived feature's probability distribution function (PDF) is computed and compared to the known user's PDF. The calculated probability verifies the similarity between the distributions, and a decision is made with 92.18% accuracy based on a predetermined verification threshold. [ABSTRACT FROM AUTHOR]

Background: The amide proton transfer‐weighted (APTw) imaging for kidney diseases is important. However, the breathing patterns on APTw imaging remains unexplored. Purpose: This study aimed to investigate the effects of intermittent breath‐hold (IBH) and free breathing (FB) on renal 3D‐APTw imaging. Study Type: Healthy volunteers were enrolled prospectively, and renal clear cell carcinoma (RCCC) patients were included retrospectively. Population: 58 healthy volunteers and 10 RCCC patients. Field Strength/Sequence: 3‐T, turbo spin echo, and fast field echo. Assessment: 3D‐APTw imaging was scanned using the IBH and FB methods in volunteers and using the IBH method in RCCC patients. The image quality was evaluated by three observers according to the 5‐point Likert scale. Optimal images rated at three points or higher were used to measure the APT values. Statistical Analysis: The measurement repeatability was assessed using the intraclass correlation coefficient (ICC) and the Bland–Altman plot. The APT values were analyzed using McNemar's test, one‐way analysis of variance, and t test. Results: 50 healthy volunteers and 8 RCCC patients were enrolled. Renal 3D‐APTw imaging using the IBH method revealed a higher success rate (88% vs 78%). The ICCs were excellent in the IBH group (ICCs > 0.74) and were good in the FB group (ICCs < 0.74). No significant differences in the APT values among various zones using the IBH (P = 0.263) or FB method (P = 0.506). The mean APT value using the IBH method (2.091% ± 0.388%) was slightly lower than the FB method (2.176% ± 0.292%), but no significant difference (P = 0.233). The APT value of RCCC (4.832% ± 1.361%) was considerably higher than normal renal using the IBH method. Conclusions: The study demonstrated that the IBH method substantially increased the image quality of renal 3D‐APTw imaging. Furthermore, APT values may vary between normal and tumor tissues. Level of Evidence: 2 Technical Efficacy: Stage 2 [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Genetics & Biodiversity Journal is the property of Genetics & Biodiversity Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Genetics & Biodiversity Journal is the property of Genetics & Biodiversity Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this work, we convert a common verification lab in the physics curriculum, measuring the charge-to-mass ratio of the electron (e/m), into an investigative lab on systematic uncertainty. The Bainbridge apparatus, commonly used to measure e/m, can have significant systematic uncertainties, leading to large discrepancies with the accepted value. Students were asked to quantify possible systematic uncertainties in the apparatus and correct them. Building upon each other's work from semester to semester, students characterized multiple sources of systematic uncertainty. Not only did the students learn about uncertainty analysis techniques that reveal systematic uncertainties, but they also dramatically improved the accuracy of the apparatus, reducing the discrepancy from 15% to 0.5%. This paper describes a pedagogical approach to exploring unknown systematic uncertainties in an intermediate laboratory setting and the student-learning benefits of such an approach. In addition, it provides detailed information about untangling and correcting the sources of systematic uncertainty in the Bainbridge apparatus. Editor's Note: This work will be very interesting to two audiences. Its most obvious audience is those who teach the e/m experiment. These readers may have observed that the statistical measurement uncertainties are seldom large enough to make the result consistent with the accepted value. This paper explains these deviations. The paper may be even more useful to the less obvious audience: those who teach upper-level laboratories or projects. This paper may inspire these instructors to involve their students in extended investigations into this and other classic experiments. [ABSTRACT FROM AUTHOR]

Biomechanical trajectories are often routed through a chain of processing steps prior to statistical analysis. As changes in processing parameter values can affect these trajectories, care is required when choosing data processing specifics. The purpose of this Research Note was to demonstrate a simple way to propagate data processing parameter uncertainty to statistical inferences regarding biomechanical trajectories. As an example application. the correlation between foot contact duration and vertical ground reaction force during constant-speed treadmill walking was considered. Uncertainty was modeled using plausible-range uniform distributions in three data processing steps, and Monte Carlo simulation was used to construct probabilistic representations of both individual vertical ground reaction force measurements and the ultimate statistical results. Whereas an initial. plausible set of parameter values yielded a significant correlation between contact duration and late-stance vertical ground reaction force, Monte Carlo simulations revealed strong sensitivity, with 'significance" being reached in fewer than 40% of simulations. with relatively little net effect of parameter uncertainty magnitude. These results indicate that propagating processing parameter uncertainty to statistical results promotes a cautious, nuanced, and robust view of observed effects. By extension, Monte Carlo simulations may yield greater interpretive consistency across studies involving data processing uncertainties. [ABSTRACT FROM AUTHOR]

Recently, the electron mobility in wedge-shaped c-GaN nanowall networks has been estimated to cross the theoretical mobility limit for bulk GaN. Significant blue-shift of the bandgap has also been observed. Both the findings are explained in terms of two-dimensional electron gas (2DEG) formed at the central vertical plane of the walls due to the polarization charges at the two inclined faces. Carrier concentration and mobility have earlier been determined from thermoelectric power and conductivity measurements with the help of a statistical model. Due to the network nature of the system, direct measurements of these quantities from Hall experiments are not possible. Search for a better way to estimate mobility in this system thus becomes important. Since, strain can also lead to the blue-shift of the bandgap, it is also imperative to evaluate carefully the role of strain. Here, using Raman spectroscopy, we have estimated carrier concentration and mobility in these nanowall networks with varied average tip-widths. Depth distribution of strain and luminescence characteristics are also studied. The study reveals that strain has no role in the bandgap enhancement. Moreover, the electron mobility, which is determined from the lineshape analysis of the A1(LO)-plasmon coupled mode in Raman spectra, has been found to be significantly higher than the theoretical limit of mobility for bulk GaN for the same electron concentration. These results thus corroborate the picture of polarization induced vertical 2DEG formation in these walls as predicted theoretically. [ABSTRACT FROM AUTHOR]

Background: Orbital floor fractures (OFFs) represent an interesting chapter in maxillofacial surgery, and one of the main challenges in orbit reconstruction is shaping and cutting the precise contour of the implants due to its complex anatomy. Objective: The aim of the retrospective study was to demonstrate, through pre- and postoperative volumetric measurements of the orbit, how the use of a preformed titanium mesh based on the stereolithographic model produced with 3D printers ("In-House" reconstruction) provides a better reconstruction volumetric compared to the intraoperatively shaped titanium mesh. Materials and Methods: The patients with OFF enrolled in this study were divided into two groups according to the inclusion criteria. In Group 1 (G1), patients surgically treated for OFF were divided into two subgroups: G1a, patients undergoing orbital floor reconstruction with an intraoperatively shaped mesh, and G1b, patients undergoing orbital floor reconstruction with a preoperative mesh shaped on a 3D-printed stereolithographic model. Group 2 (G2) consisted of patients treated for other traumatic pathologies (mandible fractures and middle face fractures not involving orbit). Pre- and postoperative orbital volumetric measurements were performed on both G1 and G2. The patients of both groups were subjected to the measurement of orbital volume using Osirix software (Pixmeo SARL, CH-1233 Bernex, Switzerland) on the new CT examination. Both descriptive (using central tendency indices such as mean and range) and regressive (using the Bravais–Pearson index, calculated using the GraphPad program) statistical analyses were performed on the recorded data. Results: From 1 January 2017 to 31 December 2021, of the 176 patients treated for OFF at the "Magna Graecia" University Hospital of Catanzaro 10 fulfilled the study's inclusion criteria: 5 were assigned to G1a and 5 to G1b, with a total of 30 volumetric measurements. In G2, we included 10 patients, with a total of 20 volumetric measurements. From the volumetric measurements and statistical analysis carried out, it emerged that the average of the volumetric differences of the healthy orbits was ±0.6351 cm3, the standard deviation of the volumetric differences was ±0.3383, and the relationship between the treated orbit and the healthy orbit was linear; therefore, the treated orbital volumes tend to approach the healthy ones after surgical treatment. Conclusion: This study demonstrates that if the volume is restored within the range of the standardized mean, the diplopia is completely recovered already after surgery or after one month. For orbital volumes that do not fall within this range, functional recovery could occur within 6 months or be lacking. The restoration of the orbital volume using pre-modeled networks on the patient's anatomical model, printed internally in 3D, allows for more accurate reconstructions of the orbital floor in less time, with clinical advantages also in terms of surgical timing. [ABSTRACT FROM AUTHOR]

Hydropower remains the largest source of renewable electricity while most hydropower plants, especially commercial hydropower plants, require accurate future energy or reservoir incoming flow ahead prediction for ensuring effective generation planning and revenue maximization as well as for flood control, and environmental protection purposes. This study develops effective daily and monthly prediction models using a Bi-Directional Long Short-Term Memory (Bi-LSTM) network-based meteorological and hydrological data, properly pre-processed by cross-correlation function (CCF), and normalization techniques for predicting the future energy portion corresponding to the natural incoming flow expected flowing into the reservoir (reservoir inflow) in the near future (days and months ahead) for Nam Theun 2 Hydropower Plant (NT2HPP) in Laos. The proposed models were tested with a separated dataset excluded from the training dataset and then evaluated using measurement or statistical indices including the root mean square error (RMSE), relative error (RE), and efficiency index (EI) in couple with Taylor diagrams. For comparison purposes, the two widely used prediction approaches namely, Feedforward Backpropagation Neural Network (FFBPNN), and Long Short-Term Memory (LSTM) based on the same datasets were also adopted and analyzed. The study results revealed that the proposed daily prediction model, with the statistical indices of EI = 74.872%, RMSE = 11.313, and RE = 4.560% for 2020; EI = 90.910%, RMSE = 7.237, and RE = 1.896% for 2021; and EI = 82.807%, RMSE = 4.918, and RE = 2.763% for 2022, was superior to the other models. Similarly, the proposed monthly prediction model, with the statistical indices of EI = 98.133%, RMSE = 55.213, and RE = 8.407% for 2020; EI = 99.819%, RMSE = 19.314, and RE = 0.611% for 2021; and EI = 97.316%, RMSE = 44.780, and RE = 6.878% for 2022, was also superior to the other models. These proved that the proposed models were more robust and efficient for future energy prediction. [ABSTRACT FROM AUTHOR]

Stitching methods allow one to measure a wider surface without the loss of resolution. The observation of small details with a better topographical representation is thus possible. However, it is not excluded that stitching methods generate some errors or aberrations on topography reconstruction. A device including confocal microscopy (CM), focus variation (FV), and coherence scanning interferometry (CSI) instrument modes was used to chronologically follow the drifts and the repositioning errors on stitching topographies. According to a complex measurement plan, a wide measurement campaign was performed on TA6V specimens that were ground with two neighboring SiC FEPA grit papers (P#80 and P#120). Thanks to four indicators (quality, drift, stability, and relevance indexes), no measurement drift in the system was found, indicating controlled stitching and repositioning processes for interferometry, confocal microscopy, and focus variation. Measurements show commendable stability, with interferometric microscopy being the most robust, followed by confocal microscopy, and then focus variation. Despite variations, robustness remains constant for each grinding grit, minimizing interpretation biases. A bootstrap analysis reveals time-dependent robustness for confocal microscopy, which is potentially linked to human presence. Despite Sa value discrepancies, all three metrologies consistently discriminate between grinding grits, highlighting the reliability of the proposed methodology. [ABSTRACT FROM AUTHOR]

Spectral characterization of noise environments that lead to the decoherence of qubits is critical to developing robust quantum technologies. While dynamical decoupling offers one of the most successful approaches to characterize noise spectra, it necessitates applying large sequences of π pulses that increase the complexity and cost of the method. Here, we introduce a noise spectroscopy method that utilizes only the Fourier transform of free induction decay or spin echo measurements, thus removing the need for the application many π pulses. We show that our method faithfully recovers the correct noise spectra for a variety of different environments (including 1/f-type noise) and outperforms previous dynamical decoupling schemes while significantly reducing their experimental overhead. We also discuss the experimental feasibility of our proposal and demonstrate its robustness in the presence of statistical measurement error. Our method is applicable to a wide range of quantum platforms and provides a simpler path toward a more accurate spectral characterization of quantum devices, thus offering possibilities for tailored decoherence mitigation. [ABSTRACT FROM AUTHOR]

In this study, a deep learning algorithm namely long short-term memory (LSTM) has been developed for forecasting daily pan evaporation at Sydney airport, Australia. The accuracy of the developed LSTM model has been compared with a commonly used machine learning model, namely multilayer perceptron neural network (MLP-NN). The evaporation rate as a single parameter was used with one time-lag based on autocorrelation function (ACF). The utilized data duration was from January 2021 to February 2022 (during Covid-19 pandemic). Different statistical measurements have been applied in order to evaluate the performance of the proposed models. The results showed that the developed LSTM model outperformed MLP-NN. The LSTM performed well with RMSE = 1.074, MAE = 0.771, R2 = 0.97, while the MLP-NN had least performance with RMSE = 2.801, MAE = 1.994, and R2 = 0.57. The developed LSTM model could be utilized in other locations for forecasting daily pan evaporation. [ABSTRACT FROM AUTHOR]

To accurately simulate the hydration process of cementitious materials, understanding the growth rate of C‐S‐H layers around clinker grains is crucial. Nonetheless, the thickness of the hydrate layer shows substantial variation around individual grains, depending on their surrounding. Consequently, it is not feasible to measure hydrate layers manually in a reliable and reproducible manner. To address this challenge, a software has been developed to statistically determine the C‐S‐H thickness, requiring minimal manual interventions for thresholding and for setting limits like particle size or circularity. This study presents a tool, which automatically identifies suitable clinker grains and and perform statistical measurements of their hydrate layer up to a specimen age of 28 days. The findings reveal a significant increase in the C‐S‐H layer, starting from 0.45 μm$\umu {\rm {m}}$ after 1 day and reaching 3.04 μm$\umu {\rm {m}}$ after 28 days. However, for older specimens, the measurement of the C‐S‐H layer was not feasible due to limited pore space and clinker grains. [ABSTRACT FROM AUTHOR]

This paper aims to give statistical significance to the measurement of spatial concentration in the context of entropy-based approaches. We simulate confidence intervals based on a null hypothesis able to capture systematic spatial concentration of firms from random patterns, and dissimilarities between the distributions of firms and employees. We implement this two-step methodology to the European manufacturing economy, and we find a substantive spatial clustering of establishments whereby the spatial divergence between employees and firms is significant both for small-scale industries typically considered as localized because of industry-specific Marshallian external economies and for those industries characterized by considerable internal scale economies. We suggest that a high heterogeneity in firm size may have positive implications for aggregate competitiveness at the sectoral level. [ABSTRACT FROM AUTHOR]

The capacity for climate governance is crucial for sustainable advancement, with data elements being a pivotal production factor in contemporary governance. This study examines the trajectory and strategy of digital transformation in climate governance, creating a three-dimensional dataset encapsulating 11 primary and 36 secondary indicators to facilitate the assessment of digital climate governance. Employing spatiotemporal analysis and coupling coordination models, this study evaluates the digitalization levels in climate governance across 30 regions in China, examining how to progress digital integration from governmental and market perspectives. Findings reveal a consistent improvement in China's regional digital climate governance, bolstering economic and social progress. Nonetheless, regional disparities and developmental lags persist, with convergence analysis indicating a divergence trend in provincial climate governance capabilities. Moreover, kernel density and Markov chain analyses suggest an ongoing evolution in regional digital climate governance efforts, aiming at achieving a higher development plateau. The study emphasizes the dual role of government and market dynamics in boosting digital governance levels, deducing from two-stage regression that effective government-market interplay is vital for elevating governance quality and fostering new productive forces, recommending an integrated governance mechanism for optimal synergy. [ABSTRACT FROM AUTHOR]

Intelligence algorithm systems rely on a large dataset to effectively extract significant features that can recognize patterns for classification purposes and extensively utilized to assist the physicians in diagnosis of lung cancer. Extracting valuable features from the available dataset is crucial, especially in cases where additional real data may not be readily accessible. In this context, we propose a novel method called feature extraction based on centroid (FE_CXY) for lesion localization, utilizing a statistical approach. The approach begins with a segmentation process that employs image processing techniques to extract features of interest which is data centroid. This extracted data is then used to compute statistical measurements, revealing hidden patterns that contribute to distinguishing between lesion and non-lesion locations. The method's efficiency is reflected in the development of robust models with improved performance in localizing lung lesions. The study's statistical findings strongly indicate that FE_CXY plays a crucial role as an important feature for detecting lesion localization supported by a student's t-test, which identifies a statistically significant difference in the patterns between lesion and non-lesion localization (p<0.05). By incorporating this method into lung cancer detection systems, we anticipate improved accuracy and efficacy, thereby benefiting early diagnosis and treatment planning. [ABSTRACT FROM AUTHOR]

Congenital heart disease (CHD) is the most common type of birth defect, representing a significant cause of perinatal morbidity and mortality. Early diagnosis of such anomalies is crucial for improving outcomes. Current protocols recommend a qualitative assessment of cardiac structures using two-dimensional ultrasound (2DUS) and color Doppler imaging. In cases of suspected abnormalities, quantitative assessments through cardiac structure measurements and reference curves can aid in accurate diagnosis. Similar to centiles widely employed in obstetrics, Z-scores provide more precise quantification of various cardiac structures, particularly at the extremes of the curve. While the development of reference curves and Z-scores has progressed over the past two decades, a lack of standardization in measurements and statistical methodology for their determination is evident. Establishing reference curves requires adherence to specific recommendations to improve their accuracy. The purpose of this study is to provide a narrative review of the major studies that have generated reference values for cardiac structures using 2DUS and Z-scores, to evaluate their methodology, and to provide a summary of the results. [ABSTRACT FROM AUTHOR]

Nanotwinned face-centered-cubic (FCC) metals have gained significant attention recently due to their exceptional mechanical and electrical properties. Among all the FCC metals, Ag has the lowest stacking fault energy, which allows twin formation to easily occur. In this study, Ag films with a thickness of 4 µm were deposited on Si (100) substrates pre-coated with Ti adhesion layers by electron beam evaporation. With ion beam assistance during the evaporation process, highly (111)-textured and high-density nanotwinned Ag films with excellent properties were produced. Both x-ray diffraction (XRD) and electron backscatter diffraction (EBSD) analyses indicate that the Ag film possesses a strong (111) preferred orientation. In addition, statistical measurements from transmission electron microscopy (TEM) images show that the average twin spacing in nanotwinned Ag films with a deposition rate of 1.8 nm/s is only 6.1 nm. However, conventional models based on thermodynamics are insufficient to describe the formation of growth twins in an ion beam-assisted deposition system. Therefore, an ion bombardment stress model is introduced in this work. The results of the mechanism of deformation twinning are also discussed. [ABSTRACT FROM AUTHOR]

Copyright of PODIUM- Revista de Ciencia y Tecnologia en la Cultura Física is the property of Universidad de Pinar del Rio and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

2D memristors have demonstrated attractive resistive switching characteristics recently but also suffer from the reliability issue, which limits practical applications. Previous efforts on 2D memristors have primarily focused on exploring new material systems, while damage from the metallization step remains a practical concern for the reliability of 2D memristors. Here, the impact of metallization conditions and the thickness of MoS2 films on the reliability and other device metrics of MoS2‐based memristors is carefully studied. The statistical electrical measurements show that the reliability can be improved to 92% for yield and improved by ≈16× for average DC cycling endurance in the devices by reducing the top electrode (TE) deposition rate and increasing the thickness of MoS2 films. Intriguing convergence of switching voltages and resistance ratio is revealed by the statistical analysis of experimental switching cycles. An "effective switching layer" model compatible with both monolayer and few‐layer MoS2, is proposed to understand the reliability improvement related to the optimization of fabrication configuration and the convergence of switching metrics. The Monte Carlo simulations help illustrate the underlying physics of endurance failure associated with cluster formation and provide additional insight into endurance improvement with device fabrication optimization. [ABSTRACT FROM AUTHOR]

This research paper delves into the effectiveness and impact of behavior change techniques fostered by information technologies, particularly wearables and Internet of Things (IoT) devices, within the realms of engineering and computer science. By conducting a comprehensive review of the relevant literature sourced from the Scopus database, this study aims to elucidate the mechanisms and strategies employed by these technologies to facilitate behavior change and their potential benefits to individuals and society. Through statistical measurements and related works, our work explores the trends over a span of two decades, from 2000 to 2023, to understand the evolving landscape of behavior change techniques in wearable and IoT technologies. A specific focus is placed on a case study examining the application of behavior change techniques (BCTs) for monitoring vital signs using wearables, underscoring the relevance and urgency of further investigation in this critical intersection of technology and human behavior. The findings shed light on the promising role of wearables and IoT devices for promoting positive behavior modifications and improving individuals' overall well-being and highlighting the need for continued research and development in this area to harness the full potential of technology for societal benefit. [ABSTRACT FROM AUTHOR]

Wetland vegetation and ecology of Lake Abaya in the southern Ethiopia was studied to determine floristic composition, plant community type and vegetation ecology. A total of 102 plots were laid along transects that were set up preferentially across areas where there were rapid changes in vegetation or marked environmental gradients to collect data on estimate of percentage aerial cover of plant species and environmental variables. Vegetation data was analyzed by agglomerative hierarchical cluster analysis using similarity ratio as a resemblance index and Ward's linkage method. Multivariate data analysis was performed using appropriate packages in R version 2.14.0. Canonical Correspondence Analysis (CCA) was used to explore the relationship between the species composition and environmental variables. The environmental data included in the CCA were determined using stepwise backward and forward selection of variables by ANOVA test. Statistical measurement regarding species diversity, richness and evenness of the plant community types was carried out by using Shannon-Wiener diversity indices. A total of 92 plant species belonging to 66 genera and 34 families were identified. Families Poaceae, Asteraceae, Fabaceae, Cyperaceae, Solanaceae, Euphorbiaceae and Amaranthaceae account for about 56.99% of the total proportion. Based on the cluster analysis, five plant community types were identified. The most important factors influencing the plant species composition and pattern of wetland plant communities were water drainage, water depth, land use, slope, altitude, and hydrogeomorphology. Therefore, these factors should be considered in future management and protection under the circumstance of climate change and human activities. [ABSTRACT FROM AUTHOR]

Congestive heart failure (CHF) is one of the primary sources of mortality and morbidity among the global population. Over 26 million individuals globally are affected by heart disease, and its prevalence is rising by 2% yearly. With advances in healthcare technologies, if we predict CHF in the early stages, one of the leading global mortality factors can be reduced. Therefore, the main objective of this study is to use machine learning applications to enhance the diagnosis of CHF and to reduce the cost of diagnosis by employing minimum features to forecast the possibility of a CHF occurring. We employ a deep neural network (DNN) classifier for CHF classification and compare the performance of DNN with various machine learning classifiers. In this research, we use a very challenging dataset, called the Cardiovascular Health Study (CHS) dataset, and a unique pre-processing technique by integrating C4.5 and K-nearest neighbor (KNN). While the C4.5 technique is used to find significant features and remove the outlier data from the dataset, the KNN algorithm is employed for missing data imputation. For classification, we compare six state-of-the-art machine learning (ML) algorithms (KNN, logistic regression (LR), naive Bayes (NB), random forest (RF), support vector machine (SVM), and decision tree (DT)) with DNN. To evaluate the performance, we use seven statistical measurements (i.e., accuracy, specificity, sensitivity, F1-score, precision, Matthew's correlation coefficient, and false positive rate). Overall, our results reflect our proposed integrated approach, which outperformed other machine learning algorithms in terms of CHF prediction, reducing patient expenses by reducing the number of medical tests. The proposed model obtained 97.03% F1-score, 95.30% accuracy, 96.49% sensitivity, and 97.58% precision. [ABSTRACT FROM AUTHOR]