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Background The incidence of sickness absence (SA) due to common mental disorders (CMDs) has increased in recent decades. It is hence important to elucidate how individuals with CMDs can maintain work. The aim was to analyse the relationship between psychosocial and organisational workplace factors and a spell of > 14 days of SA among persons with CMDs. Methods Included were respondents of the Swedish Work Environment Survey (SWES) 1993–2013, diagnosed with a CMD up to five years before the interview in the SWES (n = 3,795). Relative Risk (RR) regression models with 95% Confidence Intervals (CIs) analysed associations between psychosocial-, and organisational workplace factors and a subsequent spell of SA > 14 days. Results Low control over work (RR:1.16; CI:1.01–1.35), job strain (RR:1.25; CI:1.04–1.49), no flexible working hours (RR:1.25; CI:1.08–1.45) or no possibility to work from home (RR:1.37; CI:1.13–1.66) were significantly related to an increased risk of SA. Persons diagnosed with depression experiencing job strain had the highest increased risk of SA (RR:1.55; CI: 1.07–2.25). Conclusions A sustainable work-life among working individuals with CMDs can be provided by reducing job strain, and if possible, by increasing flexibility regarding workplace and working hours. This may prevent spells of SA, and hereby increase productivity.

Continuous Thermodilution is a novel method of quantifying coronary flow (Q) in mL/min. To account for variability of Q within the cardiac cycle, the trace is smoothened with a 2 s moving average filter. This can sometimes be ineffective due to significant heart rate variability, ventricular extrasystoles, and deep inspiration, resulting in a fluctuating temperature trace and ambiguity in the location of the “steady state”. This study aims to assess whether a longer moving average filter would smoothen any fluctuations within the continuous thermodilution traces resulting in improved interpretability and reproducibility on a test–retest basis. Patients with ANOCA underwent repeat continuous thermodilution measurements. Analysis of traces were performed at averages of 10, 15, and 20 s to determine the maximum acceptable average. The maximum acceptable average was subsequently applied as a moving average filter and the traces were re-analysed to assess the practical consequences of a longer moving average. Reproducibility was then assessed and compared to a 2 s moving average. Of the averages tested, only 10 s met the criteria for acceptance. When the data was reanalysed with a 10 s moving average filter, there was no significant improvement in reproducibility, however, it resulted in a 12% diagnostic mismatch. Applying a longer moving average filter to continuous thermodilution data does not improve reproducibility. Furthermore, it results in a loss of fidelity on the traces, and a 12% diagnostic mismatch. Overall, current practice should be maintained.

Cassava (Manihot esculenta) is one of the most important sources of dietary calories in the tropics, playing a central role in food and economic security for smallholder farmers. Cassava production is highly constrained by several pests and diseases, mostly cassava mosaic disease (CMD) and cassava brown streak disease (CBSD). These diseases cause significant yield losses, affecting food security and the livelihoods of smallholder farmers. Developing resistant varieties is a good way of increasing cassava productivity. Although some levels of resistance have been developed for some of these diseases, there is observed breakdown in resistance for some diseases, such as CMD. A frequent re‐evaluation of existing disease resistance traits is required to make sure they are still able to withstand the pressure associated with pest and pathogen evolution. Modern breeding approaches such as genomic‐assisted selection in addition to biotechnology techniques like classical genetic engineering or genome editing can accelerate the development of pest‐ and disease‐resistant cassava varieties. This article summarizes current developments and discusses the potential of using molecular genetics and genomics to produce cassava varieties resistant to diseases and pests. Combating cassava diseases and pests requires a holistic approach that combines traditional breeding methods, genomics and biotechnology innovations such as conventional genetic engineering and genome editing.

Energy efficiency plays a major role in sustaining lifespan and stability of the network, being one of most critical factors in wireless sensor networks (WSNs). To overcome the problem of energy depletion in WSN, this paper proposes a new Energy Efficient Clustering Scheme named African Vulture Optimization Algorithm based EECS (AVOACS) using AVOA. The proposed AVOACS method improves clustering by including four critical terms: communication mode decider, distance of sink and nodes, residual energy and intra-cluster distance. Through mimicking the natural scavenging behavior of African vultures, AVOACS continuously balances energy consumption on nodes resulting in an increase in network stability and lifetime. For CH selection, we use AVOACS, which considers the following parameters: communication mode decider, the distance between sink and node, residual energy, and intra-cluster distance. In comparison to the OE2-LB protocol, simulation findings demonstrate that AVOACS enhances stability, network lifetime, and throughput by 21.5%, 31.4%, and 16.9%, respectively. The results show that AVOACS is an effective clustering algorithm for energy-efficient operation in heterogeneous WSN environments as it contributes to a large increase of network lifetime and significant enhancement of performance.

This research uses the third edition of the Gaia Data Release (DR3) to re-investigate the open star cluster NGC 2509. We employed the pyUPMASK Python package and HDBSCAN algorithms to identify the cluster member stars. The current analysis introduces a new method that connects the membership probability of stars in the cluster (using the pyUPMASK tool) with the number of stars predicted by the King model at different distances from the center of the cluster. This approach divides the cluster’s area into concentric rings, or shells, and calculates the membership probability for each shell based on its specific star count, rather than using one average probability for the entire cluster. We calculated all astrophysical parameters of NGC 2509-including center, cluster radius, radial density distribution, color-magnitude diagram, distance, age, and reddening-using the photometric and astrometric data of Gaia DR3. The cluster’s relaxation time, total mass, luminosity, and mass functions are computed. The components of the proper motions ( , ), and the parallax ( ) are found to be , mas/yr and mas, respectively. According to the King model and pyUPMASK membership, we obtained stars with a total mass of . Using the PARSEC stellar isochrones fit, the mean cluster age and its relaxation time are Gyr and Myr, respectively. The cluster distance modulus and reddening are estimated to be , and mag, resulting in a distance of pc. The mass function MF for this cluster has been constructed using a piecewise powerlaw with two power laws, and , rather than the single power law as suggested by Salpeter (1955). The and are found to be and , respectively. Moreover, the is closest to the Salpeter value. Also, we identified 20 member stars as red clump that have G magnitudes between 12.6 and 13.1 mag and slightly higher temperatures than typical giants. We found that 11 members are flagged as variable stars in Gaia DR3 archive.  In addition, there are 88 stars with a radial velocity of around 57.6 ± 7.8 km. Then, we have used the galpy Python package to calculate the cluster’s kinematics and orbital parameters.

In this paper, we re-estimate the astrometric and photometric parameters of the young open star cluster King 18 based on Gaia Data Release 3 (DR3), Two Micron All-sky Survey (2MASS) and VRI CCD observations using the f/4.9 Newtonian focus of 74-inch telescope at Kottamia Astronomical Observatory (KAO) in Egypt. King 18 is a poorly studied open star cluster, for which new results are found in the current study. In order to estimate the membership and determine all the astrophysical parameters of the cluster, we have used data from Gaia DR3 and KAO. The center, cluster radius, radial density distribution, color-magnitude diagrams, distance, age, and reddening of King 18 are calculated. Also, the luminosity and mass functions, the total mass and the relaxation time of the cluster are estimated. The slope value of the mass function ( α ) of King 18 is found to be 2.27± 0.17, which is comparable with Salpeter value. Our estimates for the average cluster age and the relaxation time are 224 ± 6.3 and 28.92 Myrs, respectively. This indicates that King 18 is dynamically stable and a relaxed cluster. The cluster distance modulus from Gaia, 2Mass and VRI observations has been determined to be 12.380 ± 1.320, 12.320 ± 0.107 and 12.280 ± 0.290 mag respectively, which corresponds to distances of 2992.26, 2910.72 and 2857.59 pc, respectively. These results are in good agreement within the error. Moreover the color excesses E(V–I), E(J– K s ) and E( G BP – G RP ) are 0.850 ± 0.087, 0.380 ± 0.091 and 0.980 ± 0.130 respectively. Finally, the proper motions ( μ α cos δ , μ δ ), and parallaxes ( ϖ ) are - 2.603 ± 0.018 , - 2.106 ± 0.013 and 0.324 ± 0.040, respectively.

We present a comprehensive study of the young open cluster King 13 using photometric and astrometric data from Gaia DR3 and 2MASS. Our analysis refines the cluster’s fundamental parameters, including its structure, kinematics, and evolutionary status. To assess membership, we employed the pyUPMASK Python package with the HDBSCAN algorithm. The primary emphasis of this study is our new approach to assign a membership probability at each radius, rather than applying a single value to the entire cluster. These probabilities are calculated based on the number of stars deduced from the King model . This revealed a dense core with an elongated halo aligned with the cluster’s tangent velocity. Cluster orbital analysis suggests the cluster moves in the Galactic plane toward the Galactic center, with its tidal tail aligned with orbital motion-likely due to Galactic tidal effects. We identified 1571 41 member stars with a total mass of 2658.4 61.5 M . The mass function (MF) for the cluster has been constructed using a step function with two power lows, and , rather than the single power low suggested by Salpeter. In this cluster, the and are found to be -3.7 0.4 and 2.3 0.15 , respectively. . The cluster’s physical parameters were derived using PARSEC stellar isochrones, estimating an age of 310 28 Myr and a relaxation time of 134 13 Myr, indicating dynamical stability. The proper motions ( , ) and parallax ( ) were measured as -2.64 0.36 mas yr , -0.89 0.25 mas yr , and 0.245 0.05 mas, respectively. The corresponding distance of the cluster, derived from the parallax, is 4082 231 pc. The derived distance modulus is 13.11 1.03 mag ( 4187 262 pc), with color excess values of 1.17 0.07 mag (Gaia) and 0.44 0.03 mag (2MASS), further validating our results. Additionally, 46 member stars with radial velocity data allowed us to compute the cluster’s orbit using the galpy package. Our findings highlight the presence of a tidal tail directed toward the center of the Galaxy and underscore the role of Galactic tidal forces in shaping King 13’s morphology, reinforcing its importance in the evolution of open clusters.

This study provides a thorough investigation of the open cluster Czernik 38, employing photometric and astrometric data from Gaia DR3 and 2MASS. Our analysis refines the fundamental parameters of the cluster, including its structure, kinematics, evolutionary status, age, and morphology. To evaluate membership, we utilized the pyUPMASK Python package in conjunction with the HDBSCAN algorithm. The main focus of this research is our novel method of assigning a membership probability at each radius, instead of using a singular value for the entire cluster. One of the main outcomes of our research indicates that there is an elongated structure and a leading tidal tail that aligns with the orbital trajectory of the cluster. This tidal phenomenon arises due to orbital differential rotation. Furthermore, we have discovered a new star cluster located 32 arcmin from the center of Czernik. This cluster may serve as a companion to the Czernik 38 cluster in a binary cluster system or a complex colliding system; we will explore this further in subsequent research. According to Gaia, the distance modulus of the cluster and the color excess are measured at 12.69 0.08 mag and 2.40 0.04 mas, respectively. Additionally, from 2Mass, the distance modulus is 12.87 0.93 mag, while the color excess is 0.89 0.2 mag. Moreover, the cluster age is determined to be 115.0 20.3 Myr. The components of proper motion ( , ) and the parallax ( ) are found as -2.41 0.328 mas yr , -5.263 1.063 mas yr , and 0.21 0.083 mas, respectively. The calculated mean Gaia distances are roughly 3580.4 230.5 pc, which is in agreement with the photometric data from the Gaia and 2Mass data, within the error. There are 37 stars that exhibit radial velocity with average 46.1 8.54 , which allows us to derive orbital parameters using the galpy python package. As a result, the cluster is moving parallel to the Galactic plane towards the Galactic center. We have identified a novel category of pre-main sequence stars that form a distinct branch in the right of Color-Magnitude Diagram (CMD). These stars exhibit lower temperatures and surface gravity compared to main sequence stars. This implies that there exists a significant rate of star formation within the Czernik 38 cluster. Furthermore, we have discovered many faint blue stars in Czernik 38, as well as in the newly identified clusters, which could potentially be white dwarf stars.

Our Universe is estimated to be 13.5 ± 2 billion light years old and the observable universe is over 90 billion light years wide. Over 100 billion stars are born and die each passing day. Since the very dawn of creation, the cycle of life and death is constant at every part of the universe. But we are too tiny to comprehend such measures. Our earth where we reside in is too small and the cosmos is too vast. For anyone to measure anything from outer space is nigh impossible, or so we thought until the early 1900s. In this article, star clusters namely NGC 188 and M41 was used to show one of many convenient methods on how to find the age of a cluster or rather an individual star in that cluster using the help of the color magnitude diagram (CMD). It was observed that the ages of NGC 188 were found to be 4 ± 0.5 billion years and M41 to be around 220±30 million years.

New technologies in human emotion recognition (HER) have drawn considerable attention to use in the fields of security, intelligent customer service, healthcare, educational, human-robot interaction (HRI), and adaptive system training. To identify human emotions, our model incorporates MobileNetV3, Vision Transformer (ViT), RegNet and SE-ResNeXt into a unique deep ensemble classification structure. A Novel Multi Module Neural Networks (MMNNs) architecture is designed in this research for HER for practical application the main purpose of is to identify the human emotions. An innovative approach to improve the performance of HER by integrating MMNNs with Transfer Learning (TL) to train CNNs is researched. The MMNNs classification model is trained by combining features from four CNN models using feature pooling. The key novelty of the model is the novel DEtection TRansformer (DETR) which enhances the CNN learning block. It consists of a CNN that learns low dimensional feature representation, an encoder decoder transformer and a simple Feed Forward Network (FFN) that outputs the final detection prediction, which ultimately boosts face recognition efficiency and accuracy. The MMNNs results are validated on AffectNet, CK + and a custom-made dataset (CMD) achieving accuracy of 91.07%, 87.03% and 96.98% respectively which is further increased by data augmentation technique to 95.09%, 89.15% and 98.13% respectively.