Miracle-dz/UniFAR-FTUs huggingface dataset:
APPLICATION OF MALDI- TOF MS TECHNOLOGY TO THE IDENTIFICATION AND CHARACTERIZATION OF PATHOGENS IN DAIRY INDUSTRY.

Linda Boultifat*, assia guendouze and mohamed abdelhafid hamidechi. Laboratory of cellular and molecular biology Microbiology Department, Natural Sciences and Life Faculty, Constantine 1 University, Ain El Bey Road 25000 Constantine – Algeria. Manuscript Info Abstract . Manuscript History
Surfaces de Cauchy polyédrales des espaces temps plats singuliers

L'etude des espaces-temps plats singuliers munis d'une surface de Cauchy polyedrale est motivee par leur role de model jouet de gravite quantique propose par Deser, Jackiw et 'T Hooft. Cette these porte sur les parametrisations de certaines classes d'espaces-temps plat singuliers : les espaces-temps plats avec particules massives et BTZ Cauchy-compacts maximaux. Deux parametrisations sont proposees, l'une reposant sur une extension du theoreme de Mess aux espaces-temps plats avec BTZ et la surface de Penner-Epstein, l'autre reposant sur une generalisation du theoreme d'Alexandrov aux espaces-temps plats avec particules massives et BTZ. Ce travail propose egalement une amorce de cadre theorique permettant de considerer des espaces-temps singuliers plus generaux.
Tingkat Konversi dan Efisiensi Pakan Benih Ikan Bawal Bintang (Trachinotus blochii) dengan Frekuensi Pemberian Berbeda

Nilai tingkat konversi dan efisiensi pakan merupakan parameter penentuan kualitas, perlakuan, atau pakan yang diberikan terhadap pertumbuhan ikan Bawal Bintang (Trachinotus blochii). Tujuan penelitian ini adalah mengetahui pengaruh frekuensi pemberian pakan terhadap konversi dan efisiensi pakan dan kelangsungan hidup ikan Bawal Bintang. Metode penelitian yang digunakan adalah Rancangan Acak Lengkap (RAL) dengan 3 perlakuan (Kontrol yaitu pemberian pakan 1 hari sekali, A yaitu pemberian pakan 2 hari sekali,dan B yaitu pemberian pakan 3 hari sekali) dengan 3 ulangan. Hasil penelitian terbaik adalah perlakuan Kontrol sebesar 2.11 g (nilai konversi pakan), 47.32% (efisiensi pakan), dan 100% (kelangsungan hidup). Kesimpulan penelitian ini adalah perlakuan yang diberikan selama penelitian secara analisis ANOVA tidak memberi pengaruh yang nyata terhadap nilai konversi dan efisiensi pakan. Pemberian pakan dengan frekuensi 1 kali sehari (kontrol) menunjukkan pertumbuhan bobot terbaik dan dapat diterapkan pada budidaya. Kata kunci : Tingkat konversi, Efisiensi pakan, Ikan Bawal Bintang,
College Algebra: Past, Present, and Future

Abstract Issues within college algebra are well-documented. Over the past two decades, reform has brought about significant modifications in the course, yet to date there has been no synthesis of the efficacy of these efforts. This paper situates college algebra within a historical context, and synthesizes research on reform. I argue that, notwithstanding the successes brought about by change, the course is still not in a position to fulfill quantitative literacy requirements; that is, there exists nontrivial components of quantitative literacy that a college algebra course, by its nature, tends not to provide. I conclude with realistic steps forward for administrators and instructors.
Remote Sensing Estimates of Particulate Organic Carbon Sources in the Zhanjiang Bay Using Sentinel-2 Data and Carbon Isotopes

The source information of coastal particulate organic carbon (POC) with high spatial and temporal resolution is of great significance for the study of marine carbon cycles and marine biogeochemical processes. Over the past decade, satellite ocean color remote sensing has greatly improved our understanding of the spatiotemporal dynamics of ocean particulate organic carbon concentrations. However, due to the complexity of coastal POC sources, remote sensing methods for coastal POC sources have not yet been established. With an attempt to fill the gap, this study developed an algorithm for retrieving coastal POC sources using remote sensing and geochemical isotope technology. The isotope end-member mixing model was used to calculate the proportion of POC sources, and the response relationship between POC source information and in situ remote sensing reflectance (Rrs) was established to develop a retrieval algorithm for POC sources with the following four bands: (Rrs(443)/Rrs(492)) × (Rrs(704)/Rrs(665)). The results showed that the four-band algorithm performed well with R2, mean absolute percentage error (MAPE) and root mean square error (RMSE) values of 0.78, 33.57% and 13.74%, respectively. Validation against in situ data showed that the four-band algorithm derived calculated the proportion of marine POC accurately, with an MAPE and RMSE of 27.49% and 13.58%, respectively. The accuracy of the algorithm was verified based on the Sentinel-2 data, with an MAPE and RMSE of 28.02% and 15.72%, respectively. Additionally, we found that the proportion of marine POC sources was higher outside the Zhanjiang Bay than inside it using in situ survey data, which was consistent with the retrieved results. Influencing factors of POC sources may be due to the occurrence of phytoplankton blooms outside the bay and the impact of terrestrial inputs inside the bay. Remote sensing in combination with carbon isotopes provides important technical assistance in comprehending the biogeochemical process of POC and uncovering spatiotemporal variations in POC sources and their underlying causes.
STEP: Spatio-Temporal Progressive Learning for Video Action Detection

In this paper, we propose Spatio-TEmporal Progressive (STEP) action detector—a progressive learning framework for spatio-temporal action detection in videos. Starting from a handful of coarse-scale proposal cuboids, our approach progressively refines the proposals towards actions over a few steps. In this way, high-quality proposals (i.e., adhere to action movements) can be gradually obtained at later steps by leveraging the regression outputs from previous steps. At each step, we adaptively extend the proposals in time to incorporate more related temporal context. Compared to the prior work that performs action detection in one run, our progressive learning framework is able to naturally handle the spatial displacement within action tubes and therefore provides a more effective way for spatio-temporal modeling. We extensively evaluate our approach on UCF101 and AVA, and demonstrate superior detection results. Remarkably, we achieve mAP of 75.0% and 18.6% on the two datasets with 3 progressive steps and using respectively only 11 and 34 initial proposals.
Editorial: Youth personality development

The period between childhood and adulthood is a turbulent time of life (Hollenstein & Lougheed, 2013). While there is a solid body of research on youth social, biological, and psychological development (Sawyer et al., 2018; Steinberg & Morris, 2001), surprisingly little is known about personality processes and individual differences in them during these years. The papers in this Special Issue make important contributions to the current literature in three ways: first, by mapping developmental patterns to broader conceptualizations of personality, including personality pathology, second, by examining predictors and mechanisms in youth personality development, and third, by linking youth personality development to later life outcomes. We hope that the results of these papers will contribute to a more nuanced understanding of adolescent personality development in diverse cultural contexts and stimulate new integrative research.
Fractal behavior of the trajectories of the foot centers of pressure during pregnancy

The equilibrium of pregnant women is compromised because the falling risk increases as pregnancy progresses. The trajectories of the centers of pressure are related to equilibrium, gait and posture. The purpose of this work was to investigate the fractal behavior of the trajectories of the center of pressure of both feet in standing and walking pregnant women and how they evolve during pregnancy. By using instrumented insoles (F-Scan® system working at 40 Hz), the trajectories of the center of pressure of both feet were obtained for 71 women in the three trimesters of their pregnancy. The ‘detrended fluctuation analysis’, a technique designed to extract the fractal properties of a signal, was used to analyze the fractal behavior of both the x and y components of the measured trajectories. Specifically, the fractal characteristic exponents corresponding to the small and large end regions of the corresponding scaling functions were obtained and compared. No differences were found between the characteristic exponents obtained for x and y components nor from the signals found for left and right feet within the statistical uncertainties. In standing conditions all the exponents obtained remained almost constant, regardless of the elapsed pregnancy time, taking values around 0.5. In walking conditions, the small end exponents did not change with respect to those found in static conditions; on the contrary, the large-end exponents reduced as pregnancy progressed, reaching values of the order of −0.6 in the third trimester. The multi-scaling character of the walking signals could be related to the anteroposterior and medio-lateral displacement observed in previous experiments and that could be on the root of the development of musculoskeletal discomfort and a falling risk that increases with the pregnancy time.
YOLO-ViT-Based Method for Unmanned Aerial Vehicle Infrared Vehicle Target Detection

The detection of infrared vehicle targets by UAVs poses significant challenges in the presence of complex ground backgrounds, high target density, and a large proportion of small targets, which result in high false alarm rates. To alleviate these deficiencies, a novel YOLOv7-based, multi-scale target detection method for infrared vehicle targets is proposed, which is termed YOLO-ViT. Firstly, within the YOLOV7-based framework, the lightweight MobileViT network is incorporated as the feature extraction backbone network to fully extract the local and global features of the object and reduce the complexity of the model. Secondly, an innovative C3-PANet neural network structure is delicately designed, which adopts the CARAFE upsampling method to utilize the semantic information in the feature map and improve the model’s recognition accuracy of the target region. In conjunction with the C3 structure, the receptive field will be increased to enhance the network’s accuracy in recognizing small targets and model generalization ability. Finally, the K-means++ clustering method is utilized to optimize the anchor box size, leading to the design of anchor boxes better suited for detecting small infrared targets from UAVs, thereby improving detection efficiency. The present article showcases experimental findings attained through the use of the HIT-UAV public dataset. The results demonstrate that the enhanced YOLO-ViT approach, in comparison to the original method, achieves a reduction in the number of parameters by 49.9% and floating-point operations by 67.9%. Furthermore, the mean average precision (mAP) exhibits an improvement of 0.9% over the existing algorithm, reaching a value of 94.5%, which validates the effectiveness of the method for UAV infrared vehicle target detection.
Building a Smart Campus Digital Twin: System, Analytics, and Lessons Learned From a Real-World Project

Smart solutions increasingly involve the use of sensor data to represent the physical world in the digital world and apply intelligence to such representation. The main approach is a vertical end-to-end solution from sensors to intelligence and back to actuators. Recently, a new holistic approach, the so-called Digital Twin, has emerged. This goes beyond traditional smart solutions by replicating, with high-fidelity cross-domain aspects, a physical object into the digital world. The key differentiation is the inclusion of semantics information in the digital replica in a form of knowledge graph. Further, analytics continuously enrich Digital Twin’s information with predictions and insights. In this article, we adopt the Digital Twin approach for the creation of a digital replica of Espinardo’s campus at the University of Murcia (Spain). The starting point is the existing sensor network deployment and the infrastructure under development of Fog–Edge–Cloud computing based on a 5G private network. The smart campus Digital Twin is formed by the Digital Twins of 23 buildings for which different sets of data features have been thoroughly selected. We implement the concept of Digital Twin by merging sensor data with external open data sources, analytics models implemented, and information processed by these analytics. We report our experience showing the issues encountered handling the data and producing various analytics models for predicting energy consumption, building occupancy, room usage, solar energy, and anomaly detection. From our experience, we highlight some lessons learned and directions toward the full operational smart campus Digital Twin.
The Influences of Supportive Leadership and Family Social Support on Female Managers’ Organizational Effectiveness: The Mediating Effect of Positive Spillover between Work and Family

This study aims to examine the influence of supportive leadership and family social support for female managers on organizational effectiveness and test the mediating effect of positive spillover between work and family (PSWF). This study utilized data of 974 married female managers from the 6th Korean Female Manager Panel (KWMP) survey to analyze the relationship between the latent variables. Hypotheses of this study were tested using Structural Equation Model Analysis (SEM). This study found that supportive leadership and PSWF have a positive influence on female managers’ organizational effectiveness. However, family support had no significant effect on the organizational effectiveness of female managers. The analysis showed that supportive leadership and family social support positively influenced female manager’s PSWF. Also, PSWF mediated the relationship between family social support and organizational effectiveness as well as between supportive leadership and organizational effectiveness. This study provides a better understanding of PSWF as a mediator between family social support and organizational effectiveness. Contrary to previous studies that focused on the negative effects of work-family conflicts, this study highlighted the role of PSWF, justifying the need for governmental or organizational programs to increase PSWF.
A Cloud Microphysics Parameterization for Shallow Cumulus Clouds Based on Lagrangian Cloud Model Simulations

Cloud microphysics parameterizations for shallow cumulus clouds are analyzed based on Lagrangian cloud model (LCM) data, focusing on autoconversion and accretion. The autoconversion and accretion rates, A and C, respectively, are calculated directly by capturing the moment of the conversion of individual Lagrangian droplets from cloud droplets to raindrops, and it results in the reproduction of the formulas of A and C for the first time. Comparison with various parameterizations reveals the closest agreement with Tripoli and Cotton, such as and , where and are the mixing ratio and the number concentration of cloud droplets, is the mixing ratio of raindrops, is the threshold volume radius, and H is the Heaviside function. Furthermore, it is found that increases linearly with the dissipation rate and the standard deviation of radius and that decreases rapidly with while disappearing at > 3.5 μm. The LCM also reveals that and increase with time during the period of autoconversion, which helps to suppress the early precipitation by reducing A with smaller and larger in the initial stage. Finally, is found to be affected by the accumulated collisional growth, which determines the drop size distribution.
Comparison of post-operative outcomes and patient-surgeon satisfaction with a needle-tipped electrocautery incision and a cold scalpel incision in upper eyelid blepharoplasty: Cohort study

Purpose: The objective of the study is to compare post-operative outcomes and patient-surgeon satisfaction between a needle-tipped electrocautery incision and a cold scalpel incision in upper eyelid blepharoplasty Methods: The data from 247 patients who underwent bilateral upper eyelid blepharoplasty were retrospectively analyzed. Patients who underwent upper eyelid blepharoplasty with ptosis surgery or fat pad removal were excluded. The patients were divided into 2 groups, Group 1 - needle-tipped electrocautery incision and Group 2 - a cold scalpel incision. Pre-operative skin types of the patients, perioperative hemorrhage, and surgical time were observed. Post-operative ecchymosis on days 1 and 7 and scar cosmesis at months 1 and 6 were evaluated. Patients were asked about the level of satisfaction at 6 months. Results: One hundred and fifty-five patients, 75 patients in Group 1 and 80 patients in Group 2, were included in the study. No statistical differences were detected between the two groups for age, sex, and skin type. No serious complications were recorded. For surgeon satisfaction, surgical time and hemorrhage amount were statistically significantly lower in Group 1. Post-operative ecchymosis on days 1 and 7, scar cosmesis at months 1 and 6, and patient satisfaction at 6 months, the scores were similar between the groups. Conclusion: The clinical difference between needle-tipped electrocautery and cold scalpel incision was not observed after upper eyelid blepharoplasty. Needle-tipped electrocautery should be used conveniently and reliably for skin incisions in upper eyelid blepharoplasty for good cosmetic results.
Multimodality in the Hypergraph Lambek Calculus

The multimodal Lambek calculus is an extension of the Lambek calculus that includes several product operations (some of them being commutative or/and associative), unary modalities, and corresponding residual implications. In this work, we relate this calculus to the hypergraph Lambek calculus HL. The latter is a general pure logic of residuation defined in a sequent form; antecedents of its sequents are hypergraphs, and the rules of HL involve hypergraph transformation. Our main result is the embedding of the multimodal Lambek calculus (with at most one associative product) in HL. It justifies that HL is a very general Lambek-style logic and also provides a novel syntactic interface for the multimodal Lambek calculus: antecedents of sequents of the multimodal Lambek calculus are represented as tree-like hypergraphs in HL, and they are derived from each other by means of hyperedge replacement. The advantage of this embedding is that commutativity and associativity are incorporated in the sequent structure rather than added as separate rules. Besides, modalities of the multimodal Lambek calculus are represented in HL using the product and the division of HL, which explicitizes their residual nature.
Advanced Deep Convolutional Neural Network Approaches for Digital Pathology Image Analysis: a comprehensive evaluation with different use cases

Deep Learning (DL) approaches have been providing state-of-the-art performance in different modalities in the field of medical imagining including Digital Pathology Image Analysis (DPIA). Out of many different DL approaches, Deep Convolutional Neural Network (DCNN) technique provides superior performance for classification, segmentation, and detection tasks. Most of the task in DPIA problems are somehow possible to solve with classification, segmentation, and detection approaches. In addition, sometimes pre and post-processing methods are applied for solving some specific type of problems. Recently, different DCNN models including Inception residual recurrent CNN (IRRCNN), Densely Connected Recurrent Convolution Network (DCRCN), Recurrent Residual U-Net (R2U-Net), and R2U-Net based regression model (UD-Net) have proposed and provide state-of-the-art performance for different computer vision and medical image analysis tasks. However, these advanced DCNN models have not been explored for solving different problems related to DPIA. In this study, we have applied these DCNN techniques for solving different DPIA problems and evaluated on different publicly available benchmark datasets for seven different tasks in digital pathology including lymphoma classification, Invasive Ductal Carcinoma (IDC) detection, nuclei segmentation, epithelium segmentation, tubule segmentation, lymphocyte detection, and mitosis detection. The experimental results are evaluated with different performance metrics such as sensitivity, specificity, accuracy, F1-score, Receiver Operating Characteristics (ROC) curve, dice coefficient (DC), and Means Squired Errors (MSE). The results demonstrate superior performance for classification, segmentation, and detection tasks compared to existing machine learning and DCNN based approaches.
Biomarkers in Duchenne Muscular Dystrophy: Current Status and Future Directions

Duchenne muscular dystrophy is a severe, X-linked disease characterized by decreased muscle mass and function in children. Genetic and biochemical research over the years has led to the characterization of the cause and the pathophysiology of the disease. Moreover, the elucidation of genetic mechanisms underlining Duchenne muscular dystrophy has allowed for the design of innovative personalized therapies. The identification of specific, accurate, and sensitive biomarkers is becoming crucial for evaluating muscle disease progression and response to therapies, disease monitoring, and the acceleration of drug development and related regulatory processes. This review illustrated the up-to-date progress in the development of candidate biomarkers in DMD at the level of proteins, metabolites, micro-RNAs (miRNAs) and genetic modifiers also highlighting the complexity of translating research results to clinical practice. We highlighted the challenges encountered in translating biomarkers into the clinical context and the existing bottlenecks hampering the adoption of biomarkers as surrogate endpoints. These challenges could be overcome by national and international collaborative efforts, multicenter data sharing, definition of public biobanks and patients’ registries, and creation of large cohorts of patients. Novel statistical tools/ models suitable to analyze small patient numbers are also required. Finally, collaborations with pharmaceutical companies would greatly benefit biomarker discovery and their translation in clinical trials.