Miracle-dz/UniFAR-FTUs huggingface dataset:
Dual-TBNet: Improving the Robustness of Speech Features via Dual-Transformer-BiLSTM for Speech Emotion Recognition

Speech emotion recognition has always been one of the topics that have attracted a lot of attention from many researchers. In traditional feature fusion methods, the speech features used only come from the data set, and the weak robustness of features can easily lead to overfitting of the model. In addition, these methods often use simple concatenation to fuse features, which will cause the loss of speech information. In this article, to solve the above problems and improve the recognition accuracy, we utilize self-supervised learning to enhance the robustness of speech features and propose a feature fusion model(Dual-TBNet) that consists of two 1D convolutional layers, two Transformer modules and two bidirectional long short-term memory (BiLSTM) modules. Our model uses 1D convolution to take features of different segment lengths and dimension sizes as input, uses the attention mechanism to capture the correspondence between the two features, and uses the bidirectional time series module to enhance the contextual information of the fused features. We designed a total of four fusion models to fuse five pre-trained features and acoustic features. In the comparison experiments, the Dual-TBNet model achieved a recognition accuracy and F1 score of 95.7% and 95.8% on the CASIA dataset, 66.7% and 65.6% on the eNTERFACE05 dataset, 64.8% and 64.9% on the IEMOCAP dataset, 84.1% and 84.3% on the EMO-DB dataset and 83.3% and 82.1% on the SAVEE dataset. The Dual-TBNet model effectively fuses acoustic features of different lengths and dimensions with pre-trained features, enhancing the robustness of the features, and achieved the best performance.
Image matching based on multi-features fusion

The conventional methods of image matching are based on gray and shape of image. These two features can't describe image data well especially in tracking the faint and small targets. It's difficult to track the target with high-precision and steady. In this paper, a new algorithm of image matching was proposed. It extracts characters including gray and shape from image data and gains an integrated character using the information fusion algorithm with simplicity and effect. Then calculates the position coordinate of target using integrated characteristics by correlation degree. Simulation shows that the method is feasible and effective.
Cognitive Warfare in 21st Century Great Power Competition. Framing of Military Activity in the Black Sea

The Pivot to Asia first signaled and then the December 2017 US National Security Strategy ushered in the era of renewed great power competition, a global strategic environment in which great powers compete for influence and in which the leveraging of new and disruptive technologies, globalization, climate change and a global pandemic have added to the complexity and fluidity of strategic imperatives of nation states and alliances. The nature of war has remained the same, nevertheless, the character has evolved, perspectives on what constitutes war in its entire spectrum have taken on different meaning for different actors. One particularly complex dimension of great power conflict is cognitive warfare in all its manifestations. The article is looking to analyze the way the framing effect, a cognitive bias, has been leveraged as a means of perpetrating cognitive warfare in the Black Sea by reframing historical events of the last century in order to influence both individual and group beliefs and behaviors to favor or legitimize tactical as well as strategic objectives in great power competition.
AN OPTIMAL APPROACH TO TARGET TRACKING PROBLEM

Target tracking is an important research area widely studied in many engineering disciplines. In this paper, we present an optimal approach to solve the target tracking problem in three-dimesional space. We ini- tially define the problem as an optimization problem and then we solve it as a minimization problem. The per- formance of the approach is analyzed by simulation experiments.
Isolated Hypogonadotropic Hypogonadism in a Young Woman with Primary Amenorrhoea: A Case Report

The diagnostic approach to primary amenorrhoea includes a thorough history taking, clinical examination, laboratory testing and imaging technology. This case report describes a young woman who was diagnosed with isolated hypogonadotropic hypogonadism that resulted in delayed secondary sexual development and primary amenorrhoea. The patient had a normal female karyotype and an atrophied uterus. Based on the results of the laboratory tests, which indicated low oestradiol, FSH and LH levels, along with normal levels of other pituitary hormones, the patient was diagnosed with isolated hypogonadotropic hypogonadism. Genetic testing revealed a rare diagnosis of autosomal recessive hypogonadotropic hypogonadism 8 ( KISS1R , NM_032551.4:c.443T>C; p.Leu148Ser). The KISS1R gene encodes the kisspeptin receptor, which regulates the secretion of the gonadotropin-releasing hormone (GnRH). Treatment typically requires hormone therapy, which involves the administration of oestrogen to promote the growth of the uterus and secondary sexual characteristics. Combined contraceptive pills were initiated and repeated pelvis ultrasound after six months revealed a growing uterus.
Drones in Plant Disease Assessment, Efficient Monitoring, and Detection: A Way Forward to Smart Agriculture

Plant diseases are one of the major threats to global food production. Efficient monitoring and detection of plant pathogens are instrumental in restricting and effectively managing the spread of the disease and reducing the cost of pesticides. Traditional, molecular, and serological methods that are widely used for plant disease detection are often ineffective if not applied during the initial stages of pathogenesis, when no or very weak symptoms appear. Moreover, they are almost useless in acquiring spatialized diagnostic results on plant diseases. On the other hand, remote sensing (RS) techniques utilizing drones are very effective for the rapid identification of plant diseases in their early stages. Currently, drones, play a pivotal role in the monitoring of plant pathogen spread, detection, and diagnosis to ensure crops’ health status. The advantages of drone technology include high spatial resolution (as several sensors are carried aboard), high efficiency, usage flexibility, and more significantly, quick detection of plant diseases across a large area with low cost, reliability, and provision of high-resolution data. Drone technology employs an automated procedure that begins with gathering images of diseased plants using various sensors and cameras. After extracting features, image processing approaches use the appropriate traditional machine learning or deep learning algorithms. Features are extracted from images of leaves using edge detection and histogram equalization methods. Drones have many potential uses in agriculture, including reducing manual labor and increasing productivity. Drones may be able to provide early warning of plant diseases, allowing farmers to prevent costly crop failures.
Effectiveness of Digital Health Interventions Containing Game Components for the Self-management of Type 2 Diabetes: Systematic Review

Background Games and game components have become a major trend in the realm of digital health research and practice as they are assumed to foster behavior change and thereby improve patient-reported and clinical outcomes for patients with type 2 diabetes. Objective The aim of this systematic review was to summarize and evaluate the current evidence on the effectiveness of digital health interventions containing game components on behavioral, patient-reported, and clinical outcomes for patients with type 2 diabetes. Methods An electronic search was conducted in MEDLINE and PsycINFO in April 2020; updated in April 2022; and supplemented by additional searches via Google Scholar, Web of Science (which was used for forward citation tracking), and within the references of the included records. Articles were identified using predefined inclusion and exclusion criteria. In total, 2 reviewers independently conducted title, abstract, and full-text screening and then individually performed a critical appraisal of all the included studies using the Cochrane risk-of-bias tool version 2. A consensus was reached through discussion. Results Of 2325 potentially relevant titles (duplicates excluded), 10 (0.43%) randomized controlled trials were included in this review. Quality assessment revealed a high risk of bias for all randomized controlled trials except for 10% (1/10), with performance bias due to the lack of blinding being the major source of bias. There is evidence suggesting that digital health interventions containing game components can substantially improve motivation for physical activity (1/1, 100% of the studies dealing with PA motivation), exercise intensity (3/5, 60%), dietary behavior (4/4, 100%), health literacy (1/3, 33%), mental quality of life (2/2, 100%), glycated hemoglobin level (2/6, 33%), BMI (1/3, 33%), fasting plasma glucose level (1/2, 50%), waist circumference (1/1, 100%), and aerobic capacity (1/1, 100%). Conclusions Published studies indicated that digital health interventions containing game components might improve health behavior patterns, quality of life, and clinical outcomes in patients with type 2 diabetes. However, the intervention types and outcomes studied were heterogeneous, and study quality was mostly low, which translates to ambiguous results. Future research should focus on sound methodology and reporting as well as on identifying game components that contribute to significant positive effects. Trial Registration PROSPERO CRD42020209706; https://www.crd. york.ac. uk/prospero/display_record. php? RecordID=209706
LOCAL SHARP MAXIMAL FUNCTIONS IN SPACES OF HOMOGENEOUS TYPE

The purpose of this paper is twofold. In the first place, we extend some of the recent results of Jawerth and Torchinskyty concerning local maximal functions in R~n to the more general context of spaces of homogeneous type. The John-Nirenberg inequality is contained in our results. In the second place, we shall discuss the BMOlogk spaces considered by Long Ruilin and Yang Lo[2]. This we achieve by introducing an appropriate local sharp maximal function.
Electrodynamic Technologies in the Eco-industry of Food and Pharmaceutical Production

The growing interest in the world for research on microwave processing technologies of raw materials is shown. It has been established that information in available sources is only about laboratory-scale equipment, and theoretical information (models, mechanisms, calculation methods) is practically absent. The aim of the work is to conduct systematic studies in the “extractor — dehydrator — plant material” scheme. To achieve the goal, these electrodynamical systems are presented with parametric, mathematical, and experimental models. The most significant result of the work is that the concept of a “hybrid” process is introduced to explain the mechanism of interaction between the electromagnetic field and the raw material. Using the first law of thermodynamics, it is shown that the “hybrid” process performs work to move the solution from the volume of the material to its surface. As a result, sluggish diffusion processes are accompanied by powerful flows, the driving force of which is the pressure difference in the capillary of the material and the environment. The importance of the work is that new effects are established: mechanodiffusional and vapordynamical. Mechanodiffusional allows obtaining polyextracts in one extractor, and vapordynamical allows the dehydration of the solid phase in the form of two parallel streams — vapor and juice. Experiments were conducted with rosehip fruits, soybeans, tomato squeezes, and sunflower meal. It is shown that electrodynamical dehydrators are characterized by stable performance indicators of vapor generation up to concentrations of 85°brix, at low levels of energy consumption. The results of chemical studies of the obtained samples in electrodynamical devices are presented.
Research on the construction of knowledge graph based on multi-source heterogeneous geospatial data

Geospatial data shows an explosive growth trend, which leads to "massive data and difficult knowledge". Therefore, the transformation from data to spatial knowledge has become a scientific problem to be solved urgently. Firstly, this paper puts forward the technical process and basic ideas of Knowledge graph construction; Secondly, as geospatial knowledge has specific spatio-temporal characteristics, the representation method of geospatial data knowledge is described, and its spatio-temporal relationship is expounded; Finally, this paper takes some geospatial data in Sichuan Province as an example to study the problem of knowledge extraction and multi-source data fusion, and generate the corresponding knowledge graph. This research can promote the integration of geospatial data and semantic web technology, realize the spatialization of web text and the semantics of spatial data, and further improve the intelligent service level.
Young and Senescent Cells: Distinct Nuclear F-actin Patterns Upon Latrunculin B Induction

Both cellular senescence and cytoskeleton are involved in the formation of many diseases and cell signaling pathways. Although recent studies have shown that F-actin is involved in DNA damage repair, chromatin decompression, gene transcription regulation, and cell fate determination. But studies on F-actin and aging are still absence. It is unclear whether nuclear F-actin is present during cellular senescence. Here, by confocal optical sectioning and time-lapse imaging, we found acitn chrommobody-TagGFP2-NLS shows the beneficial on investigating senescent human fibroblast IMR-90 cells. To induce the nuclear F-actin assembly in single cell, we uesd Latrunculin B (latB) which a cytoplasmic F-actin polymerization inhibitor. It is currently unknown whether the nuclear F-actin cytoskeleton in young and senescent cells responds differently to latB treatment. Here, latB application induces distinct nuclear F-actin patterns and dynamics in young and senescent cells. Thus, after analyzing the results of actin dynamic we demonstrate a diverse effect of latB on the nuclear F-actin cytoskeleton in young and senescent cells.
A complete graph-based approach with multi-task learning for predicting synergistic drug combinations

Abstract Motivation Drug combination therapy shows significant advantages over monotherapy in cancer treatment. Since the combinational space is difficult to be traversed experimentally, identifying novel synergistic drug combinations based on computational methods has become a powerful tool for pre-screening. Among them, methods based on deep learning have far outperformed other methods. However, most deep learning-based methods are unstable and will give inconsistent predictions even by simply changing the input order of drugs. In addition, the insufficient experimental data of drug combination screening limits the generalization ability of existing models. These problems prevent the deep learning-based models from being in service. Results In this article, we propose CGMS to address the above problems. CGMS models a drug combination and a cell line as a heterogeneous complete graph, and generates the whole-graph embedding to characterize their interaction by leveraging the heterogeneous graph attention network. Based on the whole-graph embedding, CGMS can make a stable, order-independent prediction. To enhance the generalization ability of CGMS, we apply the multi-task learning technique to train the model on drug synergy prediction task and drug sensitivity prediction task simultaneously. We compare CGMS’s generalization ability with six state-of-the-art methods on a public dataset, and CGMS significantly outperforms other methods in the leave-drug combination-out scenario, as well as in the leave-cell line-out and leave-drug-out scenarios. We further present the benefit of eliminating the order dependency and the discrimination power of whole-graph embeddings, interpret the rationality of the attention mechanism, and verify the contribution of multi-task learning. Availability and implementation The code of CGMS is available via https://github.com/TOJSSE-iData/CGMS.
Stator ﬂux linkage estimation for direct torque control of permanent magnet synchronous machines

The use of highly dynamic electrical drives with permanent magnet synchronous machines (PMSMs) gained a lot of interest due to their high efficiency and reliability. These drives have to provide accurate and fast torque control, for example with direct torque control (DTC). The basic principle is to directly select stator voltage vectors to change the amplitude and the position of the stator flux linkage vector Ψs according to the differences between actual and reference value for torque and stator flux linkage in a stationary reference frame. As such Ψs has to be estimated with high accuracy. To this end several estimation methods were compared [1], both for surface and interior geometries of PMSMs [2].
Clinical and Oncological Outcomes after Uniportal Anatomical Segmentectomy for Stage IA Non-Small Cell Lung Cancer

Background and Objectives: The existing literature comparing sublobar and lobar resection in the treatment of stage IA lung cancer highlights the trend and overall need for further evaluation of minimally invasive, parenchymal-sparing techniques. The role of uniportal minimally invasive segmentectomy in the oncological therapy of early-stage non-small cell lung cancer (NSCLC) remains controversial. The aim of this study was to evaluate the clinical and midterm oncological outcomes of patients who underwent uniportal video-assisted anatomical segmentectomy for pathological stage IA lung cancer. Materials and Methods: We retrospectively analyzed all patients with pathological stage IA lung cancer (8th edition UICC) who underwent uniportal minimally invasive anatomical segmentectomy at our institution from January 2015 to December 2018. Results: 85 patients, 54 of whom were men, were included. The median length of hospital stay was 3 days (1.-3. IQR 3–5), whereas 30-day morbidity was 15.3% (13 patients), and the in-hospital mortality rate was 1.2% (1 patient). The 3-year overall survival rate was 87.9% for the total population. It was 90.5% in the IA1 group, 93.3% in the IA2 group, and 70.1% in the IA3 group, respectively. Conclusions: There were satisfactory short-term clinical outcomes with low 30-day morbidity and mortality and promising midterm oncological survival results following uniportal minimally invasive anatomical segmentectomy for pathological stage IA non-small cell lung cancer.
Development and Application of the Scale-Up Reflection Guide (SRG)

Scaling up effective interventions in public health is complex and comprehensive, and published accounts of the scale-up process are scarce. Key aspects of the scale-up experience need to be more comprehensively captured. This study describes the development of a guide for reflecting on and documenting the scale-up of public health interventions, to increase the depth of practice-based information of scaling up. Reviews of relevant scale-up frameworks along with expert input informed the development of the guide. We evaluated its acceptability with potential end-users and applied it to two real-world case studies. The Scale-up Reflection Guide (SRG) provides a structure and process for reflecting on and documenting key aspects of the scale-up process of public health interventions. The SRG is comprised of eight sections: context of completion; intervention delivery, history/background; intervention components; costs/funding strategies and partnership arrangements; the scale-up setting and delivery; scale-up process; and evidence of effectiveness and long-term outcomes. Utilization of the SRG may improve the consistency and reporting for the scale-up of public health interventions and facilitate knowledge sharing. The SRG can be used by a variety of stakeholders including researchers, policymakers or practitioners to more comprehensively reflect on and document scale-up experiences and inform future practice.
On the equivalence of flows in networks of queues

Consider a network of queues in equilibrium. When are the flows observed on two given links of that network equivalent, i.e., when do they have the same law? The verification of the equivalence of two such point processes is first reduced to an algebraic problem by a technique based on the filtering theory. This method is then used to show that the arrival and departure processes at an M/M/1 node in a Jackson network are not always equivalent, thereby contradicting a conjecture made in [8]. An example where that equivalence holds is also given; it provides new results on the time reversibility of some familiar processes.
Editorial: Membrane Trafficking in Immunology - How Membrane Transport and Exocytosis Defects Underlie Immunodeficiencies

1 Institute of Neuroscience and Physiology, University of Gothenburg, Gothenburg, Sweden, 2 Division of Bone Marrow Transplantation and Immune Deficiency, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States, 3 Department of Pediatrics, University of Cincinnati, Cincinnati, OH, United States, 4 Center for Hematology and Regenerative Medicine, Department of Medicine, Karolinska Institutet, Stockholm, Sweden, 5 Primary Immunodeficiency Unit, Clinical Immunology and Transfusion Medicine Clinic, Karolinska University Hospital Huddinge, Stockholm, Sweden, 6 Broegelmann Research Laboratory, Department of Clinical Science, University of Bergen, Bergen, Norway, 7 Department of Pediatrics, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY, United States, 8 Institute for Immunodeficiency, Center for Chronic Immunodeficiency (CCI), Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
Use of Patient-Reported Outcome Measures in Lower Extremity Research

Background A large number of patient reported outcome measures (PROMs) have been developed for specific lower extremity orthopaedic pathologies. However, a consensus as to which PROMs are recommended for use in evaluating treatment outcomes for patients with hip, knee, ankle and/or foot pathology based on the strength of their psychometric properties is lacking. Objective To identify PROMs that are recommended in systematic reviews (SRs) for those with orthopaedic hip, knee, foot, and ankle pathologies or surgeries and identify if these PROMs are used in the literature. Study design Umbrella Review Methods PubMed, Embase, Medline, Cochrane, CINAHL, SPORTDisucs and Scopus were searched for SRs through May 2022. A second search was done to count the use of PROMs in seven representative journals from January 2011 through May 2022. SRs that recommended the use of PROMs based on their psychometric properties were included in the first search. SRs or PROMs not available in the English were excluded. The second search included clinical research articles that utilized a PROM. Case reports, reviews, and basic science articles were excluded. Results Nineteen SRs recommended 20 PROMs for 15 lower extremity orthopaedic pathologies or surgeries. These results identified consistency between recommended PROMs and utilization in clinical research for only two of the 15 lower extremity pathologies or surgeries. This included the use of the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and the Copenhagen Hip and Groin Outcome Score to assess outcomes (HAGOS) for those with knee osteoarthritis and groin pain, respectively. Conclusion A discrepancy was found between the PROMs that were recommended by SRs and those used to assess clinical outcomes in published research. The results of this study will help to produce more uniformity with the use of PROMs that have the most appropriate psychometric properties when the reporting treatment outcomes for those with extremity pathologies. Level of evidence 3a