Miracle-dz/UniFAR-FTUs huggingface dataset:
Age Diversity in Neighborhoods—A Mixed-Methods Approach Examining Older Residents and Community Wellbeing

This paper focuses on age diversity in neighbourhoods and its possible impacts on community wellbeing. The aims of this paper are (a) to investigate whether age diversity in neighbourhoods contribute to older residents’ wellbeing and (b) to explore older residents’ experiences and views on age diversity in their neighbourhood. These questions are addressed using a mixed-method approach combining survey and interview data and analysis. The data is derived from a survey (n = 420) and 19 semistructured interviews addressed to the older residents of a rental house company located in Eastern Finland. The interview data is analysed using qualitative content analysis. The results of qualitative data indicate that older adults see various benefits in an age-diverse living environment. In the quantitative analysis, we apply multilevel models in our statistical analyses to take both community- and individual-level variation into account. The quantitative results show that older adults living in age-diverse neighbourhoods reported higher community wellbeing. Such association was not found among the younger residents. Overall, our study adds to the understanding of the importance of neighbouring relations on community wellbeing in later life. The results can be utilized when developing age-friendly environments and housing policies at local and national levels.
Computationally Modelling Cholesterol Metabolism and Atherosclerosis

Simple Summary Heart disease and stroke are major global health problems. There are many risk factors for these conditions. However, the main risk factor is high levels of low-density lipoprotein cholesterol (LDL-C). LDL-C is involved in the formation of plaques which eventually lead to either a heart attack or a stroke. The biology associated with this process is exceptionally complex. Computational modelling can be used to understand this complexity. In this work computational modelling was used to better understand the relationship between high levels of LDL-C and plaque progression. The model was able to identify therapeutic interventions which are effective at slowing plaque growth. Abstract Cardiovascular disease (CVD) is the leading cause of death globally. The underlying pathological driver of CVD is atherosclerosis. The primary risk factor for atherosclerosis is elevated low-density lipoprotein cholesterol (LDL-C). Dysregulation of cholesterol metabolism is synonymous with a rise in LDL-C. Due to the complexity of cholesterol metabolism and atherosclerosis mathematical models are routinely used to explore their non-trivial dynamics. Mathematical modelling has generated a wealth of useful biological insights, which have deepened our understanding of these processes. To date however, no model has been developed which fully captures how whole-body cholesterol metabolism intersects with atherosclerosis. The main reason for this is one of scale. Whole body cholesterol metabolism is defined by macroscale physiological processes, while atherosclerosis operates mainly at a microscale. This work describes how a model of cholesterol metabolism was combined with a model of atherosclerotic plaque formation. This new model is capable of reproducing the output from its parent models. Using the new model, we demonstrate how this system can be utilized to identify interventions that lower LDL-C and abrogate plaque formation.
Crowdsourced Data Validation for ASR Training

Many ASR engines are based on crowdsourced speech corpora, such as Common Voice. Although crowdsourced data is inexpensive, the utterances obtained from crowdsourcing can be noisy because of uncontrollable factors such as accents, environments, etc. Another issue with the Common Voice corpus is the lack of validators to cover a vast collection of crowd-sourced utterances. This issue presents a significant challenge to speech data validation. To mitigate this bottleneck, we pro-pose a machine-learning classifier that predicts the correctness of the data, which can act as either the validator itself or a pre-screen for the validator. Our system achieves more than 95% F1-score in the three Common Voice languages, including Thai, Japanese, and Turkish, and performs even better when we have only one human judge involved in the decision. Furthermore, we also found that the data obtained from our method outper-formed the current crowdsourcing validation method when used to train the ASR model.
Robust Monocular 3D Human Motion with Lasso-Based Differential Kinematics

This work introduces a method to robustly reconstruct 3D human motion from the motion of 2D skeletal landmarks. We propose to use a lasso (least absolute shrinkage and selection operator) optimization framework where the ℓ1-norm is computed over the vector of differential angular kinematics and the ℓ2-norm is computed over the differential 2D reprojection error. The ℓ1-norm term allows us to model sparse kinematic angular motion. The minimization of the reprojection error allows us to assume a bounded noise in both the kinematic model and the 2D landmark detection. This bound is controlled by a scale factor associated to the ℓ2-norm data term. A posteriori verification condition is provided to check whether or not the lasso formulation has allowed us to recover the ground-truth 3D human motion. Results on publicly available data demonstrates the effectiveness of the proposed approach on state-of-the-art methods. It shows that both sparsity and bounded noise assumptions encoded in lasso formulation are robust priors to safely recover 3D human motion.
Performance Analysis of E-band 12-Kilometer Long Transmission Links Based on Experimental Data

Recent advances in millimeter wave (mm-wave) technologies have inspired various communications exploiting the large spectrum resources. Among which, the E-band, i.e., the 71-76 and 81-86 GHz frequency bands, has an ultra-wide bandwidth of 10 GHz and can potentially provide tens of Gigabits-per-second (Gbps) data rates. The E-band mm-wave is thus considered a candidate for building high-capacity wireless backhauls, especially in long-range transmission scenarios. In order to evaluate E-band link performance in real propagation environments, we conducted several outdoor experiments. Here we report one experiment across a salt lake in Qinghai Province of China, and another across the sea near Sanya Bay of South China Sea, both having a line-of-sight (LOS) path distance of around 12 km. An E-band system with highly-directive and dual-polarized Cassegrain antennas, operating at a center frequency of 74 GHz and supporting a signal bandwidth up to 1 GHz, was used. Signals with different modulation schemes and symbol rates were transmitted through the long-range channels and finally recorded at the receiver side. Based on the obtained data, we analyze E-band link performance. The results show that 2-2.5 Gbps uncoded data rates can be achieved with relatively low bit error rates (BERs) by using 16- and 32-QAM in co-polarized single-input single-output (SISO) links. When higher-order modulation schemes are used, BERs increase accordingly. The 64-QAM transmission provides a maximum data rate of 3 Gbps but has a BER of 10-1. This work indicates that an E-band long-range SISO link can achieve a multiple-Gbps data rate over a below 1-GHz bandwidth, and by using multiple-antenna technologies, there is a potential of boosting data rates to reach tens of Gbps. Hence, we foresee that E-band mm-wave can replace fiber optics in various long-distance backhaul scenarios.
Drone-based Underwater Sensor Network with Optical Camera Communication

Underwater communication has attracted considerable attention for providing ubiquitous connectivity all over the world. The major constraint for laser diode (LD)-based underwater optical communication (UWOC) is the strong directivity of the transmitter and the receiver. The directivity control of laser beams is a fatal challenge due to oceanic turbulence. Underwater optical camera communication (UWOCC) is a promising option thanks to its low-directivity. However, the previous works on UWOCC have only focused on link-level propagation characteristics. Therefore, this paper proposes a drone-based underwater sensor network system with UWOCC. An underwater drone cruises around and receives optical signals from spatially distributed sensor nodes in underwater environments. The feasibility of the proposed system is confirmed via experimental results in Kobe city, Japan.
CleanUNet 2: A Hybrid Speech Denoising Model on Waveform and Spectrogram

In this work, we present CleanUNet 2, a speech denoising model that combines the advantages of waveform denoiser and spectrogram denoiser and achieves the best of both worlds. CleanUNet 2 uses a two-stage framework inspired by popular speech synthesis methods that consist of a waveform model and a spectrogram model. Specifically, CleanUNet 2 builds upon CleanUNet, the state-of-the-art waveform denoiser, and further boosts its performance by taking predicted spectrograms from a spectrogram denoiser as the input. We demonstrate that CleanUNet 2 outperforms previous methods in terms of various objective and subjective evaluations.
An Efficient Approach for the Automated Segmentation and Transcription of the People's Speech Sorpus

Advancements in speech technology have led to the integration of modern ASR systems into various applications such as chatbots, medical dictation, video transcription etc. Conversa-tional ASR training requires speech that captures the acoustic cues of spontaneous speech. With its 30k hours of conversational speech, the People’s Speech corpus is the largest available spontaneous and conversational corpus and an invaluable resource for such training. In addition, it comes with a commercial friendly license. The corpus is packaged in uniform 15-second segments, but this can lead to abrupt cutting off of speech and transcription that is not always accurate. This paper presents an effective method for automatic data mining from a small subset of 973 raw original records used by the People’s Speech corpus. The paper also proposes an approach for outlier detection and automatic data curation. Results show a 19 . 7% relative improvement in WER compared to the original segments.
Data Fusion – Resolving Data Conflicts for Integration

The amount of information produced in the world increases by 30% every year and this rate will only go up. With advanced network technology, more and more sources are available either over the Internet or in enterprise intranets. Modern data management applications, such as setting up Web portals, managing enterprise data, managing community data, and sharing scientific data, often require integrating available data sources and providing a uniform interface for users to access data from different sources; such requirements have been driving fruitful research on data integration over the last two decades [13, 15]. Data integration systems face two folds of challenges. First, data from disparate sources are often heterogeneous. Heterogeneity can exist at the schema level, where different data sources often describe the same domain using different schemas; it can also exist at the instance level, where different sources can represent the same real-world entity in different ways. There has been rich body of work on resolving heterogeneity in data, including, at the schema level, schema mapping and matching [17], model management [1], answering queries using views [14], data exchange [10], and at the instance level, record linkage (a.k.a., entity resolution, object matching, reference linkage, etc.) [9, 18], string similarity comparison [6], etc. Second, different sources can provide conflicting data. Conflicts can arise because of incomplete data, erroneous data, and out-of-date data. Returning incorrect data in a query result can be misleading and even harmful: one may contact a person by an out-of-date phone number, visit a clinic at a wrong address, carry wrong knowledge of the real world, and even make poor business decisions. It is thus critical for data integration systems to resolve conflicts from various sources and identify true values from false ones. This problem becomes especially prominent with the ease of publishing and spreading false information on the Web and has recently received increasing attention. This tutorial focuses on data fusion, which addresses the second challenge by fusing records on the same real-world
Linearized-Trajectory Model-Predictive Controller for Improving Microgrid Short-Term Stability With Real-Time Implementation

A model-predictive controller (MPC) for synchronous generator-based microgrids is introduced to enhance voltage, frequency, and transient stability in fast timescales. The centralized controller uses mathematical models of power system dynamics to predict the evolution of system states over a finite horizon based on information from local state observers and relays. The MPC dynamically adjusts the setpoints of existing primary controls to maximize system stability. To address the computational challenges of a real-time MPC implementation due to nonlinear power system dynamics and short sampling intervals, a trajectory linearization technique is applied to the MPC formulation. The proposed controller is validated via a hardware-in-the-loop (HIL) testbed. The testbed includes an implementation of the controller in hardware, which interfaces with a high-fidelity microgrid simulation model running in a real-time simulator. State observers based on the Extended Kalman Filter (EKF), which provide the controller with estimates of the system state, are also implemented. The HIL testbed is used to verify the real-time operation of the controller and evaluate its performance under modeling uncertainties.
Promoter variations in DBR2-like affect artemisinin production in different chemotypes of Artemisia annua

Abstract Artemisia annua is the only known plant source of the potent antimalarial artemisinin, which occurs as the low- and high-artemisinin producing (LAP and HAP) chemotypes. Nevertheless, the different mechanisms of artemisinin producing between these two chemotypes were still not fully understood. Here, we performed a comprehensive analysis of genome resequencing, metabolome, and transcriptome data to systematically compare the difference in the LAP chemotype JL and HAP chemotype HAN. Metabolites analysis revealed that 72.18% of sesquiterpenes was highly accumulated in HAN compared to JL. Integrated omics analysis found a DBR2-Like (DBR2L) gene may be involved in artemisinin biosynthesis. DBR2L was highly homologous with DBR2, belonged to ORR3 family, and had the DBR2 activity of catalyzing artemisinic aldehyde to dihydroartemisinic aldehyde. Genome resequencing and promoter cloning revealed that complicated variations existed in DBR2L promoters among different varieties of A. annua and were clustered into three variation types. The promoter activity of diverse variant types showed obvious differences. Furthermore, the core region (−625 to 0) of the DBR2L promoter was identified and candidate transcription factors involved in DBR2L regulation were screened. Thus, the result indicates that DBR2L is another key enzyme involved in artemisinin biosynthesis. The promoter variation in DBR2L affects its expression level, and thereby may result in the different yield of artemisinin in varieties of A. annua. It provides a novel insight into the mechanism of artemisinin-producing difference in LAP and HAP chemotypes of A. annua, and will assist in a high yield of artemisinin in A. annua.
Feeding habits of Boana raniceps (Cope, 1862) in three mountains of Brazilian semiarid

Studying the trophic ecology of animals helps us understand their life history, their relationship with the environment and other species, and their role in ecosystems. In this sense, we describe the feeding habits of the hyalid frog Boana raniceps in three high-altitude mountain ranges of Northeastern Brazil, investigating intersex differences in the diet and the relationship between the size with the volume of prey consumed. Furthermore, we investigated prey-predator interactions in the Boana genus with an ecological network approach. We analyzed the stomach contents of 62 B. raniceps specimens collected in three mountains in the state of Ceará (Northeastern Brazil) and consulted data available in the literature for information on the diet of other Boana species. We identied 82 prey items distributed in eleven categories. Overall, there were no differences in the composition of B. raniceps diet among the three mountains. Although males and females shared the majority of prey items consumed, they tended to consume prey volumetrically differently, and predator size did not influence prey volume. In addition, predator-prey interactions in the genus Boana revealed low modularity, complementary specialization, and nestedness. Therefore, species of the genus Boana follow the general pattern for the diet of most Brazilian frog species (generalist and opportunistic habits), being likely influenced by the availability of prey in the habitat. Overall, the present study constitutes a complete review of the prey items consumed by the genus Boana in Brazil, describing the general pattern of anuran-prey networks in these hylids.
Self-Assembled Peptide Hydrogels in Regenerative Medicine

Regenerative medicine is a complex discipline that is becoming a hot research topic. Skin, bone, and nerve regeneration dominate current treatments in regenerative medicine. A new type of drug is urgently needed for their treatment due to their high vulnerability to damage and weak self-repairing ability. A self-assembled peptide hydrogel is a good scaffolding material in regenerative medicine because it is similar to the cytoplasmic matrix environment; it promotes cell adhesion, migration, proliferation, and division; and its degradation products are natural and harmless proteins. However, fewer studies have examined the specific mechanisms of self-assembled peptide hydrogels in promoting tissue regeneration. This review summarizes the applications and mechanisms of self-assembled short peptide and peptide hydrogels in skin, bone, and neural healing to improve their applications in tissue healing and regeneration.
Research Progress of CXCR4-Targeting Radioligands for Oncologic Imaging

C-X-C motif chemokine receptor 4 (CXCR4) plays a key role in various physiological functions, such as immune processes and disease development, and can influence angiogenesis, proliferation, and distant metastasis in tumors. Recently, several radioligands, including peptides, small molecules, and nanoclusters, have been developed to target CXCR4 for diagnostic purposes, thereby providing new diagnostic strategies based on CXCR4. Herein, we focus on the recent research progress of CXCR4-targeting radioligands for tumor diagnosis. We discuss their application in the diagnosis of hematological tumors, such as lymphomas, multiple myelomas, chronic lymphocytic leukemias, and myeloproliferative tumors, as well as nonhematological tumors, including tumors of the esophagus, breast, and central nervous system. Additionally, we explored the theranostic applications of CXCR4-targeting radioligands in tumors. Targeting CXCR4 using nuclear medicine shows promise as a method for tumor diagnosis, and further research is warranted to enhance its clinical applicability.
Efficacy of Core Strengthening Exercise Program in Postpartum Period - A Narrative Review

Introduction: One of the most eminent phase of women’s life is pregnancy. Pregnancy is a natural physiological process but postnatal complications poses a unique and gender-based challenge for physical therapist. Aim/Objectives: To compare various exercises to strengthen the core muscles after delivery in the postpartum period. Methodology: Total 70 studies were checked and total 39 were not suitable for the study. Total 20 articles were selected which were RCT. Result: It has been reported that the therapeutic exercises can improve lung functions by increasing forced vital capacity and forced expiratory volume. Conclusion: Based on included articles, this study concluded that core strengthening exercise improved strength of pelvic floor muscles, reduced lumbopelvic pain, and improved quality of life in postpartum women.
Neurological adverse events of mitotane in adrenocortical carcinoma: results of a pilot study

Introduction Mitotane, the only drug approved by the Food and Drug Administration (FDA) for the treatment of adrenocortical carcinoma, is associated with several side effects including neurotoxicity. The aim of our study is to investigate the relationship between mitotane plasma levels and neurological toxicity. Methods We have considered five patients affected by adrenocortical carcinoma treated with mitotane. The neurological assessment included a neurological examination, an electroencephalogram, event-related potentials (P300), and a neuropsychological assessment. All of the patients were first considered at the onset of symptoms of neurotoxicity or when mitotanemia levels were above 18 mg/L, for the second time at mitotanemia normalization and subsequently at its further increase, or in case of persistent neurological abnormalities, some months after normalization. Results At the first neurotoxicity, four patients showed impaired neurological examination, electroencephalogram, and P300; three patients had impaired neuropsychological assessment; one patient, only P300. At mitotanemia normalization, the neurological examination became normal in all patients and electroencephalogram normalized in one patient, improved in another one, continuing to be altered in the other three. P300 latency and neuropsychological assessment normalized in two patients and persisted altered in the patient experiencing long-term mitotane toxicity. At the third evaluation, in the patient with prolonged mitotane toxicity, the normal mitotanemia in the previous 9 months restored P300 and improved the electroencephalogram but not the neuropsychological assessment. In the two patients experiencing a further rise of mitotanemia, neurological examination was normal but P300 and electroencephalogram were altered. Conclusion The results of our study highlighted the presence of neurophysiological and neuropsychological abnormalities associated with mitotane values above 18 mg/L.
Improving Semi-Blind Interference Suppression on Multi-Cell Massive MIMO Systems by Multi-Antenna Users

This paper presents improved semi-blind uplink interference suppression scheme which was previously proposed. When the number of spatial multiplexing is increased in a Massive MIMO system to increase capacity, the number of orthogonal sequences required for channel estimation has an upper limit, so they are used repeatedly in each cell, resulting in inter-cell interference (ICI) caused by pilot signals so called pilot contamination. It prevents sufficient suppression of not only ICI but also inter-user interference (IUI). Our previously proposed scheme is based on intra-cell channel state information (CSI) which contains pilot contamination. Blind adaptive array (BAA) signal processing such as constant modulus algorithm (CMA) can well suppress residual interference with capturing desired signals. This paper presents interference suppression performance improvement by employing beamforming at the user terminal side. User beamforming works more effectively for semi-blind interference suppression because it can suppress ICI in advance, although it cannot suppress IUI. Computer simulations demonstrates its effectiveness.