Miracle-dz/UniFAR-FTUs huggingface dataset:
Highly Sensitive Metamaterial Plasmonic Sensor Based on Nanoimprinting

In this paper, we propose and experimentally demonstrate a highly sensitive plasmonic nanohole metasurface sensor using nanoimprinting and electron beam evaporation. A refractive index sensitivity of 516 nm/RIU has been successfully achieved. With the help of nanoimprinting, the proposed method exhibits intrinsic advantages in a simple fabrication process, high repeatability and low cost. Furthermore, the proposed method enables large-area fabrication, making the mass-production of the metamaterial sensor highly feasible.
Head movements in two- and four-person interactive conversational tasks in noisy and moderately reverberant conditions

Multi-modal processing schemes are of increasing importance for adaptive hearing devices. However, more data is required to understand interactions in complex application scenarios. In this study, the speech and head movements of eight normal-hearing participants were recorded in two-and four-person interactive conversational tasks, with and without 4-talker babble noise at 75 dB(A) and reverberation times of 0.25 and 0.6 s. Two-person conversations showed a head movement (yaw) interquartile range of 11.6° while four-person conversations showed a statistically significantly different interquartile range of 21.91°. No effect of acoustic condition was observed. The recorded data were also successfully used to test a previously published hearing-device direction of arrival estimation algorithm that utilized head movement information and correlation lag between acoustic signals from the left and right ear.
Beyond Style: Synthesizing Speech with Pragmatic Functions

With recent advances in generative modelling, conversational systems are becoming more lifelike and capable of long, nuanced interactions. Text-to-Speech (TTS) is being tested in territories requiring natural-sounding speech that can mimic the complexities of human conversation. Hyper-realistic speech generation has been achieved, but a gap remains between the verbal behavior required for upscaled conversation, such as par-alinguistic information and pragmatic functions, and comprehension of the acoustic prosodic correlates underlying these. Without this knowledge, reproducing these functions in speech has little value. We use prosodic correlates including spectral peaks, spectral tilt, and creak percentage for speech synthesis with the pragmatic functions of small talk, self-directed speech, advice, and instructions. We perform a MOS evaluation, and a suitability experiment in which our system outperforms a read-speech and conversational baseline.
Acoustic characteristics of depression in older adults' speech: the role of covariates

Depression in older adults is often associated with various physical conditions and is hence different from depression at a younger age. Ageing may come with cognitive decline, medication use, and frailty, which are known to be predictors of late-life depression. One common symptom of depression is psychomotor retardation, that may also affect speech production. Most speech studies on depression so far have focused on younger or middle-aged adults. In this study, we used speech data from a large longitudinal Dutch study on late-life depression and its comorbid symptoms to compare speech acoustics in persons with depression (PWD) and controls. We investigated whether groups differed by taking several covariates into account (e.g., frailty, slowness, and medication use). Group differences were found in within-vowel F2 range, speech rate and mean pause duration. These data indicate that speech acoustics can be used to differentiate PWDs and controls, even with low-quality speech data.
Decentralized Bidirectional-Chain Equalizer for Massive MIMO

The current multiple-input multiple-output (MIMO) systems are still mainly implemented based on the centralized architecture, which thus has to process a huge amount of base-band data. In particular, the central processing unit (CPU) needs a large bus bandwidth to accommodate the prohibitive baseband data transmission, which hinders effective system implementation, especially for the massive MIMO systems. Moreover, the centralized scheme lacks flexibility and scalability when facing varying sizes of antenna arrays and diverse applications. This paper proposes an efficient decentralized bidirectional-chain (DBC) equalizer architecture. The advantages of the DBC architecture are two-fold. First, it can reduce the data traffic transmitted from the antennas to the processing unit by categorizing them into clusters, each of which is equipped with a local processing unit (LPU). Second, it can reduce the time delay by updating all clusters in parallel. To sufficiently exploit the proposed DBC architecture, we further propose efficient parallel iterative algorithms. The DBC-based parallelizable iterative algorithms achieve the state-of-the-art performance in terms of convergence rate and bit error rate. Finally, simulation results are provided to confirm the effectiveness and superiority of our proposal.
FEM-SPH Numerical Simulation of Impact Loading on Floating Laminates

The study of dynamic events such as impact and hydroelastic slamming is of great importance in determining the structural integrity of naval or maritime structures, particularly those made of composite materials. This topic has been investigated by numerous researchers using analytical, experimental, and numerical approaches. In this study, we propose using a hybrid numerical model combining smoothed-particle hydrodynamics (SPH) and the finite element method (FEM) to investigate the impact of external objects on floating laminates. The results show a good agreement with the available experimental data regarding the impact dynamic and some limitations in the damage determination.
Overview of Image Datasets for Deep Learning Applications in Diagnostics of Power Infrastructure

The power sector is one of the most important engineering sectors, with a lot of equipment that needs to be appropriately maintained, often spread over large areas. With the recent advances in deep learning techniques, many applications can be developed that could be used to automate the power line inspection process, replacing previously manual activities. However, in addition to these novel algorithms, this approach requires specialized datasets, collections that have been properly curated and labeled with the help of experts in the field. When it comes to visual inspection processes, these data are mainly images of various types. This paper consists of two main parts. The first one presents information about datasets used in machine learning, especially deep learning. The need to create domain datasets is justified using the example of the collection of data on power infrastructure objects, and the selected repositories of different collections are compared. In addition, selected collections of digital image data are characterized in more detail. The latter part of the review also discusses the use of an original dataset containing 2630 high-resolution labeled images of power line insulators and comments on the potential applications of this collection.
Modeling the Chlorine Series from the Treatment Plant of Drinking Water in Constanta, Romania

Ensuring good drinking water quality, which does not damage the population’s health, should be a priority of decision factors. Therefore, water treatment must be carried out to remove the contaminants. Chlorination is one of the most used treatment procedures. Modeling the free chlorine residual concentration series in the water distribution network provides the water supply managers with a tool for predicting residual chlorine concentration in the networks. With regard to this idea, this article proposes alternative models for the monthly free chlorine residual concentration series collected at the Palas Constanta Water Treatment Plant, in Romania, from January 2013 to December 2018. The forecasts based on the determined models are provided, and the best results are highlighted.
Derivation of Optimal Two Dimensional Rule Curve for Dualistic Reservoir Water-Supply System

In arid and semi-arid regions particularly vulnerable to climate change, optimizing the long-term operation of multi-purpose reservoirs is paramount. This study derived an optimum two-dimensional rule curve to jointly operate the parallel reservoirs of Mosul and Dukan, Northern Iraq. A hybridized optimization technique combining conventional dynamic programming with the shuffled complex evolution algorithm (SCE-UA) was developed to solve this problem. The results showed that the proportion of normal water supply areas increased from the beginning of the flood season (October) to its highest levels in April (58.77% of the total water supply area). The proportion decreased to its lowest in September (25.04% of the total water supply area). The newly derived 2D rule cure was compared with the current operation policy and was found to optimize the amount of water shortage by 21.1% during the operational period. It also reduced the shortage period and avoided catastrophic water shortages during droughts. In addition, the developed model optimized the amounts of water more than the joint water requirements, suffering from a significant deficit in meeting the demand during some months of the operational years. As a result, the storage in each reservoir was improved and thence can be adapted to face water shortages during future climate changes. This study proved the new hybridized model's applicability and can serve as a tool for sustainable water management. Doi: 10.28991/CEJ-2023-09-07-016 Full Text: PDF
A QoS harmonization strategy for Wi-Fi and Cellular Networks Convergence

Beyond 5G networks will not only present an evolution of the current 5G standards, but they will also provision a path for increased convergence with other Radio Access Technologies (RATs). The Wi-Fi Alliance and 3GPP through their standards releases have presented a lucrative inter-working opportunity for the same. Such a convergence will help to enhance the Quality of Experience (QoE) for the users. However, one of the most important challenge towards a more converged scenario is the provision of uniform Quality of Service (QoS) across cellular and Wi-Fi RATs. Hence in this paper, to the best of our knowledge, we propose the first attempt in literature to homogenize Wi-Fi and 5G QoS. Specifically, a method is presented to map Wi-Fi QoS parameters to 5G QoS Identifiers (5QI). The presented results, which show a clear possibility of the 5G and Wi-Fi QoS harmonization, have been obtained via a real testbed setup.
Enhanced Multiple DBSCAN Algorithm for Traffic Detection Using mmWave Radar

The ability to robustly and effectively detect and classify road objects is vital to an all-purpose traffic monitoring system. Recent development in mmWave radar technologies offers improved range and resolution at an affordable price, making it an ideal candidate for Intelligent Transportation System (ITS) applications. Modern mmWave radars output 3D detection point clouds representing moving objects. The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is a popular method for clustering radar point clouds. However, our study found that several variations of DBSCAN perform less than expected in a road and intersection scene. To address this, we propose an Enhanced Multiple DBSCAN algorithm tailored specifically for traffic monitoring applications, which aims to improve detection performance using radar point cloud data. By using adaptive parameters, the Enhanced Multiple DBSCAN algorithm addresses the problem of reducing cluster size over distance. Additionally, a modified Non-Maximum Suppression (NMS) variation is included to address missed detections when merging results from multiple DBSCANs. Our Enhanced Multiple DBSCAN achieves over 90% precision in detecting road objects, the best result among all tested methods. The algorithms proposed and evaluated in this study provide a valuable reference for modern radar ITS applications.
Space Geodetic Constraints on Plate Rigidity and Global Plate Motions.

The Global Positioning System (GPS) has emerged over the last 10 years as the premier space geodetic technique for geologic observation with the benefits o f both accuracy and economy. GPS data from more than 260 stations were processed utilizing GIPSY-OASIS software, and all available RINEX files for these stations beginning on January 1, 1993. The uniform processing strategy, combined with a rigorous independent error estimate o f site velocities and the broad geographical distribution o f these sites resulted in a global high-precision surface velocity data set. This data set has been used to: 1) compare the effects o f different GPS antenna and monument types on data noise; 2) examine in detail the rigidity o f the North America plate; 3) constrain the rate o f deformation in the northern Gulf o f Mexico;. 4) define a model for Recent (Holocene) time global plate motions. The weighted root mean square scatter about the best fit line through the daily position estimates are used to assess the effects o f different antenna and monument types on North America. The data set is sensitive to glacial isostatic adjustment as indicated by the decrease in Xv2 from 1.3 to 1.0 by excluding those sites on rigid North America within a radius o f 1,800 km o f Hudson Bay. The resulting 64 site solution for the angular velocity o f North America give a mean rate residual o f 1 mm/yr.
Development and Validation of a Novel UHPLC-MS/MS Method for the Quantification of Plinabulin in Plasma and Its Application in a Pharmacokinetic Study with Leukopenic Rats

Plinabulin, a new antitumor drug developed from marine natural products that targets microtubules in cancer cells, is currently being tested in a phase III clinical study. Plinabulin has been clinically proven to be effective on leukopenia. However, to our knowledge, there are no reports investigating the pharmacokinetics of plinabulin in individuals with leukopenia and healthy individuals. In this study, we developed a rapid and sensitive UHPLC-MS/MS method for the detection of plinabulin for the first time. Using a novel cyclophosphamide-induced leukopenia model, we investigated the differences in the pharmacokinetic characteristics of plinabulin between rats with leukopenia and normal rats. Plinabulin and propranolol (IS) peaks were separated by gradient elution for a total run time of 5 min. The methodological validation showed a good accuracy (101.96–109.42%) and precision (RSD ≤ 5.37%) with the lower limit of quantification at 0.5 ng/mL. The recovery of plinabulin was between 91.99% and 109.75% (RSD ≤ 7.92%). The values of the area under the plasma concentration-time curve (AUC0-t) for leukopenia groups and control groups at doses of 0.5 mg/kg, 1 mg/kg, and 3 mg/kg were 148.89 ± 78.74 h·μg/L and 121.75 ± 31.56 h·μg/L; 318.15 ± 40.00 h·μg/L and 272.06 ± 42.85 h·μg/L; and 1432.43 ± 197.47 h·μg/L and 1337.12 ± 193.56 h·μg/L; respectively. The half-lives (t1/2s) of plinabulin were 0.49–0.72 h for leukopenia groups and 0.39–0.70 h for control groups at three doses, and the clearance rates (CLs) of plinabulin were 2.13–3.87 L/h/kg for leukopenia groups and 2.29–4.23 L/h/kg for control groups. Pharmacokinetic results showed that there was no significant pharmacokinetic difference between the normal group and the leukopenia group. Based on the power model, plinabulin exhibits a lack of dose proportionality over the dose range of 0.5–3 mg/kg after intravenous administration. This study provides guidance for the development of plinabulin as a potential candidate for the treatment of chemotherapy-induced leukopenia.
Deep Learning Based Steering Angle Prediction with LiDAR for Autonomous Vehicle

End to end control using steering angle prediction of autonomous vehicle is the research problem which has been solved using camera based sensor. In our work we have developed deep learning architecture for solving steering angle prediction using real time LiDAR data. Because LiDAR sensor gives 360 degree view of autonomous vehicle. Camera is not useful for various lighting condition specially in night time while LiDAR sensor is independent of various light scenarios. We have recorded LiDAR data and corresponding steering angle and proposed small benchmark dataset for research. We have performed our experiment on recorded dataset.
Meaningful Work, Well-Being, and Health: Enacting a Eudaimonic Vision

Work is one of the most enduring and consequential life domains regarding how meaning and purpose impact health and well-being. This review first examines scientific findings from the MIDUS (Midlife in the U.S.) national longitudinal study that have linked work to well-being and health. Most have focused on adverse work or work conditions as influences on poor health, with a few recent findings investigating links to purpose and other aspects of eudaimonic well-being. Organizational scholarship is then selectively reviewed to show how meaningful work is often linked to motivation, performance, and commitment. Paradoxically, meaning can also lead to the exploitation and erosion of health and well-being when managed without regard for decent working conditions. Recent workplace phenomena known as the Great Resignation and Quiet Quitting underscore the societal consequences of work without meaning or adequate working conditions. Both the scientific and organizational literature are enriched by a vision of meaningful work rooted in Aristotle’s writings about virtue, ethics, and the realization of potential. Evidence-based practices tied to these eudaimonic ideals are examined at multiple levels, including the societal context (public policy), organizational conditions (culture, human resource practices, leadership), and individual strategies to find meaning, engagement, and fulfillment in work. A concluding section highlights strengths and omissions in the scientific and organizational literature and, going forward, calls for greater interplay among researchers, practitioners, and policymakers in enacting eudaimonic ideals.
Physical Layer Security for THz Communication

Terahertz (THz) band offers large accessible bandwidth and spatially confined highly directed beams for the realization of secure and high data rate communication. In this work, the secrecy performance is investigated for a directional THz link, which suffers from high propagation loss and attenuation due to molecular absorption. The statistical characterization of the THz link is also considered for analyzing the system over precise channel model. A threat model is developed for the scenario where the main intended user and the eavesdropper (eve) are located in line-of-sight (LOS) with respect to the source. The performance analysis is carried out by deriving the probability of strictly positive secrecy capacity (SPSC) and the lower bounds of secrecy outage probability (SOP). The proposed theoretical expressions are validated using extensive Monte-Carlo simulations. The results demonstrate that limiting the broadcast range and tuning the frequency are powerful yet simple transmission protocols that can achieve the physical layer security in the THz bands.
VIT-GADG: A Generative Domain-Generalized Framework for Chillers Fault Diagnosis Under Unseen Working Conditions

The extreme unbalance of training samples among different working conditions caused by complex and variable external environments makes the fault diagnosis of a chiller based on domain adaptation (DA) poor performance. Although recently emerging fault diagnosis methods based on domain generalization (DG) can learn domain-invariant knowledge from multiple source domains and can generalize to unseen target domains, these methods still rely on multiple similar source domain data and rarely consider how to enhance the ability to distinguish the joint distribution of features extracted from the source domain samples. To address these problems, a generative domain-generalized framework for chillers fault diagnosis, namely, vision transformer generative adversarial DG (VIT-GADG), is proposed. In VIT-GADG, a novel VIT domain generation network (VIT-DGN) is first designed to reduce DG’s dependence on multisource domain data by improving the diversity of the distribution of the source domain samples. Then, a new adversarial DG network called VIT conditional adversarial DG network (VIT-CADGN) is designed to extract domain-invariant knowledge from the source domain and latent domains that can be generalized to unseen target domains. Specifically, the VIT module can effectively extract the global statistical feature of input samples, which is conducive to the identification of joint distribution. Simultaneously, the collaborative conditional domain discrimination strategy is introduced to improve the distribution discrimination ability of the extracted global statistical features while simultaneously aligning its conditional distribution. In addition, a personalized adaptive weight strategy is proposed to improve the performance of VIT-CADGN. Finally, the comprehensive case study shows that VIT-GADG has a satisfactory ability to extract invariant features, which improves the diagnosis accuracy in the unseen target domain.
Protocol for Community-Based Exercise Training after Discharge from Hospital-Based Stroke Rehabilitation: A Multicenter, Randomized, Parallel-Group, Double-Blind Controlled Pilot and Feasibility Trial

Exercise training participation of patients with stroke in the community after discharge from the hospital has many benefits for physical, social, and psychological rehabilitation and improves their quality of life. However, in the Republic of Korea, studies on stroke survivors who can participate in an exercise training program have not been conducted. This trial aims to investigate the effectiveness of exercise training programs after patients with stroke are discharged from the hospital with a doctor’s note and referred to a community exercise center, as there is a lack of studies on this population. This multicenter, randomized, parallel-group, double-blind controlled pilot and feasibility trial will randomly assign 120 patients with stroke to either 8 weeks of a community-based exercise training program (experimental group) or activities of daily living (control group). The primary outcomes will be muscle strength, cardiorespiratory fitness, body composition, physical performance, and gait. The secondary outcomes will be quality of life and activities of daily living. This study’s results may add new insights into the effectiveness of community-based exercise training programs after patients with stroke are discharged from the hospital with a doctor’s note and referred to a community exercise center. The success of the new exercise training approach could offer valuable information for developing more inclusive protocols for patients with stroke in the future if it proves to be efficacious.