Miracle-dz/UniFAR-FTUs huggingface dataset:
Prevalence of psychiatric morbidity and cognitive impairment among non-communicable disease patients in Southern Chennai, Tamil Nadu

Introduction: According to the WHO, non-communicable diseases cause 71% of all deaths globally. Despite many studies showing a significant association between non-communicable diseases (NCDs) and cognitive decline, it is not researched in Tamil Nadu. Hence, this study was conducted to screen for psychiatric morbidity and cognitive impairment (CI) among NCD patients in Southern Chennai. Aims: The aim of this study was to estimate the prevalence of psychiatric morbidity and CI and their associated factors among NCD patients attending NCD clinics of tertiary care hospitals. Methods and Material: A cross-sectional study was carried out in NCD patients (n = 343) attending an NCD clinic in a tertiary care hospital. Basic sociodemographic and clinical details were obtained by a semi-structured questionnaire. Cognition function and psychiatric morbidity were assessed using mini-mental state examination, patient health questionnaire 9 and generalised anxiety disorder 7 tools, respectively. Results: The mean age of the study participants was 58 years. Of 343 participants, 19.2% had severe CI, 26.8% had severe depression, and 29.7% had severe anxiety. Among 180 participants aged 59–86 years, 25.5% participants had osteoarthritis; of these, 41.3% had severe CI (P < 0.0001), 82.6% had severe depression (P < 0.0001) and 63% had severe anxiety (P < 0.027), and their association was statistically significant. Conclusions: This study concludes that about one-fourth of the NCD patients suffered from CI and psychiatric morbidity, which are of rising concern. Musculoskeletal diseases are neglected to be assessed under NCDs, and in this study, osteoarthritis was found to be significantly associated with depression, anxiety and CI.
Improving accuracy of age estimates for insect evidence—calibration of physiological age at emergence (k) using insect size but without “k versus size” model

Abstract Postmortem interval may be estimated based on the age of insect evidence collected on a death scene. Reference data that are used in such estimation frequently comprise thermal summation constant (i.e. k), which is equal to the insect age upon completion of immature development expressed in accumulated degree-days or degree-hours (ADD or ADH). Essentially, k is a central point of an insect group and it may poorly represent insect evidence that is near the limits of variation for the group. Accordingly, it was postulated to calibrate k for particular insect evidence and insect size and sex were found to be useful for this purpose in some of the species. However, the calibration is only possible by using the model that correlates k with insect size. Since very few such models were published, this lack of data limits the calibration of k in forensic casework. In this article, we develop a formula that is useful for the calibration of k without the use of “k versus size” model (and related datasets). The formula uses k from the general thermal summation model for a species (with its standard error), size range for the species (retrieved from entomology literature), and size measurements for particular insect evidence. The calibration of k with the formula was validated using the Creophilus maxillosus (Coleoptera: Staphylinidae) and Necrodes littoralis (Coleoptera: Silphidae) datasets. It was particularly useful while analyzing unusually small and large insects, in case of which the formula reduced the inaccuracy of k from the general model on average by ~25 ADD in C. maxillosus and ~40 ADD in N. littoralis. We discuss the limitations and prospects of the calibration protocol that employs the formula.
Backstepping Control Collaborative Shaft Torque Observer for Limit Cycle Oscillation Suppression of Fully Closed-Loop Gear Transmission Servo System

To address the problem of gear clearance causing the limit cycle oscillation at the positioning end, further deteriorating the positioning accuracy of the servo system, and even causing tooth breakage in severe cases, a backstepping control (BC) method based on shaft torque observer (STO) is proposed in this article for a fully closed-loop gear transmission servo system. First, a gear transmission servo control system model is established, which includes a permanent magnet synchronous motor model, a backlash model, and a gear transmission system model. Second, the description function method is utilized to analyze the limit cycle oscillation mechanism of nonlinear systems. Next, speed loop and current loop BC subsystems are proposed. To avoid the problem of limit cycle oscillation and decreased positioning accuracy caused by backlash nonlinearity in the fully closed-loop gear transmission servo system, a STO is proposed and real-time observation and online compensation of shaft torque are carried out, which effectively eliminates the limit cycle oscillation at positioning end. Ultimately, a 2.0-kW gear transmission servo system is used as a case study, the validity of the proposed BC-STO is tested via experimental results, the limit cycle oscillation is effectively eliminated, and the positioning accuracy is significantly improved.
Scaling Code Pattern Inference with Interactive What-If Analysis

Programmers often have to search for similar code when detecting and fixing similar bugs. Prior active learning approaches take only instance-level feedback, i.e., positive and negative method instances. This limitation leads to increased labeling burden, when users try to control generality and specificity for a desired code pattern. We present a novel feedback-guided pattern inference approach, called SURF. To reduce users’ labelling effort, it actively guide users in assessing the implication of having a particular feature choice in the constructed pattern, and incorporates direct feature-level feedback. The key insight behind SURF is that users can effectively select appropriate features with the aid of impact analysis . SURF provides hints on the global distribution of how each feature is consistent with already labelled positive and negative instances,
Coordinated LMA, Balloon-borne Electric Field, and Polarimetric Radar Observations of a Triggered Lightning Flash at Camp Blanding

In an 18-day period of July - August 2013, coordinated observations were attempted with a balloon-borne electric field meter, a balloon-borne particle imager, the 5-cm wavelength polarimetric Shared Mobile Atmospheric Research and Teaching Radar (SMART-R), a small-baseline VHF Lightning Mapping Array (LMA), and the extensive observing facilities for triggered lightning at the International Center for Lightning Research and Testing (ICLRT) at Camp Blanding, Florida. This experiment was the first to provide vertical profiles of the electric field, from which the vertical charge distribution could be inferred, relative to radar data in Florida storms. Furthermore, mapped three-dimensional lightning structure, surface measurements of lightning current, and multiple-station electric fields and electric field derivatives were provided by the ICLRT for triggered flashes. On 1 August, an electric field meter was flown during a period in which 3 flashes were triggered and confirmed the hypothesis that the turn to horizontal lightning structure just above the melting level occurred in a layer of negative charge.
ALPACA: An Asymmetric Loss Prediction Algorithm for Channel Adaptation Based on a Convolutional-Recurrent Neural Network in URLLC Systems

A key feature of 5G systems is the Ultra-Reliable Low-Latency Communication (URLLC), which can be used for remote surgery, smart grids, industrial control, etc. URLLC requires millisecond-level delays and very high reliability, i.e., less than $10^{-5}$ packet loss probability. The ability to satisfy these very strict quality of service requirements depends on selecting the Modulation and Coding Schemes (MCS) for data transmissions. On the one hand, the selected MCS shall be robust enough to avoid multiple retransmissions within a small delay budget. On the other hand, the MCS shall be high-rate to reduce channel resource consumption and, thus, shall increase the system capacity for URLLC. The MCS selection problem is extremely challenging to capture the quickly varying wireless channel effects, e.g., in highly mobile scenarios, because the decision shall be made long before the actual transmission occurs. The paper proposes a novel MCS selection algorithm called ALPACA (Asymmetric Loss Prediction Algorithm for Channel Adaptation), which relies on a widely used class of convolutional-recurrent neural networks. However, in contrast to existing approaches, ALPACA explicitly considers the asymmetric error cost for channel prediction by utilizing quantile regression loss. Both real-life channel measurements and 3GPP channel models are used to evaluate the performance of ALPACA. Numerical results demonstrate the increase in the reliability and reduction in resource consumption compared with the existing MCS selection algorithms, which results in 40% growth of the network capacity.
Effects of lava heating on volatile‐rich slopes on Io

The upper crust of Io may be very rich in volatile sulfur and SO2. The surface is also highly volcanically active, and slopes may be warmed by radiant heat from the lava. This is particularly the case in paterae, which commonly host volcanic eruptions and long‐lived lava lakes. Patera slopes are highly variable, but some are greater than 70°. I model the heating of a volatile slope for two end‐member cases: instantaneous emplacement of a large sheet flow and persistent heating by a long‐lived lava lake. In general, single flows can briefly raise sulfur to the melting temperature or drive a modest amount of sublimation of SO2. Persistently, lava‐covered surfaces will drive much more significant geomorphic effects, with potentially significant sublimation and slope retreat. In addition to the direct effects, heating is likely to weaken slope materials and may trigger mass wasting. Thus, if the upper crust of Io is rich in these volatile species, future missions with high‐resolution imaging are likely to observe actively retreating slopes around lava lakes and other locations of frequent eruptions.
A Misalignment-Tolerant Autonomous Wireless Power Transfer System for Battery Charging Based on Detuning Control

This article proposed an autonomous wireless power transfer system with misalignment tolerance and improved control degree of freedom based on detuning control for battery charging, in which the outputs can be regulated continuously and smoothly without additional dc–dc converters while maintaining soft-switching or zero-phase-angle operation of the inverter. Theoretical analysis shows that constant current (CC) or constant voltage (CV) independent of coupling coefficient and load can be achieved without information exchange between transmitter and receiver, which eliminates wireless communication. Furthermore, when the detuning ratio is controlled to be constant, a transfer efficiency insensitive to the variation of the coupling coefficient can be obtained. A prototype with a 6-A charging current and a 36-V charging voltage is built to support the feasibility of the proposed method. Experimental results demonstrate that within the coupling coefficient range of 0.283–0.456, CC and CV with transfer efficiency greater than 91.5% can be achieved.
Imaging near-surface heterogeneities by natural migration of surface waves

We demonstrate that near-surface heterogeneities can be imaged by natural migration of backscattered surface waves in common shot gathers. No velocity model is required because the data are migrated onto surface points with the virtual Green’s functions computed from the shot gathers. Migrating shot gathers recorded by 2D and 3D land surveys validates the effectiveness of detecting near-surface heterogeneities by natural migration. The implication is that more accurate hazard maps can be created by migrating surface waves in land surveys.
Pharmacoeconomic evaluation of isavuconazole, posaconazole, and voriconazole for the treatment of invasive mold diseases in hematological patients: initial therapy prior to pathogen differential diagnosis in China

Background Invasive mold diseases (IMD) is associated with high mortality and a substantial economic burden. For high-risk patients, fever drive or diagnostic drive therapy is usually initiated prior to the differential diagnosis of the pathogen. This study evaluated the cost-effectiveness of isavuconazole, posaconazole, vs. voriconazole in the treatment of IMD from the perspective of the Chinese healthcare system, informing healthcare decision-making and resource allocation. Methods A decision analytic model was constructed using TreeAge Pro 2011 software to evaluate the cost-effectiveness of the entire disease course. We assumed that the prevalence of mucormycosis in the patients entering the model was 7.8%. Efficacy, cost, adverse events, and other data included in the model were mainly derived from clinical studies, published literature, and publicly available databases. The primary outcomes of the model output were total cost, quality-adjusted life years (QALYs), life years (Lys), and incremental cost-effectiveness ratio (ICER). The willing-to-pay (WTP) threshold was defined as one to three times China's GDP per capita in 2022. One-way sensitivity analysis and probability sensitivity analysis were used to determine the robustness of the model. At the same time, the cost-effectiveness of three triazole antifungal agents under a broader range of mucormycosis prevalence, when voriconazole was covered by medical insurance reimbursement, and after the price reduction of posaconazole was discussed. Results Compared with voriconazole, isavuconazole provided an additional 0.38 Lys (9.29 vs. 8.91 LYs) and 0.31 QALYs (7.62 vs. 7.31 QALYs); ICER was $15,702.46/QALY, well-below the WTP threshold ($38,223/QALY). However, posaconazole did not provide a significant economic advantage over voriconazole (9.40 vs. 9.36 Lys; 7.71 vs. 7.68 QALYs; ICER $64,466.57/QALY). One-way sensitivity analysis found that ICER was highly sensitive to the mortality of patients with invasive aspergillus infection. In the probabilistic sensitivity analysis, when the WTP threshold was $38,223/QALY, the probability of isavuconazole being cost-effective was 72.9%. The scenario analysis results indicated that posaconazole would become cost-effective when the price was reduced by 15% or the prevalence of mucormycosis was 14%. Conclusions Isavuconazole represents a cost-effective initial option for treating IMD in high-risk hematological patients prior to the differential diagnosis of pathogens. It will also be economical when a 15% reduction in posaconazole cost is achieved.
Characterization of the 2.4 GHz-band using a semiempirical model and a ray tracing model

A renovated interest has arisen nowadays on the unlicensed ISM (Industrial, Scientific, and Medical) 2.4 GHz band, where more and more LAN (Local Area Network) and PAN (Personal Area Network) technologies are coming into operation. We have characterized the 2.4 GHz band, for short-range applications, with a semiempirical channel model, obtained from the results of a measurement campaign carried out this year. In addition, we have developed a 3D ray tracing channel model, in MATLAB, to test the propagation conditions in the same scenarios than those already tested in the measurement campaign. A comparison between both channel models with real results will be carried out and their likeness will be performed by means of the Path Loss (PL) profile and the Line-of-Sight Factor (LOSF) profile.
Manipulation of GameXPeptide synthetase gene expression by a promoter exchange alters the virulence of an entomopathogenic bacterium, Photorhabdus temperata temperata, by modulating insect immune responses

An entomopathogenic bacterium, Photorhabdus temperata subsp. temperata, is mutualistic to its host nematode, Heterorhabditis megidis. The infective juvenile nematodes enter target insects through natural openings and release the symbiotic bacteria into the insect hemocoel. The released bacteria suppress the insect immune responses and cause septicemia through their secondary metabolites. GameXPeptide (GXP) is one of the common secondary metabolites of most Photorhabdus species and is produced by the catalytic activity of a specific non-ribosomal peptide synthetase called GxpS encoded by the gxpS gene. This study confirmed gxpS to be encoded in the P. temperata temperata genome and analyzed its expression during bacterial growth. LC-MS/MS analysis of the bacterial culture broth contained at least four different GXPs (GXP-A to GXP-D), in which GXP-A was the most abundant. To investigate GXP synthesis following gxpS expression, the gxpS promoter of P. temperata temperata was replaced with an inducible arabinose promoter by homologous recombination. The gxpS transcript levels in the mutant were altered by the addition of l-arabinose. Without the inducer, the gxpS transcript level was significantly lower compared to the wild type and produced significantly lower amounts of the four GXPs. The addition of the inducer to the mutant significantly increased gxpS expression and produced significantly higher levels of the four GXPs compared to the wild type. The metabolite extracts obtained from wild-type and mutant bacteria showed differential immunosuppressive activities according to their GXP contents against the cellular and humoral immune responses of a lepidopteran insect, Spodoptera exigua. Interestingly, the gxpS-mutant bacteria showed less insecticidal activity compared to the wild type, whereas the addition of GXP to the mutant significantly restored insecticidal activity. These results suggest that the gxpS gene encoded in P. temperata temperata is responsible for the production of at least four different GXPs, which play crucial roles in bacterial virulence.
Structural Relationships between Socio-Cognitive Mindfulness, Everyday Creativity, and Clinical Competence in Nursing Students: Mediating Effects of Everyday Creativity

Background: Incorporating socio-cognitive mindfulness, which has not received much attention from nursing researchers, this study investigated the structural relationships between socio-cognitive mindfulness, everyday creativity, and clinical competence among nursing students. This study also explored the mediating effect of everyday creativity. Methods: A cross-sectional study was performed with 222 nursing students in South Korea. Students completed the questionnaire evaluating their own socio-cognitive mindfulness, everyday creativity, and clinical competence. Data were analyzed using structural equation modeling and path analysis. Results: Socio-cognitive mindfulness positively influenced everyday creativity (β = 0.791, p < 0.01), and everyday creativity also positively influenced clinical competence (β = 0.470, p < 0.01). However, the relationship between socio-cognitive mindfulness and clinical competence was not significant. Importantly, everyday creativity fully mediated the relationship between socio-cognitive mindfulness and clinical competence (a × b = 0.372, p < 0.01). Conclusions: The results indicate that socio-cognitive mindfulness effectively enhances nursing students’ clinical competence by improving their everyday creativity. The mediating result highlights the significance of everyday creativity in nursing education, underscoring the necessity for training programs aimed at cultivating creativity. This research offers a basis for developing programs that concentrate on socio-cognitive mindfulness and everyday creativity, with the goal of enhancing the clinical competence of nursing students.
Design of a Beam Separator for FLASH Electron Therapy Facilities

A promising modality of radiation therapy is FLASH: a technique in which the full radiation dose is delivered in just 1/10th of a second. This modality has been proven successful in destroying cancerous cells while sparing healthy tissues. The aim is to develop an accelerator to treat large-volume and deep-seated tumors using high-energy electron beams in the FLASH modality. Specifically, we are designing a steady-state magnet that guides three distinct energy beams into three separate beamlines and ensures dose conformality within FLASH timescales. This paper presents a design method that incorporates beam optics and magnetic parameters into a numerical optimization process. The method is applied to the design of a magnetic spectrometer with a varying pole profile. The magnet performance is compared to pure dipole and combined dipole-quadrupole designs.
Deep Learning for Audio Signal Processing

Given the recent surge in developments of deep learning, this paper provides a review of the state-of-the-art deep learning techniques for audio signal processing. Speech, music, and environmental sound processing are considered side-by-side, in order to point out similarities and differences between the domains, highlighting general methods, problems, key references, and potential for cross fertilization between areas. The dominant feature representations (in particular, log-mel spectra and raw waveform) and deep learning models are reviewed, including convolutional neural networks, variants of the long short-term memory architecture, as well as more audio-specific neural network models. Subsequently, prominent deep learning application areas are covered, i.e., audio recognition (automatic speech recognition, music information retrieval, environmental sound detection, localization and tracking) and synthesis and transformation (source separation, audio enhancement, generative models for speech, sound, and music synthesis). Finally, key issues and future questions regarding deep learning applied to audio signal processing are identified.
Drowning Detection Algorithm in Coastal Zones

This article delves into the architecture of a system tailored for recognizing individuals at risk of drowning at sea, leveraging advanced computer vision techniques to ensure beachfront safety. The system capitalizes on a camera mounted on a lifeguard tower, capturing a comprehensive view of the beach vicinity. The proposed method integrates two stages: 1) Sea segmentation via the deep learning architecture, SegN et, trained to identify marine portions occupied by swimmers, further enhanced by the VGG16 model to extract intricate image features, and 2) Precise individual detection within the sea using the YOLO framework and the Vision Transformer model, trained to scrutinize sequential frames and detect anomalies suggestive of drowning. This approach underwent rigorous evaluation, signaling potential as a transformative measure in coastal safety protocols, aiming to substantially mitigate drowning-associated hazards.