bluuebunny/crossref_metadata_2025_split huggingface dataset:
DEPRESSION AMONG PATIENTS WITH SUBSTANCE USE DISORDER IN A REHABILITATION PSYCHIATRY UNIT: A STUDY FROM KHYBER TEACHING HOSPITAL, PESHAWAR
Authors: Iqbal Aqeela , Usman Abid , Mujtaba Muhammad , Wazir Abdul Hai , Hussain Asif , Ahmad Bashir 
Published: January 2025

Objective: To determine the frequency of depression among patients admitted for substance abuse detoxification at the psychiatry department of Khyber Teaching Hospital, Peshawar
Methods and material: This descriptive cross-sectional study was conducted in the Department of Psychiatry at Khyber Teaching Hospital from 29th November 2020 to 29th May 2021.137 patients were observed, in our study. Patients were subjected to detailed history and clinical examination. All the patients were subjected to detailed interviews using the Hamilton Depression Rating Scale in a calm environment to detect depression. All the observations were recorded in the presence of an expert psychiatrist fellow of CPSP (with a minimum of five years of experience). All above-mentioned information including name, age, and address was recorded on a pre-designed Proforma by the trainee. Care was taken during the extraction of information from all patients to avoid responder bias. Responder bias was avoided by using a structured, validated questionnaire with neutral wording, training data collectors to ensure impartiality, and conducting interviews in a private setting to encourage honest responses.
Results: Our study shows that among 137 patients, the mean age was 30 years with a standard deviation ± 10.05. 133(97%) patients were male and 4(3%) patients had female. 97(1%) patients had depression.
Conclusion: Our study concludes that the frequency of depression was 71% among patients admitted for substance abuse detoxification at the psychiatry department of Khyber Teaching Hospital, Peshawar.
EFFECTIVENESS OF POSTERIOR INTRAOSSEOUS ARTERY FLAP IN COVERAGE OF DORSAL HAND DEFECTS
Authors: Tauqeer Zahra , Afridi Riaz Ahmed Khan , Khan Firdous 
Published: January 2025

Background: Hand injuries are a challenging issue, frequently encountered by orthopedic and plastic surgeons. These injuries often involve soft tissue defects that expose muscles, tendons, bones, and neurovascular structures. Prompt coverage of these defects is crucial to prevent infection and necrosis of the exposed tissues.
Objective: To determine the effectiveness of the posterior intraosseous artery flap in coverage of dorsal hand defects.
Methodology: This descriptive study was carried out at the Burns and Plastic Surgery Unit of Lady Reading Hospital Peshawar, from November 2022 to August 2024 after ethical approval. The sample size was determined to be 75 patients (95% CI, success rate 83.3%)8. The study included patients aged 18-60 years of either gender who presented with dorsal hand defects. Patients were followed up for up to a minimum of 6 months. Data was collected in Proforma and analyzed using SPSS 22.
Results: The mean age was 39.13±12.06 years. The male-to-female ratio was 2.1:1. The average duration since trauma was 3.03±1.23 hours. The right hand was most frequently affected (61.3%). The most common mechanism was crush injury (38.7%), followed by degloving (26.7%). The overall flap success rate was 81.3% (Two-tailed p value 0.70 ± 0.045) indicating no significant difference between the observed flap success rate of 81.3% and the expected rate of 83%.8
Conclusion: The posterior interosseous artery flap is beneficial for covering dorsal hand defects due to its single-stage procedure and the ability to provide pliable coverage without the need to sacrifice any major vessels.
Optimization of the Effect of Network Information Flow Advertising Based on User Behavior Data Encryption and Deep Learning
Authors: Yan Xia , Rosli Anita Binti , Alwie Aryaty Binti 
Published: January 2025

Existing network information flow advertising mostly relies on user behavior data, which is easy to cause user privacy leakage and low precision of advertising delivery. This article combines user behavior data encryption and deep learning technology to propose a method for optimizing the effect of network information flow advertising. The user behavior data is encrypted using Paillier homomorphic encryption technology. Then, the stacked auto encoder (SAE) is used to extract high-order nonlinear features, and the shallow features are automatically combined based on Field-aware Factorization Machines (FFM) to construct a SAE-FFM comprehensive model. At the same time, dropout is used to reduce model overfitting, and the advertising effect is optimized through two-stage pre-training and fine-tuning. The experimental results show that when the number of impressions is 10,000, the average advertising click through rate and conversion rate of the SAE-FFM model are 4.50% and 0.59% respectively, and the information leakage rate is 0.052%. The results show that the SAE-FFM model can greatly improve the click through rate and conversion rate of advertisements, prevent privacy leakage, and help the development of the information flow advertising industry.
Combining Differential Privacy with FedAvgM to Optimize Data Access Control in the Information Platform
Authors: Wang Mingyue 
Published: January 2025

In response to the privacy leakage risk and low-efficiency issues of traditional data access control, especially in distributed environments, data transmission and centralized processing may expose sensitive information. To improve data access control in information platforms, ensure data security, and accelerate global model training, this article introduced a joint mechanism of differential privacy and FedAvgM (Federated Averaging with Momentum) optimization to enhance privacy protection and improve training efficiency. Firstly, the Laplacian noise mechanism was adopted to prevent user privacy information from being leaked during global model training by adding noise to the dataset, ensuring the privacy of each data access. Then, combined with the FedAvgM optimization algorithm, distributed nodes calculated local model parameters separately and merged these parameters through weighted averaging to reduce the training time of the global model and improve efficiency. Finally, layered encryption technology was adopted to add multiple layers of encryption during data transmission to ensure the security of the transmission link. At the same time, a dynamic permission allocation mechanism was introduced to limit the frequency of access to sensitive data. The experimental results demonstrate that when the privacy budget at ε=20, the privacy protection method of Laplace mechanism still maintains 73% accuracy in data transmission, with a privacy leakage risk of only 0.27. Under the same training epochs, the FedAvgM optimization algorithm achieved a correct data transmission accuracy of 97%. In comparison, the convergence speed increased by 0.29%/min in 1-20 training epochs, showing a faster convergence speed. Under four different noise attack methods, the anti-noise ability of layered encryption is the lowest at 15.4 dB, 18.5 dB, 17.1 dB, and 17.3 dB, respectively. This method effectively improves the problems of privacy leakage risk and low efficiency in traditional data access control.
The Impact of X‐Ray Radiation on Chemical and Optical Properties of Triple‐Cation Lead Halide Perovskite: from the Surface to the Bulk
Authors: Vidon Guillaume , Dally Pia , Al‐Katrib Mirella , Ory Daniel , Kim Minjin , Soret Etienne , Rangayen Eva , Legrand Marie , Blaizot Alexandre , Schulz Philip , Puel Jean‐Baptiste , Suchet Daniel , Guillemoles Jean‐François , Etcheberry Arnaud , Bouttemy Muriel , Cacovich Stefania 
Published: July 2023

AbstractUnderstanding the effects of X‐rays on halide perovskite thin films is critical for accurate and reliable characterization of this class of materials, as well as their use in detection systems. In this study, advanced optical imaging techniques are employed, both spectrally and temporally resolved, coupled with chemical characterizations to obtain a comprehensive picture of the degradation mechanism occurring in the material during photoemission spectroscopy measurements. Two main degradation pathways are identified through the use of local correlative physico‐chemical analysis. The first one, at low X‐Ray fluence, shows minor changes of the surface chemistry and composition associated with the formation of electronic defects. Moreover, a second degradation route occurring at higher fluence leads to the evaporation of the organic cations and the formation of an iodine‐poor perovskite. Based on the local variation of the optoelectronic properties, a kinetic model describing the different mechanisms is proposed. These findings provide valuable insight on the impact of X‐rays on the perovskite layers during investigations using X‐ray based techniques. More generally, a deep understanding of the interaction mechanism of X‐rays with perovskite thin films is essential for the development of perovskite‐based X‐ray detectors and solar for space applications.
Sustainable Nano‐Nonwoven Fabric Production from Recycled Polyamide 6 Waste via Electrospinning: Controlling Characteristics and Comprehensive Analytical Study
Authors: Metwally Bahaa S. , Zayed Ahmed M. , Rashed Samah A. , El‐Sheikh Mohamed N. , Hamouda Asmaa S. 
Published: July 2023

AbstractThis study investigates the recycling of polyamide 6 (PA 6) wastes to produce nonwoven fabrics based on nanofibers (PA‐NWNF) to promote sustainable textile solid waste management and generate a novel material with exceptional properties. PA‐NWNF characteristics can be controlled by adjusting the electrospinning parameters. A comprehensive characterization will be performed using various analytical techniques, such as scanning electron microscopy (SEM), Fourier‐transform infrared spectroscopy (FTIR), X‐ray diffraction (XRD), Brunauer–Emmett–Teller (BET), and dynamic mechanical analysis (DMA), to evaluate the unique properties as a function of the electrospinning parameters. According to the SEM results, concentrations of 21% are suitable to produce nanofiber with a well‐defined morphology, provided that the applied voltage is maintained at 20 kV and the collecting distance is above 10 cm, irrespective of the flow rate in the established protocol. XRD and FTIR analysis indicate a gradual decrease in the α‐phase and a simultaneous increase in the γ‐phase as the solution concentration increases. Moreover, low applied voltage favored β‐form crystallization over γ and α‐forms predominant at the higher voltage. DMA and BET data reveal that the elongated droplets formed at lower concentration exhibit a low storage modulus, high damping factor, highest surface area, and smallest pore diameters.
Multifunctional Al2O3:Eu2+/Sm2+,3+ + Y2O3:Eu3+ Coatings Created by the Plasma Electrolytic Oxidation
Authors: Ćirić Aleksandar , Stojadinović Stevan , Dramićanin Miroslav D. 
Published: July 2023

AbstractThe industrial applications of luminescent coatings for temperature sensing and high‐security QR codes have multiple inherent problems in their application (speed, cost, industrial feasibility) or properties (poor adhesion, chemical, thermal stability, or thermal contact). Luminescent coatings created by the electrochemical Plasma Electrolytic Oxidation (PEO) process have superior properties and can be grown directly within minutes, but are limited to the number of possible luminescent dopants. Having the unlimited possibility for doping on the PEO coatings would provide for superior coatings in all industrially appealing features. Herein, it is shown that adding any luminescent powder results in its partial dissociation and incorporation of its constituents, while it also gets incorporated as a whole. In this manner, alumina coatings are grown with various combinations and concentrations of divalent samarium and europium, and yttrium oxide with trivalent europium ions. These polycrystalline coatings whose microhardness is next to that of the diamond produce intense luminescence in the blue and red regions. Al2O3:Eu2+/Sm2+/3+ + Y2O3:Eu3+ coatings show an excellent temperature sensing performance from the cryogenic up to the high temperatures. Ultimately, the proof‐of‐concept of the luminescent QR code by the electrochemical process is realized creating a highly secure code with unprecedented environmental stability.
Goethite Mineral Dissolution to Probe the Chemistry of Radiolytic Water in Liquid‐Phase Transmission Electron Microscopy
Authors: Couasnon Thaïs , Fritsch Birk , Jank Michael P. M. , Blukis Roberts , Hutzler Andreas , Benning Liane G. 
Published: July 2023

AbstractLiquid‐Phase Transmission Electron Microscopy (LP‐TEM) enables in situ observations of the dynamic behavior of materials in liquids at high spatial and temporal resolution. During LP‐TEM, incident electrons decompose water molecules into highly reactive species. Consequently, the chemistry of the irradiated aqueous solution is strongly altered, impacting the reactions to be observed. However, the short lifetime of these reactive species prevent their direct study. Here, the morphological changes of goethite during its dissolution are used as a marker system to evaluate the influence of radiation on the changes in solution chemistry. At low electron flux density, the morphological changes are equivalent to those observed under bulk acidic conditions, but the rate of dissolution is higher. On the contrary, at higher electron fluxes, the morphological evolution does not correspond to a unique acidic dissolution process. Combined with kinetic simulations of the steady state concentrations of generated reactive species in the aqueous medium, the results provide a unique insight into the redox and acidity interplay during radiation induced chemical changes in LP‐TEM. The results not only reveal beam‐induced radiation chemistry via a nanoparticle indicator, but also open up new perspectives in the study of the dissolution process in industrial or natural settings.
Evaluating the GeoSnap 13‐μ$$ \mu $$m cutoff HgCdTe detector for mid‐IR ground‐based astronomy
Authors: Leisenring Jarron M. , Atkinson Dani , Bowens Rory , Douence Vincent , Hoffmann William F. , Meyer Michael R. , Auyeung John , Beletic James , Cabrera Mario S. , Greenbaum Alexandra Z. , Hinz Philip , Ives Derek , Forrest William J. , McMurtry Craig W. , Pipher Judith L. , Viges Eric 
Published: July 2023

AbstractNew mid‐infrared HgCdTe (MCT) detector arrays developed in collaboration with Teledyne Imaging Sensors (TIS) have paved the way for improved 10‐m sensors for space‐ and ground‐based observatories. Building on the successful development of longwave HAWAII‐2RGs for space missions such as NEO Surveyor, we characterize the first 13‐m GeoSnap detector manufactured to overcome the challenges of high‐background rates inherent in ground‐based mid‐IR astronomy. This test device merges the longwave HgCdTe photosensitive material with Teledyne's GeoSnap‐18 (18‐m pixel) focal plane module, which is equipped with a capacitive transimpedance amplifier (CTIA) readout circuit paired with an onboard 14‐bit analog‐to‐digital converter (ADC). The final assembly yields a mid‐IR detector with high QE, fast readout (>85 Hz), large well depth (>1.2 million electrons), and linear readout. Longwave GeoSnap arrays would ideally be deployed on existing ground‐based telescopes as well as the next generation of extremely large telescopes. While employing advanced adaptive optics (AO) along with state‐of‐the‐art diffraction suppression techniques, instruments utilizing these detectors could attain background‐ and diffraction‐limited imaging at inner working angles <10 /D, providing improved contrast‐limited performance compared with JWST MIRI while operating at comparable wavelengths. We describe the performance characteristics of the 13‐m GeoSnap array operating between 38 45 K, including quantum efficiency, well depth, linearity, gain, dark current, and frequency‐dependent () noise profile.
HNRNPU's multi‐tasking is essential for proper cortical development
Authors: Sapir Tamar , Reiner Orly 
Published: July 2023

AbstractHeterogeneous nuclear ribonucleoprotein U (HNRNPU) is a nuclear protein that plays a crucial role in various biological functions, such as RNA splicing and chromatin organization. HNRNPU/scaffold attachment factor A (SAF‐A) activities are essential for regulating gene expression, DNA replication, genome integrity, and mitotic fidelity. These functions are critical to ensure the robustness of developmental processes, particularly those involved in shaping the human brain. As a result, HNRNPU is associated with various neurodevelopmental disorders (HNRNPU‐related neurodevelopmental disorder, HNRNPU‐NDD) characterized by developmental delay and intellectual disability. Our research demonstrates that the loss of HNRNPU function results in the death of both neural progenitor cells and post‐mitotic neurons, with a higher sensitivity observed in the former. We reported that HNRNPU truncation leads to the dysregulation of gene expression and alternative splicing of genes that converge on several signaling pathways, some of which are likely to be involved in the pathology of HNRNPU‐related NDD.
The entrainment and evolution of gas bubbles in bread dough—A review
Authors: Sun Xinyang , Scanlon Martin G. , Nickerson Michael T. , Koksel Filiz 
Published: July 2023

AbstractBackground and ObjectiveFor control of bread quality to achieve high loaf volume and uniform crumb structure, gas bubble dynamics in dough needs to be better understood throughout different breadmaking processes. The objective of this review was to establish a solid theoretical basis on how flour type, water and salt content, and mixing conditions affected the incorporation, evolution, and stabilization of gas bubbles in a dough.FindingsBubble dynamics including entrainment, disentrainment, break‐up, disproportionation, growth, and coalescence were outlined and their effects on the gas phase of the dough were assessed. The application, advantages, and disadvantages of microscopy, magnetic resonance imaging (MRI) and X‐ray microtomography techniques for qualitatively or quantitatively characterizing the void fraction and bubble size distribution (BSD) in the dough at various stages of the breadmaking process have been discussed.ConclusionsSince the BSD evolution in bread dough is associated with the quality of the resultant products, to devise strategies for improving the product quality, dough formulation, and mixing conditions need to be considered.Significance and NoveltyDue to the obvious challenges of monitoring the fast evolution of BSD in yeasted dough, future research needs to focus on the effects of yeast activity on dough's BSD.
Individual tree segmentation of airborne and UAV LiDAR point clouds based on the watershed and optimized connection center evolution clustering
Authors: Li Yi , Xie Donghui , Wang Yingjie , Jin Shuangna , Zhou Kun , Zhang Zhixiang , Li Weihua , Zhang Wuming , Mu Xihan , Yan Guangjian 
Published: July 2023

AbstractLight detection and ranging (LiDAR) data can provide 3D structural information of objects and are ideal for extracting individual tree parameters, and individual tree segmentation (ITS) is a vital step for this purpose. Various ITS methods have been emerging from airborne LiDAR scanning (ALS) or unmanned aerial vehicle LiDAR scanning (ULS) data. Here, we propose a new individual tree segmentation method, which couples the classical and efficient watershed algorithm (WS) and the newly developed connection center evolution (CCE) clustering algorithm in pattern recognition. The CCE is first used in ITS and comprehensively optimized by considering tree structure and point cloud characteristics. Firstly, the amount of data is greatly reduced by mean shift voxelization. Then, the optimal clustering scale is automatically determined by the shapes in the projection of three different directions. We select five forest plots in Saihanba, China and 14 public plots in Alpine region, Europe with ULS or ALS point cloud densities from 11 to 3295 pts/m2. Eleven ITS methods were used for comparison. The accuracy of tree top detection and tree height extraction is estimated by five and two metrics, respectively. The results show that the matching rate (Rmatch) of tree tops is up to 0.92, the coefficient of determination (R2) of tree height estimation is up to .94, and the minimum root mean square error (RMSE) is 0.6 m. Our method outperforms the other methods especially in the broadleaf forests plot on slopes, where the five evaluation metrics for tree top detection outperformed the other algorithms by at least 11% on average. Our ITS method is both robust and efficient and has the potential to be used especially in coniferous forests to extract the structural parameters of individual trees for forest management, carbon stock estimation, and habitat mapping.
Predicting venous thromboembolism (VTE) risk in cancer patients using machine learning
Authors: Townsley Samir Khan , Basu Debraj , Vora Jayneel , Wun Ted , Chuah Chen‐Nee , Shankar Prabhu R. V. 
Published: July 2023

AbstractBackgroundThe association between cancer and venous thromboembolism (VTE) is well‐established with cancer patients accounting for approximately 20% of all VTE incidents. In this paper, we have performed a comparison of machine learning (ML) methods to traditional clinical scoring models for predicting the occurrence of VTE in a cancer patient population, identified important features (clinical biomarkers) for ML model predictions, and examined how different approaches to reducing the number of features used in the model impact model performance.MethodsWe have developed an ML pipeline including three separate feature selection processes and applied it to routine patient care data from the electronic health records of 1910 cancer patients at the University of California Davis Medical Center.ResultsOur ML‐based prediction model achieved an area under the receiver operating characteristic curve of 0.778 ± 0.006 (mean ± SD) when trained on a set of 15 features. This result is comparable with the model performance when trained on all features in our feature pool [0.779 ± 0.006 (mean ± SD) with 29 features]. Our result surpasses the most validated clinical scoring system for VTE risk assessment in cancer patients by 16.1%. We additionally found cancer stage information to be a useful predictor after all performed feature selection processes despite not being used in existing score‐based approaches.ConclusionFrom these findings, we observe that ML can offer new insights and a significant improvement over the most validated clinical VTE risk scoring systems in cancer patients. The results of this study also allowed us to draw insight into our feature pool and identify the features that could have the most utility in the context of developing an efficient ML classifier. While a model trained on our entire feature pool of 29 features significantly outperformed the traditionally used clinical scoring system, we were able to achieve an equivalent performance using a subset of only 15 features through strategic feature selection methods. These results are encouraging for potential applications of ML to predicting cancer‐associated VTE in clinical settings such as in bedside decision support systems where feature availability may be limited.