Practical Alternatives Research Articles

Evaluation of plasma cell sorting methods in multiple myeloma patients: flow cytometry versus magnetic beads

BackgroundThe prognosis of a plasma cell neoplasm (PCN) varies depending on the presence of genetic abnormalities. However, detecting sensitive genetic mutations poses challenges due to the heterogeneous nature of the cell population in bone marrow aspiration. The established gold standard for cell sorting is fluorescence-activated cell sorting (FACS), which is associated with lengthy processing times, substantial cell quantities, and expensive equipment. Magnetic-activated cell sorting (MACS) can be performed without the need for FACS equipment and allows for rapid sorting of many cells, making it a practical alternative. Our objective is to conduct a comparative analysis of these two sorting techniques to assess whether MACS can viably replace FACS in clinical applications.MethodsPlasma cell purity, fluorescence in situ hybridization (FISH), and next-generation sequencing analyses were performed on FACS- and MACS-sorted bone marrow samples from 31 PCN patients.ResultsThe MACS-sorted samples yielded a higher percentage of plasma cells than FACS-sorted samples under microscopy (p = 0.0156) and flow cytometry (p = 0.0313). FISH performed by two methods in 10 samples showed the same results, and the proportion of abnormal cells was significantly higher in MACS than in FACS (p = 0.001). Wilcoxon matched-pairs signed rank test analysis showed that the median of differences of variant allele frequency (VAF) of two methods (VAF of MACS minus VAF of FACS) in the DNMT3A, TET2, and ASXL1 (DTA) group was − 0.006555 (p = 0.0020), while that in the non-DTA group was 0.002805 (p = 0.0019). Ten copy number variants (CNVs) were found in both FACS- and MACS-sorted samples, eight were identified only in MACS-sorted samples, and one was detected only in FACS-sorted samples.ConclusionOur study demonstrates that MACS is a viable alternative for plasma cell sorting in bone marrow samples of patients with PCN.

Real-world multicentre study of cefiderocol treatment of immunocompromised patients with infections caused by multidrug-resistant Gram-negative bacteria: CEFI-ID.

The increase in the population of immunocompromised patients due to advances in management of end-stage diseases and transplants poses challenges in treating infections caused by multi-drug resistant (MDR) pathogens. Cefiderocol (FDC), a siderophore cephalosporin, has shown efficacy against carbapenem-resistant Gram-negative bacteria. This retrospective multicentre study investigated the real-world use of FDC in 114 immunocompromised adults treated for MDR infections in 12 French hospitals (June 2020-November 2023). Clinical and microbiological outcomes, including infection cure, relapse, as well as mortality, and resistance acquisition, were assessed at days 28 and 90. Antibiotic prescription compliance with current guidelines was investigated. At day 28, clinical success was achieved in 53.3% of cases, and overall mortality was 37.7%, consistent with other studies (33-37%). Infection-related mortality accounted for 25.4%. Relapse occurred in 17.5% of patients by day 28, rising by an additional 9.8% among survivors by day 90. Resistance acquisition was observed in two cases at day 28 (Pseudomonas aeruginosa and Stenotrophomonas maltophilia) and in three additional cases by day 90. FDC was used as monotherapy in 49.1% of cases, with a median treatment duration of 10 days. Nearly 25% of strains collected in FDC-treated patients were susceptible to best-practice alternatives. These findings highlight FDC's utility in difficult-to-treat infections, particularly S. maltophilia, but the high relapse rate and resistance acquisition underscore the need for careful monitoring, adherence to guidelines, and reconsideration of empirical use to prevent resistance and improve outcomes in fragile populations.

Validation of an improved reference freeze-dried direct agglutination test for detecting leishmaniasis in the canine reservoir.

Introduction. Proper identification and management of post-kala-azar dermal leishmaniasis (PKDL) and canine leishmaniasis (CanL) cases are among the prerequisites to the effective control of visceral leishmaniasis worldwide. Unlike PKDL, CanL still awaits effective improvement because of its cryptic nature, absence of Leishmania parasites in lesions or lymph nodes and not complete sensitivity of some diagnostic tools in use. Because of the need for certain skills and equipment, both the liquid direct agglutination test and freeze-dried direct agglutination test (FD-DAT) versions are, in comparison with the indirect immunofluorescence antibody test (IFAT) or enzyme-linked immunosorbent assay (ELISA), practical and feasible diagnostic alternatives. Aim. Validate the performance of an improved FD-DAT to suit routine and large-scale applications in CanL endemic areas. Methodology. Introducing citrate-saline formaldehyde (CSF) as an anti-clumping agent to replace normal saline for antigen reconstitution and drastically, however, eligibly lower the concentration of promastigotes (1.4×107) in comparison with the original FD-DAT reference (>5×107 ml-1). To ensure optimal safety, β-mercaptoethanol was replaced by urea or SDS as a serum-reducing agent. Results. By improving the procedure for reconstitution of FD-DAT antigen with CSF, a 150% reduction in the test application cost was achieved. Expired test batches (±4 years earlier) were successfully revitalized to full validity. As compared to the 48 h shelf-life time for the original, an FD-DAT batch reconstituted here with CSF maintained stability for ±12 months. Conclusions. The highly concordant results with IFAT and ELISA (one-way ANOVA test, P=0.142, homogeneity of variances P=0.009) as routine CanL diagnostics further motivate the application of the improved FD-DAT for the detection of the disease in endemic areas.

Perspective on Leisure Sports Participation Among Older Adults in a Super-Aging Society: Focusing on Health Concern, Athletic Passion, Leisure Satisfaction, and Intent to Continue Participating in Leisure Sports.

Background/Objectives: Today, society has a growing interest in healthy aging. This study analyzed health concerns, leisure satisfaction, athletic passion, and intention to continue participating in leisure sports of various age groups as well as the elderly, and we empirically analyzed the differences. Methods: This study conducted a survey of 306 adult men and women who regularly participated in leisure sports. Frequency, correlation, and difference analyses were performed using SPSS version 26.0. Results: The main findings indicated that older age groups showed higher levels of health concern, obsessive passion, and intention to participate in leisure sports than younger age groups, whereas younger age groups had higher levels of harmonious passion than older age groups. Conclusions: This study provides results through a comparative analysis that can indirectly enhance the quality of life by presenting practical alternatives to increase older adults' participation in leisure sports activities amid the global aging trend.

Effects of Repetitive Transcranial Magnetic Stimulation on Poststroke Hemineglect: A Systematic Review and Network Meta-Analysis of Randomized Controlled Trials.

Although various repetitive transcranial magnetic stimulation (rTMS) and theta burst stimulation (TBS) protocols are used, their comparative effectiveness for treating poststroke hemineglect remains unassessed. To investigate rTMS and TBS effects on clinical outcomes in poststroke hemineglect through a systematic review and network meta-analysis. We searched PubMed, EMBASE, and Cochrane Library databases up to March 7, 2024, for trials on rTMS or TBS in poststroke hemineglect. Included studies involved rTMS or TBS with different protocols, sham, or no stimulation, assessing hemineglect severity or impact. The quality of the included studies was evaluated using the PEDro scale. The network meta-analysis was performed using ShinyNMA (version 1.01). We analyzed 13 studies with 309 participants. All studies included participants who had experienced right hemisphere stroke. All included studies had a fair to good quality based on PEDro score evaluation. Protocols included continuous TBS (cTBS), high-frequency rTMS (HF-rTMS), and low-frequency rTMS (LF-rTMS) targeting both contralesional and lesional sites. HF-rTMS on the lesional site significantly improved short-term results on the line bisection test and Catherine Bergego Scale; LF-rTMS on the contralesional site improved short-term line bisection; and cTBS on the contralesional site improved long-term line bisection. No severe adverse events or significant inconsistencies were reported. Our findings indicate that HF-rTMS targeting the lesional site is the preferred therapeutic approach for the short-term management of poststroke hemineglect. LF-rTMS directed at the contralesional site is a practical alternative. Moreover, cTBS targeting the contralesional site is a viable option because of its long-term effect.

Multi-channel masked autoencoder and comprehensive evaluations for reconstructing 12-lead ECG from arbitrary single-lead ECG

Electrocardiogram (ECG) has emerged as a widely accepted diagnostic instrument for cardiovascular diseases (CVD). The standard clinical 12-lead ECG configuration causes considerable inconvenience and discomfort, while wearable devices offers a more practical alternative. To reduce information gap between 12-lead ECG and single-lead ECG, this study proposes a multi-channel masked autoencoder (MCMA) for reconstructing 12-Lead ECG from arbitrary single-lead ECG, and a comprehensive evaluation benchmark, ECGGenEval, encompass the signal-level, feature-level, and diagnostic-level evaluations. MCMA can achieve the state-of-the-art performance. In the signal-level evaluation, the mean square errors of 0.0175 and 0.0654, Pearson correlation coefficients of 0.7772 and 0.7287. In the feature-level evaluation, the average standard deviation of the mean heart rate across the generated 12-lead ECG is 1.0481, the coefficient of variation is 1.58%, and the range is 3.2874. In the diagnostic-level evaluation, the average F1-score with two generated 12-lead ECG from different single-lead ECG are 0.8233 and 0.8410.

Estimation of Physico-Chemical Properties of Soil Using Machine Learning

This study leverages soil images to determine its physico-chemical characteristics from two locations in Benue State, North-Central Nigeria: Vandeikya (7.099653°, 8.572102°) and Konshisha (6.783539°, 9.069549°). Practical alternatives to traditional soil quality estimation methods, often costly and inaccessible in resource-poor regions, were implemented. The study integrates Machine Learning (ML) and Deep Learning (DL) techniques with soil image analysis, employing image feature extraction through Gray-Level Co-occurrence Matrix (GLCM) and Gabor filters to enhance the feature space across 1,388 samples (each consisting of 600g of soil) and 16 properties. We evaluated different models, including Support Vector Regression (SVR), a Convolutional Neural Network (CNN), an optimized CNN using Grid Search, and a stacked ML-CNN approach. The models were assessed on their ability to predict soil properties while minimizing error rates. The SVR model showed limited predictive performance, with its best results yielding slightly over 40% R² and negative scores for physical properties. The CNN model performed better but had feature scores below 80% R². The optimized CNN model surpassed 90% R² and reduced MAE by more than 50% for several properties. The stacked ML-CNN model further improved these metrics, demonstrating the effectiveness of the integrated approach. This research highlights the potential for using machine learning and image-based analysis as a scalable, cost-effective alternative to conventional soil testing, offering practical solutions for regions where conventional measurement tools are scarce. Integration with historical soil data might also open new possibilities for enhancing the accuracy and applicability of these methods for broader soil management practices.

Scalable Slot-Die Coating of Passivation Layers for Improved Performance of Perovskite Solar Cell Modules.

Upscaling the perovskite solar cell (PSC) while avoiding losses in the power conversion efficiency presents a substantial challenge, especially when transitioning from ≤1cm2 cells to ≥10cm2 modules. In addition to the fabrication of key functional layers, scalable technologies for surface passivation, considered indispensable for achieving high-performance PSCs, are urgently required. However, studies on this topic remain limited. In this study, an industry-ready slot-die coating method for the effective passivation of perovskite films as a practical alternative is developed to the spin-coating procedures commonly used in research. The coating conditions and molecular structure of the passivation agent are systematically optimized to achieve high-quality film morphology and substantially suppress interface recombination. 2-chloro-5-(trifluoromethyl)-phenylammonium bromide exhibited the best results, improving the open-circuit voltage of cells and subcells in a module by 80±4 and 72±10mV, respectively. Correspondingly, the larger-area (active area: 10cm2) modules sustained the highest efficiency of 21.9% under simulated 1-sun irradiation. The encapsulated devices retained 94% of their initial performances after 750h of continuous operation. The proposed surface-passivation slot-die technology is compatible with high-throughput processes and is employable for large-scale PSC fabrication.

Exploring the Impact of Additive Shortcuts in Neural Networks via Information Bottleneck-like Dynamics: From ResNet to Transformer.

Deep learning has made significant strides, driving advances in areas like computer vision, natural language processing, and autonomous systems. In this paper, we further investigate the implications of the role of additive shortcut connections, focusing on models such as ResNet, Vision Transformers (ViTs), and MLP-Mixers, given that they are essential in enabling efficient information flow and mitigating optimization challenges such as vanishing gradients. In particular, capitalizing on our recent information bottleneck approach, we analyze how additive shortcuts influence the fitting and compression phases of training, crucial for generalization. We leverage Z-X and Z-Y measures as practical alternatives to mutual information for observing these dynamics in high-dimensional spaces. Our empirical results demonstrate that models with identity shortcuts (ISs) often skip the initial fitting phase and move directly into the compression phase, while non-identity shortcut (NIS) models follow the conventional two-phase process. Furthermore, we explore how IS models are still able to compress effectively, maintaining their generalization capacity despite bypassing the early fitting stages. These findings offer new insights into the dynamics of shortcut connections in neural networks, contributing to the optimization of modern deep learning architectures.

The Emperor and the New Clothes: Engaging the Frivolity of Ethical and Moral Leadership as Espoused Values Against the Values of use

University Education in South Africa has been a subject of transformation in systematic ways and over time. This has had to call upon the multi-dimensional approaches (about MLG Systems and Practices) due to the complexity of the subject. While remarkable progress has been made, and can be evidenced by empirical data, such developments remain elusive for some universities within the sector. Strangely, even the considerable highly ranked universities have not been spared from what can be argued as the complexity of power relations in the politics of knowledge and of being and becoming. This article is based on this issue about complexity in university education, which can play out more as the continuities than what should be the discontinuities from the old regimes of power and of truth. A Case study approach was applied as a methodology, with the actual methods about data management including organisational records, content analysis and critical discourse analysis, over time. Informing such an approach was the ideal of ensuring the theory-methodology-practical program aspects of a potentially credible scholarship project. As such the social realist theory, informed by the critical realist philosophy, served as the anchor of the study that this article is reporting on. The findings relate to what can become the enduring management, leadership and governance crisis in the case of university education, thus constituting the micro politics of being and becoming, despite what can be reported about the same case at macro level. Such cases call upon scholarly critiques if the practical alternatives might constitute the realms of new possibilities. The potential value of the study can be the enhanced dialogue and sustained conversations about the role of management, leadership and governance systems in university education, which have to be maintained and sustained by the idea of ethical and moral practices in pursuit of what ought to be the public and common good.

Resource Allocation in UAV-D2D Networks: A Scalable Heterogeneous Multi-Agent Deep Reinforcement Learning Approach

In unmanned aerial vehicle (UAV)-assisted device-to-device (D2D) caching networks, the uncertainty from unpredictable content demands and variable user positions poses a significant challenge for traditional optimization methods, often making them impractical. Multi-agent deep reinforcement learning (MADRL) offers significant advantages in optimizing multi-agent system decisions and serves as an effective and practical alternative. However, its application in large-scale dynamic environments is severely limited by the curse of dimensionality and communication overhead. To resolve this problem, we develop a scalable heterogeneous multi-agent mean-field actor-critic (SH-MAMFAC) framework. The framework treats ground users (GUs) and UAVs as distinct agents and designs cooperative rewards to convert the resource allocation problem into a fully cooperative game, enhancing global network performance. We also implement a mixed-action mapping strategy to handle discrete and continuous action spaces. A mean-field MADRL framework is introduced to minimize individual agent training loads while enhancing total cache hit probability (CHP). The simulation results show that our algorithm improves CHP and reduces transmission delay. A comparative analysis with existing mainstream deep reinforcement learning (DRL) algorithms shows that SH-MAMFAC significantly reduces training time and maintains high CHP as GU count grows. Additionally, by comparing with SH-MAMFAC variants that do not include trajectory optimization or power control, the proposed joint design scheme significantly reduces transmission delay.

Visible-Light-Mediated Vicinal Dihalogenation of Unsaturated C-C Bonds Using Dual-Functional Group Transfer Reagents.

The growing demand for chemical production continues to drive the development of sustainable and efficient methods for introducing molecular complexity. In this context, the exploration of unconventional functional group transfer reagents (FGTRs) has led to significant advancements in practical and atom-efficient synthetic protocols. Aiming to advance the field of valuable organic synthesis, herein we report the successful development of carbon-based, bench-stable, modular, and inexpensive reagents implemented in dual halogen transfer to unsaturated hydrocarbons via photocatalytic activation of reagents based on a radical-polar crossover mechanism. This method beneficially enables vicinal dichlorination, dibromination, and bromo-chlorination reactions of olefins, offering practical alternatives to the use of toxic binary halogens. Detailed mechanistic studies, combining experimental, spectroscopic, and theoretical investigations, revealed a distinctive photocatalytic single-electron transfer reduction of FGTR. This process triggers mesolytic carbon-halogen bond cleavage, followed by a radical 1,2-halide rearrangement, leading to the continuous generation of dihalogen species in the reaction medium. The wide applicability of the developed protocol is demonstrated through an extensive scope of unsaturated molecules, including additional operations on strain-release dihalogenation.

Effect of video-based exercise on premenstrual symptoms: a randomized controlled trial.

This study aims to investigate the impact of Pilates exercises on premenstrual syndrome (PMS) symptoms, perceived stress levels, and pain intensity. Forty-six women with PMS participated in this study and were assigned to the intervention and control groups based on their willingness to participate. The intervention group undertook Pilates practices via video recording twice a week for 8 weeks, while the control group did not engage in regular exercise during the same period. PMS symptoms were assessed using the Premenstrual Syndrome Scale (PMSS), premenstrual stress levels were evaluated using the Perceived Stress Scale (PSS), and premenstrual pain levels were assessed using the McGill Melzack Pain Questionnaire (MPQ) at both the beginning and end of the study. There was a significant difference observed in the PMSS, PSS and MPQ evaluations of the intervention group following their participation in Pilates practices (p < 0.05). Conversely, no significant difference was observed in the PMSS, PSS and MPQ evaluations of the control group at the end of the study (p > 0.05). There was no statistically significant difference between the two groups in PMSS evaluation (p > 0.05) at the end of the study. However, a statistically significant difference was detected in PSS and MPQ evaluations (p < 0.05). Pilates exercise can affectively decrease the perceived stress level and pain severity in PMS affected women. This study highlights the efficacy of Pilates for physiotherapists in PMS symptom reduction. Moreover, the implementation of a self-guided video-based home exercise program could provide patients with practical and time-efficient alternatives.

Digital interventions to prevent smoking habit among children. Is there evidence?

Abstract Smoking remains one of the foremost preventable causes of death and disease on a global scale, with its roots often traced back to early childhood. The habit typically begins at a young age, setting a pattern that becomes increasingly difficult to break over time. This enduring issue highlights the critical need for effective preventive measures. Traditional approaches to curbing smoking among the youth-such as public health campaigns, school-based education, and legislative measures like age restrictions on tobacco purchases-have had limited success. In this context, digital interventions (DIs) have begun to emerge as a promising new frontier for tobacco prevention. Leveraging the pervasive influence of technology in young people’s lives, these interventions include gamified educational programs, which transform learning about the dangers of smoking into interactive and engaging activities; social media campaigns, which can spread powerful anti-smoking messages quickly and broadly; and virtual reality experiences, which immerse users in scenarios that vividly illustrate the impact of smoking on health. These digital strategies not only offer fresh avenues for conveying important health information but also engage young audiences in a medium they find relatable and compelling. In this context, the presentation will discuss results of a systematic review aims to assess the efficacy of DI in preventing smoking, and improving knowledge and awareness about smoking damages, among children. This review is part of the PRIN project, titled: “Leo&amp;Giulia standing for public health”, whose goal is to evaluate the effectiveness of a DI aimed at school-aged children, intended to increase knowledge about the negative effects of smoking and prevent its use. Grasping the impact of DI in smoking prevention can greatly benefit public health by offering practical alternatives.

Diagnostic serology test comparison for Q fever and Rift Valley fever in humans and livestock from pastoral communities.

Q fever (QF) and Rift Valley fever (RVF) are endemic zoonotic diseases in African countries, causing significant health and economic burdens. Accurate prevalence estimates, crucial for disease control, rely on robust diagnostic tests. While enzyme-linked immunosorbent assays (ELISA) are not the gold standard, they offer rapid, cost-effective, and practical alternatives. However, varying results from different tests and laboratories can complicate comparing epidemiological studies. This study aimed to assess the agreement of test results for QF and RVF in humans and livestock across different laboratory conditions and, for humans, different types of diagnostic tests. We measured inter-laboratory agreement using concordance, Cohen's kappa, and prevalence and bias-adjusted kappa (PABAK) on 91 human and 102 livestock samples collected from rural regions in Chad. The serum aliquots were tested using ELISA in Chad, and indirect immunofluorescence assay (IFA) (for human QF and RVF) and ELISA (for livestock QF and RVF) in Switzerland and Germany. Additionally, we examined demographic factors influencing test agreement, including district, setting (village vs. camp), sex, age, and livestock species of the sampled individuals. The inter-laboratory agreement ranged from fair to moderate. For humans, QF concordance was 62.5%, Cohen's kappa was 0.31, RVF concordance was 81.1%, and Cohen's kappa was 0.52. For livestock, QF concordance was 92.3%, Cohen's kappa was 0.59, RVF concordance was 94.0%, and Cohen's kappa was 0.59. Multivariable analysis revealed that QF test agreement is significantly higher in younger humans and people living in villages compared to camps and tends to be higher in livestock from Danamadji compared to Yao, and in small ruminants compared to cattle. Additionally, RVF agreement was found to be higher in younger humans. Our findings emphasize the need to consider sample conditions, test performance, and influencing factors when conducting and interpreting epidemiological seroprevalence studies.

A PCA-based algorithm for online false data injection and jamming attacks detection in cyber-physical systems

Cyber-physical systems face cyberattacks that hinder performance, increase cost, or even collapse the system. Thus, rapid and accurate attack detection is crucial for quick activation of defense mechanisms. Model-based attack detection approaches are known for their precision; however, the unavailability of the exact system model poses a challenge. In response, model-free approaches have gained increased attention as a practical alternative. In this paper, an online model-free algorithm for detecting false data injection and jamming attacks on CPSs is proposed. The method leverages principal component analysis to reconstruct the expected observations in a reduced dimension space, emphasizing the most effective principal components. Then, deciding on attacked or normal operation relies on analyzing either the Euclidean distance or the cosine similarity of the discrepancy between the expected and actual observations. The proposed metrics effectively expose subtle deviations from expected behavior, as any alteration in these components augments the distance between the observed and reconstructed values. The proposed method was compared with the conventional cumulative sum discriminator and Kalman-based algorithm, using an IEEE-14, IEEE-30, and IEEE-118 bus systems. The results demonstrate the superiority of the proposed algorithm in terms of various evaluation metrics, including F-score, precision, recall, and miss detection ratio.

Do middle-aged and older people underreport loneliness? experimental evidence from the Netherlands

Despite the growing acknowledgment of the importance of loneliness among older individuals, questionnaire length constraints may hinder the inclusion of common multi-item loneliness scales in surveys. Direct, single-item loneliness measures are a practical alternative, but scholars have expressed concerns that such measures may lead to underreporting. Our aim was to test whether such reservations are justified. We conducted a preregistered list experiment among 2,553 people aged 50 + who participated in the Dutch Longitudinal Internet studies for the Social Sciences (LISS) panel. The list experiment method has been developed to unobtrusively gather sensitive information. We compared the list experiment estimate of the prevalence of frequent loneliness with the corresponding direct question estimate to assess downward bias in the latter. Next to pooled models, we estimated models stratified by gender to assess whether loneliness underreporting differed between women and men. Relying on the direct question, we estimated that 5.9% of respondents frequently felt lonely. Our list experiment indicated that the prevalence of frequent loneliness was 13.1%. Although substantial in magnitude, the difference between both estimates was only marginally significant (Δb: 0.072, 95% CI: − 0.003;0.148, p = .06). No evidence of gender differences was found. Although we cannot be conclusive that loneliness estimates are biased downward when a direct question is used, our results call for caution with direct, single-item measures of loneliness if researchers want to avoid underreporting. Replications are needed to gain more precise insights into the extent to which direct, single-item loneliness measures are prone to downward reporting bias.

Applications of vibrational phenomena to evaluate the effect of hydrogen solution on the elasticity and fatigue properties of industrial metal plates

Understanding resonance phenomena in materials is crucial in many fields, from controlling noise in small mobile devices to designing seismic-safe large structures or in energy harvesting. Resonance depends on material elasticity and its shape, influenced by temperature. While controlling elastic properties can control vibrations, seeking practical alternatives is essential. This research focuses on introducing hydrogen into industrial metal materials as a method to control resonance conditions, which is particularly relevant in the context of the upcoming hydrogen society. Controlled amounts of hydrogen were introduced to industrial metal plates (Ti, W) using electrochemical methods to investigate changes in their resonance characteristics. The results indicate that the introduction of hydrogen into Ti resulted in a maximum 20% reduction in Young's modulus, while W exhibited a 10% decrease. Additionally, the recovery of their modulus by hydrogen desorption was confirmed. This confirms that the control of hydrogen allows the manipulation of the modulus of elasticity at constant temperature, controlling the resonance without changing the dimensions. Furthermore, similar evaluations were performed on high hardness steel. The results showed fatigue failure due to vibration, increased Young's modulus with the introduction of hydrogen, and an observed trend toward decreased life to failure.

온라인 무역사기 사례에 관한 연구: 이메일 해킹 무역사기 사례를 중심으로

Purpose: This study focuses on the fact that fraud using email, which is a major means of communication in trade transactions, continues to occur. To raise awareness of the email hacking risks, this study suggests practical countermeasures through analysis of fraud that has caused damage and cases of attempted fraud. Research design, data, and methodology: Along with a literature review, this study focuses on successful and attempted email hacking trade fraud based on recent data from KOTRA. Results: According to the analysis, representative types of email-related trade fraud are mainly “new forms of email account hacking” and “existing forms of similar email alteration fraud cases”. Recently, in response to changes in companies' awareness of email fraud, the number of cases has decreased, but caution is required because fraudsters are becoming more detailed and sophisticated in their approach. Conclusions: This paper encourages step-by-step practical and policy alternatives in order to be suspicious of (and to confirm) account change requests based on the changing perception of fraud by email, which is the main means of communication for traders.

Potential for trans-pulmonary tumor markers in the early diagnosis of lung cancer: a case report

BackgroundMeasurement of tumor markers from peripheral venous blood is an emerging tool to assist in the early diagnosis of lung cancer. Samples from the pulmonary artery and pulmonary artery wedge position (trans-pulmonary samples) are accessible via right-heart catheterization and, by virtue of their proximity to lung tumors, may increase diagnostic yield.Case presentationWe report a case of a 64 year-old woman from whom trans-pulmonary samples were obtained and who was diagnosed 16 months later with recurrent metastatic small cell lung cancer. Carcinoembryonic antigen, cytokeratin fragment 21 − 1 (CYFRA), and human epididymis protein 4 (HE4) levels demonstrated increasing concentrations across the pulmonary circulation. These gradients exceeded the assays’ coefficient of variation by several-fold. For CYFRA and HE4, pulmonary artery wedge concentrations exceeded peripheral venous levels by more than 10% and peripheral arterial levels were up to 8% higher than peripheral venous levels.ConclusionsEvaluating the feasibility and utility of trans-pulmonary tumor markers for lung cancer diagnosis in a larger cohort should be considered. The addition of a peripheral arterial sample to standard peripheral venous samples may be a more practical alternative.