BPKEM: A biometric-based private key encryption and management framework for blockchain.

The fundamental technology behind bitcoin, known as blockchain, has been studied and used in a variety of industries especially in finance. The security of blockchain is extremely important as it will affects the assets of the clients as well as it is the lifeline feature of the entire system that needs to be guaranteed. Currently, there is a lack of a methodical approach to guarantee the security and dependability of the private key during its whole life. Furthermore, there is no quick, easy, or secure way to create the encryption key. A biometric-based private key encryption and management framework (BPKEM) for blockchain is proposed not only to solve the private key lifecycle manag- ement problem, but also it maintains compatibility with existing blockchain systems. For the problem of private key encryption, a biometric-based stable key generation method is proposed. By using the relative invariance between facial and fingerprint feature points, this measure can convert feature points into stable and distinguishable descriptors, then using a reusable fuzzy extractor to create a stable key. The correct- ness and efficiency of the newly proposed biometric-based blockchain encryption tech- nique in this paper has been validated in the experiments.

Resilient performance in building maintenance: A macro-cognition perspective during sudden breakdowns.

Building maintenance encompasses multiple tightly inter-connected agents (e.g., technicians, occupants, supervisors, and equipment). Variable working conditions and limited resources may affect the safety and sustainability of the activities. Although recent studies have explored how complex systems can perform resilient behavior in facing the complexity of everyday activities, the factors that effectively contribute to resilient performance are still paired with limited empirical evidence. We studied the performance of the maintenance team during sudden breakdowns of air-conditioning devices in a large university campus, using the Functional Resonance Analysis Method (FRAM). A FRAM diagram containing 30 functions was organized including six macro-cognitive functions (expertise, sensemaking, communication, coordination, collaboration, and adaptation/improvisation), examining their role in anticipating, and responding to emergencies, and eight functional units that are directly impacted by disturbances were analyzed in more detail. Results indicate that macro-cognitive functions can greatly impact the functionality of the maintenance team in pursuit of their goals. Moreover, we noted those macro-cognitive functions here analyzed depend on each other to produce resilient performance.

Humans create more novelty than ChatGPT when asked to retell a story.

We compare how humans retell stories to how ChatGPT retells stories in chains of three retellings by different people or different accounts on ChatGPT. ChatGPT provides competent summaries of the original narrative texts in one step of retelling. In subsequent retellings few additional changes occur. Human retellers, by contrast, reduce the original text incrementally and by creating 55-60% of novel words and concepts (synsets) at each iteration. The retellings by both ChatGPT and humans show very stable emotion ratings, which is a puzzle for human retellers given the high degree of novel inventions across retellings. ChatGPT maintains more nouns, adjectives, and prepositions and also uses language later acquired in life, while humans use more verbs, adverbs, and negations and use language acquired at a younger age. The results reveal that spontaneous retelling by humans involves ongoing creativity, anchored by emotions, beyond the default probabilistic wording of large language models such as ChatGPT.

Evaluation of the Chilean National Orthopaedic Examination Over 11 Years: Progress and Outcomes of National and International Examinees.

INTRODUCTION: The National Orthopaedics Examination (EMNOT) was initially designed for Chilean orthopaedic program graduates and is now a crucial component of the revalidation process for international orthopaedic surgeons seeking practice in Chile. This study aims to describe participation and performance of EMNOT examinees based on their origin and to analyze the difficulty and discrimination indexes during its first 11 years of implementation. METHODS: A retrospective assessment was conducted on all EMNOT results from 2009 to 2019. The study evaluated the participation and performance of examinees according to their origin and examined the difficulty and discrimination indexes of the examination. RESULTS: A total of 975 examinees were evaluated, with 41.23% from national resident programs (National Medical Graduates) and 58.77% from international examinees (International Medical Graduates). The number of participating universities increased from 4 in 2009 to 17 in 2019. National Medical Graduates examinees achieved a mean score of 66.52 ± 8.67 (0 to 100 points) while International Medical Graduates examinees scored 55.13 ± 11.42 (P < 0.001). The difficulty and discrimination indexes remained adequate throughout this period. DISCUSSION: Over the course of 11 years, the number of EMNOT examinees exhibited notable growth. The examination effectively differentiates between candidates based on their origin and maintains appropriate levels of difficulty and discrimination.

Model-based optimization of controlled release formulation of levodopa for Parkinson's disease.

Levodopa is currently the standard of care treatment for Parkinson's disease, but chronic therapy has been linked to motor complications. Designing a controlled release formulation (CRF) that maintains sustained and constant blood concentrations may reduce these complications. Still, it is challenging due to levodopa's pharmacokinetic properties and the notion that it is absorbed only in the upper small intestine (i.e., exhibits an "absorption window"). We created and validated a physiologically based mathematical model to aid the development of such a formulation. Analysis of experimental results using the model revealed that levodopa is well absorbed throughout the entire small intestine (i.e., no "absorption window") and that levodopa in the stomach causes fluctuations during the first 3 h after administration. Based on these insights, we developed guidelines for an improved CRF for various stages of Parkinson's disease. Such a formulation is expected to produce steady concentrations and prolong therapeutic duration compared to a common CRF with a smaller dose per day and a lower overall dose of levodopa, thereby improving patient compliance with the dosage regime.

DaylilyNet: A Multi-Task Learning Method for Daylily Leaf Disease Detection.

Timely detection and management of daylily diseases are crucial to prevent yield reduction. However, detection models often struggle with handling the interference of complex backgrounds, leading to low accuracy, especially in detecting small targets. To address this problem, we propose DaylilyNet, an object detection algorithm that uses multi-task learning to optimize the detection process. By incorporating a semantic segmentation loss function, the model focuses its attention on diseased leaf regions, while a spatial global feature extractor enhances interactions between leaf and background areas. Additionally, a feature alignment module improves localization accuracy by mitigating feature misalignment. To investigate the impact of information loss on model detection performance, we created two datasets. One dataset, referred to as the 'sliding window dataset', was obtained by splitting the original-resolution images using a sliding window. The other dataset, known as the 'non-sliding window dataset', was obtained by downsampling the images. Experimental results in the 'sliding window dataset' and the 'non-sliding window dataset' demonstrate that DaylilyNet outperforms YOLOv5-L in mAP@0.5 by 5.2% and 4.0%, while reducing parameters and time cost. Compared to other models, our model maintains an advantage even in scenarios where there is missing information in the training dataset.

Performance of Sensor Data Process Offloading on 5G-Enabled UAVs.

Recently, unmanned aerial vehicle (UAV)-oriented applications have been growing worldwide. Thus, there is a strong interest in using UAVs for applications requiring wide-area connectivity coverage. Such applications might be power line inspection, road inspection, offshore site monitoring, wind turbine inspections, and others. The utilization of cellular networks, such as the fifth-generation (5G) technology, is often considered to meet the requirement of wide-area connectivity. This study quantifies the performance of 5G-enabled UAVs when sensor data throughput requirements are within the 5G network's capability and when throughput requirements significantly exceed the capability of the 5G network, respectively. Our experimental results show that in the first case, the 5G network maintains bounded latency, and the application behaves as expected. In the latter case, the overloading of the 5G network results in increased latency, dropped packets, and overall degradation of the application performance. Our findings show that offloading processes requiring moderate sensor data rates work well, while transmitting all the raw data generated by the UAV's sensors is not possible. This study highlights and experimentally demonstrates the impact of critical parameters that affect real-life 5G-enabled UAVs that utilize the edge-offloading power of a 5G cellular network.

Plan de mantenimiento de plantas generadoras de oxígeno medicinal de las instituciones prestadoras de servicios de salud del Ministerio de Salud y de los Gobiernos Regionales-2023. Documento Técnico / Maintenance plan for medical oxygen generating plants of the institutions providing health services of the Ministry of Health and Regional Governments-2023. Technical document

El documento contiene las disposiciones para mantener en funcionamiento y en operatividad las plantas generadoras de oxígeno medicinal de las instituciones prestadoras de servicios de salud- IPRESS del Ministerio de Salud y de los Gobiernos Regionales

Ab initio real-time quantum dynamics of charge carriers in momentum space.

Application of the non-adiabatic molecular dynamics (NAMD) approach is limited to studying carrier dynamics in the momentum space, as a supercell is required to sample the phonon excitation and electron-phonon (e-ph) interaction at different momenta in a molecular dynamics simulation. Here we develop an ab initio approach for the real-time charge carrier quantum dynamics in the momentum space (NAMD_k) by directly introducing e-ph coupling into the Hamiltonian based on the harmonic approximation. The NAMD_k approach maintains the zero-point energy and includes memory effects of carrier dynamics. The application of NAMD_k to the hot carrier dynamics in graphene reveals the phonon-specific relaxation mechanism. An energy threshold of 0.2 eV-defined by two optical phonon modes-separates the hot electron relaxation into fast and slow regions with lifetimes of pico- and nanoseconds, respectively. The NAMD_k approach provides an effective tool to understand real-time carrier dynamics in the momentum space for different materials.

CUDM: A Combined UAV Detection Model Based on Video Abnormal Behavior.

The widespread use of unmanned aerial vehicles (UAVs) has brought many benefits, particularly for military and civil applications. For example, UAVs can be used in communication, ecological surveys, agriculture, and logistics to improve efficiency and reduce the required workforce. However, the malicious use of UAVs can significantly endanger public safety and pose many challenges to society. Therefore, detecting malicious UAVs is an important and urgent issue that needs to be addressed. In this study, a combined UAV detection model (CUDM) based on analyzing video abnormal behavior is proposed. CUDM uses abnormal behavior detection models to improve the traditional object detection process. The work of CUDM can be divided into two stages. In the first stage, our model cuts the video into images and uses the abnormal behavior detection model to remove a large number of useless images, improving the efficiency and real-time detection of suspicious targets. In the second stage, CUDM works to identify whether the suspicious target is a UAV or not. Besides, CUDM relies only on ordinary equipment such as surveillance cameras, avoiding the use of expensive equipment such as radars. A self-made UAV dataset was constructed to verify the reliability of CUDM. The results show that CUDM not only maintains the same accuracy as state-of-the-art object detection models but also reduces the workload by 32%. Moreover, it can detect malicious UAVs in real-time.

Towards 100 Gbps over 100 km: System Design and Demonstration of E-Band Millimeter Wave Communication.

Long-range E-band communication with fiber-equivalent speed is emerging extensively as a critical technology in the next-generation communication. This paper firstly reviews the relevant progress in recent research. A brief survey is presented on high-speed, long-range E-band communication systems and their relevant techniques that are essential to the link design, including antenna, power amplifier (PA), channel, and digital baseband processing. In the second part, we review our recent field trial of a long-range air-to-ground E-band link, which maintains steady transmission from a slow-moving helium balloon to the ground station with a vertical dimension of 20 km. The improvement directions and future research topics are then discussed.

Agonistic activity of fentanyl analogs and their metabolites on opioid receptors.

PURPOSE: This study aims to expose the toxicity of fentanyl analogs and their metabolites by measuring the agonistic activity of these compounds on opioid receptors. METHODS: The agonistic activity of fentanyl, four analogs of fentanyl (acetylfentanyl, butyrylfentanyl, tetrahydrofuranylfentanyl, and furanylfentanyl), and their metabolites were evaluated using a cell-based assay system, which measured the cellular cAMP level after the reaction of a test compound with cells expressing opioid receptor. RESULTS: Fentanyl and its four analogs showed agonistic activity on µ-opioid receptor at < 10 nM, whereas these compounds were inactive at δ- and κ-opioid receptors even at 100 nM. Similarly, no metabolites showed agonistic activity on δ- and κ-opioid receptors. Meanwhile, several metabolites were active at µ-opioid receptor. ß-Hydroxy metabolites exhibited strong activity nearly equivalent to those of the parent drugs. Some 4'-hydroxy metabolites and N-acyl group-hydroxylated metabolites were still active; however, their activity drastically decreased compared to the parent drugs. CONCLUSIONS: Most of the metabolic reactions drastically diminish the agonistic activity of fentanyl analogs; exceptionally, ß-hydroxylation maintains the activity at a level nearly equal to that of the parent drugs. However, ß-hydroxy metabolites should contribute less to the poisoning caused by the ingestion of fentanyl analogs.

SCGG: A deep structure-conditioned graph generative model.

Deep learning-based graph generation approaches have remarkable capacities for graph data modeling, allowing them to solve a wide range of real-world problems. Making these methods able to consider different conditions during the generation procedure even increases their effectiveness by empowering them to generate new graph samples that meet the desired criteria. This paper presents a conditional deep graph generation method called SCGG that considers a particular type of structural conditions. Specifically, our proposed SCGG model takes an initial subgraph and autoregressively generates new nodes and their corresponding edges on top of the given conditioning substructure. The architecture of SCGG consists of a graph representation learning network and an autoregressive generative model, which is trained end-to-end. More precisely, the graph representation learning network is designed to compute continuous representations for each node in a graph, which are not only affected by the features of adjacent nodes, but also by the ones of farther nodes. This network is primarily responsible for providing the generation procedure with the structural condition, while the autoregressive generative model mainly maintains the generation history. Using this model, we can address graph completion, a rampant and inherently difficult problem of recovering missing nodes and their associated edges of partially observed graphs. The computational complexity of the SCGG method is shown to be linear in the number of graph nodes. Experimental results on both synthetic and real-world datasets demonstrate the superiority of our method compared with state-of-the-art baselines.

Towards Online Ageing Detection in Transformer Oil: A Review.

Transformers play an essential role in power networks, ensuring that generated power gets to consumers at the safest voltage level. However, they are prone to insulation failure from ageing, which has fatal and economic consequences if left undetected or unattended. Traditional detection methods are based on scheduled maintenance practices that often involve taking samples from in situ transformers and analysing them in laboratories using several techniques. This conventional method exposes the engineer performing the test to hazards, requires specialised training, and does not guarantee reliable results because samples can be contaminated during collection and transportation. This paper reviews the transformer oil types and some traditional ageing detection methods, including breakdown voltage (BDV), spectroscopy, dissolved gas analysis, total acid number, interfacial tension, and corresponding regulating standards. In addition, a review of sensors, technologies to improve the reliability of online ageing detection, and related online transformer ageing systems is covered in this work. A non-destructive online ageing detection method for in situ transformer oil is a better alternative to the traditional offline detection method. Moreover, when combined with the Internet of Things (IoT) and artificial intelligence, a prescriptive maintenance solution emerges, offering more advantages and robustness than offline preventive maintenance approaches.

Unsupervised Person Re-Identification with Attention-Guided Fine-Grained Features and Symmetric Contrast Learning.

Unsupervised person re-identification has attracted a lot of attention due to its strong potential to adapt to new environments without manual annotation, but learning to recognise features in disjoint camera views without annotation is still challenging. Existing studies tend to ignore the optimisation of feature extractors in the feature-extraction stage of this task, while the use of traditional losses in the unsupervised learning stage severely affects the performance of the model. Additionally the use of a contrast learning framework in the latest methods uses only a single cluster centre or all instance features, without considering the correctness and diversity of the samples in the class, which affects the training of the model. Therefore, in this paper, we design an unsupervised person-re-identification framework called attention-guided fine-grained feature network and symmetric contrast learning (AFF_SCL) to improve the two stages in the unsupervised person-re-identification task. AFF_SCL focuses on learning recognition features through two key modules, namely the Attention-guided Fine-grained Feature network (AFF) and the Symmetric Contrast Learning module (SCL). Specifically, the attention-guided fine-grained feature network enhances the network's ability to discriminate pedestrians by performing further attention operations on fine-grained features to obtain detailed features of pedestrians. The symmetric contrast learning module replaces the traditional loss function to exploit the information potential given by the multiple samples and maintains the stability and generalisation capability of the model. The performance of the USL and UDA methods is tested on the Market-1501 and DukeMTMC-reID datasets by means of the results, which demonstrate that the method outperforms some existing methods, indicating the superiority of the framework.

Disposable Ureteroscopes in Urology: Current State and Future Prospects.

Ureteroscopy is the most common surgical modality for stone treatment. Reusable flexible ureteroscopes are delicate instruments that require expensive maintenance and repairs. Multiple single use ureteroscopes have been developed recently to combat the expensive and time-intensive sterilization and repair of ureteroscopes. Although multiple studies have looked at different aspects of reusable and single use ureteroscopes, there is significant heterogeneity in performance measures and cost between the 2 categories, and neither has a clear advantage. Both can be used successfully, and individual and institution level factors should be considered when deciding which ureteroscope to use.

Guía de práctica clínica para el tratamiento farmacológico de Nefritis lúpica / Clinical practice guideline for the pharmacological treatment of lupus nephritis

La guía contiene recomendaciones para el tratamiento farmacológico inicial de la nefritis lúpica durante la fase de inducción y mantenimiento, con el fin de contribuir a reducir las complicaciones de los pacientes con esta condición