bluuebunny/crossref_metadata_2025 huggingface dataset:
DRL-GAN: A Hybrid Approach for Binary and Multiclass Network Intrusion Detection
Authors: Strickland Caroline , Zakar Muhammad , Saha Chandrika , Soltani Nejad Sareh , Tasnim Noshin , Lizotte Daniel J. , Haque Anwar
Published: April 2024

Our increasingly connected world continues to face an ever-growing number of network-based attacks. An Intrusion Detection System (IDS) is an essential security technology used for detecting these attacks. Although numerous Machine Learning-based IDSs have been proposed for the detection of malicious network traffic, the majority have difficulty properly detecting and classifying the more uncommon attack types. In this paper, we implement a novel hybrid technique using synthetic data produced by a Generative Adversarial Network (GAN) to use as input for training a Deep Reinforcement Learning (DRL) model. Our GAN model is trained on the NSL-KDD dataset, a publicly available collection of labeled network traffic data specifically designed to support the evaluation and benchmarking of IDSs. Ultimately, our findings demonstrate that training the DRL model on synthetic datasets generated by specific GAN models can result in better performance in correctly classifying minority classes over training on the true imbalanced dataset.
Enhancing Resilience in Biometric Research: Generation of 3D Synthetic Face Data Using Advanced 3D Character Creation Techniques from High-Fidelity Video Games and Animation
Authors: Blümel Florian Erwin , Schulz Mathias , Breithaupt Ralph , Jung Norbert , Lange Robert
Published: April 2024

Biometric authentication plays a vital role in various everyday applications with increasing demands for reliability and security. However, the use of real biometric data for research raises privacy concerns and data scarcity issues. A promising approach using synthetic biometric data to address the resulting unbalanced representation and bias, as well as the limited availability of diverse datasets for the development and evaluation of biometric systems, has emerged. Methods for a parameterized generation of highly realistic synthetic data are emerging and the necessary quality metrics to prove that synthetic data can compare to real data are open research tasks. The generation of 3D synthetic face data using game engines’ capabilities of generating varied realistic virtual characters is explored as a possible alternative for generating synthetic face data while maintaining reproducibility and ground truth, as opposed to other creation methods. While synthetic data offer several benefits, including improved resilience against data privacy concerns, the limitations and challenges associated with their usage are addressed. Our work shows concurrent behavior in comparing semi-synthetic data as a digital representation of a real identity with their real datasets. Despite slight asymmetrical performance in comparison with a larger database of real samples, a promising performance in face data authentication is shown, which lays the foundation for further investigations with digital avatars and the creation and analysis of fully synthetic data. Future directions for improving synthetic biometric data generation and their impact on advancing biometrics research are discussed.
Multimodal Environmental Sensing Using AI & IoT Solutions: A Cognitive Sound Analysis Perspective
Authors: Emvoliadis Alexandros , Vryzas Nikolaos , Stamatiadou Marina-Eirini , Vrysis Lazaros , Dimoulas Charalampos
Published: April 2024

This study presents a novel audio compression technique, tailored for environmental monitoring within multi-modal data processing pipelines. Considering the crucial role that audio data play in environmental evaluations, particularly in contexts with extreme resource limitations, our strategy substantially decreases bit rates to facilitate efficient data transfer and storage. This is accomplished without undermining the accuracy necessary for trustworthy air pollution analysis while simultaneously minimizing processing expenses. More specifically, our approach fuses a Deep-Learning-based model, optimized for edge devices, along with a conventional coding schema for audio compression. Once transmitted to the cloud, the compressed data undergo a decoding process, leveraging vast cloud computing resources for accurate reconstruction and classification. The experimental results indicate that our approach leads to a relatively minor decrease in accuracy, even at notably low bit rates, and demonstrates strong robustness in identifying data from labels not included in our training dataset.
Adaptive Weighted Error-Correction Method Based on the Error Distribution Characteristics of Multi-Channel Alignment
Authors: Song Peiyu , Wang Weibo , Wu Biwei , Zou Limin , Zhan Tianpeng , Tan Jiubin , Ding Xuemei
Published: April 2024

As process nodes of advanced integrated circuits continue to decrease below 10 nm, the requirement for overlay accuracy is becoming stricter. The alignment sensor measures the position of the alignment mark relative to the wafer; thus, sub-nanometer alignment position accuracy is vital. The Phase Grating Alignment (PGA) method is widely used due to its high precision and stability. However, the alignment error caused by the mark asymmetry is the key obstacle preventing PGA technology from achieving sub-nanometer alignment accuracy. This error can be corrected using many methods, such as process verification and multi-channel weighted methods based on multi-diffraction, multi-wavelength and multi-polarization state alignment sensors. However, the mark asymmetry is unpredictable, complex and difficult to obtain in advance. In this case, the fixed-weight method cannot effectively reduce the alignment error. Therefore, an adaptive weighted method based on the error distribution characteristic of a multi-channel is proposed. Firstly, the simulation result proves that the error distribution characteristic of the multi-alignment result has a strong correlation with the mark asymmetry. Secondly, a concrete method of constructing weight values based on error distribution is described. We assume that the relationship between the weight value of each channel and the deviations of all channels’ results is second-order linear. Finally, without other prior process correction in the simulation experiment, the residual error’s Root Mean Square (RMS) of fixed weighted method is 14.0 nm, while the RMS of the adaptive weighted method is 0.01 nm, when dealing with five typical types of mark asymmetry. The adaptive weighted method exhibits a more stable error correction effect under unpredictable and complicated mark asymmetry.
An Analytical Model of IaaS Architecture for Determining Resource Utilization
Authors: Hanczewski Slawomir , Stasiak Maciej , Weissenberg Michal
Published: April 2024

Cloud computing has become a major component of the modern IT ecosystem. A key contributor to this has been the development of Infrastructure as a Service (IaaS) architecture, in which users’ virtual machines (VMs) are run on the service provider’s physical infrastructure, making it possible to become independent of the need to purchase one’s own physical machines (PMs). One of the main aspects to consider when designing such systems is achieving the optimal utilization of individual resources, such as processor, RAM, disk, and available bandwidth. In response to these challenges, the authors developed an analytical model (the ARU method) to determine the average utilization levels of the aforementioned resources. The effectiveness of the proposed analytical model was evaluated by comparing the results obtained by utilizing the model with those obtained by conducting a digital simulation of the operation of a cloud system according to the IaaS paradigm. The results show the effectiveness of the model regardless of the structure of the emerging requests, the variability of the capacity of individual resources, and the number of physical machines in the system. This translates into the applicability of the model in the design process of cloud systems.
Measuring Surface Electromyography with Textile Electrodes in a Smart Leg Sleeve
Authors: Amitrano Federica , Coccia Armando , Pagano Gaetano , Biancardi Arcangelo , Tombolini Giuseppe , Marsico Vito , D’Addio Giovanni
Published: April 2024

This paper presents the design, development, and validation of a novel e-textile leg sleeve for non-invasive Surface Electromyography (sEMG) monitoring. This wearable device incorporates e-textile sensors for sEMG signal acquisition from the lower limb muscles, specifically the anterior tibialis and lateral gastrocnemius. Validation was conducted by performing a comparative study with eleven healthy volunteers to evaluate the performance of the e-textile sleeve in acquiring sEMG signals compared to traditional Ag/AgCl electrodes. The results demonstrated strong agreement between the e-textile and conventional methods in measuring descriptive metrics of the signals, including area, power, mean, and root mean square. The paired data t-test did not reveal any statistically significant differences, and the Bland–Altman analysis indicated negligible bias between the measures recorded using the two methods. In addition, this study evaluated the wearability and comfort of the e-textile sleeve using the Comfort Rating Scale (CRS). Overall, the scores confirmed that the proposed device is highly wearable and comfortable, highlighting its suitability for everyday use in patient care.
Developing a Novel Prosthetic Hand with Wireless Wearable Sensor Technology Based on User Perspectives: A Pilot Study
Authors: Shimizu Yukiyo , Mori Takahiko , Yoshikawa Kenichi , Katane Daisuke , Torishima Hiroyuki , Hara Yuki , Yozu Arito , Yamazaki Masashi , Hada Yasushi , Mutsuzaki Hirotaka
Published: April 2024

Myoelectric hands are beneficial tools in the daily activities of people with upper-limb deficiencies. Because traditional myoelectric hands rely on detecting muscle activity in residual limbs, they are not suitable for individuals with short stumps or paralyzed limbs. Therefore, we developed a novel electric prosthetic hand that functions without myoelectricity, utilizing wearable wireless sensor technology for control. As a preliminary evaluation, our prototype hand with wireless button sensors was compared with a conventional myoelectric hand (Ottobock). Ten healthy therapists were enrolled in this study. The hands were fixed to their forearms, myoelectric hand muscle activity sensors were attached to the wrist extensor and flexor muscles, and wireless button sensors for the prostheses were attached to each user’s trunk. Clinical evaluations were performed using the Simple Test for Evaluating Hand Function and the Action Research Arm Test. The fatigue degree was evaluated using the modified Borg scale before and after the tests. While no statistically significant differences were observed between the two hands across the tests, the change in the Borg scale was notably smaller for our prosthetic hand (p = 0.045). Compared with the Ottobock hand, the proposed hand prosthesis has potential for widespread applications in people with upper-limb deficiencies.
Optical Camera Communications in Healthcare: A Wearable LED Transmitter Evaluation during Indoor Physical Exercise
Authors: Niarchou Eleni , Matus Vicente , Rabadan Jose , Guerra Victor , Perez-Jimenez Rafael
Published: April 2024

This paper presents an experimental evaluation of a wearable light-emitting diode (LED) transmitter in an optical camera communications (OCC) system. The evaluation is conducted under conditions of controlled user movement during indoor physical exercise, encompassing both mild and intense exercise scenarios. We introduce an image processing algorithm designed to identify a template signal transmitted by the LED and detected within the image. To enhance this process, we utilize the dynamics of controlled exercise-induced motion to limit the tracking process to a smaller region within the image. We demonstrate the feasibility of detecting the transmitting source within the frames, and thus limit the tracking process to a smaller region within the image, achieving an reduction of 87.3% for mild exercise and 79.0% for intense exercise.
Research on Tire Surface Damage Detection Method Based on Image Processing
Authors: Chen Jiaqi , Li Aijuan , Zheng Fei , Chen Shanshan , He Weikai , Zhang Guangping
Published: April 2024

The performance of the tire has a very important impact on the safe driving of the car, and in the actual use of the tire, due to complex road conditions or use conditions, it will inevitably cause immeasurable wear, scratches and other damage. In order to effectively detect the damage existing in the key parts of the tire, a tire surface damage detection method based on image processing was proposed. In this method, the image of tire side is captured by camera first. Then, the collected images are preprocessed by optimizing the multi-scale bilateral filtering algorithm to enhance the detailed information of the damaged area, and the optimization effect is obvious. Thirdly, the image segmentation based on clustering algorithm is carried out. Finally, the Harris corner detection method is used to capture the “salt and pepper” corner of the target region, and the segmsegmed binary image is screened and matched based on histogram correlation, and the target region is finally obtained. The experimental results show that the similarity detection is accurate, and the damage area can meet the requirements of accurate identification.
Enhancing Healthcare through Sensor-Enabled Digital Twins in Smart Environments: A Comprehensive Analysis
Authors: Adibi Sasan , Rajabifard Abbas , Shojaei Davood , Wickramasinghe Nilmini
Published: April 2024

This comprehensive review investigates the transformative potential of sensor-driven digital twin technology in enhancing healthcare delivery within smart environments. We explore the integration of smart environments with sensor technologies, digital health capabilities, and location-based services, focusing on their impacts on healthcare objectives and outcomes. This work analyzes the foundational technologies, encompassing the Internet of Things (IoT), Internet of Medical Things (IoMT), machine learning (ML), and artificial intelligence (AI), that underpin the functionalities within smart environments. We also examine the unique characteristics of smart homes and smart hospitals, highlighting their potential to revolutionize healthcare delivery through remote patient monitoring, telemedicine, and real-time data sharing. The review presents a novel solution framework leveraging sensor-driven digital twins to address both healthcare needs and user requirements. This framework incorporates wearable health devices, AI-driven health analytics, and a proof-of-concept digital twin application. Furthermore, we explore the role of location-based services (LBS) in smart environments, emphasizing their potential to enhance personalized healthcare interventions and emergency response capabilities. By analyzing the technical advancements in sensor technologies and digital twin applications, this review contributes valuable insights to the evolving landscape of smart environments for healthcare. We identify the opportunities and challenges associated with this emerging field and highlight the need for further research to fully realize its potential to improve healthcare delivery and patient well-being.
Real-Time Multi-Person Video Synthesis with Controllable Prior-Guided Matting
Authors: Chen Aoran , Huang Hai , Zhu Yueyan , Xue Junsheng
Published: April 2024

In order to enhance the matting performance in multi-person dynamic scenarios, we introduce a robust, real-time, high-resolution, and controllable human video matting method that achieves state of the art on all metrics. Unlike most existing methods that perform video matting frame by frame as independent images, we design a unified architecture using a controllable generation model to solve the problem of the lack of overall semantic information in multi-person video. Our method, called ControlMatting, uses an independent recurrent architecture to exploit temporal information in videos and achieves significant improvements in temporal coherence and detailed matting quality. ControlMatting adopts a mixed training strategy comprised of matting and a semantic segmentation dataset, which effectively improves the semantic understanding ability of the model. Furthermore, we propose a novel deep learning-based image filter algorithm that enforces our detailed augmentation ability on both matting and segmentation objectives. Our experiments have proved that prior information about the human body from the image itself can effectively combat the defect masking problem caused by complex dynamic scenarios with multiple people.
A Novel Architecture for an Intrusion Detection System Utilizing Cross-Check Filters for In-Vehicle Networks
Authors: Im Hyungchul , Lee Donghyeon , Lee Seongsoo
Published: April 2024

The Controller Area Network (CAN), widely used for vehicular communication, is vulnerable to multiple types of cyber-threats. Attackers can inject malicious messages into the CAN bus through various channels, including wireless methods, entertainment systems, and on-board diagnostic ports. Therefore, it is crucial to develop a reliable intrusion detection system (IDS) capable of effectively distinguishing between legitimate and malicious CAN messages. In this paper, we propose a novel IDS architecture aimed at enhancing the cybersecurity of CAN bus systems in vehicles. Various machine learning (ML) models have been widely used to address similar problems; however, although existing ML-based IDS are computationally efficient, they suffer from suboptimal detection performance. To mitigate this shortcoming, our architecture incorporates specially designed rule-based filters that cross-check outputs from the traditional ML-based IDS. These filters scrutinize message ID and payload data to precisely capture the unique characteristics of three distinct types of cyberattacks: DoS attacks, spoofing attacks, and fuzzy attacks. Experimental evidence demonstrates that the proposed architecture leads to a significant improvement in detection performance across all utilized ML models. Specifically, all ML-based IDS achieved an accuracy exceeding 99% for every type of attack. This achievement highlights the robustness and effectiveness of our proposed solution in detecting potential threats.
Use of Unmanned Surface Vehicles (USVs) in Water Chemistry Studies
Authors: Katsouras Georgios , Dimitriou Elias , Karavoltsos Sotirios , Samios Stylianos , Sakellari Aikaterini , Mentzafou Angeliki , Tsalas Nikolaos , Scoullos Michael
Published: April 2024

Unmanned surface vehicles (USVs) equipped with integrated sensors are a tool valuable to several monitoring strategies, offering enhanced temporal and spatial coverage over specific timeframes, allowing for targeted examination of sites or events of interest. The elaboration of environmental monitoring programs has relied so far on periodic spot sampling at specific locations, followed by laboratory analysis, aiming at the evaluation of water quality at a catchment scale. For this purpose, automatic telemetric stations for specific parameters have been installed by the Institute of Marine Biological Resources and Inland Waters of Hellenic Centre for Marine Research (IMBRIW-HCMR) within several Greek rivers and lakes, providing continuous and temporal monitoring possibilities. In the present work, USVs were deployed by the Athens Water and Sewerage Company (EYDAP) as a cost-effective tool for the environmental monitoring of surface water bodies of interest, with emphasis on the spatial fluctuations of chlorophyll α, electrical conductivity, dissolved oxygen and pH, observed in Koumoundourou Lake and the rivers Acheloos, Asopos and Kifissos. The effectiveness of an innovative heavy metal (HM) system installed in the USV for the in situ measurements of copper and lead was also evaluated herewith. The results obtained demonstrate the advantages of USVs, setting the base for their application in real-time monitoring of chemical parameters including metals. Simultaneously, the requirements for accuracy and sensitivity improvement of HM sensors were noted, in order to permit full exploitation of USVs’ capacities.
Classification of Sleep Quality and Aging as a Function of Brain Complexity: A Multiband Non-Linear EEG Analysis
Authors: Penalba-Sánchez Lucía , Silva Gabriel , Crook-Rumsey Mark , Sumich Alexander , Rodrigues Pedro Miguel , Oliveira-Silva Patrícia , Cifre Ignacio
Published: April 2024

Understanding and classifying brain states as a function of sleep quality and age has important implications for developing lifestyle-based interventions involving sleep hygiene. Current studies use an algorithm that captures non-linear features of brain complexity to differentiate awake electroencephalography (EEG) states, as a function of age and sleep quality. Fifty-eight participants were assessed using the Pittsburgh Sleep Quality Inventory (PSQI) and awake resting state EEG. Groups were formed based on age and sleep quality (younger adults n = 24, mean age = 24.7 years, SD = 3.43, good sleepers n = 11; older adults n = 34, mean age = 72.87; SD = 4.18, good sleepers n = 9). Ten non-linear features were extracted from multiband EEG analysis to feed several classifiers followed by a leave-one-out cross-validation. Brain state complexity accurately predicted (i) age in good sleepers, with 75% mean accuracy (across all channels) for lower frequencies (alpha, theta, and delta) and 95% accuracy at specific channels (temporal, parietal); and (ii) sleep quality in older groups with moderate accuracy (70 and 72%) across sub-bands with some regions showing greater differences. It also differentiated younger good sleepers from older poor sleepers with 85% mean accuracy across all sub-bands, and 92% at specific channels. Lower accuracy levels (<50%) were achieved in predicting sleep quality in younger adults. The algorithm discriminated older vs. younger groups excellently and could be used to explore intragroup differences in older adults to predict sleep intervention efficiency depending on their brain complexity.
LSTM-Autoencoder Based Anomaly Detection Using Vibration Data of Wind Turbines
Authors: Lee Younjeong , Park Chanho , Kim Namji , Ahn Jisu , Jeong Jongpil
Published: April 2024

The problem of energy depletion has brought wind energy under consideration to replace oil- or chemical-based energy. However, the breakdown of wind turbines is a major concern. Accordingly, unsupervised learning was performed using the vibration signal of a wind power generator to achieve an outlier detection performance of 97%. We analyzed the vibration data through wavelet packet conversion and identified a specific frequency band that showed a large difference between the normal and abnormal data. To emphasize these specific frequency bands, high-pass filters were applied to maximize the difference. Subsequently, the dimensions of the data were reduced through principal component analysis, giving unique characteristics to the data preprocessing process. Normal data collected from a wind farm located in northern Sweden was first preprocessed and trained using a long short-term memory (LSTM) autoencoder to perform outlier detection. The LSTM Autoencoder is a model specialized for time-series data that learns the patterns of normal data and detects other data as outliers. Therefore, we propose a method for outlier detection through data preprocessing and unsupervised learning, utilizing the vibration signals from wind generators. This will facilitate the quick and accurate detection of wind power generator failures and provide alternatives to the problem of energy depletion.
A Novel Method for Remaining Useful Life Prediction of RF Circuits Based on the Gated Recurrent Unit–Convolutional Neural Network Model
Authors: Yang Wanyu , Wu Kunping , Long Bing , Tian Shulin
Published: April 2024

The remaining useful life (RUL) prediction of RF circuits is an important tool for circuit reliability. Data-driven-based approaches do not require knowledge of the failure mechanism and reduce the dependence on knowledge of complex circuits, and thus can effectively realize RUL prediction. This manuscript proposes a novel RUL prediction method based on a gated recurrent unit–convolutional neural network (GRU-CNN). Firstly, the data are normalized to improve the efficiency of the algorithm; secondly, the degradation of the circuit is evaluated using the hybrid health score based on the Euclidean and Manhattan distances; then, the life cycle of the RF circuits is segmented based on the hybrid health scores; and finally, an RUL prediction is carried out for the circuits at each stage using the GRU-CNN model. The results show that the RMSE of the GRU-CNN model in the normal operation stage is only 3/5 of that of the GRU and CNN models, while the prediction uncertainty is minimized.
Impact of Prior COVID-19 Immunization and/or Prior Infection on Immune Responses and Clinical Outcomes
Authors: Livieratos Achilleas , Gogos Charalambos , Akinosoglou Karolina
Published: April 2024

Cellular and humoral immunity exhibit dynamic adaptation to the mutating SARS-CoV-2 virus. It is noteworthy that immune responses differ significantly, influenced by whether a patient has received vaccination or whether there is co-occurrence of naturally acquired and vaccine-induced immunity, known as hybrid immunity. The different immune reactions, conditional on vaccination status and the viral variant involved, bear implications for inflammatory responses, patient outcomes, pathogen transmission rates, and lingering post-COVID conditions. Considering these developments, we have performed a review of recently published literature, aiming to disentangle the intricate relationships among immunological profiles, transmission, the long-term health effects post-COVID infection poses, and the resultant clinical manifestations. This investigation is directed toward understanding the variability in the longevity and potency of cellular and humoral immune responses elicited by immunization and hybrid infection.
Retrospective Study of Clinicopathological Changes and Prediction Model for Canine Vascular Neoplasms
Authors: Suphonkhan Jidapa , Klaymongkol Chananchida , Khomsiri Wijittra , Wanprom Jedsada , Jeamsripong Saharuetai , Chimnakboon Narisara , Rungsipipat Anudep , Radtanakatikanon Araya
Published: April 2024

Vascular neoplasms, including hemangiosarcoma (HSA) and hemangioma (HMA), are more common in dogs than other domestic animal species; however, comprehensive laboratory screening tests for early diagnosis are currently limited. The aims of this study were to investigate general signalments, anatomic locations, and clinicopathological abnormalities of dogs diagnosed with vascular neoplasms and to determine the diagnostic significance of these abnormalities. Retrospective data of dogs with HMA, HSA, and healthy dogs were analyzed. Dogs with HMA and HSA were seniors, with mixed breeds being most affected. HMA affected predominantly non-visceral sites, while HSA was more common in visceral sites, particularly the spleen. In multivariate model analyses, the odds of HMA diagnosis were 5.5 times higher in anemic dogs and 33.0 times higher in lymphopenic dogs compared to dogs without the abnormalities. The odds of HSA diagnosis were 42.5 times higher in anemic dogs, 343 times higher in lymphopenic dogs and 92.7 times higher in dogs with hyperfibrinogenemia compared to dogs without the abnormalities. The study suggested that these identified abnormalities were nonspecific and commonly observed in various chronic diseases, and hence their combination with clinical information, such as diagnostic imaging and histopathology, is important to facilitate a more precise diagnosis of canine vascular neoplasms.
Specific Gravity Improves Identification of Clinically Significant Quantitative Proteinuria from the Dipstick Urinalysis
Authors: McAdams Meredith C. , Gregg L. Parker , Xu Pin , Zhang Song , Li Michael , Carroll Ella , Kannan Vaishnavi , Willett DuWayne L. , Hedayati S. Susan
Published: April 2024

Key Points

Urine albumin-to-creatinine ratio and urine protein-to-creatinine ratio are frequently obtained and represent possible tools for screening for proteinuria and thus early CKD.

Adding specific gravity to dipstick proteinuria improves the ability to screen patients with clinically significant proteinuria and can be used to identify patients with early CKD.

Background

CKD is often underdiagnosed during early stages when GFR is preserved because of underutilization of testing for quantitative urine albumin-to-creatinine ratio (UACR) or urine protein-to-creatinine ratio (UPCR). Semiquantitative dipstick proteinuria (DSP) on urinalysis is widely obtained but not accurate for identifying clinically significant proteinuria.

Methods

We identified all patients with a urinalysis and UACR or UPCR obtained on the same day at a tertiary referral center. The accuracy of DSP alone or in combination with specific gravity (SG) against a gold-standard UACR ≥30 mg/g or UPCR ≥0.15 g/g, characterizing clinically significant proteinuria, was evaluated using logistic regression. Models were internally validated using ten-fold cross-validation. The SG for each DSP above which significant proteinuria is unlikely was determined.

Results

Of 11,229 patients, clinically significant proteinuria was present in 4073 (36%). The area under the receiver-operating characteristic curve (95% confidence interval) was 0.77 (0.76 to 0.77) using DSP alone and 0.82 (0.82 to 0.83) in combination with SG (P < 0.001), yielding a specificity of 0.93 (SEM=0.02) and positive likelihood ratio of 9.52 (SEM=0.85). The optimal SG cutoffs to identify significant proteinuria were ≤1.0012, 1.0238, and 1.0442 for DSP of trace, 30, and 100 mg/dl, respectively. At any SG, a DSP ≥300 mg/dl was extremely likely to represent significant proteinuria.

Conclusions

Adding SG to DSP improves recognition of clinically significant proteinuria and can be easily used to identify patients with early stage CKD who may not have otherwise received a quantified proteinuria measurement for both clinical and research purposes.
Fra Bernardin Sokol, svećenik i glazbenik
Authors: Urlić Ivan , Pavlić Petra
Published: April 2024

Prateći život fra Bernardina Sokola od njegova rođenja pa do nasilne smrti, u radu ćemo se upoznati s veličinom nadarenog franjevca. U prvom dijelu govori se o životu fra Bernardina Sokola, njegovu obrazovnom putu koji ga je učinio velikim glazbenim umjetnikom. Govorimo o njegovoj mučeničkoj smrti i životnom štovanju sestre Marije Celine od Prikazanja. U središnjem dijelu govor je Sokolovoj posvećenosti glazbi iz koje nastaje njegov veliki stvaralački skladateljski opus. Stoga je donesen kratak prikaz njegovih misa i veliki broj crkvenih skladbi koje je Sokol posvetio Majci Božjoj, Isusu i drugim svetcima. Skladao je i svjetovne skladbe od kojih je dio namijenio mješovitim zborovima, a drugi dio namijenjen je djeci i objavljen pod nazivom Glazbeni monolozi. Skladateljski mu je opus obilježen i Angelusima, a okušao se i u instrumentalnim skladbama. Također iznimno je tijekom cijeloga života bio i dobar glazbeni pedagog i izdavač. Konačno, u trećem dijelu obrađena je tema koju smo nazvali „prekid šutnje“ koja je nastala nakon njegove smrti. „Ponovno rođenje“ jednoga čestitog franjevca dolazi s uspostavom slobodne i neovisne države Hrvatske kada se počinje ponovno spominjati ime i izvoditi djela fra Bernardina Sokola. Manifestacija Sokolovi dani bio je izniman događaj koji je otkrio mnoge Istine o osobi i djelu poznatog franjevca glazbenika.
Evaluación del potencial energético de biomasa residual agrícola como recurso energético renovable en Tungurahua, Ecuador
Authors:   Facultad de Ciencias Agropecuarias, Universidad Técnica de Ambato, Querochaca, Cevallos, Ecuador, López Villacis Isabel C. ,   Facultad de Ciencia e Ingeniería en Alimentos y Biotecnología, Laboratorio de la Unidad Operativa de Investigación, Universidad Técnica de Ambato, Los Chasquis y Río Payamino, Ambato, Ecuador, Santana Rita C. ,   Facultad de Ciencias Agropecuarias, Universidad Técnica de Ambato, Querochaca, Cevallos, Ecuador, Artieda Jorge R. ,   Facultad de Ciencias Agropecuarias, Universidad Técnica de Ambato, Querochaca, Cevallos, Ecuador, Vásquez Carlos L.
Published: April 2024

Los residuos agrícolas poseen características energéticas y son considerados como fuentes renovables, mediante procesos de conversión como la combustión, permiten la obtención de energía sustitutiva de la energía hidrocarburífera. Se evaluó energéticamente la biomasa residual agrícola de los cultivos de papa (Solanum tuberosum), maíz (Zea mays) y tomate de árbol (Solanum betaceum) como recurso renovable en la provincia de Tungurahua, Ecuador. Los cultivos prospectivos con mayor relevancia en cuanto a los residuos biomásicos generados fueron establecidos. Se propuso una base de datos del análisis proximal. Se determinó el poder calorífico y la cantidad de biomasa para el cálculo del potencial energético para la producción de energía limpia. Se determinó que el cultivo de tomate de árbol genera mayor cantidad de residuos agrícolas con 128.83 tn.ha/año, cuyo poder calorífico es de 15010.15 MJ/tn. Con estos parámetros se calculó el potencial energético que fue de 0.54 GWh.
El impacto de Huanglongbing, HLB, en la producción mundial de cítricos
Authors:   Centro de Biotecnología Genómica, Instituto Politécnico Nacional, Boulevard del Maestro, s/n esq. Elías Piña, Col. Narciso Mendoza, Cd. Reynosa, Tamaulipas, México, C.P. 88710, Pineda Cagua Bryan G. , Hernández Mendoza José Luis , Hernández Delgado San Juana ,   Centro de Biotecnología Genómica, Instituto Politécnico Nacional, Boulevard del Maestro, s/n esq. Elías Piña, Col. Narciso Mendoza, Cd. Reynosa, Tamaulipas, México, C.P. 88710, Gill Langarica Homar Rene ,   Centro de Biotecnología Genómica, Instituto Politécnico Nacional, Boulevard del Maestro, s/n esq. Elías Piña, Col. Narciso Mendoza, Cd. Reynosa, Tamaulipas, México, C.P. 88710, García Olivares Jesús Gerardo ,   Centro de Biotecnología Genómica, Instituto Politécnico Nacional, Boulevard del Maestro, s/n esq. Elías Piña, Col. Narciso Mendoza, Cd. Reynosa, Tamaulipas, México, C.P. 88710, De La Rosa Reyna Xóchilt Fabiola ,   Centro de Biotecnología Genómica, Instituto Politécnico Nacional, Boulevard del Maestro, s/n esq. Elías Piña, Col. Narciso Mendoza, Cd. Reynosa, Tamaulipas, México, C.P. 88710, Quiroz Velásquez Jesús Di Carlo ,   Centro de Biotecnología Genómica, Instituto Politécnico Nacional, Boulevard del Maestro, s/n esq. Elías Piña, Col. Narciso Mendoza, Cd. Reynosa, Tamaulipas, México, C.P. 88710
Published: April 2024

El cultivo de naranja se originó en el hemisferio oriental, principalmente en China, y luego se extendió por todo el mundo como fruta comercial. En 2022, la producción mundial de naranjas aumentó a un total de 49 millones de toneladas métricas. Los principales productores incluyen Brasil, China, la UE, México y Estados Unidos. Sin embargo, la industria de los cítricos enfrenta desafíos debido a la enfermedad de Huanglongbing (HLB). Los investigadores están explorando modificaciones genéticas para lograr tolerancia. Esta revisión explora el impacto de la enfermedad, las especies de cítricos de importancia, las variedades mejoradas y la evolución global del HLB.
Almidón, una plataforma versátil en la industria de alimentos
Authors:   Laboratorio de Tecnología de Alimentos, Universidad Autónoma de Tamaulipas, Unidad Multidisciplinaria Reynosa - Aztlán, calle 16 y lago de Chapala col. Aztlán CP. 88740, Reynosa, México, Trejo Cuevas Karen , Rodríguez Castillejos Guadalupe ,   Laboratorio de Biotecnología Experimental, Centro de Biotecnología Genómica del Instituto Politécnico Nacional. Boulevard del Maestro s/n esq. Elías Piña, Col. Narciso Mendoza, Cd. Reynosa Tamaulipas México, C.P. 88710, Reyes Gallardo Jared ,   Laboratorio de Tecnología de Alimentos, Universidad Autónoma de Tamaulipas, Unidad Multidisciplinaria Reynosa - Aztlán, calle 16 y lago de Chapala col. Aztlán CP. 88740, Reynosa, México, Lizarazo Ortega Cristian ,   Laboratorio de Biotecnología Experimental, Centro de Biotecnología Genómica del Instituto Politécnico Nacional. Boulevard del Maestro s/n esq. Elías Piña, Col. Narciso Mendoza, Cd. Reynosa Tamaulipas México, C.P. 88710, Hernández Jiménez Cristina ,   Laboratorio de Tecnología de Alimentos, Universidad Autónoma de Tamaulipas, Unidad Multidisciplinaria Reynosa - Aztlán, calle 16 y lago de Chapala col. Aztlán CP. 88740, Reynosa, México
Published: April 2024

La presente revisión versa sobre el almidón (amilosa y amilopectina) que constituye una fuente de energía importante en la alimentación humana. Existen almidones de lenta digestión o resistentes que se han asociado a efectos benéficos en la salud humana y animal. Esta macromolécula es tan versátil que tiene diversas aplicaciones en la industria alimentaria ya sea en la producción de alimentos o como aditivo (espesante, espumante, hidrocoloide); además puede utilizarse en la industria textil por sus propiedades termoplásticas. Por ello, esta molécula es una fuente de energía fundamental y también representa una plataforma versátil para la obtención de diversos productos.
Implication of metal complexes in biology and medicine, the system cadmium (II)/iron (II)/zinc (II) – sarcosine
Authors:   Department of Chemistry, Faculty of Natural Sciences, University of Guyana, P.O. Box 101110 Georgetown, Guyana, Bhushan Tewari Brij
Published: April 2024

Implication of metal complexes in biology and medicin, the system cadmium (II)/iron (II) / zinc (II) – sarcosine. A technique involving the use of paper Electrophoretic Technique is described for the study of equilibria in binary complex system in solution. This methodis based upon the migration of a spot of metal ion on a paper strip at different pH values of background electrolyte containing 0.1 mol L -1 perchloric acid and 0.01 mol L -1 sarcosine. A graph of pH against mobility gives information about the binary complexes and permit to calculate their stability constants. The first and second stability constants of [Cd (II) – sarcosine], [Fe (II) – sarcosine] and [Zn (II) – sarcosine] complexes were found to be (3.94 ± 0.03; 2.66 ± 0.07), (3.70 ± 0.01; 2.54 ± 0.03) and (4.39 ± 0.01; 2.74 ± 0.07) logarithm stability constant values for Cd (II), Fe (II) and Zn (II) complexes, respectively at ionic strength 0.1 Mol L -1 and temperature of 35 0 C. The stability constants follow the order: Cd (II) > Fe (II) > Zn (II).
Synthesis of lithium and silver nanoparticles by green chemistry and its antimicrobial effect on Staphylococcus aureus and Klebsiella pneumoniae
Authors:   Laboratory of Heavy Metals and Organic Solvents, Biomedical Research Institute BIOMED, University of Carabobo, Carabobo, Venezuela, ucarabobo.oficial@gmail.com, Pacheco Coello Franklin Jesus
Published: April 2024

Synthesis of lithium and silver nanoparticles by green chemistry and its antimicrobial effect on Staphylococcus aureus and Klebsiella pneumoniae. The synthesis of NPs (nanoparticles) using lithium and silver salts and its characterization by scanning electron microscopy (SEM) was carried out. The effect of silver (AgNPs) and Lithium (LiNPs) nanoparticles (NPs) on the proliferation of Staphylococcus aureus and Klebsiella pneumoniae, was evaluated. For all concentrations evaluated, inhibition of bacterial growth was observed with a MIC close to 0.8 mg/mL-AgNPs and 0.6 mg/mL-LiNPs against S. aureus and 1 mg/mL- AgNPs and 0.6 mg/mL- LiNPs against K. pneumonia. Given the actual development of bacterial resistance to antibiotics, we contributed with an alternative for the future treatment of bacterial infections in humans and animals by evaluation of activities of synthetic metallic nanoparticles.
Brittle Asthma -What next in management?
Authors: Goyal Virendra Kr. , Gupta Atul , Singh Ayushya Pal , Soni Keyur Harshad
Published: April 2024

The term ‘brittle asthma’ was first used by Turner-Warwick (1977) to describe patients with asthma whose peak expiratory flow (PEF) varied ‘chaotically’ (follow no set pattern); which could lead to death from an a cute severe attack. Morbidity from brittle asthma is considerable, and we aim to identify causes and management strategies for such patients. Two types of Brittle asthma has been suggested; a feature of both types being a susceptibility to repeated severe attacks resulting in hospitalization & considerable morbidity and mortality. Data from asthma register (UK), suggests that the prevalence is the order of 0.05% of all asthmatics.
ENVIRONMENTAL CONSEQUENCES OF RUSSIAN AGGRESSION AGAINST UKRAINE IN 2022-2023 (APPLIED AND COMMUNICATIVE ASPECTS)
Authors: Aleynyk V. , Bilousov M. , Chernenko D. , Grygorieva L.
Published: April 2024

Abstract. The article raises the resonant issue of the environmental consequences of the aggressive military actions of the Russian Federation against Ukraine in 2022-2023, in particular in the context of the need to document them and provide sufficient coverage to attract the attention of specialized organizations dealing with environmental protection and the international community.
The authors provide concrete examples of the devastating impact on Ukrainian nature and ecosystem, including the shelling of residential and critical infrastructure, mining of a large part of the country's south and east, and contamination of soil and water bodies by shells. The article also attempts to predict how long it will take to eliminate the consequences of these impacts.
The authors also analyze the level of coverage of this issue in the global and Ukrainian media and communication and propose mechanisms for greater coverage of this range of problems, including the involvement of specialized environmental organizations, activists, and opinion leaders.
The article is an important contribution to the study of the environmental consequences of military conflicts and may be useful for researchers in both environmental and political sciences, public and government officials working in this field, as well as representatives of the political establishment.
Advances in Prostate Cancer Biomarkers and Probes
Authors: Li Keyi , Wang Qiao , Tang Xiaoying , Akakuru Ozioma Udochukwu , Li Ruobing , Wang Yan , Zhang Renran , Jiang Zhenqi , Yang Zhuo
Published: April 2024

Prostate cancer is one of the most prevalent malignant tumors in men worldwide, and early diagnosis is essential to improve patient survival. This review provides a comprehensive discussion of recent advances in prostate cancer biomarkers, including molecular, cellular, and exosomal biomarkers. The potential of various biomarkers such as gene fusions (TMPRSS2-ERG), noncoding RNAs (SNHG12), proteins (PSA, PSMA, AR), and circulating tumor cells (CTCs) in the diagnosis, prognosis, and targeted therapies of prostate cancer is emphasized. In addition, this review systematically explores how multi-omics data and artificial intelligence technologies can be used for biomarker discovery and personalized medicine applications. In addition, this review provides insights into the development of specific probes, including fluorescent, electrochemical, and radionuclide probes, for sensitive and accurate detection of prostate cancer biomarkers. In conclusion, this review provides a comprehensive overview of the status and future directions of prostate cancer biomarker research, emphasizing the potential for precision diagnosis and targeted therapy.
ELEMENTY KULTURY ORGANIZACYJNEJ JAKO DETERMINANTY KSZTAŁTUJĄCE WSPÓŁCZESNE UCZELNIE WYŻSZE
Authors: Ziemkiewicz-Gawlik Ilona , Miętkiewicz Monika , Brzychcy Katarzyna , Jaroszewski Karol
Published: April 2024

We współczesnym społeczeństwie od osób dorosłych na zajmowanych stanowiskach pracy wymaga się wiedzy praktycznej oraz podbudowy teoretycznej, którą studiujący zdobywają w ramach procesu kształcenia. Oferta studiów w Polsce jest obecnie bardzo bogata. Chcący uzyskać wykształcenie mogą wybierać w ofercie zarówno w uczelniach publicznych, jak i niepublicznych, oferujących studia w systemie stacjonarnym, niestacjonarnym, wieczorowym, a także hybrydowym (połączenie studiów tradycyjnych z kształceniem na odległość) lub on-line (kształcenie na odległość). Należy zwrócić uwagę na fakt, iż odpowiednia jakość kształcenia jest możliwa przede wszystkim dzięki wykwalifikowanej i zaangażowanej w ten proces kadrze. Z tego obszaru wywodzą się podstawowe procesy poznawcze opisane przez autorów, takie jak: kształcenie, doskonalenie i dokształcanie. W niniejszym artykule autorzy skupili się na czynnikach wewnętrznych, kształtujących kulturę organizacyjną uczelni, mających wpływ na jakość kształcenia oraz cały system z tym związany. Prowadzone badania dotyczyły wybranych czynników wewnętrznych kształtujących kulturę organizacyjną w uczelniach publicznych i niepublicznych o profilu praktycznym, a następnie przedstawiono rekomendację zmian w badanych obszarach.
‘‘Aggiornamento’ dalam Visi Sinodal untuk Merespon Tantangan Gereja pada Millenium Ketiga
Authors: Situmorang Markus
Published: April 2024

Gereja tidak pernah berjalan sendirian tetapi Roh Kudus selalu membimbing perjalanannya. Tantangan dan hambatan yang dihadapinya tidak pernah membatalkan tugas perutusan yang telah diterima dari Yesus. Janji Yesus untuk menyertai Gereja-Nya termanifestasi dalam sejarah baik lewat tokoh-tokoh yang dipilih-Nya maupun Roh-Nya yang menerangi hati dan pikiran umat-Nya. Tujuan penelitian ini untuk melihat semangat ‘aggiornamento’ dalam konteks gereja sinodal. Semangat ‘aggiornamento’ dalam Vatikan II dihidupkan kembali lewat seruan untuk berjalan bersama. Penelitian ini menemukan bahwa terdapat kontinuitas dari Vatikan II dan eklesiologi sinodal dalam roh ‘aggiornamento’. Metode yang digunakan dalam penelitian ini adalah metode kualitatif dengan tinjauan Pustaka. Peneliti mengkaji dan menganalisa visi Fransiskus tentang sinodalitas yang tertuang dalam berbagai kesempatan, dokumen Vatikan II dan pemikiran para teolog. Roh ‘aggiornamento’ yang bertiup kencang dalam Vatikan II terus berhembus hingga dewasa ini untuk membarui hidup Gereja baik ke dalam maupun ke luar agar karya keselamatan hadir bagi dunia.