bluuebunny/crossref_metadata_2025 huggingface dataset:
Determination of the maximum flow of the river with anthropogenic factors
Authors: Masharif Bakiev , Umidbek Masharifov , Murod Mukhammedov , Aziz Yavov , Sarvinoz Kayumova
Published: August 2023

This study was aimed to determine the anthropogenic and natural factors affecting hydrological regime of rivers, reliability and safety of hydroelectric facilities. The work of the Uchkurgan hydroelectric complex before and after the commissioning of the Toktogul reservoir with a hydroelectric power station is considered. The observations were conducted over a period of 92 years. A significant decrease in the unevenness of the average long-term water flow has been established for various periods. A homogeneity graph was built, which showed the periods of influence of anthropogenic factors in 1975 and 1990. The following scenarios for determining the maximum water discharges were considered: local water consumption regime, commissioning of the Toktogul reservoir, and energy mode. The estimated verification flow of 0.01% security satisfies the design capacity of the hydroelectric complex of 2720 m3/s..
Improvement of damless water intake methods taking into account the hydraulic and sediment regimes of the river
Authors: Bazarov Dilshod , Norkulov Bekhzod , Jamolov Farhod , Islamov Karim , Nazarova Shokhida , Shodiev Bobur
Published: August 2023

This article examines the movement of suspended and bottom sediments in a stream, which is considered as a complex problem in the process of water intake from rivers and canals for the needs of energy or the national economy with damless water intakes. The article presents the results of analyzes of experimental and field studies conducted by the authors of the existing problems in assessing the intensity of channel processes in the territory of the damless water intake of the Amudarya River. As well as the development of hydraulic regimes and flow regimes of sediment flow for the proposed structures based on experimental and field studies for the proposed structures is the main goal of research work. In the course of experimental studies, measurements of depth, flow and, on this basis, changes in water flow were made, which were initially elements of flow in straight and curved channels.
FPGA Implementation of Enhanced Montgomery Modular for Fast Multiplication
Authors: Jain Akanksha , khtri Rajesh , Bansod PP
Published: August 2023

This Paper proposed an enhanced Montgomery and efficient implementation of Modular Multiplication. Cryptographyoprocess is usedufor providingphigh informationmsecurity when a data is transferredmfrom transmitter to receiver. Various using methods like RSA, ECC, the Digital Signature Algorithm. The propose Montgomery algorithm usin RSA algorithim of cryptography is implemented in two different input both the inputs are 8 bit input. Coding have been done in Verilog language and the results are simulated on Vivado Software. For physical testing, we have used an FPGA NESYS 4 DDR hardware board that have Artix-7 FPGA chip on it produced by digilent company. The propose method shows good results in term of the number of slice flip flop, LUTs, and number of IOBs and power consumption. The proposed method shows better results as compare to other previous methods in term of different result parameters.
Articulated Robot Arm for Garbage Disposal in Hospital Environment
Authors: Jain Shreyansh Kumar , Monish Mittapalli , Gupta Neeraj , Raj Shivam Kumar , Subramanian Karpagavalli
Published: August 2023

The use of robotic arms is crucial in the medical industry, particularly in hospital settings. It can be used for a wide range of things, including as an aide in the operating room or for the removal of medical waste, among many other things. In this work, the robotic arm is designed to segregate medical waste as hazardous or non-hazardous. A dataset with five classes was created because there was no readily available data set for medical waste. The primary challenge in doing so is to programme the robotic arm’s movements and train the image dataset to classify objects as hazardous or non-hazardous. The 3D-printed robotic arm model has 6-Degree of Freedom(DOF) and is coupled with MG996R and SG90 servo motors. The robotic arm that is attached to an Arduino uno board is operated by the Blynk IoT platform. It uses YOLO V5 (You Only Look Once) algorithm to detect objects, and it favours intersection over union (IOU). To demonstrate, static robotic arm model was placed near the pile of medical waste to identify the waste and segregate it accordingly.
An Improved Music Recommendation System for Facial Recognition and Mood Detection
Authors: S Shakthi Sri , S Sathya , T Pandiarajan
Published: August 2023

A user can choose the song using a number of strategies. This system finds the user’s mood and recommends music in accordance with that mood. The main objective is to accurately assess the user’s mood in a friendly way. Listeners are introduced to a machine learning application that plays song based on the user’s mood. Finding the user’s expression using Artificial Neural Network model, which expresses their emotions, can help with this. Compared to speaking, facial expressions reveal a lot more about a person. Users are finding it challenging to select the song that they want to listen to due to the daily increase in the number of songs being created. In order to address, our recommendation system gives users a selection of songs that are automatically added to their customized playlist based on the user’s mood.
Resource Centric Analysis of RSA and ECC Algorithms on FPGA
Authors: Sudarshan Deeksha , Khandelwal Chirag , B M Linge Gowda , Bijjaragi Kiran Kumar , S S Rekha
Published: August 2023

The electronics industry’s shadow side is counterfeiting, and the doom is growing. Almost every business in the supply chain is impacted by the issue, including component suppliers, distributors, Electronics Manufacturing Services (EMS) providers, Original Design Manufacturers (ODMs), Original Equipment Manufacturers (OEMs), and their clients. In fact, any electronics firm that wishes to benefit from the cheap costs associated with globalization must be aware that someone along the supply chain may be persuaded to acquire fake items and sell them as genuine. A thorough grasp of chip designs, including partitioning and prioritizing data transit and storage, as well as a range of obfuscation techniques and activity monitoring, is necessary to reduce the danger of future hardware breaches. To battle this problem, we need to enforce various security measures at different levels of the supply chain. The recent methods include implementing cryptographic ciphers into the devices. The commonly used ciphers are the hard ciphers. But owing to the advancements and increase in the number of low power and resource constrained devices, there has been a dire need to design ciphers that support such devices. This paper talks about the advantages of lightweight ciphers, aiming to secure low power devices and other embedded devices. This work mainly compares two algorithms, RSA(hard cipher) and ECC(light cipher) in terms of their device utilization and power consumption on a Kintex-7. The presented results are justified from simulations performed on the Vivado design suite.
Categorical Embeddings for Tabular Data using PyTorch
Authors: Khedkar Sanskruti , Lambor Shilpa , Narule Yogita , Berad Prathamesh
Published: August 2023

Deep learning has received much attention for computer vision and natural language processing, but less for tabular data, which is the most prevalent type of data used in industry. Embeddings offer a solution by representing categorical variables as continuous vectors in lowdimensional space. PyTorch provides excellent support for GPU acceleration and pre-built functions and modules, making it easier to work with embeddings and categorical variables. In this research paper, we apply a feedforward neural network model in PyTorch to a multiclass classification problem using the Shelter Animal Outcome dataset. We calculate the probability of an animal's outcome belonging to each of the 5 categories. Additionally, we explore feature importance using two common techniques: MDI and permutation. Understanding feature importance is crucial for building better models, improving performance, and interpreting and communicating results. Our findings demonstrate the usefulness of embeddings and PyTorch for deep learning with tabular data and highlight the importance of feature selection for building effective machine learning models.
Crop Yield Prediction Using Improved Random Forest
Authors: T. Padma , Sinha Dipali
Published: August 2023

Agriculture has an important role in India’s economic development. Crop productivity is affected by the rising population and the country’s ever-changing climate. Crop yield estimation is a challenge in the farming sector. Numerous studies have been conducted in the agricultural sector to better estimate crop yield through machine learning techniques. It is an efficient method for anticipating crop yields and determining which crops to cultivate. Random Forest has been widely utilized for this purpose. A set of parameters in the Random Forest classifier must be stay tuned. The machine learning algorithm will yield better results with correct hyper parameter adjustment. This work presents a hybrid approach to agricultural yield estimation using a Random Forest classifier and the Random Search method with a 0.99 R2 score, 0.045 MSE, and 0.022 MAE, the suggested method outperformed other existing approaches such as Decision Tree (DT), Multiple Linear Regression (MLR), Random Forest (RF), and Grid Search (GS) optimized RF. Validation methods such as R2, Mean Squared Error, and Mean Absolute Error to cross-validation have been used to confirm the authenticity of the outcomes. The purpose of this study is to apply the crop yield prediction approach into action to assist farmers in solving agricultural production concerns.
A Comparative Study of Deep Learning and Traditional Methods for Environmental Remote Sensing
Authors: Farooq Bazila , Manocha Ankush
Published: August 2023

Because of the accessibility of massive data from remote sensing data and developments in ML, machine learning (ML) techniques have been extensively applied in environmental remote sensing research. Modern machine learning (ML) frameworks like deep learning (DL) have significantly outperformed older models in terms of performance. This study focuses on the software that uses a traditional neural network (NN) as well as Deep Learning (DL) approaches in environmental remote sensing, which also covers land cover mapping, retrieval of environmental parameters, data fusion, image compression, and information reconstruction and prediction. It is also explained how DL may be used to monitor other aspects of the ecosystem, including the environment, water management, ground and air surface temperatures, transpiration, ultraviolet (UV) rays, and sea color all factors to consider. Following that, the essay explores the challenges and prospective uses of DL in environmental remote sensing.
Colored MRI biomedical image tumor classification and segmentation based on transfer learning of modified Y-Net
Authors: Nafeaa Khamis Nassr , Saadi Mustaf Rahma
Published: August 2023

By incorporating the colored MRI identification synthesis into the MRI segmentation model with transfer learning AI Y-Net, this study clearly shows the high potential of a multidisciplinary system-level study for diagnoses. This way, such a system can provide integrity of the goal without compromising the quality of each one and saving time consumption. Another alternative to such integration is to be used for enhancement and segmentation that is accurate and robust to the variabilities in scanner and acquisition protocols. System Level Simulator is the deep learning based on Kearse AI deep learning network specified to Y-VGG16 net results of outstanding performance in medical image segmentation. Based on the literature, there are different AI models for the diagnosis system, which are different of what is proposed in this paper. A partial-frozen network is applied to the U-net to compare results between different fine-tuning FT strategies. The network operation is also evaluated depending on the dataset size, showing the importance of the combination of dataset, TL and data augmentation (DA). Transfer learning (TL) helps us for MRI medical image segmentation deep learning with more accurate performances of the TL technique. The system hybrid the Y-Net architecture with Transfer learning to reduce the domain-shift effect in brain MRI segmentation results of the automated deep learning segmentation approach.
Comparative Analysis Between Different Lung Segmentation Techniques
Authors: Bhosle Sanket , Kanade Dnyaneshwar , Bhosale Shourya S. , Bhosale Vaibhavi R. , Bhople Rutuja S. , Bhegade Sakshi B.
Published: August 2023

For the purpose of identifying various lung illnesses, computed tomography (CT) pictures of the lung must be segmented. The most significant aspect of medical imaging is image segmentation. Via an automated process, the ROI (region of interest) is extracted. The process of segmentation separates an image into sections according to a particular interest, such as segmenting human organs or tissue. Several medical disorders can benefit from the segmented image of the lung. We specifically compared and analysed various threshold segmentation algorithms in this paper in an effort to determine which one would be the best to use moving forward with image processing. We have used Computed Tomography (CT) images of Lungs with Tuberculosis (TB) dataset from Kaggle for image processing and compared them with finely masked CT images to infer the best Threshold algorithm. We have decided to do the analysis on Threshold algorithms named as Binary Threshold, Otsu’s Threshold, and Adaptive Threshold. Comparison has been done based on performance parameters such as Accuracy, Precision, Recall Value, f1-score, etc. The results are also represented in Graphical format for better understanding of performed comparison study.
Brain tumor segmentation using UNet-few shot schematic segmentation
Authors: L K Pavithra , Paramanandham Nirmala , Sharan Tanya , Kumar Sarkar Ronit , Gupta Samraj
Published: August 2023

Early finding and determination of a proper therapy technique will build the endurance of people with cancer. A key step in the diagnosis and treatment of brain tumors is accurate and reliable segmentation. Given its uneven shape and opaque borders, gliomas are among the most difficult brain cancers to detect. Because of significant differences in their design, programmed division of glioma brain growths is a fluid topic. Improved UNet-based designs for the automatic segmentation of brain tumors from MRI images are reported in this article. Training semantic division models requires an enormous measure of finely clarified information, making it challenging to quickly acclimatize to unfamiliar classes that don’t meet this requirement. The original Few Shot Segmentation attempts to address this issue but has other flaws. Hence in this paper a generalized Few-Shot Schematic Segmentation is discussed to break down the speculation capacity of at the same time sectioning the original classifications with the base classes and adequate models. A Context-Aware Prototype Learning (CAPL) which is used for improving the performance by utilizing the co-occurrence of earlier information from help tests and progressively enhancing logical data to the classifier, molded on the substance of each question picture. Results reveal the outperformance of the developed model.
Aasna: Kinematic Yoga Posture Detection And Correction System Using CNN
Authors: Jadhav Ranjana , Ligde Vaidehi , Malpani Rushikesh , Mane Phinehas , Borkar Soham
Published: August 2023

Yoga is a very popular form of exercise that originated in India that has numerous benefits for the mind and body. According to recent statistics, there are over 300 million yoga practitioners worldwide, with the number of yoga instructors increasing annually. However, incorrect yoga postures can lead to injuries and health complications. This highlights the importance of correct yoga posture and the need for a system that can detect and correct improper poses. This abstract presents a yoga posture detection and correction system designed using OpenCV for computer vision, and kinematic representation of the human body considering 17 points mapped on the human body, utilizing the tf-pose estimation algorithm for precise pose estimation. The system also includes a convolutional neural network (CNN) model developed using the Keras API and trained on the TensorFlow platform's MoveNet architecture for handling training of the model. The MoveNet pose estimation module has been used to detect keypoints of the human body which achieved an accuracy of 99.88%. The system works by live capturing of the yoga practitioner using a camera, extracting the key features of the pose, and comparing them with a trained data model of known yoga poses. If the pose is incorrect, the system provides real-time feedback to correct the pose.
Improved Intrusion Detection System That Uses Machine Learning Techniques to Proactively Defend DDoS Attack
Authors: T Rajendran , E Abishekraj , U Dhanush
Published: August 2023

This abstract aims to provide a comprehensive analysis of the intricacies of DDoS attacks, which are increasingly prevalent and malicious cyber-attacks that disrupt the normal flow of traffic to a targeted server by exponentially increasing network traffic. To secure distributed systems against DDoS attacks, intrusion detection mechanisms and machine learning techniques are commonly employed. The CICDDoS2019 dataset is often utilized for the detection and prevention of these attacks. The dataset undergoes pre-processing and is split into training and test datasets. Machine learning techniques are then utilized to predict and classify the attacks using the test dataset. The protocols which are examined during the attack are SNMP, NTP, UDP, and DNS. The accuracy is obtained by comparing the predicted results with the training dataset. Machine learning algorithms such as K- Nearest Neighbor(K-NN)-96.49%, Support Vector Machine (SVM)-79.61%, Random Forest (RF)-99.10%, and Gaussian Naïve Bayes (GNB)-78.75% have been found to produce high levels of accuracy for attack classification.
Adoption of Novel Technologies to Boost Precision Agriculture (BPA) using Internet of Things (IOT)
Authors: Anbumozhi Anna , A Shanthini
Published: August 2023

Precision Farming (PF) or Precision Agriculture (PA) is a management of farming that uses modern technology to ensure that the soil gets what it needs to remain crop healthy for producing good productivity. Precision agriculture uses various ICT technologies such as Sensors, Actuators, GIS software, GPS, Variable Rate Technology, Robotics, Drone and other aerial imagery. Agriculture is important to our country’s economic structure since it provides food, raw resources, and job opportunities to a big portion of the people. Previously farmers use the traditional approaches to cultivate the crops like without use of technological advancement for cultivating the crops, that reduces the farmers yield and profit. Now a day the farmers are coming forward to convert their farms from conventional approach to smart approach by using modern technologies. By using modern technological equipments, the farmers can monitor the crop cultivation and reduce the wastage of excess amount of input to the farms. With the help of drones we can know the status of the growth and condition of the crop which is cultivated in the farm filed, it reduces the human interventions. This paper discusses about the modern technologies based on Precision methodology, components involves in IOT with sensors, field in which the Precision farming is applied, where the Internet of Things used by precision agriculture and difficulties faced by farmers.
Optimal path finding in 3D environment: Application for the exhibition space through 3D visualization and BIM
Authors: Song Yimin
Published: August 2023

The application of 3D visualization technology in building construction has also increased. The study used Revit software to construct a 3D building information model (BIM) for the exhibition space of Chuzhou Higher Education City Development Collaborative Innovation Center to achieve a 3D visualization display; based on the 3D visualization, a particle swarm optimization (PSO) algorithm was used to find the optimal path for the exhibition space, so as to achieve the layout design of the exhibition space. The PSO algorithm was optimized in terms of inertia weight, acceleration coefficient, and initial population to obtain the improved PSO (IPSO) algorithm. The experimental results showed that the optimal path found by the IPSO algorithm was 78.56 meters in distance, 98.2 seconds in time consumption, and 50.11% in smoothness, which were better than the other two algorithms. Meanwhile, the IPSO algorithm had a lower value of particle fitness function, indicating that the IPSO algorithm had the highest performance and the strongest path finding ability among the three algorithms. It is confirmed that it is feasible to use the IPSO algorithm for optimal visit path finding in 3D environment. It is effective to visualize the exhibition space in 3D by constructing a BIM.
Cancer Survivors – wie müssen diese Patienten kardiologisch angebunden werden?
Authors: Evertz Ruben , Braulke Friederike , El Shafie Rami A. , von Haehling Stephan
Published: August 2023

Die kardioonkologische Nachsorge von Patienten mit stattgehabter oder anhaltender Tumortherapie umfasst sowohl kardial asymptomatische Patienten, die aufgrund der tumorspezifischen Therapie
ein erhöhtes Risiko besitzen, kardiovaskuläre Ereignisse zu erleiden, als auch Patienten, die bereits manifeste kardiovaskuläre Erkrankungen unter der tumorspezifischen Therapie erlebt
haben. Bei asymptomatischen Patienten mit stattgehabter Anthrazyklin- und/oder mediastinaler Strahlentherapie richten sich der Zeitpunkt der kardiologischen Erstkonsultation sowie das
Intervall der kardioonkologischen Nachbetreuung nach dem individuellen Patientenrisiko und der erhaltenen Bestrahlungs- und Anthrazyklindosis. Die frühe Identifikation von Risikopatienten
und die Erkennung von Spätfolgen einer Krebserkrankung und/oder -therapie stellen interdisziplinäre Herausforderungen dar. Die optimale Therapie und Nachsorge von Cancer Survivors mit
kardiovaskulärem Risiko bedarf einer engen Absprache und Zusammenarbeit aller behandelnden onkologischen und kardiologischen Spezialisten.
Sepsis geht alle an!
Authors: Hecker Ruth
Published: August 2023

Sepsis gilt weltweit als eine der am häufigsten auftretenden Erkrankungen und Todesursachen. Mindestens 85 000 Todesfälle treten allein in Deutschland auf und doppelt so viele Menschen versterben an Sepsis im Krankenhaus wie an einem Schlaganfall und Herzinfarkt gemeinsam. Das Tückische an der lebensbedrohlichen Erkrankung: Sepsis kann in jedem Lebensalter (vom Säugling bis ins hohe Alter) und in jeder Lebenssituation entstehen. Dieser Artikel gibt einen Überblick zu Entstehung und Symptomatik von Sepsis bei Erwachsenen und geht im Besonderen auf Sepsis bei Kindern und Neugeborenen ein.
„Ich will so eine!“ – Implementierung einer APN
Authors: Riewoldt Christina , Wiedemann Regina
Published: August 2023

In deutschen Kliniken werden Advanced Practice Nurses bis heute kaum eingesetzt – dabei gibt es dieses Konzept schon viele Jahre, und Länder wie die Schweiz agieren damit sehr erfolgreich. Der folgende Beitrag stellt das Berufsmodell einer APN vor und beschreibt ihren Einsatz in einer pädiatrisch onkologischen und hämatologischen Abteilung. Und er geht der Frage nach, warum sich Deutschland bei der Umsetzung von APN-Modellen immer noch so schwertut.
Monoklonale Antikörper zur Prävention der Malaria
Authors: Piepel Christiane , Ramharter Michael
Published: August 2023

Die Malaria stellt trotz verschiedener Präventionsmöglichkeiten weiter eine große medizinische Herausforderung dar. Monoklonale Antikörper sind in vielen medizinischen Fachbereichen inzwischen fester Bestandteil von Therapiestrategien – für ihren Einsatz in der Malariaprävention schreitet die Forschung nun erfreulicherweise ebenso voran. In einer Phase-II-Studie zum monoklonalen Antikörper CIS43LS beschreiben Forscher aus Mali und den USA Daten zur Sicherheit und Effektivität gegen Neuinfektionen mit dem Malariaerreger Plasmodium falciparum.
Moderation of Physical Activity between Cardiometabolic Risk and
Adiponectin in Adolescents
Authors: Tadiotto Maiara Cristina , Corazza Patricia Ribeiro Paes , Menezes Junior Francisco Jose de , Moraes Junior Frederico Bento de , Lopes Maria de Fátima Aguiar , Tozo Tatiana Aparecida Affornali , Purim Kátia Sheylla Malta , Mota Jorge , Leite Neiva
Published: August 2023

The aims of the study were to examine the moderating role of physical activity in
the relationship between cardiometabolic risk factors and adiponectin
concentration in adolescents. This is a cross-sectional study conducted with 96
adolescents of both sexes, between 11 and 17 years old. Body mass, height, fat
mass (FM), fat-free mass, systolic blood pressure, diastolic blood pressure,
high density lipoprotein, low density lipoprotein (LDL-c), triglycerides,
insulin, adiponectin, C-reactive protein, and level of physical activity (energy
expenditure questionnaire) were measured. Body mass index (BMI), triponderal
mass index (TMI), homeostasis model to assessment insulin resistance (HOMA-IR),
and quantitative insulin sensitivity check index (QUICKI) were calculated.
Macro-PROCESS for SPSS was used for moderation analyses. Direct interactions
were found for BMI, TMI, FM, insulin, and HOMA-IR and inverse for LDL-c, and
QUICKI. Protection against cardiometabolic risk was found when the PA-coeff was
completed above 1.57 coeff (BMI), 1.62 coeff (TMI), 1.55 coeff (FM), 1.41 coeff
(LDL-c)1.60 coeff (insulin), 1.59 coeff (HOMA-IR) and 1.35 coeff (QUICKI). We
conclude that physical activity was a moderator in the relationship with
adiposity, insulin resistance and sensitivity, LDL-c, and adiponectin. In this
context, we evidenced a relevant clinical impact on the health of adolescents,
demonstrating the interaction between anthropometrics variables and physical
activity.
Current Research on Vitamin D Supplementation against Sarcopenia: A
Review of Clinical Trials
Authors: Kressel Heidi , Matsakas Antonios
Published: August 2023

Vitamin D plays an important role in skeletal muscle function and metabolism. The
aim of this review was A) to discuss the clinical evidence of vitamin D
supplementation either alone or combined with other strategies in the prevention
of sarcopenia in non-sarcopenic individuals and B) to critically discuss the
clinical evidence on the effect of vitamin D combined with other strategies on
muscle strength, mass and function in sarcopenic individuals without vitamin D
deficiency. Sparse clinical data on non-sarcopenic individuals indicate that
vitamin D alone has a subtle beneficial effect on knee extensor strength at
doses 880–1600 IU/day without improving handgrip strength or
muscle mass. When co-administered with other supplements such as protein, mixed
effects appear to prevent the decline of muscle mass, possibly delaying the
onset of sarcopenia in non-sarcopenic individuals, at doses of 800–1,000
IU/day over 6–12 weeks. In sarcopenic individuals, vitamin D
100–1,000 IU/day co-supplementation with protein results in
increased handgrip strength between 9.8–40.5%. However, there is
no strong clinical evidence that vitamin D dosage correlates with changes in
muscle strength or mass. Potential sources of discrepancy among studies are
discussed. Future studies with appropriate experimental design are essential to
dissect the net effect of vitamin D on sarcopenia.
Thyroid-Related Hormones Changes Predict Changes in Anthropometric
Measures and Incidence of Obesity in Chinese Euthyroid Persons
Authors: Deng Ling , Zheng Xiaoxia , Shuai Ping , Yu Xijie
Published: August 2023

The purpose of this study was to investigate the correlation of thyroid-related
hormones changes within the reference range with the changes in anthropometric
measures and incidence of obesity. The study included 4850 subjects with normal
thyroid-related hormones at baseline and at follow-up. We evaluated the
relationship of changes in thyroid-related biomarkers with anthropometric
measures changes and incidence of obesity. In euthyroid persons, changes in
serum thyroid stimulating hormone (TSH), free triiodothyronine (FT3), and free
thyroxine (FT4) concentrations and FT3/FT4 ratio were independent
predictors of changes in body mass index (BMI) and waist circumference (WC) in
men, changes in serum FT3 and FT4 concentrations and FT3/FT4 ratio were
independent predictors of changes in BMI and WC in women. Every single unit
increment in ΔFT3/FT4 was accompanied by a 7.144 and 7.572 times
risk of having obesity in men and women, respectively. Every single unit
decrement in ΔFT4 was accompanied by a 21.0% and 26.9%
lower risk of having obesity in men and women, respectively. In conclusion, in
euthyroid individuals, changes in thyroid-related hormones were associated with
anthropometric measures changes and incidence of obesity.
Cosmetic Treatments with Energy-Based Devices in Skin of Color
Authors: Chao Janet Ren , Porter Jennifer Parker , Hessler Jill
Published: August 2023

The use of energy-based devices in cosmetic treatments for patients with higher Fitzpatrick skin types is a topic of concern due to the increased risk of complications such as hyperpigmentation and scarring. Advancements in technology have enabled the development of safe and effective energy-based modalities for improving skin tone, texture, and signs of aging in patients with higher Fitzpatrick skin types. This article reviews various modalities that have shown positive outcomes in Fitzpatrick skin types 4 to 6.

Radiofrequency (RF) skin tightening devices have emerged as a noninvasive method for improving skin laxity by stimulating collagen and elastin production without generating excessive heat, making them suitable for darker skin tones. Microfocused ultrasound (MFUS) is another noninvasive option for skin tightening and lifting. By producing thermal coagulation in the deeper layers of the skin, MFUS stimulates collagen production, resulting in improved skin tightness. Fractional laser resurfacing, both ablative and nonablative, offers effective treatment options for various concerns such as acne scars, skin texture, and dyspigmentation. Nonablative fractional lasers, which target water rather than melanin, are particularly safer for darker skin types. Broadband light devices that emit different wavelengths have shown success in skin rejuvenation, improving wrinkles, pore size, erythema, telangiectasias, and dyspigmentation. Additionally, RF microneedling combines the benefits of both modalities, delivering safe treatment for skin laxity and textural concerns. The short-pulse neodymium-doped yttrium aluminum garnet (Nd:YAG) laser has become a transformative tool for patients with Fitzpatrick skin types 4 to 6. This laser targets melanin, hemoglobin, and water, providing improvements in hyperpigmentation, acne, fine lines, and scar texture. Combining the short-pulse Nd:YAG laser with other treatments like chemical peels or microneedling has shown synergistic effects and enhanced outcomes. Understanding these modalities and tailoring treatments to specific concerns is crucial for safe and effective solutions in patients with higher Fitzpatrick skin types.
Fisetin's Promising Antitumor Effects: Uncovering Mechanisms and Targeting for Future Therapies
Authors: Qaed Eskandar , Al-Hamyari Bandar , Al-Maamari Ahmed , Qaid Abdullah , Alademy Haneen , Almoiliqy Marwan , Munyemana Jean Claude , Al-Nusaif Murad , Alafifi Jameel , Alyafeai Eman , Safi Mohammed , Geng Zhaohong , Tang Zeyao , Ma Xiaodong
Published: August 2023

Background Cancer remains a critical global health challenge and a leading cause of mortality. Flavonoids found in fruits and vegetables have gained attention for their potential anti-cancer properties. Fisetin, abundantly present in strawberries, apples, onions, and other plant sources, has emerged as a promising candidate for cancer prevention. Epidemiological studies linking a diet rich in these foods to lower cancer risk have sparked extensive research on fisetin’s efficacy.

Objective This review aims to comprehensively explore the molecular mechanisms of fisetin's anticancer properties and investigate its potential synergistic effects with other anticancer drugs. Furthermore, the review examines the therapeutic and preventive effects of fisetin against various cancers.

Methods A systematic analysis of the available scientific literature was conducted, including research articles, clinical trials, and review papers related to fisetin’s anticancer properties. Reputable databases were searched, and selected studies were critically evaluated to extract essential information on fisetin’s mechanisms of action and its interactions with other anticancer drugs.

Results Preclinical trials have demonstrated that fisetin inhibits cancer cell growth through mechanisms such as cell cycle alteration, induction of apoptosis, and activation of the autophagy signaling pathway. Additionally, fisetin reduces reactive oxygen species levels, contributing to its overall anticancer potential. Investigation of its synergistic effects with other anticancer drugs suggests potential for combination therapies.

Conclusion Fisetin, a bioactive flavonoid abundant in fruits and vegetables, exhibits promising anticancer properties through multiple mechanisms of action. Preclinical trials provide a foundation for further exploration in human clinical trials. Understanding fisetin’s molecular mechanisms is vital for developing novel, safe, and effective cancer prevention and treatment strategies. The potential synergy with other anticancer drugs opens new avenues for combination therapies, enhancing cancer management approaches and global health outcomes.
Best Practices and Educator Strategies for Facilitating a Flipped Classroom in Graduate Medical Education
Authors: Falck Alison , French Heather , Dadiz Rita , Gray Megan M. , Mendres-Smith Amber , Nolan Bret , Martin Kirsti , Vasquez Margarita M.
Published: August 2023

Objective Effective flipped classroom (FC) education fosters learner engagement, promoting higher-level cognitive skills. FC learning in graduate medical education (GME) has increased, but few educators have significant experience with FC facilitation. There are no evidence-based practices to support professional development of FC facilitation skills in GME. The objective of this study is to identify best practices for effective FC facilitation in GME.

Study Design We conducted a mixed-methods, cross-sectional study of faculty educators who participated in a randomized controlled trial (RCT) using FC for physiology education in neonatal–perinatal medicine. Educators completed a 25-question survey about effective strategies for FC facilitation. A subset of educators participated in interviews to share their FC facilitation experiences and strategies to maximize learner engagement. Quantitative survey data were analyzed with descriptive statistics. Qualitative survey and interview data were coded and analyzed inductively to identify themes.

Results Seventy-five educators completed the survey (75/136, 55% response rate), and 11 participated in semistructured interviews. While educators facilitated a median of two FC sessions (interquartile range: 1, 5) during the RCT, 43 (57%) had not received prior training in FC facilitation. Qualitative data analyses generated five themes that aligned with quantitative survey results: (1) educator preferences, (2) unique FC facilitation skills, (3) learning environment optimization, (4) subject matter expertise, and (5) learner behavior management. Sixty-two educators (83%) felt they were well prepared to lead FC sessions. Thirty-six educators (48%) reported that unprepared learners disrupt the learning environment, and the provision of clear expectations and adequate time to prepare for FCs improves learner preparation. Strategies to facilitate effective FC sessions included creating a safe learning environment and engaging learners in critical thinking.

Conclusion Educators highlighted faculty development needs, strategies, and actions to promote effective FC facilitation. Further exploration through learner interviews will provide additional evidence for the development of best practices and resources for FC facilitation.

Key Points
Risk Factors for Prolonged Intertwin Delivery Interval and Associated Maternal and Second Twin Morbidity
Authors: Justman Naphtali , Goldfreind Roee , Abu-Rass Hiba , Siegler Yoav , Shahak Gilad , Wolfovitz Amir , Ginsberg Yuval , Zipori Yaniv , Khatib Nizar , Weiner Zeev , Vitner Dana
Published: August 2023

Objective This work aimed to identify possible risk factors and the morbidity associated with prolonged intertwin delivery interval (IDI).

Study Design A retrospective cohort study at a single tertiary care center. Women with twin gestations who reached the second stage of labor between January 2010 and December 2019 were included in the study. Demographic and clinical characteristics were compared between short IDI (≤15 minutes) and prolonged IDI (>15 minutes). The primary outcome was the rate of 5-minute Apgar score ≤ 7.

Results A total of 461 women were included; 312 of whom were in the short IDI group and 149 were in the prolonged IDI group. Rates of 5-minute Apgar score ≤ 7 and neonatal acidemia were significantly higher in the prolonged IDI group (3.5 vs. 9.7%, p = 0.008; 4.3 vs. 15.7%, p = 0.01, respectively). Vaginal delivery was less likely to occur in the prolonged IDI group (75.8 vs. 93.3%). Placental abruption and hemoglobin drop ≥ 3 g/dL were more prevalent in the prolonged IDI group (4 vs. 1%, p = 0.03; 39.1 vs. 24.7%, p = 0.01, respectively). In the multivariate analysis, age ≥ 30 years (adjusted odds ratio [aOR]: 1.76, p = 0.01), nulliparity (aOR: 1.66, p = 0.03), and birth weight ratio ≥ 1.2 (aOR: 1.92, p < 0.05) were associated with prolonged IDI.

Conclusion Prolonged IDI is associated with an increased risk for neonatal acidemia and low 5-minute Apgar score, and with an increased rate of cesarean delivery, placental abruption, and hemoglobin drop ≥ 3 g/dL. Advanced maternal age, nulliparity, and twin birth weight ratio ≥ 1.2 are associated with prolonged IDI.

Key Points
Spectral stability of periodic waves for the Zakharov system
Authors: Hakkaev Sevdzhan , Stanislavova Milena , Stefanov Atanas G.
Published: August 2023

This paper is concerned with the stability of periodic traveling waves of dnoidal type, of the Zakharov system. This problem was considered in a study of Angulo and Brango [Nonlinearity 24, 2913 (2011)]. In particular, it was shown that under a technical condition on the perturbation, such waves are orbitally stable, with respect to perturbations of the same period. Our main result fills up the gap created by the aforementioned technical condition. More precisely, we show that for all natural values of the parameters, the periodic dnoidal waves are spectrally stable.
Radial dependence diagnosis of inductively coupled Ar plasma based upon optical emission spectroscopic measurement with spectral reflectance compensation
Authors: Yamashita Yuya , Doi Kenta , Kiyota Tetsuji , Asakawa Keiichiro , Hosoya Sotaro , Kikuchi Wataru , Nezu Atsushi , Akatsuka Hiroshi
Published: August 2023

This study proposed a simple method to evaluate the spectral reflectance of the inner wall of a vacuum chamber. A method for calculating spectral emission coefficients by taking the spectral reflectance of the chamber inner wall into account was proposed. Furthermore, plasma diagnosis based on optical emission spectroscopic (OES) measurement was performed so as to obtain radial dependence of electron temperature Te and density Ne of a radio frequency inductively coupled Argon (Ar) plasma by applying a collisional-radiative model to radially resolved emission spectra of the Ar plasma assuming axial symmetry. In addition, Langmuir probe measurement and electromagnetic simulation were performed and compared with the OES-based plasma diagnosis results. The spectral radiance compensation improved the diagnostic result by 0.6% and 3.1% for Te and Ne, respectively.
A gated-time-of-flight top-hat electrostatic analyzer for low energy ion measurements
Authors: Gershman Daniel J. , Avanov Levon A. , Collinson Glyn , Tucker Corey J. , Barrie Alexander , Chornay Dennis J. , Paschalidis Nikolaos P. , Rowland Douglas , Moore Thomas E.
Published: August 2023

When incorporated into a top-hat electrostatic analyzer, a gate electrode enables the separation of ions by their mass-per-charge with modest mass resolution (M/∆M ∼ 10). Gated-time-of-flight (TOF) instruments avoid the energy straggling and angular scattering effects prevalent in foil-based detection systems, providing more pristine measurements of three-dimensional distribution functions of incident ions. Gated-TOF implementations are ideal for measuring the properties of low-energy (i.e., <100 eV) thermal ions in various space environments. We present an instrument prototype capable of separating H+, He+, O+, and O2+ in Earth’s ionosphere and demonstrate that in addition to providing species determination, precise operation of the gate electrode provides an electronically adjustable geometric factor that can extend a single instrument’s dynamic range by several orders of magnitude.
Comment on: “Quasi-periodic and chaotic structure of Alfvén waves in a plasma containing double spectral distributed electrons” [Phys. Plasmas 29, 122103 (2022)]
Authors: Chatterjee Prasanta , Prasad Punam Kumari , Saha Asit
Published: August 2023

Recently, El-Labany et al. reported on “Quasi-periodic and chaotic structure of Alfvén waves in a plasma containing double spectral distributed electrons” [Phys. Plasmas 29, 122103 (2022)]. The paper contains some fallacies, which are designated in this Comment.
Absolute calibration of a streaked optical pyrometer at nanosecond time scale with a luminescent concentrator
Authors: Nourry-Martin M. , Denoeud A. , Chollet C. , Bonneau M. , Vinci T. , Ravasio A. , Brygoo S. , Darbon S. , Balembois F.
Published: August 2023

The Streaked Optical Pyrometer (SOP) is a visible diagnostic widely used to study the warm dense matter regime at high energy laser facilities, gas guns, or ion accelerators. It is usually coupled with a Velocity Interferometer System for Any Reflector (VISAR) diagnostic for simultaneous shock wave velocity, reflectivity, and temperature measurements to study the Equation of State (EOS) of materials. While VISAR is a well-mastered technology that provides velocity measurements with low relative uncertainties (close to percent), SOP diagnostics still suffer from high imprecision. In this article, we present a new calibration method in order to obtain absolute temperature measurements with reduced uncertainties. This approach is based on a novel light source: a Ce:YAG luminescent concentrator pumped by LEDs. This device produces enough optical power for calibration at the nanosecond sweep duration of the streak camera. As a demonstration, it has first been installed at the LULI facility and tested on quartz samples shocked at temperatures above 4000 K.
Characterization of 87Rb MEMS vapor cells for miniature atomic magnetometers
Authors: Jiang Minwei , Zhai Hao , Jiang Chunyu , Wang Jian , Chen Chen , Zhang Qi , Wu Dongmin , Zhang Baoshun , Zeng Zhongming , Lin Jie , Wang Yiqun , Jin Peng
Published: August 2023

Accurate characterization of atomic vapor cells is crucial for enhancing the sensitivity of miniature atomic magnetometers. In this study, a fast and efficient approach is developed to characterize relaxation properties of the inner surface of the 87Rb micro-electro-mechanical systems (MEMS) vapor cell. Based on the zero-field level crossing effect, the transverse relaxation rate was experimentally determined, and the depolarization coefficient of 0.097 for atom-wall collisions in the 87Rb MEMS vapor cell was obtained. The experiments demonstrated that the alkali-wall collision relaxation gradually dominates the depolarization of the 87Rb ensemble as the radius of the vapor cell decreases. This research provides a quantitative method to evaluate the effectiveness of the MEMS vapor cell anti-relaxation coatings and offers the design inspiration for MEMS atomic vapor cells from the perspective of relaxation characterization. It is beneficial for the development of weak magnetic field detections such as biomagnetism detection in ambient environments.
Absolute calibration up to 20 MeV of an online readout CMOS system suitable to detect high-power lasers accelerated protons
Authors: Burdonov K. , Lelièvre R. , Forestier-Colleoni P. , Ceccotti T. , Cuciuc M. , Lancia L. , Yao W. , Fuchs J.
Published: August 2023

We present the design and absolute calibration of a charged particle online readout CMOS system tailored for high-power laser experiments. This system equips a Thomson parabola spectrometer, which is used at the Apollon petawatt scale laser facility to measure the spectra of protons produced by high-intensity laser–target interactions. The RadEye1 CMOS matrices array detectors are paired with a custom triggering system for image grabbing. This allows us to register the proton and ion signals remotely. The repetition rate is presently 1 shot/min, but the frame grabbing enables the system to be compatible with modern high-power lasers running, e.g., at 1 Hz. We detail here the implementation, in the harsh electromagnetic environment of such interactions, of the system, and its absolute calibration, which was performed for proton energies from 4 to 20 MeV.
Effect of a guide field on the turbulence like properties of magnetic reconnection
Authors: Adhikari S. , Shay M. A. , Parashar T. N. , Matthaeus W. H. , Pyakurel P. S. , Stawarz J. E. , Eastwood J. P.
Published: August 2023

The effect of an external guide field on the turbulence-like properties of magnetic reconnection is studied using five different 2.5D kinetic particle-in-cell (PIC) simulations. The magnetic energy spectrum is found to exhibit a slope of approximately −5/3 in the inertial range, independent of the guide field. On the contrary, the electric field spectrum in the inertial range steepens more with the guide field and approaches a slope of −5/3. In addition, spectral analysis of the different terms of the generalized Ohm's law is performed and found to be consistent with PIC simulations of turbulence and MMS observations. Finally, the guide field effect on the energy transfer behavior is examined using the von Kármán–Howarth (vKH) equation based on incompressible Hall-MHD. The general characteristics of the vKH equation with constant rate of energy transfer in the inertial range are consistent in all the simulations. This suggests that the qualitative behavior of energy spectrum and energy transfer in reconnection are similar to that of turbulence, indicating that reconnection fundamentally involves an energy cascade.
Terahertz broadband near-perfect absorber with a single-layer coating on doped semiconductor
Authors: Shi Fenghua , Chen Yihang , Leung Chi-Wah
Published: August 2023

We demonstrate that a single-layer coating on a doped GaAs or Si substrate enables broadband antireflection and, hence, broadband perfect absorption in the terahertz frequency range. This broadband behavior can be generally expected when the substrate material has a Drude-type dispersion. Our mathematical analyses show that the reflection from Drude-type material may have an anomalously dispersive phase shift. The anomalous dispersion of the reflection phase is used to compensate for the normal dispersion of the accumulation phase in the single-layer coating film. Consequently, the antireflection conditions are satisfied in a wide frequency range, and broadband antireflection is achieved. Thus, broadband perfect absorption is realized with only a single-layer coating film on the substrate. Our method provides a simple and efficient approach to achieving broadband perfect absorption, which is critical in many applications such as radar stealth techniques and solar cells.