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Yeme tutumları ile beden algısı arasındaki ilişkide öz şefkat ve duygu düzenlemenin aracı rolünün sınanması
Get PDFYeme bozuklukları gün geçtikçe görülme sıklığı artan, komorbidite ve mortalite oranları
oldukça yüksek bir psikopatoloji grubudur. Yeme bozukluklarının temelini ise bozulmuş
yeme tutumlarıyla birlikte bozuk ve olumsuz beden algısı oluşturur. Bu çalışmanın temel
amacı yeme tutumları ile beden algısı arasındaki ilişkide öz şefkat ve duygu düzenlemenin
aracı rolünün incelenmesidir. 18-65 yaş arasındaki 200 kişi kolayda örneklemle yürütülmüş
bu çalışmada yemeğe ilişkin tutumlar ve kişilerde yeme bozukluğu olup olmaması Yeme
Tutum Testi (YTT-40) ve Yeme Bozukluğu Değerlendirme Ölçeği (YBDÖ) ile öz şefkat
düzeyi Öz Şefkat Ölçeği Kısa Formu (ÖŞÖ-KF) ile, duygu düzenleme güçlüğü düzeyi
Duygu Düzenleme Güçlüğü Ölçeği (DDGÖ) ile, beden algısı ise Beden Algısı Ölçeği (BAÖ)
ile ölçülmüştür. Yapılan çoklu doğrusal aracı etki analizi öz şefkat ve duygu düzenlemenin
beden algısı ile yeme tutumları arasındaki ilişkide kısmı aracılık etkisini göstermiştir.Eating disorders are a psychopathological group with increasing prevalence, high rates of
comorbidity, and mortality. The core of eating disorders is formed by distorted eating
attitudes along with a disrupted and negative body image. The primary aim of this study is
to investigate the mediating role of self-compassion and emotion regulation in the
relationship between eating attitudes and body image. Conducted with a convenience sample
of 200 individuals aged between 18 and 65, this study assessed eating attitudes and the
presence of eating disorders using the Eating Attitudes Test (EAT-40) and the Eating
Disorder Evaluation Scale (EDE-Q), respectively. Self-compassion level was measured
using the Short Form of the Self-Compassion Scale (SF-SCS), emotion regulation difficulty
level was assessed using the Difficulty in Emotion Regulation Scale (DERS), and body
image was evaluated using the Body Image Scale (BIS). The conducted multiple linear
mediator effect analysis demonstrated the partial mediating effect of self-compassion and
emotion regulation on the relationship between body image and eating attitudes
An effective mechanism for FOG computing assisted function based on Trustworthy Forwarding Scheme (IOT)
No full textAs the Internet of Things (IoT) continues to proliferate, the demand for efficient and secure data processing at the network edge has grown exponentially. Fog computing, a paradigm that extends cloud capabilities to the edge of the network, plays a pivotal role in meeting these requirements. In this context, the reliable and trustworthy forwarding of data is of paramount importance. This paper presents an innovative mechanism designed to ensure the trustworthiness of data forwarding in the context of MQTT (Message Queuing Telemetry Transport), a widely adopted IoT communication protocol. Our proposed mechanism leverages the inherent advantages of MQTT to establish a robust and secure data-forwarding scheme. It integrates fog computing resources seamlessly into the MQTT ecosystem, enhancing data reliability and security. The mechanism employs trust models to evaluate the credibility of IoT devices and fog nodes involved in data forwarding, enabling informed decisions at each stage of the transmission process. Key components of the mechanism include secure communication protocols, authentication mechanisms, and data integrity verification. The proposed secure communication protocols (TLS/SSL, MQTTS, and PKI) and data integrity verification methods (MAC, digital signatures, checksums, and CRC) provide a robust framework for ensuring secure and trustworthy data transmission in IoT systems. These elements collectively contribute to the establishment of a reliable data forwarding pipeline within MQTT. Additionally, the mechanism prioritizes low-latency communication and efficient resource utilization, aligning with the real-time requirements of IoT applications. Through empirical evaluations and simulations, the research demonstrates the effectiveness of our proposed mechanism in improving the trustworthiness of data forwarding, while minimizing overhead, as the experiment was conducted with 15 fog nodes, and the maximum Level of Trust (LoT) score was 0.968, which is very high, with an estimated accuracy of 97.63%. The results indicate that our approach significantly enhances data security and reliability in MQTT-based IoT environments, thereby facilitating the seamless integration of fog computing resources for edge processing
THE IMPORTANCE OF PRACTICAL EDUCATION AND INTERNSHIP IN RADIOTHERAPY TECHNICIAN EDUCATION
No full textThe purpose of this study was to assess the practical training provided by associate degree programs in radiotherapy education in the United States, as well as the preparedness of students for internships and their post-internship learning levels. A total of 317 participants, including both face-to-face and online students, as well as graduates of the radiotherapy program, completed a survey consisting of 68 questions. Of the participants, 66.2% were female (n=210) and 33.8% were male (n=107). Lab facilities are available at institutions where the majority of participants (70.3%) have received education. Binary logistic regression tests were used to investigate whether there was a difference between the pre- and post-internship status. It was statistically significant that interns who had experienced professional growth prior to the internship performed better in achieving such gains after the internship (p < 0.05). The rate of error among participants who underwent laboratory training was 58.4% lower, and the time it took for them to begin working with patients independently was 61.1% less compared to those who were trained in an educational institution with inadequate practical training. Our survey underscores the significance of both theoretical and hands-on training in the education of radiotherapy technicians. Starting internships with theoretical training in a laboratory environment reinforces students' knowledge and improves their success during the internship. This type of training also enhances self-confidence, strengthens their connection to their profession, and prepares them for professional life after graduation
Investigation the performance of new designed solar still integrated with solar cells
Get PDFThe aim of this research is to conduct a design and analysis of solar still with specific
specifications and sizes under certain conditions to choose the optimal angle of inclination
of the glass cover. Where the city of Baghdad was chosen to choose the characteristics of its
location and temperature at a specific time of the year, due to the large city's need for
desalinated water. The SOLIDWORKS software will be used in drawing and designing.
Then, the ANSYS Fluent software will be used in the CFD simulation process to extract
some results such as prisms and density contours, in addition to some other important results.
In addition to the analysis, the MATLAB software will be used by writing a complete code
using the specific city characteristics and weather conditions on that day, in addition to using
heat equations in order to extract results and graphs of the amount of water produced every
hour, as the working time for this device was chosen from 9 am until 5:00 p.m. Results and
fees will also be extracted for some other factors such as evaporation rate and solar radiation,
as well as the temperature of the glass and absorber during the day. Through these results,
the optimal angle will be known. The optimal angle is the angle that gives us the largest
amount of desalinated water at the same conditions
An efficient faults and attacks categorization model in IoT-based cyber physical systems using Dilated CNN and BiLSTM with multi-scale dense Attention module
No full textThe physical process with the digital computing channel and electronic computing is integrated by the Cyber-Physical Systems (CPS). The abnormality and failures are the two main sources that affect the performance of the CPS. In the CPS, the research on security analysis and fault diagnosis has attracted lots of interest from the researcher. However, the existing model does not find the difference between the fault and attacks, the existing approaches need adequate development for identifying the difference between the fault and attack in the Internet of Things (IoT)-CPS. In this research, a deep learning-based approach is developed to detect the attacks and faults in the IoT-CPS for enhancing the security of the network. The IoT-based data is garnered from a standard online source. After collecting the data, the extraction of the deep features is performed using the Conditional Variational Autoencoder (CVA). The attained deep attributes are further taken for the weighted feature fusion process in which the required weights are chosen in an optimal manner using the Enhanced Egret Swarm Optimization (EESO) algorithm. The obtained weighted fused features are inputted into the Dilated Convolutional Neural Network and Bidirectional Long Short-Term Memory with Multi-Scale Dense Attention (DCNN-Bi-LSTM-MSDA). The classification outcomes are obtained from the DCNN-Bi-LSTM-MSDA module. Throughout the result analysis, the accuracy and NPV rate of the designed model is 94.16% and 99.38%. The validation of the fault and attack classification offered by the implemented deep learning-oriented failure and abnormality classification scheme in IoT-based CPS is done against several traditional models
The impact of strategic awareness on enhancing organizational agility in hospitals in Iraq
Get PDFCurrent study sought to investigates the association between strategic awareness and
organizational agility, this study was direct to hospitals in Salah Al-Din governorate in Iraq,
the following approaches were applied to conduct the study: quantitative, descriptive and
analytical approaches, further, administrative employees in top management levels were the
targeted population in this study, by applying convenience sampling responses from
administrative employees were collected using online questionnaire, the researcher was able
to gather 139 questionnaires, of which 137 questionnaires were valid for analysis. To
complete data analysis for this study, the statistical package for social sciences v27 was
chosen, by applying a set of tools and tests, main results by the study were as follows:
Surveyed hospitals practice strategic awareness to high level, this result was based on the
total mean value that scored (Mean= 3.93) and surveyed hospitals enjoy overall moderate
level of organizational agility, as this variable has a mean value of (Mean= 3.61). In viewing
Strategic awareness as an overall variable, significant positive correlation was scored with
Organizational agility as an overall variable [r= 0.769**], also at the levels of organizational
agility dimensions, as scored a positive correlation with Sensing [r= 0.617**], and a positive
correlation with Decision-making [r= 0.715**] and with Acting [r= 0.547**]. Hence, strategic awareness as an overall variable was seen significantly correlating with
organizational agility overall and with its dimensions.Mevcut çalışma, stratejik farkındalık ile örgütsel çeviklik arasındaki ilişkiyi araştırmaya
çalışmıştır, bu çalışma doğrudan Irak'taki Salah Al-Din Valiliği'ndeki hastanelere
yönlendirilmiştir, çalışmayı yürütmek için aşağıdaki yaklaşımlar uygulanmıştır: nicel,
tanımlayıcı ve analitik yaklaşımlar, ayrıca, bu çalışmada hedeflenen nüfus üst yönetim
kademelerindeki idari çalışanlardı. araştırma, idari çalışanlardan kolaylık örneklemesi
yanıtları uygulanarak çevrimiçi anket kullanılarak toplanmış, araştırmacı 139 anket
toplayabilmiştir, bunlardan 137 anket analiz için geçerliydi. Bu çalışmanın veri analizini
tamamlamak için, bir dizi araç ve test uygulanarak sosyal bilimler istatistik paketi v27
seçildi, çalışmanın ana sonuçları şu şekildeydi: Ankete katılan hastaneler stratejik
farkındalığı yüksek düzeyde uyguluyor, bu sonuç puanlanan toplam ortalama değere
dayanıyordu (Ortalama = 3,93) ve ankete katılan hastaneler, bu değişkenin ortalama değeri
(Ortalama = 3,61) olduğu için genel olarak orta düzeyde örgütsel çevikliğe sahiptir. Stratejik
farkındalığı genel bir değişken olarak görürken, Örgütsel çeviklik ile genel bir değişken
olarak [r = 0.769 **], ayrıca örgütsel çeviklik boyutları düzeyinde, Algılama ile pozitif bir
korelasyon [r= 0.617 **] ve Karar Verme ile pozitif bir korelasyon olarak anlamlı pozitif
korelasyon kaydedildi -yapma [r = 0.715 **] ve Oyunculuk ile [r = 0.547 **]. Bu nedenle, genel bir değişken olarak stratejik farkındalığın, genel olarak örgütsel çeviklik ve boyutları
ile önemli ölçüde ilişkili olduğu görülmüştü
Examining the potential of deep learning in the early diagnosis of Alzheimer's disease using brain MRI images
No full text- Alzheimer's disease is a severe public health problem affecting millions worldwide. Deep Learning (DL) models can aid in detecting the disease using MRI data, and we evaluated three DL models for this purpose. We used detailed MRI images of Alzheimer's patients and healthy controls to train these models. A convolutional neural network (CNN) with two convolutional and two fully connected layers was employed in the initial model, which had a 95% accuracy rate. The second model, which included a leaky ReLU activation function, more fully connected layers, and a bigger kernel size, was an enhanced version of the previous one and had a 96% accuracy rate. The third model was a transfer learning model with two dense layers built on top of the VGG16 architecture, achieving an accuracy of 80%. Our findings imply how neural network models may assist with MRI data-based the disease assessment via evaluations of reliability, precision, recollection, and the F1 ranking. For enhancing the precision and usability of these gadgets for therapeutic usage, more study must be conducted
VGG-based feature extraction for face recognition system
Get PDFFacial recognition technologies are one of the main aspects of many things for example;
security, biometrics, and social media. That is where we go ahead to present a feature
extraction for our face recognition system based on the VGG approach. We assemble a
collection of facial images and then process them to keep all the images consistent and
properly set to avoid poor-quality images. The prioritized model exemplifies the use of
VGG16, employed to extract high-level features from faces, that follow identification by the
classification algorithm. System efficiency is evaluated concerning indicators of quality, for
instance, accuracy precision, recall, and F1-Score. The results show that our model, based
on feature extraction using VGG, has high accuracy and an accuracy rate with an LR model
is 91%, ANN 0.87, SVM 0.89, KNN 0.74, DT0,39, GB0.75, and RF 0.74for FR.
The results show that our proposed works well and is efficient in facial recognition functions.
We believe that this kind of research takes facial recognition technology to a new level of
development and will be a great example for other studies.Yüz tanıma teknolojileri, güvenlik, biyometri ve sosyal medya gibi birçok şeyin temel
unsurlarından biridir. İşte tam da bu noktada, VGG yaklaşımına dayalı yüz tanıma
sistemimiz için bir özellik çıkarma sunmaya devam ediyoruz. Bir dizi yüz görüntüsü
oluşturuyoruz ve ardından düşük kaliteli görüntülerden kaçınmak için tüm görüntüleri tutarlı
ve düzgün bir şekilde ayarlayarak işliyoruz. Öncelikli model, sınıflandırma algoritmasıyla
tanımlamayı izleyen yüzlerden yüksek seviyeli özellikler çıkarmak için kullanılan
VGG16'nın kullanımını örneklemektedir. Sistem verimliliği, doğruluk, hassasiyet, geri
çağırma ve F1 Puanı gibi kalite göstergeleri açısından değerlendirilir. Sonuçlar, VGG
kullanılarak özellik çıkarımına dayanan modelimizin yüksek doğruluğa sahip olduğunu ve
LR modeliyle doğruluk oranının %91, ANN 0,87, SVM 0,89, KNN 0,74, DT0,39, GB0,75
ve FR için RF 0,74 olduğunu göstermektedir.
Sonuçlar, önerdiğimiz modelin iyi çalıştığını ve yüz tanıma işlevlerinde etkili olduğunu
göstermektedir. Bu tür araştırmaların yüz tanıma teknolojisini yeni bir gelişim düzeyine
taşıdığına ve diğer çalışmalar için harika bir örnek olacağına inanıyoruz
Comparison of human breast milk vs commercial formula-induced early trophic enteral nutrition during postoperative prolonged starvation in an animal model
Get PDFThe present study aimed to characterize the changes in macromolecular composition and structure in ileal tissue induced by postoperative prolonged starvation (PS), human breast milk feeding (HM) and commercial formula feeding (CF) for 48 and 72 h (h). Forty-two Wistar albino rats underwent an ileal transection and primary anastomosis and were then divided into six subgroups. Two groups of seven rats were food-deprived for 48 and 72 h with free access to water only in metabolic cages (48 h PS, 72 h PS). Then, two groups of seven rats received early enteral trophic nutrition (EEN) either using HM, and CF at 48 h post-operation (48 h HM, 48 h CF). The other two groups of seven rats received the same trophic enteral nutrition at 72 h post-operation (72 h HM, 72 h CF). An additional seven rats were fed normal rat chow (control), after which the ileal tissues were harvested and freeze-dried overnight. Then sample spectra were recorded by Fourier transform infrared (FTIR) spectroscopy. PS at 48 and 72 h resulted in an increase in the concentration of lipids and a decrease in the concentration of proteins. CF and HM trophic feeding induced a decrease in membrane fluidity and an increase in lipid order. Ileal tissues showed similar compositional and structural changes in lipids and proteins in the PS and CF groups after 48 and 72 h. A marked decrease in nucleic acid concentration was seen in CF at 48 h compared to HM. The human milk feeding groups did not induce any significant alterations and showed compositional and structural data similar to the controls. In conclusion, EEN application seems to be safer when introduced at 48 h rather than 72 h and time of this nutrition is crucial to maintain ileum structure and therefore immunity and well-being. HM-induced trophic nutrition is seen to protect the ileal tissue from significant alterations within lipid and protein compositions, whereas CF caused notable changes. HM is absolutely the best nutritional source for gut health in this animal model