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Machine Learning-Based Temperature Forecasting for Sustainable Climate Change Adaptation and Mitigation
Get PDFIn this study, temperature estimation was achieved by utilizing artificial neural network (ANN) and machine learning models (linear model, support vector machine, K-nearest neighbor, random forest) to assist with sustainable environmental planning and climate change adaptation solutions. The research compared monthly humidity, wind speed, precipitation, and temperature data of the Istanbul province from 1950 to 2023. Estimates with 96% accuracy were achieved with the ANN model, and amongst the machine learning models, the random forest (RF) model demonstrated the highest performance. Generalization capability of the models was enhanced by the k-fold cross-validation method. The analysis found input variables (humidity, wind, precipitation) to be negatively associated with temperature. The current results show that the application of artificial intelligence/machine learning techniques is a useful instrument in the context of sustainable climate monitoring and temperature estimation. This study achieves sustainability targets through certain reliable methodologies for climate change evaluation, sustainable energy design, and agricultural adaptation plans. The methodology is transferable to other regional climate analyses and has the potential to underpin evidence-based, decision making for sustainable development and climate resilience
The Effect of Robotics and Coding Education on Girls' STEM Motivation, Attitude and Career Aspirations
Get PDFSTEM education aims to develop 21st-century skills, support economic growth and promote gender equality in STEM fields. It is known that gender stereotypes play a significant role in the formation of STEM identity. The most important factor preventing some high school-level female students from pursuing STEM careers is their lack of participation in STEM activities. Female students in high schools have limited opportunities to explore or learn about STEM careers due to the emphasis on verbal and religious courses in their curriculum. However, it is known that women can work more autonomously in scientific activities compared to men. The current study examines the effect of robotics and coding education on the development of girls' STEM careers. The study was conducted at an all-girls high school in Turkey, where the curriculum is predominantly centered on verbal and religious subjects. In the study, a pre-test and post-test experimental design with control group was used. A total of 76 volunteer female students (34 in the experimental group and 42 in the control group) participated in robotics and coding education over a period of 12 weeks. The data were collected using the validated STEM career, motivation and attitude scales and analyzed using t-tests, ANOVA and Pearson correlation. The findings revealed that robotics and coding training significantly improved the participants' STEM career aspirations, attitudes and motivations. A strong positive correlation was found between career interest, attitude and motivation. The study also showed that STEM career scores are significantly higher among students who wish to become teachers compared to those considering a career in the fields of health or engineering. However, no significant correlation was found between the participants' parents' education levels, family income and STEM career aspirations.Scientific Research Projects Coordination Unit of Mus Alparslan University [BAP-24-MMYO-4901-01]The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by the Scientific Research Projects Coordination Unit of Mus Alparslan University. Project Number: BAP-24-MMYO-4901-01
The effectiveness of nature-based interventions in combating PTSD: A meta-analysis and systematic review
No full textPost-traumatic stress disorder is generally characterized by the totality of problematic behaviours that occur after exposure to one or more events with a traumatic effect. Different treatment techniques have been developed in the field of psychology for a long time for the treatment of this disorder, which creates a significant expenditure burden for the health system. Nature-based interventions, which are a relatively new approach, have recently been more frequently preferred in the treatment of post-traumatic stress disorder. In this systematic review and meta-analysis study, it was aimed to examine the effect of nature-based interventions on post-traumatic stress disorder. Data were searched in Cochrane Library (CENTRAL), Pubmed, Web of Science (WOS), Scopus, CINAHL (EBSCOhost), Proquest databases in English and YOKTEZ database in Turkish. The last scanning process for all databases was performed on March 03, 2024. 13 experimental studies conducted with a total of 1022 people were included in the meta-analysis. Joanna Briggs Institute (JBI) Critical Appraisal Checklist was used to evaluate the risk of bias of the studies and to conduct the quality study. On the other hand, while Standardized mean difference (SMD) was used to determine the effect size of the study, its heterogeneity was evaluated according to the I2 test. Five different methods were used in the process of evaluating publication bias. In addition, moderator analysis and sensitivity analyses were performed. As a result of the analyses, it was found that nature-based interventions moderately and negatively affected posttraumatic stress symptoms (SMD =-0.558; 95 % CI =-0.678 to-0.437; p = 0.000). Considering the heterogeneity value (I2 = 27.469 %), fixed effect model was used to calculate the effect size since it might to be important level. According to the moderator analysis in which the variable of nature-based intervention techniques (Fishing Activities, Hiking Activities, Recreational Activities) was considered, no significant difference was found between nature-based intervention techniques. It was concluded that nature-based interventions provided significant welfare by moderately reducing the symptoms of posttraumatic stress disorder and that it is a reliable treatment technique that can be preferred by clinicians working in this field
Predicting optimal energy usage in buildings using artificial neural networks based on heuristic algorithms
Get PDFBu tez, enerji ile ilgili sorunları ele almak için sezgisel üstü algoritmalarla desteklenen yapay sinir ağlarının (YSA) uygulanmasını araştırmaktadır. Enerji verimliliğinin önemini vurgulamakta ve akıllı binalarda ısıtma, havalandırma ve iklimlendirme (HVAC) sistemlerinin enerji kullanımı üzerindeki etkisini araştırmaktadır. Araştırma, yapılardaki ısı yükünü (HL) ve soğutma yükünü (CL) en aza indirmenin nasıl enerji tasarrufu sağlayabileceğini detaylandırmaktadır. Çalışmada, enerji verimliliğini artırmak için Karşıtlık Tabanlı Öğrenmeyi (OBL) Çok Katmanlı Algılayıcı (MLP) modeliyle bütünleştiren HGS algoritması (OBL-HGS) kullanılmıştır. OBL-HGS algoritmasının HGS algoritmasının sınırlamalarını aştığı ve karmaşık optimizasyon senaryolarında olağanüstü iyi performans gösterdiği bulunmuştur. Araştırma, UCI Enerji Verimliliği veri setini kullanarak binalardaki HL ve CL değerlerini tahmin ediyor ve OBL-HGS-MLP modelinin geleneksel HGS ve geleneksel yöntemlere kıyasla üstün doğruluk ve genelleme elde ettiğini gösteriyor. Sonuçlar, OBL-HGS algoritmasının MLP ile birleştirilmesinin enerji verimliliğini optimize etmek için yeni bir strateji sağladığını ve enerji yönetimi ve sürdürülebilirlik konusunda yeni perspektifler sunduğunu göstermektedir. Tez, enerji verimli akıllı binalar tasarlamayı ve HVAC sistemlerinde enerji yükü tahminini iyileştirmeyi amaçlamaktadır. Bu bağlamda, geliştirilen modelin HVAC sistemlerinde istenen verimliliğin elde edilmesinde bina tasarımcılarına yardımcı olabileceği iddia edilmekte ve küresel enerji krizinin ortasında iklim kontrol kalitesini artırmak için bina tasarımında uygun çevresel kontrol ekipmanlarının seçilmesinin önemi vurgulanmaktadır.This thesis investigates the networks of artificial neural networks (ANNs) that are purposed to address energy-related issues. The expansion of the energy system is investigated in the cooling and air conditioning of heating, ventilation and air conditioning (HVAC) energy usage in smart buildings. The research details how minimizing heat storage (HL) and cooling load (CL) in the building can save energy. The study uses the OBL-HGS integrated region (OBL-HGS) with the Multi-Layer Perceptron (MLP) model to increase the energy utilization. It is seen that the OBL-HGS software overcomes the limitations of the HGS software and performs exceptionally well in complex view scenarios. The research estimates the HL and CL values in buildings using the UCI Energy Efficiency dataset and shows that the OBL-HGS-MLP model achieves superior accuracy and generalization compared to the traditional HGS and conventional methods. The results show that combining OBL-HGS applications with MLP provides a new strategy demonstration for optimizing energy distribution and new perspectives on energy management and sustainability. The thesis allows to design energy-efficient smart buildings and to estimate energy load in HVAC systems. It is claimed that this flexible, regular model can help building designers to achieve desired changes in HVAC systems and emphasizes the importance of using appropriate cooling control equipment in building design for the moderate climate control constraint of global energy crises
The Effect of Secondary Traumatic Stress and Cognitive Flexibility on Psychological Well-Being in Health Education Students
Get PDFAim The aim of this study is to examine the effects of secondary traumatic stress and cognitive flexibility on the psychological well-being of nursing and midwifery students and to model these relationships with machine learning approaches. Background While nursing and midwifery students are at risk of secondary traumatic stress (STS), cognitive flexibility is an important factor in coping with this stress. This study aims to develop strategies to improve students' mental health by examining the effects of STS and cognitive flexibility on psychological well-being using machine learning methods. Methods This cross-sectional descriptive study was conducted with 620 nursing and midwifery students between March and August 2024. Data were collected using a Personal Information Form, the Cognitive Flexibility Scale, the Psychological Well-Being Scale, and the Secondary Traumatic Stress Scale. Data analysis was performed using SPSS 22.0, G*Power 3.1, and R programming language 4.1.3. Results Hierarchical regression estimation showed that the model was significant and usable (F(2,617) = 112.473, p = 0.001). Secondary traumatic stress level and cognitive flexibility levels together explained 26.7% (R2 = 0.267) of the total variance in psychological well-being. It was determined that the decrease in students' secondary traumatic stress level (t = -7.724, p < 0.001) and the increase in cognitive flexibility level (t = 10.755, p < 0.001) caused a statistical increase in the level of Psychological Well-Being. Shapley Additive Explanations (SHAP) were used to understand the importance and contribution of each variable in the model. Cognitive Flexibility was found to be the most important variable in the prediction of Psychological Well-Being. Conclusions It was determined that the decrease in the level of secondary traumatic stress and the increase in the level of cognitive flexibility caused an increase in the level of psychological well-being. Longitudinal studies on students' psychological well-being levels are recommended. Clinical implications This study emphasises the importance of cognitive flexibility strategies to support health education (nurse and midwife) candidates to cope with secondary traumatic stress. It may contribute to the training of healthier and more resilient professionals by increasing the psychological well-being of students in nursing and midwifery education.Sakarya niversitesiWe would like to extend our heartfelt thanks to everyone who supported and contributed to this research. We especially wish to express our deepest gratitude to the health education students (nursing and midwifery) who played a crucial role in this study; without their participation, this research would not have been possible
Radioactive sources in betavoltic nuclear batteries
Get PDFBetavoltaik teknolojisi, uzun süreler boyunca elektrik enerjisi üretmek için beta radyasyonu ihtiva eden radyoizotopların bozunma enerjisinden yararlanan doğrudan bir enerji dönüşümü yaklaşımını temsil eder. Bu tür nükleer piller, özellikle uzak veya erişilemeyen ortamlarda, minimum bakımla uzun çalışma ömrü gerektiren uygulamalar için avantajlar sunar. Bu tez, betavoltaik pillerin en önemli kısmı olan radyoaktif kaynaklara odaklanan bilimsel araştırmaların kapsamlı bir incelemesini ve analizini sunmaktadır. Trityum (3H), Nikel-63 (63Ni), Prometyum−147 (147Pm), Karbon−14 (14C) ve Stronsiyum−90/İtriyum−90 (90Sr/ 90Y) dahil olmak üzere önemli radyoizotoplar, yarı ömür, beta emisyon enerjisi, spesifik aktivite, güç yoğunluğu, bozunum özellikleri, kullanılabilirlik, maliyet ve güvenlik hususları gibi kritik seçim kriterlerine göre incelenir. İzotop üretimi, kaynak üretimi ve kaynak karakterizasyonu için yöntemler incelenmiştir.Betavoltaic technology represents a direct energy conversion approach that utilizes the decay energy of radioisotopes containing beta radiation to generate electrical energy over long periods of time. Such nuclear batteries offer advantages for applications requiring long operating life with minimal maintenance, particularly in remote or inaccessible environments. This thesis provides a comprehensive review and analysis of scientific research focusing on radioactive sources, which are the most important component of betavoltaic batteries. Important radioisotopes such as Strontium-90/Therium-90 (90Sr/90Y), Promethium-147 (147Pm), Carbon-14 (14C), Nickel-63 (63Ni), Tritium (3H) are examined based on critical selection criteria such as half-life, beta emission energy, specific activity, power density, decay characteristics, availability, cost, and safety considerations. Methods for isotope production, source production, and source characterization are examined
Effect of neuro-linguistic programming applied prior to uterine curettage for fetal demise on pain score, fear of pain and pain catastrophizing: Randomized controlled trial
No full textObjective: This study aimed to determine the effect of neuro-linguistic programming applied prior to uterine curettage for fetal demise on pain score, fear of pain, and pain catastrophizing. Design: This is a single-blind, two-arm, parallel-group randomized controlled trial. Women with a gestational age of 12 weeks and/or less who underwent uterine curettage for the first time due to fetal death were included in the sample. The study was conducted in S,anl & imath;urfa, Turkey. Data were collected between July 2, 2024, and March 28, 2025, with a sample of 100 women randomly assigned to either the NLP (n = 50) or control (n = 50) groups. Before curettage, experimental group received only one 30-minute NLP practice session. Control group did not receive the NLP intervention but had one usual conversation with women for 30 min. Data were collected at three different time points: pre-intervention, post-intervention, and post-curettage. The primary outcomes of the study were the mean scores of the Visual Analog Scale, Fear of Pain Questionnaire-III, and Pain Catastrophizing Scale. Results: Compared to the control group, pain score measured over time decreased by 77.4 %, fear of severe pain by 88.2 %, fear of mild pain by 78.3 %, fear of medical pain by 82.2 %, total fear of pain score by 85.4 %, helplessness towards pain by 80.7 %, magnification by 77.4 %, rumination by 78.5 %, total pain catastrophizing score by 81.9 % more in the NLP group (p < 0.05). Conclusion: The practice of NLP can be safely used as a care intervention to reduce pain, fear of pain, and negative thoughts about pain. Trial and protocol registration: ClinicalTrials.gov registration number NCT06493305. Name of the registry: The Effect of NLP on Pain Before Uterine Curettage Due to the Fetal Demise. Registration number: NCT06493305. This research was registered in the NIH U.S. National Library of Medicine Clinical Trial Registry on July 1, 2024. The first woman participant was recruited on July 2, 2024. NCT registration URL link: https://clinicaltrials.gov/study/NCT06493305
Efficiency Assessment of Healthcare Resources: An In-Depth Exploration Using SFA, DEA, and Capacity Utilization Indicators
No full textThe demand for health care has increased. This demand causes an increase in the resources allocated and pressures on health managers and policymakers. Thus, it is important to evaluate the efficiency. It aimed to investigate the efficiency of the hospitals operating in Turkey. Stochastic Frontier Analysis (SFA), Data Envelopment Analysis (Charnes, Cooper and Rhodes Model [CCR] and Banker, Charnes and Cooper Model [BCC]), and capacity utilization indicators (bed occupancy ratio [BOR], bed turnover rate [BTR], and average length of stay [LoS]) were used. Thirty-nine of the 96 hospitals were found to be efficient according to BCC. It found that there was a strong correlation between the CCR model and SFA. It was moderate between BCC and SFA. Also, it was seen that there were significant differences between the SFA and BTR averages of the hospitals that were found to be efficient and inefficient. However, there were no significant differences between the averages of BOR and LoS. It is thought that the results obtained from different methods will help decision makers to better understand and monitor the performance of hospitals.La demanda de atenci & oacute;n sanitaria ha aumentado. Esta demanda genera un aumento en la asignaci & oacute;n de recursos y ejerce presi & oacute;n sobre los gestores y responsables pol & iacute;ticos del sector salud. Por lo tanto, es fundamental evaluar la eficiencia. El objetivo del estudio fue investigar la eficiencia de los hospitales que operan en Turqu & iacute;a. Se utilizaron el An & aacute;lisis de Frontera Estoc & aacute;stica (AFE), el An & aacute;lisis Envolvente de Datos (Modelo de Charnes, Cooper y Rhodes [CCR] y Modelo de Banker, Charnes y Cooper [BCC]) e indicadores de utilizaci & oacute;n de la capacidad (tasa de ocupaci & oacute;n de camas [BOR], tasa de rotaci & oacute;n de camas [BTR] y duraci & oacute;n media de la estancia [LOS]). 39 de los 96 hospitales resultaron eficientes seg & uacute;n el BCC. Se observ & oacute; una fuerte correlaci & oacute;n entre el modelo AFE y el ADC, y moderada entre el BCC y el AFE. Adem & aacute;s, se observaron diferencias significativas entre los promedios de AFEy BTR de los hospitales considerados eficientes e ineficientes. Sin embargo, no se observaron diferencias significativas entre los promedios de BOR y LoS. Se considera que los resultados obtenidos con diferentes m & eacute;todos ayudar & aacute;n a los responsables de la toma de decisiones a comprender y monitorear mejor el desempe & ntilde;o de los hospitales
RE: A Comparison of the short-term effects of steroid injection, prolotherapy and home-based physiotherapy in patients with chronic lateral elbow tendinopathy
Get PDFRE: A Comparison of the short-term effects of steroid injection, prolotherapy and home-based physiotherapy in patients with chronic lateral elbow tendinopath
Rapid high precision analysis of uranium content in Texas ores using gamma-ray spectrometry
No full textThe nuclear fuel cycle relies on the accurate and reliable quantification of uranium content in ores. Here, we offer a gamma-ray spectrometry method employing high purity germanium (HPGe) detectors to determine the total uranium content of a small ore sample from south Texas after accounting for gamma-ray self-attenuation. We show the quantification of uranium content using the 1001.0 keV gamma-ray produced by the daughter product 234mPa from the 238U series. We utilized a152Eu point-source to determine an attenuation correction factor for the ore sample at varying gamma-ray energies to significantly improve the accuracy of the method. Through the comparator method, we show a quick non-destructive method for quantifying the total uranium content in a Texas ore of 6.17 +/- 0.09 wt% with a 1.52 % relative uncertainty within a 2 h count time utilizing 26.5 g of material.Los Alamos National Laboratory [LA-UR-25-23151]The support of Los Alamos National Laboratory is gratefully appre-ciated. LA-UR-25-23151