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After Total Knee Arthroplasty, Monitored Active Ankle Pumping Improves Lower Leg Circulation More Than Unmonitored Pumping: A Pilot Study
No full text[[abstract]]1) Background: deep venous thrombosis (DVT) has long been recognized as the most devastating complication after total knee replacement (TKR). To prevent DVT, intermittent pneumatic compression to improve venous return in the lower leg has been advocated by surgeons. Physical activities such as active ankle pumping and early mobilization have been recommended as auxiliary measures to increase venous return in the lower leg and help in ambulation after TKR. In this study, in order to remind patients to exercise their ankle actively and efficiently after TKR, a foot band with motion sensor and reminder alarm was used. (2) Methods: The patients were randomly allocated into three groups according to the therapeutic protocols. The patients in group 1 conducted active ankle pumping without any reminders, those in group 2 underwent intermittent pneumatic compression, and those in group 3 conducted active ankle pumping with ankle motion sensor/reminder. The parameters of blood flow, namely, peak flow velocity and flow volume, in the bilateral common femoral vein and popliteal vein on the 1st, 3rd, and 14th days after surgery were measured using the echo technique, an index to evaluate the effect on promotion of venous return, among the three groups. (3) Results: The peak flow velocity and flow volume of the operative limb in group 3 (with motion sensor/reminder) were significantly higher than those in other groups. The peak flow velocity and flow volume in the popliteal vein in group 3 increased by 112% and 93.8%, respectively, compared to group 1 on the 14th day. No significant difference in peak flow velocity or flow volume was found in the nonoperative limb between the groups. (4) Conclusions: According to the results, a motion sensor/reminder with vibration alarms can improve the performance of active ankle pumping exercises in improving lower leg circulation, and hence may reduce the risk of DVT
Triangular configuration with headless compression screws in the fixation of transverse patellar fracture
No full text[[abstract]]A triangular configuration with three parallel cannulated screws is an established treatment for fixing transverse patellar fractures; however, the stability achieved with this approach is slightly lower than that attained with cannulated screws combined with anterior wiring. In the present study, triangular configurations were modified by partially or totally replacing the cannulated screws with headless compression screws (HCSs). Through finite element simulation involving a model of distal femoral, patellar, and proximal tibial fractures, the mechanical stability levels of the modified triangular configurations were compared with that of two cannulated screws combined with anterior wiring. Four triangular screw configurations were developed: three HCSs in a forward and backward triangular configuration, two deep cannulated screws along with one superficial HCS, and two superficial cannulated screws with one deep HCS. Also considered were two parallel cannulated screws (inserted superficially or deeply) combined with anterior wiring. The six approaches were all examined in full knee extension and 45° flexion under physiological loading. The highest stability was obtained with the three HCSs in a backward triangular configuration, as indicated by the least fragment displacement and the smallest fracture gap size. In extension and flexion, this size was smaller than that observed under the use of two deeply placed parallel cannulated screws with anterior wiring by 50.3% (1.53 vs. 0.76 mm) and 43.2% (1.48 vs. 0.84 mm), respectively. Thus, the use of three HCSs in a backward triangular configuration is recommended for the fixation of transverse patellar fractures, especially without the use of anterior wiring
自我效能團體對經濟弱勢少年自我效能的影響之評估研究
No full text[[abstract]]伴隨著社會與經濟結構的快速變遷，學術界對貧窮議題也有更多的認識，尤其是經濟弱勢對少年產生多方面的負面影響，因此如何提升經濟弱勢少年的自我效能是一重要的課題。本研究目的為了解自我效能團體對經濟弱勢少年自我效能影響，研究採用單案設計、參與觀察法、深度訪談法，針對6名經濟弱勢少年設計並帶領自我效能團體，使用研究工具為中學生自我效能量表、團體工作紀錄、團體滿意度調查、訪談大綱，所得資料進行分析，最後得到之研究結果如下：
1、自我效能團體有助於經濟弱勢少年建構成功經驗並提升自我效能。
2、經濟弱勢少年於自我效能團體過後兩個月內產生學習遷移，於日常生活中產生持續性的正向影響。
3、本研究設計之自我效能團體方案有助於提升經濟弱勢少年之自我效能。[[abstract]]Under the development of welfare diversification, it is necessary to cross different organizations to respond to complex social issues and provide services. In order to integrate these services, it is necessary to handle boundary activities. Middle managers usually become boundary spanners and need to adjust their management methods to adapt to the current environment. Through the transformation of the management model, this article uses the research notes method to explore the response of social service organizations, the new thinking and core value reflections that the managers of social service organizations should have in the face of the qualitative change and rise of management. This article quotes Paul Williams(2019) from the United Kingdom as mentioning that social service organization managers need to take on the role of network management ?Boundary spanner?. From the internal perspective of the organization, the coordination and cooperation between managers and various departments is the role of network management outside the organization needs to be held or actively participated in the discussion. The author also through reflection, facing the shift of managerialism and professional values, the ?boundary spanner? plays the role of speculation
結合遊戲軟體情境之探究學習法：程式學習之個案研究
No full text[[abstract]]本研究提出透過探究式學習及遊戲情境學習基礎程式設計，探討學習者是否可從學習遊戲程式設計過程中提升學習成就、態度及技能。部分學習者認為，程式設計難以理解，也沒有足夠的學習動機。然而，過去研究顯示，學習程式設計可培養邏輯思考及問題解決之能力。儘管學習程式設計有許多好處，但仍有學習者因找不到適合的學習方法而放棄學習。為解決上述的問題，本研究提出結合遊戲軟體情境之探究式學習法。目的是讓學習者藉由玩遊戲產生經驗，進而透過情境的關聯來學習原本陌生的程式語法，產生有意義的學習，並以Python「打磚塊」小遊戲為例，讓學習者學習基礎程式設計。本研究採個案研究法，研究對象為就讀資訊相關科系，卻對學習程式設計沒有信心的兩位學生；透過探究與結合遊戲情境進行學習，並觀察於學習程式設計前、中、後的過程與結果，探討學習前後成就、態度、技能的變化。研究結果發現，於教學實驗後，個案在學習成就方面有明顯的提升；學習態度方面則透過訪談及問卷顯示個案認同本研究之教學方法，且朝著正向積極的態度學習程式設計；學習技能方面則顯示個案提升成就後可獨立運用基礎程式設計技能。[[abstract]]The purpose of this research is to explore whether learners can improve their learning achievements, attitudes and skills in the process of learning game programming through inquiry-based learning and game scenario learning basic programming. This research proposes an inquiry-based learning method combined with game scenario. The purpose is to allow learners to generate experience by playing games, and then learn the unfamiliar programming syntax through contextual relevance to achieve meaningful learning. This research takes the ?Breakout? game written in Python as an example, allowing learners to learn basic programming through game code and scenario. This research adopts a case study method. The research participants are female students studying in the Department of Information Science, but they think it is very difficult to learn programming. This study proposes to learn through a combination of inquiry and game scenario, and observe the process and results of students before, during and after learning programming, and explore the changes in achievements, attitudes and skills before and after learning. The results of the study found that after the study of the game code proposed by this research, the cases have a significant improvement in learning achievement. In terms of learning attitude, interviews and questionnaire surveys show that the case agrees with the teaching method of this research and has a positive attitude towards learning program design. In terms of learning skills, the case can use these skills immediately after improving achievement
A novel intelligent deep learning predictive model for meteorological drought forecasting
No full text[[abstract]]The advancements of artificial intelligence models have demonstrated notable progress in the field of hydrological forecasting. However, predictions of extreme climate events are still a challenging task. This paper presents the development and verification procedures of a new hybrid intelligent model, namely convolutional long short-term memory (CNN-LSTM) for short-term meteorological drought forecasting. The CNN-LSTM conjugates the long short-term memory (LSTM) network with a convolutional neural network (CNN) as the feature extractor. The new model was implemented to forecast multi-temporal drought indices, three-month and six-month standardized precipitation evapotranspiration (SPEI-3 and SPEI-6), at two case study points located in Ankara province, Turkey. Statistical accuracy measures, graphical inspections, and comparison with benchmark models, including genetic programming, artificial neural networks, LSTM, and CNN, were considered to verify the efficiency of the proposed model. The results showed that the CNN-LSTM outperformed all the benchmarks. In quantitative visualization, it attained minimal root mean square error (RMSE?=?0.75 and 0.43) for the SPEI-3 and SPEI-6 at Beypazari station and (RMSE?=?0.73 and 0.53) for the SPEI-3 and SPEI-6 at Nallihan station over the testing periods. The proposed hybrid model was a promising and reliable modeling approach for the SPEI prediction and increased our knowledge about meteorological drought patterns
Deep learning versus gradient boosting machine for pan evaporation prediction
No full text[[abstract]]In the present study, two innovative techniques namely, Deep Learning (DL) and Gradient boosting Machine (GBM) models are developed based on a maximum air temperature ‘univariate modeling scheme’ for modeling the monthly pan evaporation (Epan) process. Monthly air temperature and pan evaporation are used to build the predictive models. These models are used for evaluating the evaporation prediction for the Kiashahr meteorological station located in the north of Iran and Ranichauri station positioned in Uttarakhand State of India. Findings indicated that the deep learning model was found best at Kiashahr station for testing datasets MAE (0.5691, mm/month), RMSE (0.7111, mm/month), NSE (0.7496), and IOA (0.9413). It can be concluded that in the semi-arid climate of Iran both of the used methods had the good capability in modeling of monthly Epan. However, DL predicted monthly Epan better than GBM. Moreover, the highest accuracy of the deep learning model was also observed for the Ranichauri station in terms of MAE?=?0.3693?mm/month, RMSE?=?0.4357?mm/month, NSE?=?0.8344, & IOA?=?0.9507 in testing stage. Overall, results expose the superior performance of DL-based models for both study stations and can also be utilized for various other environmental modeling
Multifunctionalities of mycosynthesized zinc oxide nanoparticles (ZnONPs) from Cladosporium tenuissimum FCBGr: Antimicrobial additives for paints coating, functionalized fabrics and biomedical properties
No full text[[abstract]]The prime focus of this investigation was to synthesize zinc oxide nanoparticles (ZnONPs) and test its potential applications as antimicrobials on textile fabrics, utilize ZnONP-functionalized building paints, evaluate its dye degradation efficiency, and determine its anticancer activity. ZnONPs were synthesized extracellularly in minimal medium at a fixed pH range 7.2 using an endophytic fungus Cladosporium tenuissimum FCBGr isolated from the Aegle marmelos (Vilva tree), a medicinal tree. The molecular identification of endophytic isolate FCBGr was performed using PCR amplification of ITS rDNA. The synthesized particles were characterized using microscopic and spectral analysis viz., SEM, XRD, and FTIR. The mycosynthesized ZnONPs exhibited enhanced antimicrobial and antioxidant properties at minimal concentration. Additionally, ZnONP-coated/functionalized fabrics exhibited a high degree of antimicrobial activity against most clinical pathogens tested. ZnONP-functionalized paints inhibited Aspergillus molds on building walls. Moreover, ZnONPs exhibited anti-angiogenic property by inhibiting blood vessel formation and cytotoxicity against HeLa cell lines even at the lowest concentration. These assessments evinced the promising nature of ZnONPs coatings in bio-medical applications and bioremediation
Association of Interleukin-8 Promoter Genotypes With Taiwan Lung Cancer Risk
No full text[[abstract]]Background/Aim: Chronic inflammation is believed to play a critical role in the pathogenesis of lung cancer. Interleukin-8 (IL-8) is an inflammatory cytokine and plays an important role in cancer development. Few studies have investigated the association between interleukin-8 - 251T/A (rs4073) genotype and lung cancer risk in various populations. Materials and Methods: In the current study, genotypes of interleukin-8 rs4073 were analyzed in 358 lung cancer patients and 716 healthy controls in Taiwan, by the PCR-RFLP methodology. Results: The distribution frequencies of interleukin-8 rs4073 genotypes between control and case groups were compared, and the homozygous variant AA genotypes showed a lower percentage in the case group compared to the control group (OR=0.57, 95%CI=0.39-0.85, p=0.0059). The distributions of alleles frequencies also exhibited statistical difference (p=0.0066). There was an interaction between interleukin-8 rs4073 and smoking habits (p=0.0051). Conclusion: Interleukin-8 rs4073 genotypes were associated with lung cancer susceptibility, especially for smokers
A mental account-based portfolio selection model with an application for data with smaller dimensions.
No full text[[abstract]]With the rapid development of Robo-adviser, behavioral portfolio theory (BPT) has been drawing good attention. However, the existing BPT models always assume the assets’ returns are normally distributed and cannot be solved efficiently when the number of assets is large. To circumvent these limitations, this paper proposes a new portfolio selection model with two mental accounts in which the lower-level (safety) is set up to avoid loss while the upper-level (self-actualization) need corresponding to the mental account is set up to get a good profit. To relax the normality assumption, we formulate our proposed model by replacing the probability terms with the expectation of indicator function and designing a sequential convex approximation algorithm to solve the proposed model. Also, we prove that the optimal portfolio obtained by our proposed algorithm converges when analyzing data with small dimensions.
Last, we carry out empirical studies by using trading data with 30 stocks from the American stock market to demonstrate the superiority, effectiveness, and robustness of our proposed portfolio selection in a smaller dimension case because our method is suitable only for small dataset. By comparing the characteristics of the optimal portfolio and the out-of-sample performance of our proposed portfolio selection model with the corresponding traditional portfolio selection models, we find that our new model not only derives the optimal portfolio with moderate diversification, but also obtain the highest average return and the highest Omega ratio in the out-of-sample testing period. Extensive experiment results by using different sample sizes, different frequencies, and employing the rolling-time window approach also confirm that our proposed portfolio selection model performs the best when we compare both the highest cumulative return and the Omega ratio in the out-of-samples