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Fabrication of Nicu Interconnected Porous Nanostructures for Highly Selective Methanol Oxidation Coupled With Hydrogen Evolution Reaction
No full textElectrocatalytic water electrolysis is the most promising clean and efficient process for pure and clean generation of hydrogen. However, water oxidation reaction requires a large overpotential owing to its slow kinetics, causing a lower efficiency of hydrogen production and high energy consumption. Herein, we report the bimetallic NiCu interconnected porous nanostructures on copper foil (NiCu@Cu) prepared by hydrogen bubbles templating electrodeposition technique for methanol oxidation reaction (MOR), which replaces the kinetically sluggish water oxidation reaction and enhances the hydrogen production with lower energy input. With their high macroporosity, interconnected growth on copper foil with excellent conductivity and easy flow of electrolyte on electrode interface, and stabilization of active sites due to bimetallic synergistic effects, the NiCu@Cu electrocatalysts exhibit outstanding activities for HER and MOR. The NiCu@Cu requiring just 1.32 V anodic potential vs RHE at 10 mA cm?2 for MOR which is significantly lower than that for water oxidation reaction. Moreover, the electrolyzer using NiCu@Cu/NiCu@Cu for anodic MOR and cathodic H2 production only needs a low input voltage of 1.45 V to deliver a current density of 10 mA cm?2 with impressive durability. © 2022 Hydrogen Energy Publications LLCThe authors FA and FS appreciate the financial support provided by the Lahore University of Management Sciences , Pakistan.Lahore University of Management Sciences, LUM
Tükenmişlik Sendromu. Tanım, Altta Yatan Nedenler ve Önleme Yaklaşımları
No full text[No Abstract Available
Predictors of Enhancing Human Physical Attractiveness: Data From 93 Countries
No full textPeople across the world and throughout history have gone to great lengths to enhance their physical appearance. Evolutionary psychologists and ethologists have largely attempted to explain this phenomenon via mating preferences and strategies. Here, we test one of the most popular evolutionary hypotheses for beauty-enhancing behaviors, drawn from mating market and parasite stress perspectives, in a large cross-cultural sample. We also test hypotheses drawn from other influential and non-mutually exclusive theoretical frameworks, from biosocial role theory to a cultural media perspective. Survey data from 93,158 human participants across 93 countries provide evidence that behaviors such as applying makeup or using other cosmetics, hair grooming, clothing style, caring for body hygiene, and exercising or following a specific diet for the specific purpose of improving ones physical attractiveness, are universal. Indeed, 99% of participants reported spending >10 min a day performing beauty-enhancing behaviors. The results largely support evolutionary hypotheses: more time was spent enhancing beauty by women (almost 4 h a day, on average) than by men (3.6 h a day), by the youngest participants (and contrary to predictions, also the oldest), by those with a relatively more severe history of infectious diseases, and by participants currently dating compared to those in established relationships. The strongest predictor of attractiveness-enhancing behaviors was social media usage. Other predictors, in order of effect size, included adhering to traditional gender roles, residing in countries with less gender equality, considering oneself as highly attractive or, conversely, highly unattractive, TV watching time, higher socioeconomic status, right-wing political beliefs, a lower level of education, and personal individualistic attitudes. This study provides novel insight into universal beauty-enhancing behaviors by unifying evolutionary theory with several other complimentary perspectives. © 2022 The AuthorsThis work is the result of the research project funded by the National Science Center, Poland (2019/33/N/HS6/00054).Kavitha Nalla Muthu and Chee-Seng Tan were supported by the UTAR Research Centre Excellence Award 2019 – CAP (6401/0019) from the Universiti Tunku Abdul Rahman.The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article:, This work is the result of the research project funded by the National Science Center, Poland (2019/33/N/HS6/00054). Dmitry Grigoryev was supported by the Basic Research Program at the National Research University Higher School of Economics (HSE University). Dmitrii Dubrov was supported by the Basic Research Program at HSE University, RF. Patrícia Arriaga was supported by the FCT through funds from the research center UID/PSI/03125/2021. Anabela C. Santos was supported by the FCT through funds from a PhD grant SFRH/BD/126304/2016. Kavitha Nalla Muthu and Chee-Seng Tan were supported by the UTAR Research Centre Excellence Award 2019 – CAP (6401/0019) from the Universiti Tunku Abdul Rahman. The authors would like to thank the following scholars for their help with the translation: Christin-Melanie Vauclair Melanie, Cátia Carvalho, Diogo Lamela, Elena Piccinelli, and Isabel Pinto (Portuguese), Stanislava Stoyanova (Bulgarian), Vira Hrabchuk and Anne MacFarlane (Ukrainian). The authors would also like to thank the following organizations and individuals for their help with organizing data collection in El Salvador: the Escuela de Comunicación Mónica Herrera, Directora Nicole Paetz, asistente María Erlinda Ávalos, Diego Infante, and Gabriela Quintanilla.Dmitry Grigoryev was supported by the Basic Research Program at the National Research University Higher School of Economics ( HSE University ).Patrícia Arriaga was supported by the FCT through funds from the research center UID/PSI/03125/2021. Anabela C. Santos was supported by the FCT through funds from a PhD grant SFRH/BD/126304/2016.Fundação para a Ciência e a Tecnologia, FCT: SFRH/BD/126304/2016, UID/PSI/03125/2021; Universiti Tunku Abdul Rahman, UTAR: 6401/0019; Narodowe Centrum Nauki, NCN: 2019/33/N/HS6/00054; National Research University Higher School of Economics, ??
Evaluation of the Effects of Transforming Growth Factor-Beta 3 (tgf-Beta 3) Loaded Nanoparticles on Healing in a Rat Achilles Tendon Injury Model
No full textBackground: Achilles tendon (AT) midsubstance injuries may heal suboptimally, especially in athletes. Transforming growth factor-beta 3 (TGF-beta 3) shows promise because of its recently discovered tendinogenic effects. Using poly(lactic-co-glycolic acid)-b-poly(ethylene glycol) (PLGA-b-PEG) nanoparticles (NPs) may enhance the results by a sustained-release effect. Hypothesis: The application of TGF-beta 3 will enhance AT midsubstance healing, and the NP form will achieve better outcomes. Study Design: Controlled laboratory study. Methods: A total of 80 rats underwent unilateral AT transection and were divided into 4 groups: (1) control (C); (2) empty chitosan film (Ch); (3) chitosan film containing free TGF-beta 3 (ChT); and (4) chitosan film containing TGF-beta 3-loaded NPs (ChN). The animals were sacrificed at 3 and 6 weeks. Tendons were evaluated for morphology (length and cross-sectional area [CSA]), biomechanics (maximum load, stress, stiffness, and elastic modulus), histology, immunohistochemical quantification (types I and III collagen [COL1 and COL3]), and gene expression (COL1A1, COL3A1, scleraxis, and tenomodulin). Results: Morphologically, at 3 weeks, ChT (15 +/- 2.7 mm) and ChN (15.6 +/- 1.6 mm) were shorter than C (17.6 +/- 1.8 mm) (P = .019 and = .004, respectively). At 6 weeks, the mean CSA of ChN (10.4 +/- 1.9 mm(2)) was similar to that of intact tendons (6.4 +/- 1.1 mm(2)) (P = .230), while the other groups were larger. Biomechanically, at 3 weeks, ChT (42.8 +/- 4.9 N) had a higher maximum load than C (27 +/- 9.1 N; P = .004) and Ch (29.2 +/- 5.7 N; P = .005). At 6 weeks, ChN (26.9 +/- 3.9 MPa) had similar maximum stress when compared with intact tendons (34.1 +/- 7.8 MPa) (P = .121); the other groups were significantly lower. Histologically, at 6 weeks, the mean Movin score of ChN (4.5 +/- 1.5) was lower than that of ChT (6.3 +/- 1.8). Immunohistochemically, ChN had higher COL3 (1.469 +/- 0.514) at 3 weeks and lower COL1 (1.129 +/- 0.368) at 6 weeks. COL1A1 gene expression was higher in ChT and ChN at 3 weeks, but COL3A1 gene expression was higher in ChN. Conclusion: The application of TGF-beta 3 had a positive effect on AT midsubstance healing, and the sustained-release NP form improved the outcomes, more specifically accelerating the remodeling process
Ottoman Central Administration and Primacy in the Orthodox Church: Sources and Agents of Knowledge and Inter-Patriarchal Conflicts
No full text[No Abstract Available
Assigning Meaning To Light Verbs in Turkish
No full textLight verb constructions (e.g., give a kiss to somebody) syntactically reflect typical ditransitive structures (e.g., give a violin to somebody) yet it is not clear whether these two structures thematically similar as well. We tested Turkish-speaking adults on their construal of light verb constructions with respect to the number of thematic roles1 they perceive in the event structure in a rating study. The light verb give with a different noun phrase was used in all critical utterances (e.g., give a response or give an assignment). The noun phrases of these constructions denominalized either by-lA or-lAn+dIr morpheme (e.g., response-lA and assignment-lAn+dIr) were also used as control items. The study concluded that the light verbs with their noun phrase complements contribute to the semantic construal of the event structure and the thematic role assignment processes for the denominalized versions of these structures reflect this meaning construal. © 2022 Boğaziçi Üniversitesi Yayınevi, İstanbul
Duvarlardaki İzler: Mübin Orhon’un Renkleri, Phoebe Cummıngs’in Bitkileri
No full text[No abstract available
Kimyasalların Gen Düzenleyici Etkilerinin Tahmini için Transfer Öğrenimi
No full textChemogenomics is the study of the genomic and/or proteomic reaction of biological targets to chemical compounds, with the goal of aiding drug design and screening. One of the many difficulties in chemogenomics comes from the dependency on real-life experiment data; the combination of different chemical compounds and drug targets creates an unrealistic number of possible experiments, which results in datasets that are biased towards certain chemicals and targets. The recent developments in machine learning resulted in an over-saturation of powerful models that easily pushed the limits of these datasets. To undo the effects of these biases, we employ transfer learning, the method of leveraging knowledge from similar problems. The most important bias of chemogenomics datasets is the bias towards drug targets. The availability and significance of certain cell lines greatly increase the chance of it being used as a drug target for these experiments, while others have barely enough data to train machine learning models. We experimentally demonstrate that transfer learning greatly increases the trainability of various drug targets, while using the work done on the Deep Compound Profiler (DeepCOP) as a basis. While focused on one of the simple methods of transfer learning, our experiments showed up to 22.81\% and an average of 9.00\% improvement on the area under ROC curve scores and showed great potential to be improved upon if accompanied by hyperparameter optimization and correct cell line as the transfer source.Kemogenomik, ilaç tasarımına ve taramaya yardımcı olmak amacıyla biyolojik hedeflerin kimyasal bileşiklere genomik ve/veya proteomik reaksiyonunun incelenmesidir. Kemogenomikteki birçok zorluktan biri, gerçek yaşam deney verilerine bağımlılıktan kaynaklanmaktadır; farklı kimyasal bileşiklerin ve ilaç hedeflerinin kombinasyonu, gerçekçi olmayan sayıda olası deney yaratır ve bu da belirli kimyasallara ve hedeflere yönelik önyargılı veri kümeleriyle sonuçlanmaktadır. Yapay öğrenmedeki son gelişmeler, bu veri kümelerinin sınırlarını kolayca zorlayan güçlü modellerin aşırı doygunluğuyla sonuçlanmıştır. Bu yatkınlıkların etkilerini nötrlemek için, benzer problemlerden bilgi edinme yöntemi olan transfer öğrenmeyi kullanmaktayız. Kemogenomik veri setlerindeki en önemli yanlılık, ilaç hedeflerine yönelik olandır. Bazı hücre dizilerinin erişebilirliği ve önemi, bu deneyler için bir ilaç hedefi olarak kullanılma şansını büyük ölçüde artırırken, diğerlerinin yapay öğrenme modellerini eğitmek için ancak yeterli verisi vardır. Derin Bileşik Profil Oluşturucu (DeepCOP) üzerinde yapılan çalışmayı temel olarak kullanırken, transfer öğreniminin, çeşitli ilaç hedeflerinin eğitilebilirliğini büyük ölçüde artırdığını deneysel olarak göstermekteyiz. Deneyler için kullanılan model yapısı değiştirilmemiştir. DeepCOP'da kullanılan veri bölme yöntemine ek olarak iki yöntem daha eklenmiştir. Deneylerimiz transfer öğrenmenin basit yöntemlerinden biri olan parametre tabanlı transfer öğrenimine odaklanırken, ROC eğrisi altında kalan alan puanlarında \%22,81'e varan ve ortalama \%9,00 iyileşme göstermiştir; bununla birlikte hiperparametre optimizasyonu uygulandığı ve transfer kaynağı olarak doğru hücre hattı seçildiğinde bu iyileşmelerin arttırılabileceğine yönelik potansiyel göstermiştir
Inter-Numerology Interference Pre-Equalization for 5g Mixed-Numerology Communications
No full textThis article proposes a pre-equalization method to remove inter-numerology interference (INI) that occurs in multi-numerology OFDM frame structures of fifth-generation New Radio (5G-NR) and beyond, on the transmitter side. In the literature, guard bands, filters, and interference cancellation methods are used to reduce the INI. In this work, we mathematically model how the INI is generated and show how it can be removed completely for multi-numerology systems by deploying a pre-equalization matrix on the transmitter side. With this pre-equalization method, the need for guard bands and filters is eliminated and spectral efficiency is improved