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Turquaz at Checkthat! 2024: a Hybrid Approach of Fine-Tuning and In-Context Learning for Check-Worthiness Estimation
No full text25th Working Notes of the Conference and Labs of the Evaluation Forum, CLEF 2024 -- 9 September 2024 through 12 September 2024 -- Grenoble -- 201493This paper presents our participation in the CLEF2024 CheckThat! Lab's Task-1 which focuses on determining whether passages from tweets or transcriptions are check-worthy. Task 1 covers three languages including English, Arabic, and Dutch. We propose utilizing several different instruct-tuned large language models (LLM) and aggregating their results for the Dutch dataset. In English and Arabic datasets, in addition to LLMs, we also use a fine-tuned XLM-R classifier. Our proposed method is ranked first in the Dutch dataset, fourth in the Arabic dataset, and eleventh in the English dataset. © 2024 Copyright for this paper by its authors
Savunma Sanayi İhracatı Üzerinden Abd ve Türkiye'nin Siyasi Karar Alma Modellerinin Karşılaştırmalı Analizi
No full textBu çalışmada, "Türkiye'nin savunma sanayi ihracatında izlediği karar alma süreci, ABD'nin izlediği karar alma süreci ile karşılaştırılarak Dış Politika Analizi modelleri üzerinden açıklanabilir mi?" sorusuna yanıt aranmıştır. Savunma sanayi ihracatı konusunda özel sektör, kamu, akademi ve diplomasi alanlarında uzman isimlerle yapılandırılmış mülakat gerçekleştirilmiştir. Yapılandırılmış mülakat verileri MAXQDA Analytics Pro 24 nitel veri analizi programı aracılığıyla işlenmiştir. ABD'de savunma sanayi ihracatı konusunda, yasama ve yürütme erklerinin karar verme süreçleri ile Türkiye'nin yasama ve yürütme erklerinin bu konudaki karar verme süreçleri, karşılaştırmalı olarak analiz edilmiştir. Savunma sanayi ihracatı konusunda, her iki devletin tarihsel ve toplumsal süreç içerisinde inşa ettiği bürokratik süreçler farklılıkları ve benzerlikleri vurgulanarak incelenmiştir. Çalışmada, Dış Politika Analizi yaklaşımlarından, kurumların ve bürokratik yapıların karar vermede standart davranış ilkelerini esas alan, Örgütsel Süreç Modeli yaklaşımının temel savlarına başvurulmuştur. Araştırmanın bulguları, savunma sanayi ihracatının bir dış politika meselesi olarak ele alınması gerektiğini ve savunma sanayi ihracatı karar alma sürecinin revizyon ihtiyacını ortaya koymaktadır.This study seeks to answer the question of whether the decision-making process in Türkiye's defense industry exports can be explained with a comparative analysis with the USAs' process using Foreign Policy Analysis models. Structured interviews were conducted with experts from the private sector, public sector, academia, and diplomacy regarding the defense industry. The data from these structured interviews were processed with the MAXQDA Analytics Pro 24 qualitative data analysis software. The decision-making processes of the legislative and executive branches in the United States regarding defense industry exports were comparatively analyzed with those of Türkiye. The bureaucratic processes that have been constructed historically and socially in both countries regarding the defense industry exports will be examined by emphasizing their similarities and differences. In that way, from the approaches of foreign policy analysis, the basic arguements of the organizational process model approach, which emphasizes standard behavioral principles in decision-making of institutions and structures, will be used. The findings of the research indicate that defense industry exports should be considered as a foreign policy issue, highlighting the need for revision in the decision-making process for defense industry exports accordingly
Kore Cumhuriyeti Ceza Muhakemesi Hukukunda Alternatif Çözüm Yolları
No full textKore Cumhuriyeti'nde ceza muhakemesi hukukunda alternatif yollara ilişkin olarak; savcının soruşturma başlatabileceği suçların kapsamının daraltıldığı, savcı ile polis arasındaki emir ve talimat ilişkisinin iş birliği ilişkisine dönüştürüldüğü, bu sebeplerle soruşturma evresini sonlandıran ceza muhakemesi kurum ve kararlarının kıyasla daha çeşitli olduğu görülmüştür. Kovuşturmayı sonlandıran alternatif yolların da ülkemize kıyasla, ceza bağlamında farklılıklar taşıdığı sonucuna varılmıştır. Uzlaştırmaya sevk edilebilecek suçların kapsamının ceza muhakemesi sistemimize göre daha geniş olduğu görülmektedir. Kamu davasının açılmasının ertelenmesinde; şüphelinin sabıka kaydına işlenmesi ve şüphelinin tek itiraz yolunun Kore Cumhuriyeti Anayasa Mahkemesine bireysel başvuru olması, önemli farklılıklar olarak öne çıkmaktadır.In the study, it was observed that in the Republic of Korea, the scope of crimes which the prosecutor can initiate an investigation has been narrowed down, the police have a more active role in the investigation and the order and instruction relationship between the prosecutor and the police has been transformed into a cooperation relationship. For the aforementioned reasons, the procedural institutions and decisions that end the investigation phase are more diverse compared to the Turkish system. The scope of crimes that can be referred to conciliation is wider than our system. In the postponement (suspension) of the opening of a public lawsuit; suspension of prosecution/indictment is recorded in the suspect's criminal record, and the only way the suspect can appeal against the postponement decision is to individually apply to the Constitutional Court of the Republic of Korea
Measurements of Lund Subjet Multiplicities in 13 Tev Proton-Proton Collisions With the Atlas Detector
No full textGregor, Ingrid Maria/0000-0002-5976-7818; Ke, Yan/0000-0001-5798-6665; Costanzo, Davide/0000-0003-4920-6264; Volkotrub, Yuriy/0000-0002-3114-3798; Rompotis, Nikolaos/0000-0003-2577-1875; Etzion, Erez/0000-0001-6871-7794; Wang, Zirui/0000-0002-0928-2070; Teixeira-Dias, Pedro/0000-0001-9977-3836; Panwar, Lata/0000-0003-2461-4907; Abdelrazek Aboelela, Mohammed/0009-0003-6578-220X; Sala, Alessandro/0000-0003-0824-7326; Rohne, Ole Myren/0000-0001-7744-9584; Taylor, Wendy/0000-0002-6596-9125; Elsing, Markus/0000-0002-1213-0545; Gonella, Laura/0000-0002-4919-0808This Letter presents a differential cross-section measurement of Lund subjet multiplicities, suitable for testing current and future parton shower Monte Carlo algorithms. This measurement is made in dijet events in 140 fb(-1) of root s = 13 TeV proton-proton collision data collected with the ATLAS detector at CERN's Large Hadron Collider. The data are unfolded to account for acceptance and detector-related effects, and are then compared with several Monte Carlo models and to recent resummed analytical calculations. The experimental precision achieved in the measurement allows tests of higher-order effects in QCD predictions. Most predictions fail to accurately describe the measured data, particularly at large values of jet transverse momentum accessible at the Large Hadron Collider, indicating the measurement's utility as an input to future parton shower developments and other studies probing fundamental properties of QCD and the production of hadronic final states up to the TeV-scale.We gratefully acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; ANID, Chile; CAS, MOST and NSFC, China; Minciencias, Colombia; MEYS CR, Czech Republic; DNRF and DNSRC, Denmark; IN2P3-CNRS and CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, HGF and MPG, Germany; GSRI, Greece; RGC and Hong Kong SAR, ChNSERC, Canada ina; ISF and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MEiN, Poland; FCT, Portugal; MNE/IFA, Romania; MESTD, Serbia; MSSR, Slovakia; ARIS and MVZI, Slovenia; DSI/NRF, South Africa; MICIU/AEI, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Canton of Bern and Geneva, Switzerland; NSTC, Taipei; TENMAK, Turkiye; STFC/UKRI, United Kingdom; DOE and NSF, United States of America. Individual groups and members have received support from BCKDF, Canarie, CRC and DRAC, Canada; CERN-CZ, PRIMUS 21/SCI/017 and UNCE SCI/013, Czech Republic; COST, ERC, ERDF, Horizon 2020, ICSC-NextGenerationEU and Marie Sklodowska-Curie Actions, European Union; Investissements d'Avenir Labex, Investissements d'Avenir Idex and ANR, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF, Greece; BSF-NSF and MINERVA, Israel; Norwegian Financial Mechanism 2014-2021, Norway; NCN and NAWA, Poland; La Caixa Banking Foundation, CERCA Programme Generalitat de Catalunya and PROMETEO and GenT Programmes Generalitat Valenciana, Spain; Goran Gustafssons Stiftelse, Sweden; The Royal Society and Leverhulme Trust, United Kingdom. In addition, individual members wish to acknowledge support from CERN: European Organization for Nuclear Research (CERN PJAS); Chile: Agencia Nacional de Investigacion y Desarrollo (FONDECYT 1190886, FONDECYT 1210400, FONDECYT 1230812, FONDECYT 1230987); China: Chinese Ministry of Science and Technology (MOST-2023YFA1605700), National Natural Science Foundation of China (NSFC -12175119, NSFC 12275265, NSFC-12075060); Czech Republic: PRIMUS Research Programme (PRIMUS/21/SCI/017); EU: H2020 European Research Council (ERC -101002463); European Union: European Research Council (ERC -948254, ERC 101089007), Horizon 2020 Framework Programme (MUCCA -CHIST-ERA-19-XAI00), European Union, Future Artificial Intelligence Research (FAIRNextGenerationEU PE00000013), Italian Center for High Performance Computing, Big Data and Quantum Computing (ICSC, NextGenerationEU); France: Agence Nationale de la Recherche (ANR-20-CE310013, ANR-21-CE31-0013, ANR-21-CE31-0022, ANR-22-EDIR-0002), Investissements d'Avenir Labex (ANR-11-LABX-0012); Germany: Baden-Wurttemberg Stiftung (BW Stiftung-Postdoc Eliteprogramme), Deutsche Forschungsgemeinschaft (DFG -469666862, DFG -CR 312/5-2); Italy: Istituto Nazionale di Fisica Nucleare (ICSC, NextGenerationEU), Ministero dell'Universita e della Ricerca (PRIN -20223N7F8K -PNRR M4.C2.1.1); Japan: Japan Society for the Promotion of Science (JSPS KAKENHI JP21H05085, JSPS KAKENHI JP22H01227, JSPS KAKENHI JP22H04944, JSPS KAKENHI JP22KK0227); Netherlands: Netherlands Organisation for Scientific Research (NWO Veni 2020 -VI.Veni.202.179); Norway: Research Council of Norway (RCN-314472); Poland: Polish National Agency for Academic Exchange (PPN/PPO/2020/1/00002/U/00001), Polish National Science Centre (NCN 2021/42/E/ST2/00350, NCN OPUS nr 2022/47/B/ST2/03059, NCN UMO-2019/34/E/ST2/00393, UMO-2020/37/B/ST2/01043, UMO-2021/40/C/ST2/00187, UMO-2022/47/O/ST2/00148); Slovenia: Slovenian Research Agency (ARIS grant J1-3010); Spain: BBVA Foundation (LEO22-1-603), Generalitat Valenciana (Artemisa, FEDER, IDIFEDER/2018/048), Ministry of Science and Innovation (MCIN ; NextGenEU PCI2022-135018-2, MICIN ; FEDER PID2021-125273NB, RYC2019-028510-I, RYC2020-030254-I, RYC2021-031273-I, RYC2022-038164-I), PROMETEO and GenT Programmes Generalitat Valenciana (CIDEGENT/2019/023, CIDEGENT/2019/027); Sweden: Swedish Research Council (VR 2018-00482, VR 2022-03845, VR 2022-04683, VR grant 202103651), Knut and Alice Wallenberg Foundation (KAW 2017.0100, KAW 2018.0157, KAW 2018.0458, KAW 2019.0447, KAW 2022.0358); Switzerland: Swiss National Science Foundation (SNSF -PCEFP2_194658); United Kingdom: Leverhulme Trust (Leverhulme Trust RPG-2020-004), Royal Society (NIF-R1-231091); United States of America: U.S. Department of Energy (ECA DE-AC02-76SF00515), Neubauer Family Foundation.ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW, Austria; FWF, Austria; ANAS, Azerbaijan; CNPq, Brazil; FAPESP, Brazil; NSERC, Canada; CFI, Canada; NSFC, China; MEYS CR, Czech Republic; DNRF, Denmark; DNSRC, Denmark; IN2P3-CNRS, France; CEA-DRF/IRFU, France; BMBF, Germany; MPG, Germany; Hong Kong SAR, China; ISF, Israel; INFN, Italy; MEXT, Japan; JSPS, Japan; CNRST, Morocco; RCN, Norway; MEiN, Poland; FCT, Portugal; MNE/IFA, Romania; MESTD, Serbia; MSSR, Slovakia; ARRS, Slovenia; MIZS, Slovenia; MICINN, Spain; Wallenberg Foundation, Sweden; SERI, Switzerland; MOST, Taiwan; DOE, United States of America; NSF, United States of America; BCKDF, Canada; CANARIE, Canada; Compute Canada, Canada; Czech Republic [PRIMUS 21/SCI/017, UNCE SCI/013]; COST, European Union; ERC, European Union; ERDF, European Union; Horizon 2020, European Union; Marie Skodowska-Curie Actions, European Union; Investissements d'Avenir Labex, France; Investissements d'Avenir Idex , France; ANR, France; DFG , Germany; AvH Foundation, Germany; Herakleitos programme - EU-ESF, Greece; Thales programme - EU-ESF, Greece; Aristeia programme - EU-ESF, Greece; Greek NSRF, Greece; BSF-NSF, Israel; MINERVA, Israel; Norwegian Financial Mechanism 2014-2021, Norway; NCN, Poland; NAWA, Poland; La Caixa Banking Foundation, Spain; CERCA Programme Generalitat de Catalunya, Spain; PROMETEO Programme Generalitat Valenciana, Spain; GenT Programme Generalitat Valenciana, Spain; Goran Gustafssons Stiftelse, Sweden; Royal Society, United Kingdom; Leverhulme Trust, United Kingdom; STFC, United Kingdom; TENMAK, Turkiye; Canton of Geneva, Switzerland; Canton of Bern, Switzerland; SNSF, Switzerland; SRC, Sweden; DSI/NRF, South Africa; NWO, Netherlands; Benoziyo Center, Israel; RGC, China; GSRI, Greece; HGF, Germany; SRNSFG, Georgia; Minciencias, Colombia; MOST, China; CAS, China; ANID, Chile; CERN; NRC, Canada; CERN: European Organization for Nuclear Research (CERN PJAS); Chile: Agencia Nacional de Investigacion y Desarrollo [FONDECYT 1190886, FONDECYT 1210400, FONDECYT 1230812, FONDECYT 1230987]; China: National Natural Science Foundation of China [NSFC - 12175119, NSFC 12275265, NSFC-12075060]; Czech Republic: PRIMUS Research Programme [PRIMUS/21/SCI/017]; European Union: European Research Council [ERC - 948254]; European Union: Horizon 2020 Framework Programme [MUCCA - CHIST-ERA-19-XAI-00]; European Union, Future Artificial Intelligence Research (FAIR-NextGenerationEU) [PE00000013]; Italian Center for High Performance Computing, Big Data and Quantum Computing (ICSC, NextGenerationEU); Marie Sklodowska-Curie Actions (EU H2020 MSC IF GRANT) [101033496]; France: Agence Nationale de la Recherche [ANR-20-CE31-0013, ANR-21-CE31-0013, ANR-21-CE31-0022]; France: Investissements d'Avenir Idex [ANR-11-LABX-0012]; France: Investissements d'Avenir Labex [ANR-11-LABX-0012]; Germany: Baden-Wurttemberg Stiftung (BW Stiftung-Postdoc Eliteprogramme); Germany: Deutsche Forschungsgemeinschaft [DFG - 469666862, DFG - CR 312/5-1]; Italy: Istituto Nazionale di Fisica Nucleare (FELLINI) [754496]; Japan: Japan Society for the Promotion of Science (JSPS KAKENHI) [22KK0227, JP21H05085, JP22H01227, JP22H04944]; Netherlands: Netherlands Organisation for Scientific Research (NWO Veni 2020) [VI.Veni.202.179]; Norway: Research Council of Norway [RCN-314472]; Poland: Polish National Agency for Academic Exchange [PPN/PPO/2020/1/00002/U/00001]; Poland: Polish National Science Centre [NCN 2021/42/E/ST2/00350, 2022/47/B/ST2/03059, NCN UMO-2019/34/E/ST2/00393, UMO-2020/37/B/ST2/01043, UMO-2021/40/C/ST2/00187]; Slovenia: Slovenian Research Agency (ARIS grant) [J1-3010]; Spain: BBVA Foundation [LEO22-1-603]; Generalitat Valenciana (Artemisa, FEDER) [IDIFEDER/2018/048]; La Caixa Banking Foundation [LCF/BQ/PI20/11760025]; Ministry of Science and Innovation (MCIN NextGenEU) [PCI2022-135018-2]; Ministry of Science and Innovation (MICIN FEDER) [PID2021-125273NB, RYC2019-028510-I, RYC2020-030254-I, RYC2021-031273-I, RYC2022-038164-I]; PROMETEO Programme Generalitat Valenciana [CIDEGENT/2019/023, CIDEGENT/2019/027]; GenT Programme Generalitat Valenciana [CIDEGENT/2019/023, CIDEGENT/2019/027]; Sweden: Swedish Research Council [VR 2018-00482, VR 2022-03845, VR 2022-0468, 2021-03651]; Knut and Alice Wallenberg Foundation [KAW 2017.0100, KAW 2018.0157, KAW 2018.0458, KAW 2019.0447]; Switzerland: Swiss National Science Foundation [SNSF - PCEFP2_194658]; United Kingdom: Leverhulme Trust [RPG-2020-004]; United States of America: Neubauer Family Foundatio
Derin Öğrenme Yoluyla Akıllı Yansıtıcı Yüzeylerin Fazlarının Ayrık Olarak Yapılandırılması ve Pilot Yükü Azaltılması
No full textHaberleşme sistemleri, birinci nesil haberleşme olan 1G teknolojisinden beri gelişmektedir. Son zamanlarda ortaya çıkan teknolojilerle birlikte dünya üzerindeki veri trafiği artmıştır, artan veri trafiğiyle birlikte haberleşme sistemlerinin de gelişmesi gerekmiştir. Beşinci nesil haberleşme teknolojisiyle birlikte çoklu antene sahip baz istasyonları hayatımıza girmiştir. Yeni nesil baz istasyonlarıyla birlikte haberleşme sistemlerinin güç verimliliği, bant genişliği verimliliği artmıştır ve çok antene sahip baz istasyonlarıyla birlikte kullanılan bazı teknolojiler ortaya çıkmıştır. Altıncı nesil haberleşme sistemleri için anahtar tekniklerden birisi olarak öne çıkması beklenen teknoloji Akıllı Yansıtıcı Yüzeylerdir. Akıllı Yansıtıcı Yüzeyler, haberleşme sistemlerinin enerji ve spektrum verimliliğini arttırmak için uygun maliyetli bir teknolojidir. Çok sayıda meta malzemenin kullanılmasıyla üretilen Akıllı Yansıtıcı Yüzeyler pasif elemanlardan oluşur ve bu elemanlar kendisine gelen sinyale bir faz kayması uygular. Elemanların ortak olarak optimizasyonu yapılırsa eleman sayısının karesiyle doğru orantılı olarak güç kazancı elde edilir. Akıllı Yansıtıcı Yüzeyler, getirdiği pek çok avantaja karşın mevcut haberleşme sistemlerine eklendiklerinde daha karmaşık bir sistem ortaya çıkar. Bu tezde, Akıllı Yansıtıcı Yüzeylerin faz kaymalarının konfigürasyonu Derin Öğrenme kullanılarak yapılmıştır. Akıllı Yansıtıcı Yüzeyler pasif elemanlardan oluştuğu için geleneksel yöntemlerle kanal tahmini yapılması ve bu tahmini kullanarak fazların optimize edilmesi zorlayıcı bir görevdir. Ayrıca geleneksel tahmin ediciler kanal tahmini için çok sayıda pilot periyoduna ihtiyaç duyarlar. Bu tez kapsamında baz istasyonunda alınan pilot sinyaller kullanılarak bir Derin Öğrenme modeli eğitilmiştir, modelin eğitiminde baz istasyonu tarafından alınan pilot sinyaller kullanılır. Eğitilen bu model çıkış olarak optimum faz değerlerini verir ve daha az sayıda pilot sinyal kullanılarak eğitim tamamlanabilir. Ayrıca Akıllı Yansıtıcı Yüzeylerin faz kaymalarının sürekli değerler alması pratik uygulamaları zorlaştırır, bu tez kapsamında eğitilen model ayrık değerli faz kaymalarını çıktı olarak verir. Bu faz kayması değerleri elde edildikten sonra, baz istasyonundaki hüzme oluşturma vektörü de optimum faz kayması değerleri kullanılarak optimize edilir. Çalışma sonucunda Akıllı Yansıtıcı Yüzeylerin mevcut haberleşme sistemlerine sağladığı veri hızı katkısı incelenmiş, sürekli faz değerleri kullanılan durum ile ayrık faz değerleri kullanılan durumun performansı karşılaştırılmıştır. Çalışma sonunda ayrık değerli Akıllı Yansıtıcı Yüzeylerin kullanımının sürekli değerlere yakın sonuçlar verdiği gözlemlenmiş, mükemmel kanal durum bilgisi mevcutken elde edilen fazlar referans olarak kullanılmış, normalize ortalama kare hataları karşılaştırılmış ve sunulmuştur.Communication systems have been evolving since the first generation of communication technology, 1G. With the emergence of recent technologies, data traffic worldwide has increased, necessitating further developments in communication systems. The fifth generation of communication technology introduced base stations equipped with multiple antennas, enhancing the power and bandwidth efficiency of communication systems and leading to the emergence of certain technologies used alongside these multi-antenna base stations. Intelligent Reflecting Surfaces are expected to be a key technology for sixth-generation communication systems. Intelligent Reflecting Surfaces are a cost-effective technology that increases the energy and spectrum efficiency of communication systems. Made using numerous metamaterials, these surfaces consist of passive elements that apply a phase shift to the incoming signal. If these elements are optimized collectively, a power gain proportional to the square of the number of elements can be achieved. Despite their many advantages, when integrated into existing communication systems, IRS introduce a more complex system structure. This thesis involves configuring the phase shifts of IRS using Deep Learning. Since IRS are made up of passive elements, traditional methods of channel estimation and using this estimation to optimize phases pose challenging tasks. Moreover, traditional estimators require numerous pilot periods for channel estimation. In this study, a Deep Learning model was trained using pilot signals, and this trained model provides optimum phase values as outputs. Additionally, continuous phase shifts in practical applications make implementation difficult; thus, the model trained in this thesis provides discrete phase values. After these phase shift values are obtained, the beamforming vector at the base station is also optimized accordingly. The study examines the contribution of IRS to existing communication systems in terms of data speed, compares the performance loss relative to continuous conditions, and presents normalized mean square errors compared to the optimum scenario
Effectiveness of Macroprudential Policies on Credit Surge and Stop Episodes
No full textWhen faced with capital flow and credit growth waves in recent years, policymakers have relied upon macroprudential regulation. This paper sheds light on a relatively less-analyzed policy issue: how macroprudential regulatory measures mitigate extreme credit growth episodes. We use a dynamic panel data approach to estimate the impact of MaPPs on credit growth volatility and the likelihood of credit growth boom and bust episodes. We find that MaPPs reduce credit growth volatility in both advanced economies (AEs) and emerging market economies (EMEs). In addition, MaPPs help to prevent credit surges in EMEs and stops in AEs. Our results show that there is a strong link between net capital flows and credit growth stop episodes. Net capital flow surges trigger a credit surge for EMEs. This suggests that policymakers should consider both MaPPs and capital flow management measures when designing policies to mitigate the risks associated with these phenomena
İnguinal Herni Onarımı Sonrası Kronik Postoperatif Ağrının Risk Faktörleri ve Preoperatif Depresyon-Anksiyete İlişkisi: Gözlemsel Çalışma
No full textAmaç: Amaç, inguinal herni onar ımı sonrası postoperatif kronik ağrı ile ilişkili insidans ve risk faktörlerini belirlemek ve preoperatif dep- resyon/anksiyete ile postoperatif kronik ağrı arasındaki ilişkiyi incelemekti. Gereç ve Yöntemler: Haziran 2021-Aralık 2022 tarihleri aras ında ingui- nal herni onarımı yapılan 260 hasta çalışmaya alındı. Preoperatif kronik ağrı ve postoperatif kronik ağrı görsel analog skala skoru değerlendirildi ve has- taların operasyon sonrası 3 ve 6. aylardaki sonuçlar ı kaydedildi. Ameliyat öncesi depresyon ve anksiyete değerlendirmeleri Beck Depresyon Envanteri ve Beck Anksiyete Envanteri kullanılarak yapıldı. Ameliyat öncesi, ameli- yat sırası ve ameliyat sonrası detaylar kaydedildi ve kronik postoperatif ağrı ile ilişkisi istatistiksel olarak analiz edildi. Bulgular: Postoperatif 3. ayda hastaların %22,3’ünde, 6. ayda ise hastalar ın %12,3’ünde ağrı devam etti. Hastaların %56,53’ünde preoperatif a ğrı mevcuttu ve bu hastalar ın %39,4’ünde kronik ağrı gelişti. Preoperatif kronik ağrı, postoperatif analje- zik kullanımı, ağrı süresi, kadın cinsiyet, reoperasyon, postoperatif kompli- kasyon ve postoperatif kronik a ğrı arasında istatistiksel olarak anlaml ı bir ilişki bulundu (p0,001). Rekürren operasyon geçiren 24 hastan ın 21’inde postoperatif kronik a ğrı gelişti. Hastaların %53,4’ünde a ğrı nöropatik ka- rakterdeydi. Ameliyat öncesi anksiyete/depresyon ile kronik a ğrı arasında anlamlı bir ilişki gözlendi (p0,001). Sonuç: Preoperatif ağrı, postoperatif akut ağrının şiddeti ve reoperasyon preoperatif dönemde sorgulanmal ı ve postoperatif kronik ağrıyı önlemek için yeterli ağrı palyasyonu sağlanmalı- dır. Ek olarak, preoperatif psikolojik durum postoperatif kronik ağrı gelişi- minde önemli bir etkiye sahiptir
Relocation Experiences and Psychological Aspects: Example of 2023 Kahramanmaras Earthquakes
No full text[No abstract available
The Role of Spatial Uncertainty in the Context-Specific Proportion Congruency Effect
No full textThe prime-probe version of the Stroop task has been predominantly used to demonstrate the context-specific proportion congruency (CSPC) effect. In this version, the location of the color is not known until its presentation, creating a spatial uncertainty for the color dimension. We propose that spatial uncertainty plays an important role in observing the CSPC effect. In this study, we investigated the role of spatial uncertainty with two experiments. In Experiment 1 (N = 53), we used a spatially separated version of the Stroop task having spatial uncertainty on the color dimension, and observed a significant CSPC effect. For Experiment 2, we conducted a preregistered prime-probe CSPC experiment with a considerably large sample (N = 128), eliminating the uncertainty of only the color dimension in one condition and both the color and the word dimensions in the other. Results showed that the CSPC effect was not observed in the first condition, while it was very small yet significant in the second condition. The Bayesian approach confirmed frequentist analyses of Experiment 1 and the first condition of Experiment 2. However, in the second condition of Experiment 2, there was no evidence regarding the existence of the CSPC effect. These findings support our claim that the spatial uncertainty of the color dimension, inherent in the prime-probe version Stroop task, contributed to the CSPC effect.Scientific and Technological Research Council of Turkiye (TUBIdot;TAK) [113K530]; Council of Higher Education of Turkiye (Yuksekogbreve;retim Kurulu)-100/2000 scholarship program (2020-2022); National PhD Scholarship Program of The Scientific and Technological Research Council of Turkiye (TUBIdot;TAK) [2211/A]Part of this work was supported by The Scientific and Technological Research Council of Turkiye (TUB ; Idot;TAK) under Grant No. 113K530. O.B. was supported by the Council of Higher Education of Turkiye (Yukseko ; gbreve;retim Kurulu)-100/2000 scholarship program (2020-2022) and 2211/A-National PhD Scholarship Program of The Scientific and Technological Research Council of Turkiye (TUB ; Idot;TAK)