bluuebunny/crossref_metadata_2025 huggingface dataset:
Kentsel Yeşil Alanlar İçin Mekânsal Yeterlilik ve Ulaşabilirlik Analizi: Gaziantep/Şahinbey Örneği
Authors: ŞİMŞEK Yasin , GÜNGÖR Şenay
Published: November 2022

Kentler, günümüzde insanların yoğun olarak yaşadığı yerleşim birimleridir ve kentsel yerleşim alanlarının sayısı her geçen gün artmaktadır. Kentlerde yaşayan insanların, yoğun iş tempoları ve günlük hayatları dışında kendilerine kalan boş zamanlarında dinlenme, eğlenme, fiziksel ve psikolojik olarak rahatlama gibi çeşitli ihtiyaçları bulunmaktadır. Kentsel yeşil alanlar bu ihtiyaçların giderilmesi konusunda önemli işleve sahiptir. Hızla büyüyen kent alanlarında yeşil alanların varlığının yeterli miktarda olup olmadığı ayrıca bu alanlara kentlerde yaşayan insanların eşit düzeyde ve kolay bir şekilde ulaşıp ulaşamadığı önemli bir tartışma konusudur. Bu çalışmanın amacı Gaziantep kenti, Şahinbey ilçesinde bulunan kentsel mahalle statüsündeki 95 mahallenin mevcut yeşil alanlarının yeterliliklerinin ve ulaşılabilirliklerinin değerlendirilmesidir. Çalışma kapsamında, 95 mahallenin nüfus, mahalle ve yeşil alan miktarları incelenmiştir. ArcGIS 10.8.1 yazılımı ile mevcut yeşil alanların durumu tespit edilmiş ve altlık haritalar hazırlanmıştır. Şahinbey kenti toplam alanı ve nüfus yoğunluğu hesaba katılarak mekânsal analiz yöntemleriyle aktif yeşil alanların yoğunluğu ve ulaşılabilirlik mesafesi analizleri yapılarak haritalar üretilmiştir. Sonuç olarak kişi başına düşen yeşil alan miktarı 3,23 m² olarak saptanmıştır. İmar Kanunu’nda belirtilen kişi başına düşen yeşil alan miktarı standardının 10 m² olduğunu düşündüğümüzde bu oranın yetersiz kaldığını söyleyebiliriz. Çalışma alanında bulunan mevcut yeşil alanların mahallelere göre ulaşılabilirlik düzeylerinin ise genel olarak 0-200 metre yürüme mesafesinde bulunduğu sonucuna varılmıştır. Tüm bulgular kapsamında yeşil alanların yeterlilik ve ulaşılabilirlik durumları tartışılmış ve çözüm önerileri sunulmuştur.
Yerel- Ulusal ve Uluslararası Medya Arasındaki Benzeşmeler
Authors: ÖZDEMİR Mehmet
Published: November 2022

Kitle iletişim araçlarının en önemli görevleri arasında; haber ve bilgi verme, kamuoyu oluşturma, denetim-eleştiri, eğitim ve eğlence yer almaktadır. Kitle iletişim araçları, bünyesinde bulundurduğu görsel ve işitsel araçlar sayesinde topluma haber ve bilgi vermenin yanında toplumu yönlendirme ve yönetme anlamında da önemli bir görev üstlenmiştir. Kitle iletişim araçları bu görevlerini; uluslararası medya, yaygın medya ve yerel medya aracılığı ile gerçekleştirebilmektedir.

Tarihsel süreç içerisinde yerel medya, ulusal medyanın, ulusal medya da uluslararası medyanın gelişimine önemli katkılar sağlamıştır. Tarihsel süreç içinde ulusal medyanın gelişimine ve içeriğine renk katan yerel medya artık daha çok kendi varlığını sürdürebilme çabası içerisinde bulunmaktadır. Bir dönem Türkiye’de ulusal basın ile yarış halinde olan yerel medya günümüzde bu özelliğini kaybetmiştir.

Bu çalışmada yerel, ulusal ve uluslararası medyanın yapısal ve işlevsel anlamda benzerlikleri ve bu benzerliklerin haber üretim ve dağıtım süreçlerini nasıl etkilediği tartışılmakla birlikte medyanın sahiplik yapısı, istihdam, ücret politikası, tiraj kaygısı, çalışanların eğitim durumu ve medya siyaset ilişkisi gibi benzerlikler üzerinde durulmuştur.
Behçet Necatigil’in Şiirlerinde Kadının Halleri
Authors: TÜRK Emine Bilgehan
Published: November 2022

Sanat; hayata, doğaya, zaman ve mekâna başka ufuklardan bakmasını öğretir. Şiir ise okurunda gündelik hayatın dağdağası içinde bazen uzun sürecek düşünceler uyandırırken bazen çağrışımlarıyla ânı anlamlı kılar. İnsanı kuşatan onun içinde izler bırakan olayları, şiir sanatının imkânlarıyla dile getiren nesirleriyle de şiir ve sanata dair görüşlerini açıklayan bir şairdir Behçet Necatigil. Türk şiirinin kendi üslubunu oluşturabilmiş isimlerinden olan şair, sıradan insanın hallerini şiirlerinde etkili bir söyleyiş ve yalın bir ifadeyle dile getirir. İdeoloji, sınıf gözetmeksizin toplumun hemen her kesiminden okuru ortak duygularda birleştirebilecek insani bir söyleyiş, derin bir gözlemin varlığı şiirlerinde kendini hissettirir. En sessiz, en görünmez haliyle dahi hayatın kurucusu olan kadın ve hayatın hemen her sahnesinden kadın fotoğrafları onun şiirlerinde özgün bir yere sahiptir. Küçük kız çocuğundan, geçim derdiyle bir evin sıkıntılarını omuzlayan kadınlara, gelenek ve arzuları arasında sıkışmış genç kızlardan sokak kadınlarına, hasta çocuğunun kaygısıyla uykusuz kalan annelere her yaştan ve her kesimden kadın, Necatigil’in şiirlerinde yerini bulur. Şiirlerde insanı iç dünyasından yakalamaya çalışan yargılamayan mutedil bir yaklaşımın sesi duyulur. Behçet Necatigil insanı gündelik yaşamın içinde gözlemleyen, onu yaşadığı hayatın içinde yorumlamaya çalışan bir şairdir. Bu yaklaşımıyla polemikleri, siyasi olayları, zamanı aşan bir isim olarak her devrin okurunun duygularına hitap edecektir. Necatigil’in şiirlerinde “kadın konusunun” ele alınmaya çalışıldığı bu makalede şairin; şiir ve şair algısına dair kısaca bilgi verilmiştir. Bütün şiirleri içinde yapılan taramada kadın konulu şiirler tasnif edilerek onlar da kendi içinde sınıflandırılmaya çalışılmıştır.
Comparative Analysis of Turkey’s Covid-19 Management Through the European Union 2020 and 2021 Turkey Reports
Authors: AKDOĞAN Muzaffer , GEÇER Ekmel , TUTAR Necmiye , ÜNKÜR Pınar
Published: November 2022

The European Commission, each year, prepares reports that screen the progress made by the candidate countries of the European Union (EU). EU Turkey reports are considered to have a positive influence on Turkey’s COVID-19 pandemic process management. Therefore, this study aims to find out whether the EU Turkey reports have an impact on Turkey’s COVID-19 pandemic administration. Through analysing the EU Turkey reports published in 2020 and 2021, this study also tries to establish an association between Turkey’s COVID-19 pandemic management and the reports published in previous years. In this context, the thematic distribution of the content of Covid-19 in the EU 2020 and 2021 Turkey reports in the light of the progress related to epidemics and infectious diseases in Turkey and within the framework of this distribution, answers to the questions of how Turkey’s Covid-19 management is were sought. The main finding of the study is that Turkey has improved its capacity to fight epidemics and is thus prepared for the Covid-19 pandemic process, with the influence of the guidance in previous reports.
Long-term follow-up of an overexposure radiation incident in a cohort treated with linear accelerator–based stereotactic radiosurgery for intracranial arteriovenous malformations
Authors: Borius Pierre-Yves , Januel Anne Christine , Plas Jean Yves , Duthil Pierre , Lotterie Jean Albert , Latorzeff Igor , Sabatier Jean
Published: November 2022

OBJECTIVE

Dosimetric radiosurgery incidents are rare and probably insufficiently reported in scientific publications. After a long follow-up (FU), the authors studied the outcomes of patients treated with overexposure radiation for arteriovenous malformation (AVM) administered via stereotactic radiosurgery (SRS) at their department.

METHODS

Between May 2006 and June 2007, 22 patients were treated for AVM with SRS. The mean (range) patient age was 43.5 (11.8–78) years. Previous treatments were embolization (n = 10), SRS (1), and surgery (1). The average (range) volume was 2.1 (0.2–6.4) cm3. The median prescribed minimal dose was 18.0 Gy. An initial error in the estimation of scatter factors led to overexposure to radiation. Due to this incident, the median delivered minimum dose was 25.0 Gy. All patients were prospectively followed with clinical examination and imaging.

RESULTS

The mean (range) clinical FU was 14.5 (12.0–15.2) years. AVM obliteration after SRS was completed in 90.9% of patients at a mean (range) of 39.4 (24.4–70.4) months. No patient had post-SRS AVM bleeding. Three patients (13.6%) had new permanent deficits due to radiation-induced changes (RICs). Obliteration without new deficits was achieved in 18 patients (81.8%). Two patients had new epilepsy that was probably due to RIC but well controlled. The median (range) MRI FU was 13.8 (2.5–14.9) years. During MRI FU, two RIC periods were observed: one classic period during the first 3 years showed T1-weighted annular irregular enhancement (13%), and the other period between 5 and 15 years after SRS showed the occurrence of cystic and hemorrhagic lesions (22.7%). There were no cases of radiation-induced tumor.

CONCLUSIONS

The present long-term report showed that this overexposure incident probably increased the AVM obliteration rate. This overexposure seems to have induced RIC and in particular a higher rate of cystic and hemorrhagic late lesions with nevertheless moderate clinical consequences. Long-term FU for AVM is mandatory due to the risk of late RIC.
Stereotactic radiosurgery and local control of brain metastases from triple-negative breast cancer
Authors: Kowalchuk Roman O. , Niranjan Ajay , Hess Judith , Antonios Joseph P. , Zhang Michael Y. , Braunstein Steve , Ross Richard B. , Pikis Stylianos , Deibert Christopher P. , Lee Cheng-chia , Yang Huai-che , Langlois Anne-Marie , Mathieu David , Peker Selcuk , Samanci Yavuz , Rusthoven Chad G. , Chiang Veronica , Wei Zhishuo , Lunsford L. Dade , Trifiletti Daniel M. , Sheehan Jason P.
Published: November 2022

OBJECTIVE

Stereotactic radiosurgery (SRS) is an effective treatment for intracranial metastatic disease, but its role in triple-negative breast cancer requires further study. Herein, the authors report overall survival (OS) and local tumor control in a multiinstitutional cohort with triple-negative breast cancer metastases treated with SRS.

METHODS

Patients treated from 2010 to 2019 at 9 institutions were included in this retrospective study if they had biopsy-proven triple-negative breast cancer with intracranial metastatic lesions treated with SRS. Patients were excluded if they had undergone prior SRS, whole-brain radiation therapy, or resection of the metastatic lesions. A retrospective chart review was conducted to determine OS, local control, and treatment efficacy.

RESULTS

Sixty-eight patients with 315 treated lesions were assessed. Patients had a median Karnofsky Performance Status of 80 (IQR 70–90) and age of 57 years (IQR 48–67 years). Most treated patients had 5 or fewer intracranial lesions, with 34% of patients having a single lesion. Treated lesions were small, having a median volume owf 0.11 cm3 (IQR 0.03–0.60 cm3). Patients were treated with a median margin dose of 18 Gy (IQR 18–20 Gy) to the median 71% isodose line (IQR 50%–84%). Overall, patients had a 1-year OS of 43% and 2-year OS of 20%. Most patients (88%) were followed until death, by which time local tumor progression had occurred in only 7% of cases. Furthermore, 76% of the lesions demonstrated regression. Tumor volume was correlated with local tumor progression (p = 0.012). SRS was very well tolerated, and only 3 patients (5%) developed symptomatic radiation necrosis.

CONCLUSIONS

SRS is a safe and efficacious treatment for well-selected patients with triple-negative breast cancer, especially for those with a favorable performance status and small- to moderate-volume metastatic lesions.
Interhospital transfer of pediatric patients with malignant brain tumor not associated with increased mortality, but safe routine discharge
Authors: Rangwala Shivani D. , Han Jane S. , Ding Li , Mack William J. , Krieger Mark D. , Attenello Frank J.
Published: November 2022

OBJECTIVE

Interhospital transfer (IHT) to obtain a higher level of care for pediatric patients requiring neurosurgical interventions is common. Pediatric patients with malignant brain tumors often require subspecialty care commonly provided at specialized centers. The authors aimed to assess the impact of IHT in pediatric neurosurgical patients with malignant brain tumors to identify areas of improvement in treatment of this patient population.

METHODS

Pediatric patients (age < 19 years) with malignant primary brain tumors undergoing craniotomy for resection between 2010 and 2018 were retrospectively identified in the Nationwide Readmissions Database. Patient and hospital data for each index admission provided by the Nationwide Readmissions Database was analyzed by univariate and multivariate analyses. Further analysis evaluated association of IHT on specific patient- or hospital-related characteristics.

RESULTS

In a total of 2279 nonelective admissions for malignant brain tumors in pediatric patients, the authors found only 132 patients (5.8%) who underwent IHT for a higher level of care. There is an increased likelihood of transfer when a patient is younger (< 7 years old, p = 0.006) or the disease process is more severe, as characterized by higher pediatric complex chronic conditions (p = 0.0004) and increased all patient refined diagnosis-related group mortality index (p = 0.02). Patients who are transferred (OR 1.87, 95% CI 1.04–3.35; p = 0.04) and patients who are treated at pediatric centers (OR 6.89, 95% CI 4.23–11.22; p < 0.0001) are more likely to have a routine discharge home. On multivariate analysis, transfer status was not associated with a longer length of stay (incident rate ratio 1.04, 95% CI 0.94–1.16; p = 0.5) or greater overall costs per patient ($20,947.58, 95% CI −$35,078.80 to $76,974.00; p = 0.50). Additionally, IHT is not associated with increased likelihood of death or major complication.

CONCLUSIONS

IHT has a significant role in the outcome of pediatric patients with malignant brain tumors. Transfer of this patient population to hospitals providing subspecialized care results in a higher level of care without a significant burden on overall costs, risks, or mortality.
Intrauterine closure of myelomeningocele defects with primary linear repair versus bipedicle fasciocutaneous flaps: a post-MOMS cohort study with long-term follow-up
Authors: Pontell Matthew E. , Yengo-Kahn Aaron M. , Taylor Emily , Kane Morgan , Newton J Michael , Bennett Kelly A. , Wellons John C. , Braun Stephane A.
Published: November 2022

OBJECTIVE

The objective of this study was to determine the effects of in utero bipedicle flaps on maternal-fetal morbidity/mortality, the need for CSF diversion, and long-term functional outcomes.

METHODS

Eighty-six patients who underwent fetal myelomeningocele repair from 2011 to 2021 at a single institution were reviewed. Primary outcomes included intrauterine fetal demise, postnatal death, postnatal myelomeningocele repair dehiscence, and CSF diversion by final follow-up.

RESULTS

The cohorts were no different with regard to race, ethnicity, maternal age at fetal surgery, body mass index, gravidity, parity, gestational age at fetal surgery, estimated fetal weight at fetal surgery, or fetal lesion level. Of the 86 patients, 64 underwent primary linear repair and 22 underwent bipedicle flap repair. There were no significant differences in rates of intrauterine fetal demise, postnatal mortality, midline repair site dehiscence, or the need for CSF diversion by final follow-up. Operative times were longer (32.5 vs 18.7 minutes, p < 0.001) and gestational age at delivery was lower (232 vs 241 days, p = 0.01) in the bipedicle flap cohort, but long-term functional outcomes were not different.

CONCLUSIONS

Analysis of the total cohort affirms the long-term benefits of fetal myelomeningocele repair. In utero bipedicle flaps are safe and can be used for high-tension lesions without increasing perioperative risks to the mother or fetus. In utero flaps preserve the long-term benefits seen with primary linear repair and may expand inclusion criteria for fetal repair, providing life-changing care for more patients.
Multi-Growth Period Tomato Fruit Detection Using Improved Yolov5
Authors: Yang Yingyan , Han Yuxiao , Li Shuai , Li Han , Zhang Man
Published: November 2022

Abstract: In agricultural mechanized production, in order to ensure the efficiency of hand-eye cooperative operation of tomato picking robot, the recognition accuracy and speed of multi-growth period tomato fruit is an important basis. Therefore, in order to improve the recognition speed of multi-growth period tomato fruit while ensuring or improving the accuracy, this paper improves the Yolov5s model by adding the architecture of the lightweight mobilenetv3 model. Firstly, the deep separable convolution is replaced in the backbone network of Yolov5s, which reduces the amount of convolution operation. Secondly, the linear bottleneck inverse residual structure is fused to obtain more features in high-dimensional space and perform convolution operation in low-dimensional space. Third, the attention mechanism is inserted into the last layer of the network to highlight features and improve accuracy. The research results show that the recognition accuracy of the improved Yolov5 model remains above 98%, the CPU recognition speed is 0.88f·s-1 faster than Yolov5s, and the GPU recognition speed is 90 frames per second faster than Yolov5s. Finally, a set of the recognition software system of multi-growth period tomato fruit is designed and developed by using RealSense D435i depth camera and PYQT. The software system further verifies the feasibility of the improved Yolov5 model, and lays a foundation for the visual software design of agricultural picking robot picking recognition.
Specialised English: New Directions in ESP and EAP Research and Practice by Ken Hyland & Lillian L.C. Wong (2019): Routledge
Authors: MUHAMMAD Anugrah Puspita Ayu , MUSTAKIM Mustakim
Published: November 2022

The Book “Specialised English: New Directions in ESP and EAP Research and Practice by Ken Hyland & Lillian L.C. Wong (2019): Routledge” is a must-read book for ESP and EAP researchers and practitioners. It consists of three main sections: 1) conceptualizing issues in specialized language use (five chapters), 2) focusing on texts (six chapters), and 3) focusing on practice (six chapters). This book addresses some challenges in teaching and researching ESP courses across disciplines (Enesi et al., 2021). For example, contextualizing ESP teaching as “workplace English” is currently demanding, thus allowing ESP curriculum developers to formulate and infuse English materials in the workplace (V. K. Bhatia & Bremner, 2012).
Geometry Optimization of Hot Water Storage Tank Based on Numerical Simulation
Authors: Liu Tingsen , Yan Mian , Song Xiangbo , He Yongtai
Published: November 2022

Using ANSYS software, the heat transfer process of hot water storage tank in spherical, cylindrical and square shapes was numerically simulated, and the distribution of the external wind velocity field and the temperature field under the same working conditions was studied. According to the change of average hot water temperature with time, it is concluded that spherical hot water storage tank can help reduce heat loss. Based on velocity field and temperature field distribution, it is found that the heat loss of the spherical hot water storage tank mainly occurs on the annular surface perpendicular to the windward side. By thickening the thermal insulation layer, it is possible to reduce the heat loss on the surface of the spherical water storage tank and improve the thermal insulation effect.
The Effect of Leakage Current on the Performance of Proton-Conducting Solid Oxide Fuel Cells
Authors: Li Qiangqiang , Zhang Yanghui , Shen Lili , Zhao Ning , Zhang Tao , Sun Xiaoxia
Published: November 2022

In this study, a numerical model is built to analyze the leakage current of a proton conductor solid oxide fuel cell. The transports of electron-hole and proton transfer in a mixed conductor electrolyte is described by the Nernst-Planck equation. The model is validated using experimental data. The leakage current, potential distribution, Faraday efficiency and energy efficiency are analyzed. When the output voltage of the fuel cell is greater than 0.5 V, the leakage current will cause the energy efficiency in a significant reduction. The lower the temperature, the lower the leakage current density. Lowering the temperature will improve the Faraday efficiency and energy efficiency of the fuel cell. Lower output voltage helps improve the Faraday efficiency.
Cleaning Time Prediction Method of Photovoltaic Power Station Based on Short-Term Environmental Change
Authors: Zhong Ying , Zhang Bo , Zheng Li , Feng Dantong , Ji Xu
Published: November 2022

In view of the cleaning problems of photovoltaic (PV) plants, existing studies focus on the optimization of cleaning frequency or cleaning period equivalent. However, few consider the possible influence of short-term weather changes on the optimal cleaning time in this period. This paper proposes a cleaning time prediction algorithm for PV plants. By predicting the comprehensive efficiency of the system, the peak sun power, and the peak sun hour, a recursive algorithm was used to calculate the difference between the expected power generation income and the cleaning cost under different cleaning time stamps, so as to determine the optimal cleaning time in a specific period. The proposed method was verified in outdoor PV plants. The comparison and analysis of the calculation results based on the measured data and predicted data show that the proposed method can accurately predict the optimal cleaning time, control the cleaning income, and provide an effective means and basis for the later operation and maintenance of PV plants.
Rotor Position Estimation of Permanent Magnet Synchronous Motor Based on Disturbance Model
Authors: Men Qiang , Cai Jiaming , Tan Junbi , Fang Xiangtao
Published: November 2022

Aiming at the high reliability of sensorless control of permanent magnet synchronous motor (PMSM), a back EMF estimator based on disturbance model is proposed. The back EMF in the assumed coordinate system is taken as the disturbance item of slow change, and the motor disturbance observation model is established. According to the characteristics of the observed back EMF, a phase-locked loop is designed to estimate the rotor speed and position. Through the analysis of the software simulation and physical verification results of the control model, it is proved that the observer model has high stability and adaptability for the estimation of the high and low speed operation region of the motor.
The Cost of Climate Change Heightened Sexual and Gender-based Violence: A Challenge for International Law
Authors: Desai Bharat H. , Mandal Moumita
Published: November 2022

There is a reality of creeping adverse effects of climate change. The human imprint on it has been affirmed by various global processes including 21 May 2019 recognition by the Anthropocene Working Group. It has emerged as a planetary crisis. By 2050 climate change could see 4% of global annual economic output lost to the tune of $23 trillion and may hit many poorer parts of the world disproportionately. Though entire populations are affected by climate change, women and girls suffer the most. Due to their traditional roles, women are heavily dependent on natural resources. As a consequence of natural disasters and during Covid-19 pandemic in 2020-22, women have faced heightened risks to different forms of sexual and gender-based violence (SGBV). They suffer from a lack of protection, privacy, and mental trauma. Effects of climate change results in the feminization and intensification of vulnerability of women and girls. As there is double victimization of women both as human beings and because of their gender. Growing evidence suggests role of climate change heightened violence against women and girls. There is no specific international legal instrument dealing with SGBV against women during and after the climate change induced disasters. The texts of the three specific climate change treaties (1992 UNFCCC, 1997 Kyoto Protocol and 2015 Paris Agreement) do not address this crucial aspect. It has been given attention only through the recent decisions of the Conference of the Parties (COP). Due to serious psychological and bodily harm SGBV causes to women, it needs to be explicitly factored in respective international legal instruments on climate change and disasters. There are ignorance, denials and lack of adequate attention by scholars and decision-makers in the field to address adverse effects of climate change in causing heightened violence against women and girls. Hence, this study makes a modest effort to deduce and analyze –from scattered initiatives, scholarly literature in different areas, existing international legal instruments and intergovernmental processes – the growing causal relationship between climate change and SGBV especially against women and girls as well as the phenomenal cost so as to suggest a way out for our better common future. It is a new challenge for international law that needs to be duly addressed in a timely manner.
Regulating Global Climate Change: From Common Concern to Planetary Concern
Authors: Desai Bharat H.
Published: November 2022

Since the recognition by the UN General Assembly resolution 43/53 (6 December 1988) that “climate change is a common concern of mankind” as well as adoption of the UN Framework Convention on Climate Change (UNFCCC) at the 1992 Rio Earth Summit, climate change has emerged as one of the most pressing global environmental challenges. The IPCC AR6 (April 2022) curated scientific evidence has explicitly observed that “Net anthropogenic GHG emissions have increased since 2010 across all major sectors globally.” The cumulative effect of GHG emissions appears to exacerbate the abnormal weather events, melting the polar ice caps and cause other cataclysmic climatic changes. The effects of climate change transcend territorial boundaries and continents. It has provided a normative basis for the concerted international law-making process underneath the existing UNFCCC led global regulatory regime. It designated climate change as a common concern of humankind. The resultant soft normativity has been shaped into the hard law through the trajectory of three international legal instruments that took the forms such as common but differentiated responsibilities and respective capabilities (1992 UNFCCC) to international legal commitments only for Annex I countries (1997 Kyoto Protocol) and the nationally determined commitments by the parties (2015 Paris Agreement). This study has sought to place under scanner the graded evolution of the climate change regime through the in-built law-making process premised upon a common concern of humankind. In the aftermath of the UN Secretary-General’s warning about climate emergency as part of “triple planetary crisis”, it is high time the international law scholars, the UN General Assembly and the UNFCCC regulatory process shift into the higher trajectory of climate change as a planetary concern.
Empirical Evaluation of the Asymptotic Behavior of the Analysis Complexity of Hard Random 3-CNF Formulas
Authors: Uvarov Sergey I.
Published: November 2022

This paper continued the study of the complexity behavior of the satisfiability analysis of hard random formulas given in the conjunctive normal form (CNF). In the 3-CNF formula, each clause contains three literals of logical variables. The number of logical variables in the formula is N. In this paper, the SAT solver is improved by introducing equality reduction and pure literal identification procedures. The solver improvement reduced the exponent (with base 2) from N/20.86 to N/21.41 with an R=4.6 ratio of the number of clauses to the number of variables. The results show that the efficiency of the pure literals identification procedure decreases as R increases. An important part of the study is an empirical estimation of the algorithmic complexity of the SAT problem with large number of variables. The proposed method gives a convenient lower bound on the complexity of analysis for random 3-CNF formulas. We estimated the algorithmic complexity for the range N=256÷8192. The exponential dependence of complexity on N for random 3-CNF formulas at a fixed value of R is demonstrated in this range.
Machine Learning-Based Current Density Simulation for Direct Borohydride Fuel Cell
Authors: Mohamed Amira , Ibrahem Hatem , Lee Jung-Hyun , Kim Kibum
Published: November 2022

Recent studies on fuel cell design have showed that the use of simulation tools is beneficial in terms of saving time and money. Current density management is still a key research problem for several technologies, including Direct Borohydride Fuel Cell (DBFC). This paper describes a systematic machine learning technique for estimating the cell current density for DBFC as a function of various input factors. Artificial Neural Networks (ANN) and Decision Tree Regressor (DTR) are two popular machine learning models that were trained and evaluated for the current density simulation using a conducted fuel cell experiments presented in previous research. The ANN model performed the better than the DTR model in the simulation, with a mean absolute error of 3.00015 for training and 5.57614 for testing. The simulation exhibits very small error values, indicating that the suggested approaches accurately mirror real-world DBFC process.
SD-Depth: Light-Weight Monocular Depth Estimation Using Space Depth CNN for Real-Time Applications
Authors: Ibrahem Hatem , Salem Ahmed , Kang Hyun-Soo
Published: November 2022

With the help of the space-to-depth and depth-to-space modules, we provide a convolutional neural network design for depth estimation. We show designs that down sample the spatial information of the picture utilizing space-to-depth (SD) as opposed to the widely used pooling methods (Max-pooling and Average-pooling). The space-to-depth module may shrink the image while maintaining the spatial information of the image in the form of additional depth information. This technique is far superior to Max-pooling, which diminishes the image’s information and features. We also suggest a lightweight decoder step that builds a high-resolution depth map out of many low-resolution feature maps using the depth-to-space (DS) module. The suggested architecture effectively learns depth estimation with high processing speed and accuracy. We trained and evaluated our suggested model on NYU-depthV2 dataset and attained low error values (RMSE=0.342) and high delta accuracies (δ3=0.996) at a fast-processing speed (25Fps).
Wavelet Pooling Scheme in the Convolution Neural Network (CNN) for Breast Cancer Detection
Authors: Onjun Ratapong , Sriwichai Kittikorn , Dungkratoke Narongdech , Kaennakham Sayan
Published: November 2022

In this work, the wavelet transformation (WT) under the context of convolution neural network (CNN) is developed and applied for breast cancer detection. The main objective is to investigate the effectiveness of the WCNN pooling architecture when compared to other two famous pooling strategies; max and average pooling, particularly targeting at the features extraction and classifying the phases of breast cancer by avoiding the under and overfitting problems. It is discovered in this work that the combination of WT and CNN outperforms the traditional and typical CNNs (with 96.49% of accuracy 95.81% of precision, 96.73% of recall and 96.23% of F measure).
IoT-Based Driver Distraction Prevention System
Authors: Lin Liang-Sian , Jiang Li-Wei , Wang Yu , Chen Zhi-Yu , Li Yi-Jie
Published: November 2022

Each year, many people pass away in car accidents. Drunk driving, distracted driving, and fatigued driving are the leading causes of car accidents. Some studies have noted that the features relevant to drunk driving and fatigued driving are driver’s alcohol consumption, smoking, and carbon monoxide (CO) concentration in the car. Hence, it is important and necessary to detect these factors during driving to avoid driver drowsiness and prevent car collisions. In this paper, we develop an IoT-based preventative system to monitor in-vehicle air quality and check for driver head bending. To ensure users can easily deploy the proposed system, we used low-cost sensors and tiny IoT devices to run the system. The suggested system uses low-cost gas sensors including MQ-2, MQ-3, and MQ-7 to track air quality in the car using Arduino D1. Additionally, we developed a Banana Pi camera setup to run a customized Teachable Machine model to ascertain whether or not the driver is paying attention to the road. We showcase the proposed system on the ThingSpeak cloud platform. Finally, in the proposed system, the buzzer and speaker will sound alarm warnings to the driver and occupants if the gas concentration in the car exceeds a specified value or driver distraction is detected.
An Education Tool That Supports Junior Learners in Studying Machine Learning
Authors: Horanai Hayato , Maejima Yuto , Ding Liya
Published: November 2022

This article presents an education tool that offers junior learners, such as junior high school students. basic knowledge of machine learning through designed games or quizzes. Learners can study basic ideas of k-NN classification, linear regression, and k-means clustering when enjoying their game playing. The system can also run on a smartphone for the convenience of learners. With such a tool, learners are expected to gain a better understanding of fundamentals of AI and machine learning and increase their interests in future development of AI applications.
A reduced order model for FSI of tank walls subject to wave impacts during sloshing
Authors: Bos R.W. , van der Eijk M. , den Besten J.H. , Wellens P.R.
Published: November 2022

Loads due to wave impacts are a limiting factor in the design of liquefied natural gas (LNG) tankers and their insulation. The current methodology considers the load independent from the response of the tank. Better tanks can be designed by knowing the effect of the interaction between the wave loads and the response, however predicting these effects is computationally expensive. In this paper a new application of the non-hydrostatic shallow water equations are presented, namely as a reduced order model (ROM) for fluid structure interaction for wave impacts. Our ROM is compared to a high fidelity model. The proposed ROM is fast and accurately predicts the total impulse and added mass, and therefore the general behaviour of the structure during the free vibration phase. It does however not always accurately predict the maximum force. It is therefore considered an appropriate tool for a first screening of the loads for which fluid-structure interaction is important, after which a more accurate method can be used to evaluate the most interesting cases. A sensitivity study is performed for various impact angles and velocities, showing that the importance of fluid structure interaction depends highly on the specific situation.
Lithium Provides Broad Therapeutic Benefits in an Alzheimer’s Disease Mouse Model
Authors: Wiseman Alyssa L. , Briggs Clark A. , Peritt Ariel , Kapecki Nicolas , Peterson Daniel A. , Shim Seong S. , Stutzmann Grace E.
Published: November 2022

Background: Alzheimer’s disease (AD) is a chronic neurodegenerative disorder with a progressive loss of cognitive function. Currently, no effective treatment regimen is available. Lithium, a mood stabilizer for bipolar disorder, exerts broad neuroprotective and neurotrophic actions and improves cognitive function. Objective: The study investigated if lithium stabilizes Ca2+ signaling abnormalities in hippocampal neurons and subsequently normalize downstream effects on AD neuropathology and synaptic plasticity in young AD mice. Methods: Four-month-old 3xTg-AD mice were treated with a LiCl diet chow for 30 days. At the end of the lithium treatment, a combination of two-photon Ca2+ imaging, electrophysiology, and immunohistochemistry assays were used to assess the effects of the LiCl treatment on inositol trisphosphate receptor (IP3R)-dependent endoplasmic reticulum (ER) Ca2+ and voltage-gated Ca2+ channel (VGCC)-mediated Ca2+ signaling in CA1 neurons, neuronal nitric oxide synthase (nNOS) and hyperphosphorylated tau (p-tau) levels and synaptic plasticity in the hippocampus and overlying cortex from 3xTg-ADmice. Results: Thirty-day LiCl treatment reduced aberrant IP3R-dependent ER Ca2+ and VGCC-mediated Ca2+ signaling in CA1 pyramidal neurons from 3xTg-AD mice and restored neuronal nitric oxide synthase (nNOS) and hyperphosphorylated tau (p-tau) levels to control levels in the hippocampal subfields and overlying cortex. The LiCl treatment enhanced post-tetanic potentiation (PTP), a form of short-term plasticity in the hippocampus. Conclusion: The study found that lithium exerts therapeutic effects across several AD-associated early neuronal signaling abnormalities including aberrant Ca2+ signaling, nNOS, and p-tau formation and enhances short-term synaptic plasticity. Lithium could serve as an effective treatment or co-therapeutic for AD.
Mindfulness Prevents Depression and Psychopathology in Elderly People with Mild to Moderate Alzheimer’s Disease: A Randomized Clinical Trial
Authors: Quintana-Hernández Domingo J. , Rojas-Hernández Jaime , Santana-del Pino Angelo , Céspedes Suárez Carmen , Pellejero Silva Mónica , Miró-Barrachina María Teresa , Ibáñez Fernández Ignacio , Estupiñán López José Antonio , Borkel Lucas F.
Published: November 2022

Background: This longitudinal study addressed whether mindfulness practice prevents psychological and behavioral symptoms, especially mood disorders, in Alzheimer’s disease (AD). Objective: To assess the incidence of depression in the course of AD and to determine which non-pharmacological treatment (NPT) is most effective in preventing psychopathological symptoms. Methods: We conducted a longitudinal, non-inferiority and equivalence randomized clinical trial, repeated-measures design, with a control group and three experimental treatments: mindfulness, cognitive stimulation, and relaxation. Each experimental group performed three weekly sessions for two years. The pharmacological treatment of all participants was donepezil (10 mg). Participants were patients with probable AD without diagnosed depression from the public neurology services of the Canary Health Service, Spain. Psychological evaluation was performed using the Geriatric Depression Scale (GDS), Hamilton Depression Rating Scale (HDRS), and Neuropsychiatric Inventory (NPI-Q). The statistical analysis included only patients who attended at least 75% of the sessions. A nonparametric, repeated-measures analysis was performed with Kruskal-Wallis H test and between-group differences with Mann-Whitney U test with Bonferroni correction (p < 0.008). Effect size was calculated with partial eta-squared. Results: The results showed significant differences with large effect sizes (η2p>0.14) between mindfulness and the rest of the experimental groups as well as the control in the GDS, HDRS, and NPI-Q scales. Conclusion: Compared to the other experimental groups, only mindfulness prevented the onset of depression and other psychopathologies in early-stage AD. Based on its effectiveness in maintaining cognitive functions and preventing psychopathology, we recommend mindfulness as the first-choice NPT for mild to moderate AD.
A multi-criteria decision-making with regret theory-based MULTIMOORA method under interval neutrosophic environment
Authors: Peng Lijuan , Xu Dongsheng
Published: November 2022

The MULTIMOORA (multiple multi-objective optimization by ratio analysis) method is useful for multiple criteria decision-making method. It is based on expected utility theory and assumes that decision makers are completely rational. However, some studies show that human beings are usually bounded rational, and their regret aversion behaviors play an important role in the decision-making process. Interval neutrosophic sets can more flexibly depict uncertain, incomplete and inconsistent information than single-valued neutrosophic sets. Therefore, this paper improves the traditional MULTIMOORA method by combining the regret theory under interval neutrosophic sets. Firstly, the regret theory is used to calculate the utility value and regret-rejoice value of each alternatives. Secondly, the criteria weights optimization model based on the maximizing deviation is constructed to obtain the weight vector. Then, the MULTIMOORA method is used to determine the order of the alternatives. Finally, an illustrative example about school selection is provided to demonstrate the feasibility of the proposed method. Sensitivity analysis shows the validity of the regret theory in the proposed method, and the ranking order change with different regret avoidance parameter. Comparisons are made with existing approaches to illustrate the advantage of the proposed method in reflecting decision makers’ psychological preference.
Image classification using gaussian curvature based local tetra pattern descriptor with enhanced feature selection technique
Authors: Lakshmi Narayanan S. , Ignatia K. Majella Jenvi , Alfurhood Badria Sulaiman , Bhat Nagaraj
Published: November 2022

A Gaussian Curvature-based Local Tetra Descriptor (GCLTrP) is proposed in this paper to incorporate geometric discriminative feature extraction using a hybrid combination of Gaussian Curvature (GC) and Local Terta Pattern (LTrP). The texture of an image is locally discriminant, capturing the equivalent binary response from the gaussian curvature. The extracted feature value is fed into the Enhanced Grey Wolf Optimization (EGWO), a lightweight metaheuristic searching algorithm that selects the best optimal textural features. The proposed GCLTrP with EGWO method’s effective performance is validated using the benchmarks dataset, and the results are tested using the performance evaluation metric. In comparison to other cutting-edge methods, the proposed method achieves the highest overall classification accuracy of 100% on the Brodatz and RS datasets. In terms of computational redundancy and noise reduction, the proposed technique outperforms the other existing techniques.
RETRACTED: Multiple vehicles tracking and detection using weight high order singular value decomposition dimensionality reduction and double classifiers
Authors: Ranjeeth Kumar C. , Kalaiarasu M.
Published: November 2022

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433.
Design of optimal power supply parameters for industrial expansion
Authors: Wu Guanghua , Li Hongsheng , Li Hongyu , Guo Shiping , Ma Wenjian , Dong Jing
Published: November 2022

The business expansion installation can only simply record the most basic business information, which leads to the problems of complex power supply procedures and low efficiency. Therefore, a study on the optimal power supply parameters of the business expansion installation based on grey correlation degree and fuzzy C-means clustering algorithm is proposed. Firstly, the grey correlation degree is used to process the optimal power supply parameter data of industrial expansion and installation, and the parameters of fuzzy C-means clustering algorithm are set. On this basis, an intelligent management system for the optimal power supply process of industrial expansion and installation is constructed, and the system development conditions are set up; According to the four business links of project reserve, business acceptance, collaborative operation and performance evaluation, the customer business expansion and installation function module is constructed, so as to realize the calculation of the optimal power supply line of the business expansion and installation and complete the research on the optimal power supply parameters. The experimental results show that the output stability, output throughput performance and parameter optimization ability of this method for the line impedance characteristic control of the power supply of the industrial expansion device are good and are always on the rise. At 3 cm, the output throughput reaches 1.9%, and the parameter analysis ability can reach 350 pixels, which has certain application value.
Investment decision making in the fuzzy context: An integrated model approach
Authors: Zhang Shasha , Liu Xiaodi , Garg Harish , Zhang Shitao
Published: November 2022

With the implementation and steady progress of the Belt and Road (B&R) initiative, China’s investment in countries along the B&R has maintained a high growth trend. Generally speaking, investment problems are often accompanied by high risk and uncertainty, and how to make the suitable investment decision is a difficult issue. This paper investigates an investment decision approach under the probabilistic hesitant fuzzy environment. Firstly, a new probabilistic hesitant fuzzy distance and correlation coefficient are defined to overcome the defects of the existing probabilistic hesitant fuzzy information measures. Secondly, an attribute weight integrated model is constructed by combining the maximum deviation method, the CRITIC method and the maximum entropy principle, which is able to take into account the correlation between attributes and make full use of the decision information. In addition, a disappointment theory-based probabilistic hesitant fuzzy multi-attribute decision making (PHFMADM) method is proposed to solve the investment decision problem, which can integrate the psychological behavior of decision makers into the decision making process and make the decision results more authentic and reliable. Finally, the rationality and validity of the method are verified by comparing with the existing methods.
Lemon-juice derived highly efficient S-GQD/GO composite as a photocatalyst for regeneration of coenzyme under solar light
Authors: Yadav Shesh Nath , Kumar Brijesh , Yadav Rajesh K. , Gupta Sarvesh Kumar , Singh Pooja , Singh Chandani , Singh Atul P.
Published: November 2022

The innovation of a highly efficient and inexpensive graphene oxide-based photocatalyst is a challenging task for selective solar chemical regeneration/coenzyme such as nicotinamide adenine dinucleotide (NADH). Herein, we have designed lemon-juice derived highly efficient S-GQD/GO composite as a photocatalyst for regeneration of NADH under solar light. The rational design of a highly efficient photocatalytic system through the orientation of S-GQD on graphene oxide as solar light harvesting photocatalyst is explored for the first time for NADH regeneration. This highly solar light active S-GQD/GO composite photocatalyst upon integration with the NAD+ is used for highly regioselective regeneration of coenzyme (76.36%). The present work provides the benchmark instances of graphene oxide-based material as a photocatalyst for selective regeneration of NADH under solar light and opens a new door for green synthesis.
Evaluation of the antibacterial activity of zinc oxide nanoparticles doped with copper ions and co-doped with copper and silver ions
Authors: Nohegar Shadi Ashraf , Nejaei Arazoo , Fataei Ebrahim , Ramezani Mohammad Ebrahim , Eslami Parvin Alizadeh
Published: November 2022

The current feasibility study deals with the elimination of Escherichia coli (gram-negative) and Staphylococcus aureus (gram-positive) bacterial strains isolated from swimming pools using zinc oxide (ZnO) nanoparticles (NPs) doped with copper (Cu2+) ions (CuX%/ZnO NPs) and co-doped with copper (Cu2+) and silver (Ag+) ions (AgX%/CuY%/ZnO NPs) synthesized by sol-gel method. Antibacterial activity was evaluated by Agar well diffusion assay. As-produced NPs were characterized by X-ray diffraction, Field emission-scanning electron microscopy, Energy Dispersive X-Ray and Transmission electron microscopy techniques. The results showed that the size of the co-doped NPs was smaller than that of mono-doped NPs. Meanwhile, co-doped Ag5%/Cu5%/ZnO NPs had the maximum bactericidal activity, and the destructive effect on Gram-positive bacteria was greater than that on Gram-negative bacteria. The lowest effective nanoparticle concentrations were 0.1 and 0.05 g/mL. The main bactericidal mechanism, in addition to the size of co-doped NPs, was due to the formation of reactive oxygen species, so that the destruction of the bacterial cell wall and finally death occurred through the radicals formed.