bluuebunny/crossref_metadata_2025 huggingface dataset:
Implementasi Kontrol PID Untuk Optimasi Suhu Boiler Pada Mesin Kopi Espresso
Authors: Hidayatulloh Fajar Suryansyah , Dirgantara Wahyu , Permatasari Delila Cahya
Published: November 2023

Teknologi pada mesin kopi pembuat espresso mulai banyak digunakan pada coffee shop maupun rumahan dan menjadi perceppatan dalam pembuatan kopi saat ini. Teknologi yang umum digunakan pada mesin kopi adalah proses ekstraksi pada pembuatan kopi hanya menggunakan satu sistem saja dimana untuk pembuatan jenis kopi lain akan menseting ulang.  Namun pada penelitian ini teknologi mesin kopi menggunakan tiga jenis tombol yang dimana memiliki sistem variann ekstraksi di masing-masing sistem. Mesin kopi espresso adalah pengekstrak bubuk kopi. Bubuk kopi dimasukkan pada portafilter, kemudian pompa mendorong air dari boiler yang sudah dipanaskan oleh heater menuju group head dan bubuk kopi akan didorong keluar oleh air untuk diekstrak sari kopi tersebut. Untuk menghasilkan kopi secara kontinyu maka suhu pada boiler    harus diatur secara tepat dan dikontrol dengan heater secara tepat. Sistem kontrol suhu pada mesin kopi ini menggunakan metode PID, pengaturan suhu pada bagian boiler dengan heater yang akan memanaskan air dalam boiler. Hasil penelitian menggunakan metode PID untuk kontrol suhu mesin kopi ini diperoleh nilai Kp=51, Ki=2,8 dan Kd=229,5 dapat stabil disuhu  93°C.
Optimizing Neural Networks Using Particle Swarm Optimization (PSO) Algorithm for Hypertension Disease Prediction
Authors: Sudriyanto Sudriyanto
Published: November 2023

Penyakit hipertensi atau tekanan darah tinggi merupakan masalah kesehatan yang signifikan secara global. Prediksi yang akurat tentang risiko hipertensi dapat membantu dalam pencegahan, diagnosa, dan pengobatan dini. Dalam penelitian ini, kami mengusulkan penggunaan algoritma Particle Swarm Optimization (PSO) untuk mengoptimalkan jaringan saraf dalam prediksi penyakit hipertensi. Metode ini menggabungkan keunggulan jaringan saraf dalam pemodelan yang kompleks dengan kemampuan PSO dalam mencari solusi optimal. Proses optimisasi dilakukan dengan mengatur bobot dan biases dalam jaringan saraf menggunakan PSO. Kami menggunakan dataset yang telah dipersiapkan dengan fitur-fitur yang relevan untuk prediksi hipertensi. Performa jaringan saraf sebelum dan sesudah optimisasi dievaluasi menggunakan metrik evaluasi seperti Root Mean Squared Error (RMSE) dan Squared Error. Neural Network plus Particle Swarm Optimization (NN + PSO) menghasilkan nilai Root Mean Square Error (RMSE) yang lebih rendah (0.170) dibandingkan dengan metode Neural Network (NN) tanpa Particle Swarm Optimization (0.197). Selain itu, metode NN berbasis PSO juga menghasilkan squared error yang lebih rendah (0.029) dibandingkan dengan NN (0.039). Hasil eksperimen menunjukkan bahwa penggunaan algoritma PSO meningkatkan performa neural network dalam prediksi hipertensi. Terdapat penurunan yang signifikan dalam nilai RMSE dan squared error setelah mengoptimalkan neural network menggunakan PSO. Hal ini menunjukkan bahwa prediksi yang dihasilkan lebih dekat dengan nilai sebenarnya, dan tingkat akurasi prediksi meningkat.
Pengembangan Sistem Informasi Pelayanan Masyarakat Desa (Simpelmase) Berbasis Web
Authors: Anam Choirul , Susanto Heri , Yanto Dwi , R.G. Fiqri
Published: November 2023

Desa Sumberkledung merupakan penyelenggara urusan pemerintahan dalam bidang pelayanan kependudukan dan administrasi lainnya di lingkup masyarakat Desa. Pelayanan yang dilakukan selama ini belum  memikirkan kenyamanan masyarakat serta kecepatan dalam mengurus suatu dokumen karena semua layanan dilakukan secara konvensional. Di era digital saat ini, teknologi informasi telah memberikan dampak yang signifikan terhadap peningkatan efisiensi dan aksesibilitas pelayanan publik. Tujuan dari sistem ini adalah untuk meningkatkan pelayanan masyarakat di tingkat desa dengan menggunakan teknologi Internet, sehingga desa dapat merespon kebutuhan warga dengan lebih baik dan efisien. Dalam konteks ini, sistem informasi pelayanan masyarakat desa berbasis web nampaknya bisa menjadi solusi yang tepat. Dengan menggunakan teknologi berbasis web, desa dapat memperluas akses warga terhadap informasi dan layanan, mengurangi beban kerja dan mengoptimalkan proses administrasi. Metode pengembangan yang digunakan dalam penelitan ini adalah waterfall. Metode Waterfall merupakan pendekatan SDLC paling awal yang digunakan untuk pengembangan perangkat lunak. Pengujian fungsional sistem dilakukan dengan metode black box testing menggunakan metode uji validasi. Pengujian non fungsional yaitu pengujian kesesuaian dengan aplikasi sortsite. Hasil uji kompatibilitas menunjukkan bahwa Sistem Informasi Pelayanan Desa dapat berjalan pada semua browser. Kesimpulan dengan adanya sistem ini, proses permintaan pelayanan dapat menjadi lebih cepat dan efisien. Warga masyarakat dapat mengajukan permohonan atau melaporkan masalah melalui platform online, dan petugas desa dapat dengan cepat menindaklanjuti permintaan tersebut.
Sistem Informasi Geografis Untuk Pemetaan Penerima Bantuan Pangan Non Tunai di Desa Petunjungan
Authors: Khairi Matlubul
Published: November 2023

Kemiskinan merupakan permasalahan yang dihadapi oleh seluruh negara termasuk Indonesia. Kemiskinan tidak hanya terbatas pada dimensi ekonomi, tetapi juga mencakup keterbatasan akses terhadap sumber daya dan peluang yang mendasar bagi kehidupan yang layak. Salah satu upaya pemerintah untuk mengurangi angka kemisikinan dengan bantuan sosial baik secara tunai maupun non tunai. Bantuan pangan non tunai (BPNT) merupakan salah satu program bantuan pemerintah untuk meringan dan mengurangi beban keuangan bagi keluarga miskin dalam kehidupan sehari-hari menggunakan alat pembayaran non tunai seperti kartu elektonik dan aplikasi digital. Penelitian ini mengangkat permasalahan di Desa Petunjungan dalam pengelolaan dan mendistribusikan BPNTserta mengetahui lokasi penerima manfaat BPNT. Sistem dibangun menggunakan tahapan system development life cycle (SDLC) menggunakan pendekatan waterfall. Perancangan sistem dimodelkan dengan menggunakan flowchart system, data flow diagram (DFD) dan entity relationship diagram (ERD). Sistem dibangung dengan menggunakan pemrograman server-side PHP dengan framework codeigneter dan database MySQL. Penelitian ini menghasilkan sistem informasi geografis pemetaan peneriman bantuan pangan non tunai (BPNT) yang dapat membantu dan mempermudah pengelolaan penerima BPNT dan pendistribuian BPNT dengan persentase kelayakan sebesar 85% dengan kategori sangat layak berdasarkan user acceptance testing (UAT). Berdasarkan pengujian blackbox testing Semua fitur dari sistem ini berjalan sesuai dengan fungionalitas tanpa ada kesalahan baik sintaks ataupun logika.
Anlık Gönüllülük Olgusunun Türkiye'deki Afet ve Acil Durum Yönetimi Açısından Değerlendirilmesi
Authors: YÜKSELER Mustafa , YAZGAN Jale , TENİKLER Gökhan
Published: November 2023

Anlık gönüllüler, afet ve acil durumlar sonrası müdahale ve iyileştirme çalışmalarında sıklıkla görülen doğal fenomenlerden biridir. Konuya ilişkin literatür taraması sonucunda, anlık gönüllüler bir yönüyle önemli bir iş gücü ve ekonomik kaynak olarak nitelendirilebilirken, diğer yandan organize olunamaması, afet ve acil durumlar konusunda yeterli bilgi ve beceriden yoksun kişileri de barındırabilmesi, güvenlik açısından risk oluşturulması ve idarenin söz konusu kişilere yönelik yasal sorumluluklarındaki belirsizlik gibi başlıklar yönüyle çeşitli sorunların da birer parçası olabilmektedirler. Bu çalışmada, afet ve acil durumlarda anlık gönüllülerin potansiyel rollerine ilişkin literatür taraması yapılarak, “anlık gönüllü” kavramının Türkçe literatüre kazandırılması ve gelecekte bu konuda yapılacak çalışmalara temel oluşturacak ve ivme kazandıracak bir farkındalığın yaratılması amaçlanmıştır. Bu amaç doğrultusunda, Türkiye’de afet ve acil durumlar ile yasal ve idari düzenlemelere ilişkin olarak ilgili kanunlar, kanun hükmünde kararnameler, kalkınma planları ve stratejik planlar incelenmiştir. Bunun sonucunda, ilgili düzenlemelerde anlık gönüllülere ilişkin düzenleyici hükümlere yer verilmediği ve gelecek planlarında söz konusu gönüllülerin, afet ve acil durum yönetimine ilişkin politika ve stratejilerde dikkate alınmadığı görülmüştür. Farklı afet türlerinin sıklıkla yaşandığı Türkiye’de, afet ve acil durumlar sonrası yürütülen faaliyetlerde yer alan anlık gönüllüler ile ilgili yasal zeminin oluşturulması, afet yönetim sistemine entegre edilmesi ve afetlere dirençli bir toplum oluşturmak için anlık gönüllü faaliyetleri konusunda bilinçlendirilmesi gerekmektedir.
SECURITIES ACCOUNT OPENING PADA PT. PHINTRACO SEKURITAS CABANG SURABAYA
Authors: Rachma Agustina  , Supriyatna
Published: November 2023

The process of involving investors to participate in the capital market requires a form of complete information relating to the status of investors/potential investors, both internally and externally to the company, which allows accuracy and precision to be obtained in an effort to anticipate the risks of investing in the capital market. PT. Phintraco Sekuritas, which in this case is a securities broker, requires several documents to open a client's securities account. The implementation of this service is carried out to guide the mechanism for opening a securities account at PT. Phintraco Sekuritas Surabaya Branch. The method in this activity is carried out by conducting an in-depth study of the mechanism for opening a securities account based on facts. Furthermore, providing assistance and guidance to each investor who intends to open a securities account. The results of the activity show that the opening of stock accounts is divided into 2, namely conventional and sharia, while the documents required for opening include administrative documents in the form of e-KTP, Student Identity Card (for those who are students), 10,000 stamp duty and NPWP. The next requirement that must be completed is the initial account balance of 100,000 which will be sent to the customer's RDN account.
Predicting the Gap in the Day-Ahead and Real-Time Market Prices Leveraging Exogenous Weather Data
Authors: Nizharadze Nika , Farokhi Soofi Arash , Manshadi Saeed
Published: November 2023

Predicting the price gap between the day-ahead Market (DAM) and the real-time Market (RTM) plays a vital role in the convergence bidding mechanism of Independent System Operators (ISOs) in wholesale electricity markets. This paper presents a model to predict the values of the price gap between the DAM and RTM using statistical machine learning algorithms and deep neural networks. In this paper, we seek to answer these questions: What will be the impact of predicting the DAM and RTM price gap directly on the prediction performance of learning methods? How can exogenous weather data affect the price gap prediction? In this paper, several exogenous features are collected, and the impacts of these features are examined to capture the best relations between the features and the target variable. An ensemble learning algorithm, namely the Random Forest (RF), is used to select the most important features. A Long Short-Term Memory (LSTM) network is used to capture long-term dependencies in predicting direct gap values between the markets stated. Moreover, the advantages of directly predicting the gap price rather than subtracting the price predictions of the DAM and RTM are shown. The presented results are based on the California Independent System Operator (CAISO)’s electricity market data for two years. The results show that direct gap prediction using exogenous weather features decreases the error of learning methods by 46%. Therefore, the presented method mitigates the prediction error of the price gap between the DAM and RTM. Thus, the convergence bidders can increase their profit, and the ISOs can tune their mechanism accordingly.
Minimally Invasive Sampling Methods for Molecular Sexing of Wild and Companion Birds
Authors: Turcu Maria-Carmen , Paștiu Anamaria Ioana , Bel Lucia-Victoria , Pusta Dana Liana
Published: November 2023

Birds are highly social and must be paired in order to increase their welfare. Most bird species are monomorphic; therefore, molecular sexing helps provide appropriate welfare for birds. Moreover, early sex determination can be of great value for bird owners. The aim of this study was to demonstrate that sex identification in birds achieved using molecular methods and samples collected via minimally invasive methods is fast, efficient, and accurate. A total of 100 samples (29 paired samples of feathers and oral swabs and 14 tripled samples of feathers, oral swabs, and blood) from 43 birds were included in this study, as follows: wild birds (Falconiformes, Accipitriformes, landfowl—Galliformes, waterfowl—Anseriformes) and companion birds (Passeriformes, Psittaciformes—large-, medium-, and small-sized parrots). Amplification of CHD1-Z and CHD1-W genes was performed via conventional PCR. The results obtained from feathers were compared to those obtained from oral swabs and to those obtained from blood samples, where applicable. The obtained results show that all types of samples can be used for molecular sexing of all studied bird species. To the best of our knowledge, the present study reports, for the first time, molecular sex identification in Red Siskin (Carduelis cucullata) and Goldfinch (Carduelis carduelis major). For higher accuracy, our recommendation is to use minimally invasive samples (oral swabs and feathers) and to test both types of samples for each bird instead of blood samples.
Dietary Oyster Mushroom (Pleurotus ostreatus) Waste Inhibits Experimentally Induced Eimeria tenella Challenge in Japanese Quails Model
Authors: Nasir Jamal Abdul , Chand Naila , Naz Shabana , Alhidary Ibrahim A. , Khan Rifat U. , Batool Sajida , Zelai Noha T. , Pugliese Gianluca , Tufarelli Vincenzo , Losacco Caterina
Published: November 2023

The aim of this study was to investigate the potential of dietary 3% oyster mushroom (Pleurotus ostreatus) waste in enhancing the anticoccidial effects in broilers challenged with Eimeria tenella infection. The experiment involved a total of 600 Japanese quails, raised from one to thirty-five days of age, which were divided into four treatment groups. These included a negative control group that received a basal diet (BD) without any anticoccidial or antibiotic supplementation in the non-challenged birds (negative control, NC); a positive control (PC) group consisting of NC birds challenged with E. tenella; a group that received the BD with an anticoccidial drug (standard); and a group that received the BD supplemented with 3% waste from oyster mushrooms (3% Pleurotus ostreatus). The results showed that the feed intake, body weight gain, and feed efficiency were significantly lower in the PC (p < 0.05). However, the growth traits were similar in the standard and 3% Pleurotus ostreatus-treated groups. Similarly, there was no difference (p < 0.05) in the mortality rate, oocyst count in the feces, and lesion score between the standard and 3% Pleurotus ostreatus groups. Based on intestinal histology evaluation, the villi height and width were significantly higher in the standard and 3% Pleurotus ostreatus-treated groups compared to those of the PC (p < 0.01). In conclusion, it was found that 3% Pleurotus ostreatus effectively mitigated the low growth rate of Japanese quails induced by coccidial infection.
Differential Modulation of Markers of Oxidative Stress and DNA Damage in Arterial Hypertension
Authors: Kreutzmann Moritz , Kraus Bettina J. , Christa Martin , Störk Stefan , Jansen Eugène H. J. M. , Stopper Helga , Schupp Nicole
Published: November 2023

Patients with arterial hypertension have an increased risk of developing tumors, particularly renal cell carcinoma. Arterial hypertension is linked to DNA damage via the generation of oxidative stress, in which an upregulated renin–angiotensin–aldosterone system plays a crucial role. The current study investigated surrogates of oxidative stress and DNA damage in a group of hypertensive patients (HypAll, n = 64) and subgroups of well (HypWell, n = 36) and poorly (HypPoor, n = 28) controlled hypertensive patients compared to healthy controls (n = 8). In addition, a longitudinal analysis was performed with some of the hypertensive patients. Markers for oxidative stress in plasma (SHp, D-ROM, and 3-nitrotyrosine) and urine (8-oxodG, 15-F2t-isoprostane, and malondialdehyde) and markers for DNA damage in lymphocytes (γ-H2AX and micronuclei) were measured. In HypAll, all markers of oxidative stress except malondialdehyde were increased compared to the controls. After adjustment for age, this association was maintained for the protein stress markers SHp and 3-nitrotyrosine. With regard to the markers for DNA damage, there was no difference between HypAll and the controls. Further, no significant differences became apparent in the levels of both oxidative stress and DNA damage between HypWell and HypPoor. Finally, a positive correlation between the development of blood pressure and oxidative stress was observed in the longitudinal study based on the changes in D-ROM and systolic blood pressure. In conclusion, we found increased oxidative stress in extensively treated hypertensive patients correlating with the level of blood-pressure control but no association with DNA damage.
A Review on Local Failure Probability Sensitivity Analysis
Authors: Chiron Marie , Morio Jérôme , Dubreuil Sylvain
Published: November 2023

When assessing the reliability of a system, a mathematical model is often defined to replicate the system’s behavior. The inputs of the system are then gathered into two categories, random inputs and deterministic inputs. The failure of the system depends on both categories and here we focus on the influence of the deterministic inputs. Local failure probability sensitivity analysis consists in computing the derivatives of the failure probability with respect to these deterministic parameters and is a fundamental step in reliability-based design optimization. These sensitivities also provide valuable insights into how specific model parameters affect the failure probability, allowing engineers and designers to make informed decisions about adjusting those parameters to enhance reliability. This article explores various techniques developed in the literature for assessing the sensitivity of failure probability with respect to distribution or design parameters. Depending on the nature of the deterministic parameters and the selected input space, different methods are available. The statistical characteristics of the resulting estimates as well as their computational cost are discussed here, for comparison purpose.
A Review of Pool-Boiling Processes Based on Bubble-Dynamics Parameters
Authors: Xiao Longhuang , Zhuang Yuan , Wu Xilei , Yang Jialiang , Lu Yongjie , Liu Ying , Han Xiaohong
Published: November 2023

Immersion cooling is widely used for thermal management of servers. The two-phase immersion cooling, which transfers heat by boiling, possesses efficient temperature control ability under intensive heat generation. In the process of temperature control through boiling, the generation and transportation of bubbles play a crucial role in calculating the heat-transfer capacity. Therefore, it holds immense significance to obtain a profound understanding of the mechanisms underlying bubble formation and detachment. Currently, numerous mechanistic explanations and empirical correlations have been proposed to elucidate the various parameters of bubbles during the boiling process. These findings were considered to be valuable references when selecting appropriate boiling media and designing efficient heating surfaces. To comprehensively present the progress of bubble formation and heat transfer in the boiling system, the forces exerted on the bubbles are highlighted in this article. A meticulous review of bubble-force analysis and correlation formulae pertaining to various relevant parameters (e.g., nucleation sites density, bubble growth rate, bubble growth period, and detachment frequency) was conducted. This review article was also expected to provide a novel foundation for further exploration of enhanced boiling heat transfer.
Classification of Alzheimer’s Disease Based on White Matter Connectivity Network
Authors: Yang Xiaoli , Xia Yuxin , Li Zhenwei , Liu Lipei , Fan Zhipeng , Zhou Jiayi
Published: November 2023

Alzheimer’s disease (AD) is one of the most common irreversible brain diseases in the elderly. Mild cognitive impairment (MCI) is an early symptom of AD, and the early intervention of MCI may slow down the progress of AD. However, due to the subtle neuroimaging differences between MCI and normal control (NC), the clinical diagnosis is subjective and easy to misdiagnose. Machine learning can extract depth features from neural images, and analyze and label them to assist the diagnosis of diseases. This paper combines diffusion tensor imaging (DTI) and support vector machine (SVM) to classify AD, MCI, and NC. First, the white matter connectivity network was constructed based on DTI. Second, the nodes with significant differences between groups were screened out by the two-sample t-test. Third, the optimal feature subset was selected as the classification feature by recursive feature elimination (RFE). Finally, the Gaussian kernel support vector machine was used for classification. The experiment tested and verified the data downloaded from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, and the area under the curve (AUC) of AD/MCI and MCI/NC are 0.94 and 0.95, respectively, which have certain competitive advantages compared with other methods.
Residual Transformer YOLO for Detecting Multi-Scale Crowded Pedestrian
Authors: Ye Hechao , Wang Yanni
Published: November 2023

Crowding and occlusion pose significant challenges for pedestrian detection, which can easily lead to missed and false detections for small-scale and occluded pedestrian objects in dense pedestrian scenarios. To enhance dense pedestrian detection accuracy, we propose the Residual Transformer YOLO (RT-YOLO) algorithm in this paper. The RT-YOLO algorithm enhances the multi-scale fusion strategy based on YOLOv7 and introduces a dedicated detection layer for small-scale occluded targets. It also integrates Resnet and Transformer structures to improve the small-scale feature layer and detection head, enhancing feature extraction capabilities. Additionally, the RT-YOLO algorithm incorporates the Normalization-based Attention Module (NAM) into the backbone and neck networks to identify the region of interest. The experiments demonstrate that on the CrowdHuman and WiderPerson datasets, at IOU (Intersection over Union) = 0.5, the overall improvement in mAP50 is 3.8% and 3.4%. In the IOU range from 0.5 to 1, the improvement in mAP50: 95 is 5.1% and 4%. RT-YOLO achieves an FPS of 67, maintaining real-time performance. On the VOC2007 dataset, mAP50 has been enhanced by 5.1%, indicating higher effectiveness and robustness.
Deep Learning-Enabled Heterogeneous Transfer Learning for Improved Network Attack Detection in Internal Networks
Authors: Wang Gang , Liu Dong , Zhang Chunrui , Hu Teng
Published: November 2023

Cybersecurity faces constant challenges from increasingly sophisticated network attacks. Recent research shows machine learning can improve attack detection by training models on large labeled datasets. However, obtaining sufficient labeled data is difficult for internal networks. We propose a deep transfer learning model to learn common knowledge from domains with different features and distributions. The model has two feature projection networks to transform heterogeneous features into a common space, and a classification network then predicts transformed features into labels. To align probability distributions for two domains, maximum mean discrepancy (MMD) is used to compute distribution distance alongside classification loss. Though the target domain only has a few labeled samples, unlabeled samples are adequate for computing MMD to align unconditional distributions. In addition, we apply a soft classification scheme on unlabeled data to compute MMD over classes to further align conditional distributions. Experiments between NSL-KDD, UNSW-NB15, and CICIDS2017 validate that the method substantially improves cross-domain network attack detection accuracy.
Semi-Supervised Drivable Road Segmentation with Expanded Feature Cross-Consistency
Authors: Ma Shangchen , Song Chunlin
Published: November 2023

Drivable road segmentation aims to sense the surrounding environment to keep vehicles within safe road boundaries, which is fundamental in Advance Driver-Assistance Systems (ADASs). Existing deep learning-based supervised methods are able to achieve good performance in this field with large amounts of human-labeled training data. However, the process of collecting sufficient fine human-labeled data is extremely time-consuming and expensive. To fill this gap, in this paper, we innovatively propose a general yet effective semi-supervised method for drivable road segmentation with lower labeled-data dependency, high accuracy, and high real-time performance. Specifically, a main encoder and a main decoder are trained in the supervised mode with labeled data generating pseudo labels for the unsupervised training. Then, we innovatively set up both auxiliary encoders and auxiliary decoders in our model that yield feature representations and predictions based on the unlabeled data subjected to different elaborated perturbations. Both auxiliary encoders and decoders can leverage information in unlabeled data by enforcing consistency between predictions of the main modules and those perturbed versions from auxiliary modules. Experimental results on two public datasets (Cityspace and CamVid) verify that our proposed algorithm can almost reach the same performance with high FPS as a fully supervised method with 100% labeled data with only utilizing 40% labeled data in the field of drivable road segmentation. In addition, our semi-supervised algorithm has a good potential to be generalized to all models with an encoder–decoder structure.
Stability Analysis of Trench Wall for Diaphragm Wall in Ultra-Deep Circular Foundation Pit: A Comprehensive Investigation
Authors: Xu Qianwei , Xie Jinli , Sun Zili , Lu Linhai , Yu Hangfei
Published: November 2023

Circular diaphragm walls are increasingly being used in ultra-deep foundation pits due to their arch-shaped bearing system, which provides reasonable structural support. The trench walls that form the circular ground connection wall are typically double-angled in shape, and their stability analysis remains a challenge. In this paper, an instability model for double-angled trench walls based on 3D sliding body analysis is proposed. The objective of this paper is to determine the minimum amount of slurry needed to maintain the integrity of the trench wall. The results show that the center of symmetry on the inside of the wall is the most vulnerable to damage, followed by the inside corner, and then the center and corner on the outside. The consideration of sliding bodies in overall and local stability calculations for double-angled trench wall shapes can provide a reasonable stability assessment.
Investigation of the Gas Pressure Field and Production Rate for Two Typical Proppants: Small-Sized Continuous and Large-Sized Discontinuous Proppants
Authors: Gu Wei , Cheng Qingying , Xu Dalong , Yao Sumeng , Li Heng
Published: November 2023

The small-sized proppant is widely used in the traditional hydraulic fracturing reservoir stimulation, but the theoretical research shows that the large-sized proppant can greatly improve the fracture permeability. Although many scholars have proposed the method of using large-sized proppant, the characteristics of the pressure field change and gas drainage rate during reservoir development are still unclear after pumping large-sized proppant. In this article, to study the change regularity of the reservoir pressure field, two representative proppants are selected: the small-sized proppant and large-sized proppants. First, the Navier–Stokes equations are solved using the numerical simulation method, and the characteristics of the reservoir pressure field are finely reproduced. Then, the production rate is discussed to reveal the huge potential of large-sized proppant for the natural gas development. The results show that under the same conditions, if the particle size of the proppant increases by 5 times, then the reservoir permeability will increase by 27 times approximately, and the single well production efficiency will increase by 17~19 times in the first 3000 days. In addition, a new quantitative model is proposed to evaluate the permeability magnification of the fracture and reservoir when adopting the large-sized proppant. This study further confirms that the method of large-sized proppant proposed by the author in the earlier stage has great potential. This study is helpful for the researchers and engineers to better understand the evolution regularity of the reservoir pressure field and gas production rate in the process of oil and gas exploitation after using the large-sized proppant.
The RNA-Binding Function of Ribosomal Proteins and Ribosome Biogenesis Factors in Human Health and Disease
Authors: Catalanotto Caterina , Barbato Christian , Cogoni Carlo , Benelli Dario
Published: November 2023

The ribosome is a macromolecular complex composed of RNA and proteins that interact through an integrated and interconnected network to preserve its ancient core activities. In this review, we emphasize the pivotal role played by RNA-binding proteins as a driving force in the evolution of the current form of the ribosome, underscoring their importance in ensuring accurate protein synthesis. This category of proteins includes both ribosomal proteins and ribosome biogenesis factors. Impairment of their RNA-binding activity can also lead to ribosomopathies, which is a group of disorders characterized by defects in ribosome biogenesis that are detrimental to protein synthesis and cellular homeostasis. A comprehensive understanding of these intricate processes is essential for elucidating the mechanisms underlying the resulting diseases and advancing potential therapeutic interventions.
Study on the Frost Resistance of Composite Limestone Powder Concrete against Coupling Effects of Sulfate Freeze–Thaw
Authors: Zhang Mingming , Lv Henglin , Zhou Shuchun , Wu Yuanzhou , Zheng Xiaowei , Yan Qiyao
Published: November 2023

Concrete in saline or coastal settings exposed to freezing temperatures is frequently affected by coupling actions of sulfate assault and freeze–thaw degradation, reducing the service life of concrete structures significantly. This study conducted an accelerated freeze–thaw cycle test in pure water and Na2SO4 solution with a mass proportion of 5% to examine the coupling impact of sulfate freeze–thaw on the frost resistance of composite limestone powder (CLP) concrete. Combined with SEM and XRD methods, the performance degradation mechanisms of composite limestone powder (CLP) concrete in coupling sulfate freeze–thaw conditions were analyzed with a microscopic point of view. The findings demonstrated that limestone powder has a filling effect but the activity is low. When the content is 10~20%, the chemical response is higher than the physical response. The pozzolanic effect of fly ash and slag can improve the pore structure and improve the compactness of concrete. The “superposition effect” of limestone powder, fly ash, and slag can improve the frost resistance of CLP concrete. The scenario of salt freezing cycles has negative effects that are worse than those of water freezing cycles on the antifreeze performance of CLP concrete, including apparent morphology, mass loss, relative dynamic modulus of elasticity, and compressive strength. Sulfate’s activation effect boosts slag’s activity effect, which significantly promotes the antifreeze performance of concrete subjected to salt frozen cycles over water frozen cycles. The freeze–thaw damage model of CLP concrete under coupling sulfate freeze–thaw is established through theorem analysis and experiment statistics, laying a theoretical framework for the popularization and use of this concrete.
Adolescent Intermittent Ethanol Drives Modest Neuroinflammation but Does Not Escalate Drinking in Male Rats
Authors: Wooden Jessica I. , Peacoe Lauren E. , Anasooya Shaji Chinchusha , Melbourne Jennifer K. , Chandler Cassie M. , Bardo Michael T. , Nixon Kimberly
Published: November 2023

During adolescence, the brain is highly susceptible to alcohol-induced damage and subsequent neuroimmune responses, effects which may enhance development of an alcohol use disorder (AUD). Neuroimmune reactions are implicated in adolescent alcohol exposure escalating adulthood drinking. Therefore, we investigated whether intermittent alcohol exposure in male, adolescent rats (AIE) escalated adult drinking via two-bottle choice (2BC). We also examined the influence of housing environment across three groups: standard (group-housed with enrichment during 2BC), impoverished (group-housed without enrichment during 2BC), or isolation (single-housed without bedding or enrichment throughout). In the standard group immediately after AIE/saline and after 2BC, we also examined the expression of microglial marker, Iba1, reactive astrocyte marker, vimentin, and neuronal cell death dye, FluoroJade B (FJB). We did not observe an escalation of adulthood drinking following AIE, regardless of housing condition. Further, only a modest neuroimmune response occurred after AIE in the standard group: no significant microglial reactivity or neuronal cell death was apparent using this model, although some astrocyte reactivity was detected in adolescence following AIE that resolved by adulthood. These data suggest that the lack of neuroimmune response in adolescence in this model may underlie the lack of escalation of alcohol drinking, which could not be modified through isolation stress.
Impact of an Educational Clinical Video Combined with Standard Helping Babies Breathe Training on Acquisition and Retention of Knowledge and Skills among Ethiopian Midwives
Authors: Heard Stittum Amara J. , Edwards Erika M. , Abayneh Mahlet , Gebremedhin Asrat Demtse , Horn Delia , Berkelhamer Sara K. , Ehret Danielle E. Y.
Published: November 2023

Helping Babies Breathe (HBB) is an evidence-based neonatal resuscitation program designed for implementation in low-resource settings. While HBB reduces rates of early neonatal mortality and stillbirth, maintenance of knowledge and skills remains a challenge. The extent to which the inclusion of educational clinical videos impacts learners’ knowledge and skills acquisition, and retention is largely unknown. We conducted a cluster-randomized controlled trial at two public teaching hospitals in Addis Ababa, Ethiopia. We randomized small training group clusters of 84 midwives to standard HBB vs. standard HBB training supplemented with exposure to an educational clinical video on newborn resuscitation. Midwives were followed over a 7-month time period and assessed on their knowledge and skills using standard HBB tools. When comparing the intervention to the control group, there was no difference in outcomes across all assessments, indicating that the addition of the video did not influence skill retention. Pass rates for both the control and intervention group on bag and mask skills remained low at 7 months despite frequent assessments. There is more to learn about the use of educational videos along with low-dose, high-frequency training and how it relates to retention of knowledge and skills in learners.
Regional to Mesoscale Influences of Climate Indices on Tornado Variability
Authors: Corey Cooper P. , Senkbeil Jason C.
Published: November 2023

Tornadoes present an undisputable danger to communities throughout the United States. Despite this known risk, there is a limited understanding of how tornado frequency varies spatially at the mesoscale across county or city area domains. Furthermore, while previous studies have examined the relationships between various climate indices and continental or regional tornado frequency, little research has examined their influence at a smaller scale. This study examines the relationships between various climate indices and regional tornado frequency alongside the same relationships at the mesoscale in seven cities with anomalous tornado patterns. The results of a correlation analysis and generalized linear modeling show common trends between the regions and cities. The strength of the relationships varied by region, but, overall, the ENSO had the greatest influence on tornado frequency, followed in order by the PNA, AO, NAO, MJO, and PDO. However, future research is critical for understanding how the effects of climate indices on tornado frequency vary at different spatial scales, or whether other factors are responsible for the atypical tornado rates in certain cities.
How Sn(IV) Influences on the Reaction Mechanism of 11, tri-Butyl p-Coumarate and Its tri-Butyl-tin p-Coumarate Considering the Solvent Effect: A DFT Level Study
Authors: Delgado-Alfaro Rogelio A. , Gómez-Sandoval Zeferino
Published: November 2023

Antioxidants are molecules that neutralize free radicals. In general, the reaction mechanisms of antioxidants are well known. The main reaction mechanisms of antioxidants are electron transfer (ET), hydrogen transfer (HT), and radical adduction formation (RAF). The study of these mechanisms is helpful in understanding how antioxidants control high free radical levels in the cell. There are many studies focused on determining the main mechanism of an antioxidant to neutralize a wide spectrum of radicals, mainly reactive oxygen species (ROS)-type radicals. Most of these antioxidants are polyphenol-type compounds. Some esters, amides, and metal antioxidants have shown antioxidant activity, but there are few experimental and theoretical studies about the antioxidant reaction mechanism of these compounds. In this work, we show the reaction mechanism proposed for two esters, 11, tri-butyl p-coumarate and its tri-butyl-tin p-coumarate counterpart, using Sn(IV). We show how Sn(IV) increases the electron transfer in polar media and the H transfer in non-polar media. Even though the nature of esters could be polar or non-polar compounds, the antioxidant activity is good for the Sn(IV)-p-coumarate complex in non-polar media.
Applying Eye Tracking with Deep Learning Techniques for Early-Stage Detection of Autism Spectrum Disorders
Authors: Ahmed Zeyad A. T. , Albalawi Eid , Aldhyani Theyazn H. H. , Jadhav Mukti E. , Janrao Prachi , Obeidat Mansour Ratib Mohammad
Published: November 2023

Autism spectrum disorder (ASD) poses a complex challenge to researchers and practitioners, with its multifaceted etiology and varied manifestations. Timely intervention is critical in enhancing the developmental outcomes of individuals with ASD. This paper underscores the paramount significance of early detection and diagnosis as a pivotal precursor to effective intervention. To this end, integrating advanced technological tools, specifically eye-tracking technology and deep learning algorithms, is investigated for its potential to discriminate between children with ASD and their typically developing (TD) peers. By employing these methods, the research aims to contribute to refining early detection strategies and support mechanisms. This study introduces innovative deep learning models grounded in convolutional neural network (CNN) and recurrent neural network (RNN) architectures, employing an eye-tracking dataset for training. Of note, performance outcomes have been realised, with the bidirectional long short-term memory (BiLSTM) achieving an accuracy of 96.44%, the gated recurrent unit (GRU) attaining 97.49%, the CNN-LSTM hybridising to 97.94%, and the LSTM achieving the most remarkable accuracy result of 98.33%. These outcomes underscore the efficacy of the applied methodologies and the potential of advanced computational frameworks in achieving substantial accuracy levels in ASD detection and classification.
MiRNAs and Their Role in Venous Thromboembolic Complications
Authors: Gareev Ilgiz , Pavlov Valentin , Du Weijie , Yang Baofeng
Published: November 2023

Venous thromboembolic complications (VTCs), which include deep vein thrombosis (DVT) and pulmonary embolism (PE), have remained a pressing problem in modern clinical medicine for a long time. Despite the already wide arsenal of modern methods for diagnosing and treating this disease, VTCs rank third in the structure of causes of death among all cardiovascular diseases, behind myocardial infarction (MI) and ischemic stroke (IS). Numerous studies have confirmed the importance of understanding the molecular processes of VTCs for effective therapy and diagnosis. Significant progress has been made in VTC research in recent years, where the relative contribution of microRNAs (miRNAs) in the mechanism of thrombus formation and their consideration as therapeutic targets have been well studied. In this case, accurate, timely, and as early as possible diagnosis of VTCs is of particular importance, which will help improve both short-term and long-term prognoses of patients. This case accounts for the already well-studied circulating miRNAs as non-invasive biomarkers. This study presents currently available literature data on the role of miRNAs in VTCs, revealing their potential as therapeutic targets and diagnostic and prognostic tools for this terrible disease.
Building Automation Pipeline for Diagnostic Classification of Sporadic Odontogenic Keratocysts and Non-Keratocysts Using Whole-Slide Images
Authors: Mohanty Samahit , Shivanna Divya B. , Rao Roopa S. , Astekar Madhusudan , Chandrashekar Chetana , Radhakrishnan Raghu , Sanjeevareddygari Shylaja , Kotrashetti Vijayalakshmi , Kumar Prashant
Published: November 2023

The microscopic diagnostic differentiation of odontogenic cysts from other cysts is intricate and may cause perplexity for both clinicians and pathologists. Of particular interest is the odontogenic keratocyst (OKC), a developmental cyst with unique histopathological and clinical characteristics. Nevertheless, what distinguishes this cyst is its aggressive nature and high tendency for recurrence. Clinicians encounter challenges in dealing with this frequently encountered jaw lesion, as there is no consensus on surgical treatment. Therefore, the accurate and early diagnosis of such cysts will benefit clinicians in terms of treatment management and spare subjects from the mental agony of suffering from aggressive OKCs, which impact their quality of life. The objective of this research is to develop an automated OKC diagnostic system that can function as a decision support tool for pathologists, whether they are working locally or remotely. This system will provide them with additional data and insights to enhance their decision-making abilities. This research aims to provide an automation pipeline to classify whole-slide images of OKCs and non-keratocysts (non-KCs: dentigerous and radicular cysts). OKC diagnosis and prognosis using the histopathological analysis of tissues using whole-slide images (WSIs) with a deep-learning approach is an emerging research area. WSIs have the unique advantage of magnifying tissues with high resolution without losing information. The contribution of this research is a novel, deep-learning-based, and efficient algorithm that reduces the trainable parameters and, in turn, the memory footprint. This is achieved using principal component analysis (PCA) and the ReliefF feature selection algorithm (ReliefF) in a convolutional neural network (CNN) named P-C-ReliefF. The proposed model reduces the trainable parameters compared to standard CNN, achieving 97% classification accuracy.
Diagnostic Approach to Lower Limb Entrapment Neuropathies: A Narrative Literature Review
Authors: Drăghici Nicu Cătălin , Văcăraș Vitalie , Bolchis Roxana , Bashimov Atamyrat , Domnița Diana Maria , Iluț Silvina , Popa Livia Livinț , Lupescu Tudor Dimitrie , Mureșanu Dafin Fior
Published: November 2023

Entrapment neuropathies of the lower limb are a misunderstood and underdiagnosed group of disorders, characterized by pain and dysesthesia, muscular weakness, and specific provoking movements on physical examination. The most frequent of these syndromes encountered in clinical practice are fibular nerve entrapment, proximal tibial neuropathy, sural nerve neuropathy, deep gluteal syndrome or sciatic nerve entrapment, and lateral femoral cutaneous nerve entrapment, also known as meralgia paresthetica. These are commonly mistaken for lumbar plexopathies, radiculopathies, and musculotendinous diseases, which appear even more frequently and have overlapping clinical presentations. A comprehensive anamnesis, physical examination, and electrodiagnostic studies should help clarify the diagnosis. If the diagnosis is still unclear or a secondary cause of entrapment is suspected, magnetic resonance neurography, MRI, or ultrasonography should be conducted to clarify the etiology, rule out other diseases, and confirm the diagnosis. The aim of this narrative review was to help clinicians gain familiarity with this disease, with an increase in diagnostic confidence, leading to early diagnosis of nerve damage and prevention of muscle atrophy. We reviewed the epidemiology, anatomy, pathophysiology, etiology, clinical presentation, and EDX technique and interpretation of the entrapment neuropathies of the lower limb, using articles published from 1970 to 2022 included in the Pubmed, MEDLINE, Cochrane Library, Google Scholar, EMBASE, Web of Science, and Scopus databases.
The Development of a Compact Pulsed Power Supply with Semiconductor Series Connection
Authors: Zaizen Shohei , Asami Kyohei , Furukawa Takashi , Hatta Takeshi , Nakamura Tsubasa , Sakugawa Takashi , Ueno Takahisa
Published: November 2023

In this study, high-voltage switching was performed by connecting semiconductors in series. By employing Snubber circuits and voltage divider resistors for each semiconductor, the destruction of the semiconductors was prevented. Additionally, a pulse transformer was installed between the function generator and the photocoupler to isolate the gate circuit, preventing electrical discharges in the circuit and enabling operation at an output voltage of 10 kV and an operating frequency of 200 Hz. The temperature of the semiconductors increased with the increase in operating frequency, which was counteracted by connecting charging resistors and capacitors to limit the current to the semiconductors. As a result, operation at 430 Hz became possible. Furthermore, a saturable inductor (SI) was connected to enable continuous operation. The SI delays the rise of the current and creates a phase difference, thereby reducing the power consumption of the conductor and mitigating the temperature rise, enabling continuous operation at 300 Hz. Moreover, by increasing the number of semiconductor series stages to six, an output voltage of 20 kV was confirmed in tests. By using two semiconductor series circuits, the pulsed power supply that can be changed to any pulse width was also created. As a result, output voltages with arbitrary pulse widths from 5 μs to 30 μs were confirmed.