bluuebunny/crossref_metadata_2025_split huggingface dataset:
Вплив технологічних параметрів при виготовленні кумулятивного облицювання на ефективність бронепробиття боєприпасу калібру 40 мм
Authors: Pimanov Valerii , Savchenko Denys , Orliuk Mykhailo , Bisyk Serhiy 
Published: July 2024

Мета роботи: розглянути можливість та особливості виготовлення кумулятивного облицювання боєприпасу калібру 40 мм методами об’ємного та листового штампування; оцінити вплив способів штампування на якість та точність кінцевої геометрії кумулятивного облицювання; за умови появи недосконалостей у геометрії штампованого облицювання дослідити їх вплив на ефективність бронепробиття.
Метод: комп'ютерне моделювання, натурний експеримент.
Результати дослідження: запропоновано технологію виготовлення кумулятивного облицювання вісесиметричним витягуванням; встановлено, що неточності геометрії штампованого облицювання, зумовлені особливостями процесу витягування, практично не впливають на формування кумулятивного струменю та глибину бронепробиття боєприпасу калібру 40 мм.
Теоретична цінність дослідження: результати проведених досліджень можуть бути використанні при оптимізації геометрії кумулятивних облицювань різного типу та уточненні вимог до точності кінцевої геометрії кумулятивного облицювання.
Практична цінність дослідження: запропонована технологія виготовлення кумулятивного облицювання може бути реалізована як у дрібносерійному, так і у масовому виробництві без використання складного технологічного оснащення та обладнання. Це суттєво зменшує собівартість як самого облицювання, так і готового виробу в цілому за збереження необхідної ефективності, що особливо важливо для забезпечення ЗСУ відповідними боєприпасами у необхідній кількості в умовах жорсткого дефіциту ресурсів для їх виготовлення.
Тип статті: науково-практична.
Analisis Daerah Potensi Persebaran Demam Berdarah Dengue di DKI Jakarta dengan Menggunakan Metode Clustering K-Means
Authors: Nurkhairiyyah Hani Dzikra , Zaidiah Ati , Irmanda Helena Nurramdhani 
Published: July 2024

Salah satu penyakit yang masih belum bisa dibendung di Indonesia adalah demam berdarah dengue, penyakit ini dibawa oleh gigitan nyamuk Aedes Aegypti dan Aedes Albopictus. Motivasi di balik penelitian ini adalah untuk mengkarakterisasi daerah yang diperkirakan menjadi titik penyebaran demam berdarah, khususnya di wilayah DKI Jakarta. Teknik data mining digunakan pada penelitian ini, serta clustering sebagai metodenya dengan menerapkan algoritma k-means. Nilai K yang digunakan pada penelitian ini sudah dievaluasi oleh Silhouette yang menghasilkan Silhouette Score sebesar 0,41642 pada K=2, maka cluster pada penelitian ini dibagi menjadi 2 cluster. Clustering menghasilkan daerah sporadis dan daerah endemis. Daerah sporadis mencakup 30 kecamatan, yaitu Gambir, Menteng, Tanah Abang, Cempaka Putih, Senen, Johar Baru, Sawah Besar, Kemayoran, Pasar Rebo, Pulo Gadung, Matraman, Palmerah, Tambora, Taman Sari, Grogol Petamburan, Koja, Kelapa Gading, Pademangan, Tanjung Priok, Pesanggrahan, Mampang Prapatan, Kebayoran Baru, Tebet, Kebayoran Lama, Cilandak, Pancoran, Jagakarsa, Setiabudi, Kepulauan Seribu Selatan, dan Kepulauan Seribu Utara. Daerah endemis mencakup 14 kecamatan, yaitu Cilincing, Penjaringan, Cengkareng, Kalideres, Kebon Jeruk, Kembangan, Pasar Minggu, Ciracas, Cakung, Makasar, Duren Sawit, Kramat Jati, Jatinegara, dan Cipayung. Kata kunci: data mining, clustering, k-means, demam berdarah dengue
Segmentasi Data Sinyal EEG Berdasarkan Domain Waktu Sebagai Dasar Dalam Pengolahan Sinyal Pengambilan Keputusan Dalam Rehabilitasi Stroke
Authors: Sulistyono MY Teguh , Ernawati Dyah , Amodia Stalina Anggraeny Dewi , Putra Davin Hernanda 
Published: July 2024

Penyakit stroke adalah salah satu penyakit kardiofaskuler jika menyerang akan menyebabkan cacad permanen dan meninggal dunia. Proses pemeriksaan membutuhkan dokter hanya berdasarkan visual unruk mendiagnosa penyakit, jika pemeriksaan dilakukan banyak dokter maka diagnose akan berbeda-beda. Unruk menghindari hal tersebut dibutuhkan alat EEG untuk mengambil aktivitas gelombang otak yang hasil pengambilan data tersebut dalam bentuk data mentah. Data mentah agar dapat dihasilkan untuk proses analisis diperlukan pemrosesan sinyal yang terdiri dari band pass filter, cleaning data, segmentasi dan decomposisi. Permasalahan selama ini yang timbul bahwa data mentah tersebt masih dalam bentuk data yang masih banyak noise baik dari pergerakan mata ataupun aktivitas otot., sehingga data mentah yang telah diolah akan menjadi dasar dalam pemilihan feature. Penelitian ini menggunakan metode penelitian menggunakan 2 tahapan yaitu data mentah dan pre processing, dimana pre processing memiliki 3 langkah yaitu band pass filter, cleaning data dan segmentasi.Hasil akhir dari penelitian ini adalah Segmentasi Data Sinyal EEG Berdasarkan Domain Waktu Sebagai Dasar Dalam Pengolahan Sinyal Pengambilan Keputusan Dalam Rehabilitasi Stroke. Kata kunci: EEG, Stroke, Segmentasi, Cleaning Data, Band Pass Filter, Feature
Cycle-based high-intensity sprint exercise elicits acute cognitive dysfunction in psychomotor and memory task performance
Authors: Dufner Trevor J. , Moon Jessica M. , Wells Adam J. 
Published: July 2024

PurposeTo examine the impact of an acute high-intensity sprint exercise protocol (HISEP) for eliciting post-exercise cognitive dysfunction in psychomotor, attentional, executive, and memory tasks.MethodsTwenty-four recreationally active adults (22 ± 4 yrs, 169.39 ± 10.07 cm, 75.80 ± 14.73 kg, 27.03 ± 9.55 BF%) performed a HISEP on a cycle ergometer. Average psychomotor reaction time (avgRT; Dynavision D2 Mode A &amp; Mode B), mood (Profile of Mood States Questionnaire; POMS), and cognition (Automated Neuropsychological Assessment Metrics; ANAM) were assessed pre- (PRE), post- (POST) and 60-min post (60POST) HISEP. One-way repeated measures ANOVAs were used to assess changes across time.ResultsFatigue (main effect: p &lt; 0.001, ηp2 = 0.309) was significantly higher at POST compared to PRE (p = 0.007). Tension (main effect: p = 0.021, ηp2 = 0.154) was significantly lower at 60POST compared to PRE (p = 0.029). Mode A avgRT (main effect: p = 0.022, ηp2 = 0.153) was significantly slower at POST compared to PRE (p = 0.026). Throughput (TP) scores for ANAM code substitution-delayed (CSD) task (main effect: p &lt; 0.001, ηp2 = 0.284) and matching to sample (M2S) tasks (main effect: p = 0.014, ηp2 = 0.169) were significantly lower at POST compared to PRE (p = 0.001 and p = 0.025, respectively), while mathematical processing (main effect: p = 0.002, ηp2 = 0.232) was significantly higher at 60POST compared to both PRE (p = 0.019) and POST (p = 0.005). No other significant changes in cognitive task performance were observed (p's &gt; 0.05).ConclusionsThe HISEP is a feasible and time-effective fatiguing exercise stimulus capable of eliciting acute cognitive dysfunction in psychomotor and memory task performance. NCT05100589.
The association between pulse wave velocity and heart failure: a systematic review and meta-analysis
Authors: Esmaeili Zahra , Bahiraie Pegah , Vaziri Zahra , Azarboo Alireza , Behnoush Amir Hossein , Khalaji Amirmohammad , Bazrgar Aida , Tayebi Pouya , Ziaie Naghmeh 
Published: July 2024

BackgroundThe arterial stiffness measured by pulsed wave velocity (PWV) is associated with heart failure (HF). However, the effectiveness of arterial stiffness and PWV as prognostic indicators in patients with HFpEF and HFrEF is still unclear. In this systematic review and meta-analysis, we synthesized the prognostic value of PWV and arterial stiffness in HF patients.MethodsFour databases, including Embase, PubMed, Scopus, and Web of Science, were systematically searched for published studies assessing the relationship between PWV and HF from inception up to August 31, 2023. The Newcastle-Ottawa Scale (NOS) was used to assess the quality of the included studies. The standardized mean difference (SMD) and their corresponding 95% confidence intervals (CI) were used to compare PWV in HF (HFrEF and HFpEF) and controls. Meta-regressions based on age, year of publication, sample size, and gender (male percentage) were also conducted.ResultsThe systematic search yielded 5,977 results, of which 58 met our inclusion criteria and 24 were analyzed quantitatively. Studies included 64,687 patients with a mean age of 53.7 years, and 41,803 (67.3%) were male. Meta-analysis of 19 studies showed that PWV was significantly higher in HF patients compared to the controls (SMD 1.04, 95% CI 0.43–1.66, P &lt; 0.001, I2 = 93%). Moreover, nine studies have measured PWV among HFrEF and HFpEF patients and found no significant difference (SMD −0.51, 95% CI −1.03 to 0.02, P = 0.057, I2 = 95%). Moreover, increased PWV was linked to an increased chance of developing new-onset HF in individuals with cardiovascular risk factors.ConclusionsPatients with HF exhibit significantly higher arterial stiffness, as indicated by PWV, compared to the normal population. However, this association was not significant between HFrEF and HFpEF patients. Future research is warranted to establish the potential prognostic role of PWV in HF.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023479683, PROSPERO (CRD42023479683).
A stacking ensemble model for predicting the occurrence of carotid atherosclerosis
Authors: Zhang Xiaoshuai , Tang Chuanping , Wang Shuohuan , Liu Wei , Yang Wangxuan , Wang Di , Wang Qinghuan , Tang Fang 
Published: July 2024

BackgroundCarotid atherosclerosis (CAS) is a significant risk factor for cardio-cerebrovascular events. The objective of this study is to employ stacking ensemble machine learning techniques to enhance the prediction of CAS occurrence, incorporating a wide range of predictors, including endocrine-related markers.MethodsBased on data from a routine health check-up cohort, five individual prediction models for CAS were established based on logistic regression (LR), random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost) and gradient boosting decision tree (GBDT) methods. Then, a stacking ensemble algorithm was used to integrate the base models to improve the prediction ability and address overfitting problems. Finally, the SHAP value method was applied for an in-depth analysis of variable importance at both the overall and individual levels, with a focus on elucidating the impact of endocrine-related variables.ResultsA total of 441 of the 1669 subjects in the cohort were finally diagnosed with CAS. Seventeen variables were selected as predictors. The ensemble model outperformed the individual models, with AUCs of 0.893 in the testing set and 0.861 in the validation set. The ensemble model has the optimal accuracy, precision, recall and F1 score in the validation set, with considerable performance in the testing set. Carotid stenosis and age emerged as the most significant predictors, alongside notable contributions from endocrine-related factors.ConclusionThe ensemble model shows enhanced accuracy and generalizability in predicting CAS risk, underscoring its utility in identifying individuals at high risk. This approach integrates a comprehensive analysis of predictors, including endocrine markers, affirming the critical role of endocrine dysfunctions in CAS development. It represents a promising tool in identifying high-risk individuals for the prevention of CAS and cardio-cerebrovascular diseases.
Fast dynamic prediction of consequences of heavy gas leakage accidents based on machine learning
Authors: Fan Chenqing , Gong Haixing , Zhang Yan , Ma Weichun , Yu Qi 
Published: July 2024

The field of emergency risk management in chemical parks has been characterized by a lack of fast, precise and dynamic prediction methods. The application of computational fluid dynamics (CFD) models, which offer the potential for dynamic and precise prediction, has been hindered by high computational costs. Therefore, taking liquid benzene as a case study, this paper combined machine learning (ML) algorithms with a CFD-based precise prediction model, to develop an ML model for fast dynamic prediction of heavy gas leakage consequences in chemical parks. Employing the CFD data as the input, the prediction models were developed using ML algorithms, refined with Bayesian optimization for parameter tuning. This study utilized PHOENICS software to establish a dynamic prediction model for the diffusion of liquid benzene leakage, validated by Burro nine experiment data. Comparative analyses of models based on five ML algorithms were conducted to evaluate the reliability of their predictions using both CFD standard and noisy data. The results indicated that temperature had the most significant effect on the consequences of the leakage accidents among four key factors (wind speed, temperature, leakage aperture and atmospheric stability), followed by wind speed. These factors served as input variables for ML model training. Among the models evaluated, the eXtreme Gradient Boosting (XGBoost) model showed superior performance, irrespective of the presence of noise in the data. An XGBoost-based fast prediction model was ultimately developed for predicting the consequences of liquid benzene leakage. A case analysis was conducted to validate the feasibility of the model prediction. The relative errors between the predicted and actual values of the model for acute exposure guideline level-1 (AEGL-1), AEGL-2, and AEGL-3 distances were 2.70%, 2.58%, and 0.23%, respectively. Furthermore, the XGBoost model completed the prediction in only 0.218 s, a stark contrast to the hours necessitated by the CFD model, thus offering substantial computational time savings while maintaining high accuracy levels. This paper introduces an ML model for fast dynamic prediction of heavy gas leakage, enabling chemical parks to make more timely and accurate decisions in emergency risk management.
Deciphering the genomic insights into the coexistence of congenital scoliosis and congenital anomalies of the kidney and urinary tract
Authors: Wang Haojun , Wen Wen , Yao Mingxi , Yang Tongwang , Chen Dongshan , Wang Wei 
Published: July 2024

BackgroundCongenital scoliosis and congenital anomalies of the kidney and urinary tract are distinct genetic disorders with differing clinical manifestations. Clinically, their coexistence is not rare, but the etiologies of these complex diseases remain largely unknown, especially their shared genetic basis.MethodsWe sequenced the genomes of 40 individuals diagnosed with both CS and CAKUT, alongside 2,764 controls from a Chinese Han population cohort. Our analyses encompassed gene-based and pathway-based weighted rare variant association tests, complemented by copy number variant association analyses, aiming to unravel the shared genomic etiology underlying these congenital conditions.ResultsGene-based analysis identified PTPN11 as a pivotal gene influencing both skeletal and urinary system development (P = 1.95E-21), participating in metabolic pathways, especially the MAPK/ERK pathway known to regulate skeletal and urinary system development. Pathway-based enrichment showed a significant signal in the MAPK/ERK pathway (P = 3E-04), reinforcing the potential role of PTPN11 and MAPK/ERK pathway in both conditions. Additionally, CNV analysis pinpointed IGFLR1 haploinsufficiency as a potential influential factor in the combined CS-CAKUT phenotypic spectrum.ConclusionThis study enriches our understanding of the intricate genomic interplay underlying congenital scoliosis and kidney and urinary tract anomalies, emphasizing the shared genetic foundations between these two disorders.
HIGD1B, as a novel prognostic biomarker, is involved in regulating the tumor microenvironment and immune cell infiltration; its overexpression leads to poor prognosis in gastric cancer patients
Authors: Wang Shibo , Zhang Siyi , Li Xiaoxuan , Li Xiangxue , Zhao Shufen , Guo Jing , Wang Shasha , Wang Rui , Zhang Mengqi , Qiu Wensheng 
Published: July 2024

BackgroundHIGD1B (HIG1 Hypoxia Inducible Domain Family Member 1B) is a protein-coding gene linked to the occurrence and progression of various illnesses. However, its precise function in gastric cancer (GC) remains unclear.MethodsThe expression of HIGD1B is determined through the TCGA and GEO databases and verified using experiments. The association between HIGD1B and GC patients’ prognosis was analyzed via the Kaplan-Meier (K-M) curve. Subsequently, the researchers utilized ROC curves to assess the diagnostic capacity of HIGD1B and employed COX analysis to investigate risk factors for GC. The differentially expressed genes (DEGs) were then subjected to functional enrichment analysis, and a nomogram was generated to forecast the survival outcome and probability of GC patients. Additionally, we evaluated the interaction between HIGD1B and the immune cell infiltration and predicted the susceptibility of GC patients to therapy.ResultsHIGD1B is markedly elevated in GC tissue and cell lines, and patients with high HIGD1B expression have a poorer outcome. In addition, HIGD1B is related to distinct grades, stages, and T stages. The survival ROC curves of HIGD1B and nomogram for five years were 0.741 and 0.735, suggesting appropriate levels of diagnostic efficacy. According to Cox regression analysis, HIGD1B represents a separate risk factor for the prognosis of gastric cancer (p&lt;0.01). GSEA analysis demonstrated that the HIGD1B is closely related to cancer formation and advanced pathways. Moreover, patients with high HIGD1B expression exhibited a higher level of Tumor-infiltration immune cells (TIICs) and were more likely to experience immune escape and drug resistance after chemotherapy and immunotherapy.ConclusionThis study explored the potential mechanisms and diagnostic and prognostic utility of HIGD1B in GC, as well as identified HIGD1B as a valuable biomarker and possible therapeutic target for GC.
Is Broca's area critical for speech and language? Evidence from lesion-symptom mapping in chronic aphasia
Authors: Herron Timothy J. , Schendel Krista , Curran Brian C. , Lwi Sandy J. , Spinelli Maria G. , Ludy Carl , Dronkers Nina F. , Baldo Juliana V. 
Published: July 2024

IntroductionThe specific role that Broca's area plays in speech and language has been hotly debated in the literature. Some research has pointed to a specific role in particular aspects of speech production, while other findings have suggested additional roles in aspects of language comprehension. In the current study, we had the opportunity to take a broad approach by analyzing lesion and behavioral data from a large cohort of left hemisphere stroke patients. In this brief report, our objective was to identify which speech-language measures show a significant association with Broca's area, specifically pars opercularis and pars triangularis.MethodsLesion site and neuropsychological data from 173 chronic left hemisphere stroke patients were analyzed in the current study. Univariate lesion-symptom mapping (LSM) with rigorous correction was used to identify brain regions associated with individual test performance on a large battery of speech and language tasks. Multivariate LSM analyses were conducted in subsequent runs to confirm findings.ResultsThe LSM results identified many predictable left hemisphere gray and white matter regions significantly associated with the speech-language data, but Broca's area was not implicated in performance on any speech or language measure. Regions adjacent to Broca's area, however, in left central opercular, precentral, and insular cortices were associated with speech production and motor speech performance.DiscussionThe current study failed to identify a single speech or language measure in our comprehensive test battery that was dependent on Broca's area. This finding could not be attributed to a lack of power, as Broca's area had among the highest power values and substantial lesion coverage. Interrogation of data at the individual patient level revealed the likely source of this null finding: Patients with lesions involving Broca's area varied widely in their speech-language performance, with profiles ranging from non-aphasic to Broca's to global aphasia. Given previous studies in acute stroke patients and healthy participants implicating Broca's area in speech-language, the current findings suggest that Broca's area plays a more supplementary than critical role and can be compensated by surrounding brain regions in chronic stroke.
The clinical utility of handgrip strength as a malnutrition screening tool in hospitalized older adults: a cross-sectional study in Saudi Arabia
Authors: Alamri Sultan H. , Simbawa Mayar M. 
Published: July 2024

BackgroundMalnutrition is prevalent among hospitalized older patients. Early identification is therefore essential to implementing appropriate therapeutic interventions. This study aimed to explore the correlation between handgrip strength (HGS) and nutritional status in hospitalized older adults.Materials and methodsThis observational cross-sectional study was conducted at King Abdulaziz University Hospital, where a consecutive cohort of older adult inpatients was enrolled for participation. Shortly after admission, HGS and nutritional status were assessed using a dynamometer and the most recent version of the Mini-Nutritional Assessment Short Form (MNA-SF) test, respectively. Key anthropometric and biochemical indicators were also collected.ResultsA total of 135 consecutive patients were evaluated. Among participants with low HGS, 18 (16.22%) were malnourished, 43 (38.74%) were at risk of malnutrition, and 50 (45.05%) had normal nutrition status. The participants with low HGS had low hemoglobin, low lymphocyte levels, high creatinine levels, high BUN levels, high CRP levels, high HbA1c levels, and high vitamin B12 levels. Multiple logistic regression analysis showed that age, hemoglobin, and HbA1C were independently associated with low HGS. Based on the cut-off values for the HGS by the European Working Group on Sarcopenia in Older People-2 (EWGSOP2), low HGS showed high sensitivity to detect “malnourished and at risk of malnutrition” as well as “malnourished alone;” however, the specificity was low to exclude “malnourished and at risk of malnutrition” as well as “malnutrition alone.”ConclusionAge over 75 years, low hemoglobin, and elevated HbA1C levels were independent risk factors for low HGS. While HGS was sensitive in detecting malnutrition or risk, its specificity was low. Therefore, HGS may not be adequate as a standalone tool to assess nutritional status in hospitalized older adults. Replication of this study using locally reliable and validated HGS cut-off values is warranted to confirm these findings.