bluuebunny/crossref_metadata_2025 huggingface dataset:
Penerapan Sistem SCADA Audit Energi Listrik Berbasis Raspberry-Pi Codesys Dengan Komunikasi Modbus TCP Di Workshop Elektro Politeknik Enjinering Indorama.
Authors: Hamdani Yuda Muhammad , Abadi Sarosa Castrena , Usman Dani
Published: April 2023

The Covid-19 pandemic has an impact on almost all sectors of people's lives including the industrial sector.. According to Director of Energy Conservation Haryanto, the efforts that the industry can do to survive the current pandemic period is with conservation and energy efficiency, because when an industry has implemented an energy efficiency system, the industry will be able to reduce energy costs and reduce carbon dioxide (CO2) emissions.  This research aims to create a prototype system that can support effective and efficient energy utilization efforts through the monitoring system of electricity use in the Politeknik Enjinering indorama Electrical department Workshop building so that it is expected to be used as a reference for energy audit systems both in the community environment and in the Politeknik Enjinering Indorama in order to aim for energy utilization. In this system Nodemcu ESP 8266 which acts as an RTU (Remote Transfer Unit) will send the results of processing data measurement sensor PZEM 004t to MTU (Master Transfer Unit) Raspberry pi using the Communication Protocol Modbus TCP, then the data received by MTU from RTU will be displayed to the display HMI (Human Machine Interface) Webvisu from Codesys so that sensor measurement data can be monitored in real time in the Politeknik Enjinering indorama Electrical department Workshop building either through the Computer or mobile phone. Based on the results of PZEM04T sensor testing for energy use for 30 days obtained the largest energy usage data, namely on line 1 of 83235.31 KWh, on line 2 of 79122.49 KWh and on line 3 of 26289.15 with accuracy rates for each line1, 2 and 3 which is 4.46%, 4.87% and 9.89%. The conclusion of this study that the system successfully functioned in accordance with the purpose of the study is to monitor the use of electrical energy in the Electrical Engineering wokshop building both through mobile phones and PCs in real time to be further referenced to the process of effective and efficient energy utilization in the future.Keywords: Energy Audit; PZEM04T; Raspberry Pi Codesys; Modbus TCP.
ANALISIS SAMBUNGAN LAS GMAW BAJA ASTM A36 YANG TERPAPAR PANAS TINGGI DAN PENDINGINAN CEPAT
Authors: Nurudin Nurudin , Salim Alfi Tranggono Agus , Yuwono Indarto , Rahmatika Amelia , Suparman Suparman
Published: April 2023

Fires in steel construction, especially in welding joints, cause damage, changes in crystal structure, and mechanical properties of the material. Another effect of the fires, the material undergoes rapid cooling due to extinguishing using water. This research aims to simulate fires on ASTM A36 steel material to analyze the impact of fires. The method of this research is to perform GMAW welding on A36 steel material with heat treatment at 400 °C unti 700 °C and rapid cooling using water. The results of the research are the mechanical properties of the material in the form of crystal structure, hardness level and ultimate tensile strength. The crystal structure of ferrite and pearlite was observed, there was a change in structure, namely coarse ferrite, fine ferrite, and a mixture of bainite martensite. The highest hardness values of coupons temperature is 550 °C until 700 °C, comparison coupons, and coupons temperature at 400 °C-550 °C, which value 169,3 HV, 166.3 HV, and 141,3 HV in the weld zone. Tensile test fractures on the base metal with the ultimate tensile strength coupon with temperature 550 until 700 °C of 481,75 N/mm², the comparison coupon of 478,5 N/mm², and the temperature coupon of 400 until 550 °C which is 456 N/mm². The material is ductile.
EDUKASI MANAJEMEN STRES UNTUK MENINGKATKAN KETAHANAN DIRI MERAWAT PADA CAREGIVER KELUARGA LANSIA DENGAN PENYAKIT KRONIK DI LEMPAKE KOTA SAMARINDA
Authors: Bahtiar Bahtiar , Muda Iskandar , Khumaidi Khumaidi , Aminuddin Muhammad , Nopriyanto Dwi , Widiastuti Ida
Published: April 2023

Latar belakang: Lansia di Indonesia dominan dirawat oleh keluarga lansia. Keluarga lansia sering kesulitan dalam merawat dan mendampingi lansia sehingga berpotensi memicu masalah psikologis. Tujuan: Pengabdian masyarakat ini dilakukan untuk meningkatkan pengetahuan, sikap dan keterampilan caregiver keluarga lansia dalam menerapkan manajemen stres. Metode: Kegiatan dilaksanakan selama dua hari dengan pendekatan ceramah, diskusi dan demonstrasi. Hasil:  Terdapat peningkatan pengetahuan, sikap dan keterampilan sebelum dan sesudah pemberian edukasi manajemen stress pada caregiver keluarga lansia. Kegiatan ini dapat diterapkan sebagai kegiatan promosi kesehatan mental bagi caregiver keluarga lansia dengan penyakit kronik. Kesimpulan: Kegiatan pengabdian masyarakat ini mampu meningkatkan pengetahuan, sikap dan keterampilan  caregiver keluarga lansia dengan penyakit kronik sehingga diharapkan mampu meningkatkan ketahanan diri keluarga.
SOSIALISASI MODEL PEMBELAJARAN PROBLEM BASED LEARNING BERBASIS BUDAYA CIANJUR DI SMP ISLAM AT-TAQWA
Authors: Sugiarni Rani , Setiawan Erwan , Kusumah Putri Neneng Pitaloka
Published: April 2023

Pembelajaran matematika menjadi pembelajaran yang sulit dan tidak diminati oleh siswa. Kesulitan yang dihadapi siswa SMP Islam At-Taqwa pada materi aljabar terutama dalam pemahaman konsep. Guru belum memberikan pengalaman yang bermakna pada pembelajarannya melalui pendekatan matematika dengan budaya lokal. Metode pelaksanaan yang digunakan terdiri dari tahap: 1) Persiapan, 2) Pelaksanaan, dan 3) Evaluasi. Hasil pelaksanaan dari Analisis Data Angket Respon Siswa terhadap Pembelajaran dengan Menerapkan Model Problem Based Learning (PBL) Berbasis Budaya Cianjur menyatakan  bahwa respon siswa dikatakan positif karena rata-rata persentase respon siswa pada tiap aspeknya yaitu 99% dan ini memenuhi kriteria respon siswa yang telah ditetapkan.
ТРИВОЖНІСТЬ У ВІЙСЬКОВОСЛУЖБОВЦІВ ПЕРЕД ЇХНІМ ВИСУВАННЯМ ДО ЗОНИ БОЙОВИХ ДІЙ: ПСИХОЛОГІЧНИЙ АСПЕКТ
Authors: БЕЛІЧЕНКО Людмила
Published: April 2023

Стаття присвячена психологічним аспектам зниження тривоги у військовослужбовців перед їхнім висуванням до району бойових дій. Проведення дослідження дало можливість визначили рівень ситуативної та особистісної тривожності у військовослужбовців перед відправленням до зони бойових дій.
Tvrdi gradovi Cesargrad i Kunšperk u kontekstu
granice na rijeci Sutli do početka 17. stoljeća
Authors: Iveković Misirača Robert
Published: April 2023

Na brdima koji zatvaraju klanac rijeke Sutle zvan Zelenjak smjestila su se dva tvrda grada – (utvrde) Cesargrad i Kunšperk. Oba grada predstavljala su dio obrambenog kompleksa Zemalja Krune sv. Stjepana, odnosno Svetog Rimskog Carstva Njemačke Narodnosti. Njihova geografska blizina i smještaj na granici utjecali su na njihov povijesni razvoj te razvoj njihove okolice, prije svega na razvoj njihovih podgrađa. S razvojem tih tvrdih gradova, koji je zasebno trajao od 12. do 17. stoljeća, usporedno se „razvijala“ i rijeka Sutla kao primarno prirodna granica prije navedenih političkih entiteta. Od granica rimskih provincija do bivanja granicom formiranih političkih i kulturnih entiteta, Sutla je zajedno sa svojim stanovnicima proživjela mnoga povijesna zbivanja. Izuzev manjih događaja vojne prirode tijekom 13. stoljeća, granica na Sutli u predjelu klanca Zelenjak do akindžijskih je provala u 15. stoljeću nosila epitet konstantne i mirne granice. Ulaskom Zemalja Krune sv. Stjepana u okrilje Habsburgovaca, Sutla i time predio klanca Zelenjak, od granice političkih entiteta postaje zonom koja nastoji odvratiti Osmanlije od daljnjeg prodiranja u Štajersku i posljedično time dolaska Osmanlija do Beča.
Frauenfußball: Auf dem langen Weg zum Profisport
Authors: Keller Berndt
Published: April 2023

Frauenfußball ist aus dem deutschen Sportgeschehen nicht mehr wegzudenken und wird doch oft vergessen. Seine aktuellen Entwicklungen haben bisher in Öffentlichkeit, Medien und Wissenschaft zu wenig Beachtung gefunden. Dieses Buch befasst sich deshalb mit einer Vielzahl von Entwicklungen im Frauenfußball, vor allem mit der allmählichen Professionalisierung. Dabei haben sich nicht nur Organisation und Qualität erheblich verändert, sondern vor allem im vergangenen Jahrzehnt auch seine wirtschaftlichen Wettbewerbsbedingungen und Rezeption. Der Autor analysiert zudem ein breites Spektrum finanzieller Fragen im Frauenfußball, das von Vereinsbudgets über Gehälter bis hin zur rasch fortschreitenden Kommerzialisierung und internationalen Entwicklungen reicht.
Association of Caffeine Consumption and Brain Amyloid Positivity in Cognitively Normal Older Adults
Authors: Zheng Yong-Bo , Sun Jie , Shi Le , Su Si-Zhen , Chen Xuan , Wang Qian-Wen , Huang Yue-Tong , Wang Yi-Jie , Zhu Xi-Mei , Que Jian-Yu , Zeng Na , Lin Xiao , Yuan Kai , Yan Wei , Deng Jia-Hui , Shi Jie , Bao Yan-Ping , Lu Lin
Published: April 2023

Background:

Several epidemiological studies have reported the protective role of caffeine on health outcomes; however, it remained debatable on caffeine consumption and brain amyloid positivity.

Objective:

We aimed to determine the relationship between caffeine consumption and brain amyloid pathology in cognitively normal older adults.

Methods:

The dataset used for analysis in this cross-sectional study was selected from the Anti-Amyloid Treatment in Asymptomatic Alzheimer’s (A4) Study. Multivariable logistic regression analyses were performed to explore the association between caffeine consumption and amyloid positivity using odds ratios (ORs) and 95% confidence intervals (CIs).

Results:

In total, 4,394 participants were included in the final analysis. No significant association between caffeine consumption and amyloid positivity was observed in the whole participants (OR, 0.95; 95% CI, 0.78–1.14; p = 0.558). Subgroup analysis showed that caffeine intake was significantly associated with decreased amyloid positivity in males (OR, 0.72; 95% CI, 0.54–0.97; p = 0.032) but not in females (OR, 1.14; 95% CI, 0.90–1.46; p = 0.280), and the association between caffeine and amyloid positivity was not affected by age or APOE genotypes. In addition, different levels of caffeine were not associated with amyloid positivity.

Conclusion:

The findings suggest that caffeine consumption was not significantly associated with amyloid positivity in the whole sample. However, caffeine consumption may be inversely associated with amyloid positivity among males but not females. More studies are needed to explore the mechanisms underlying caffeine consumption and brain amyloid positivity.
Mediation of Reduced Hippocampal Volume by Cerebral Amyloid Angiopathy in Pathologically Confirmed Patients with Alzheimer’s Disease
Authors: Nagaraja Nandakumar , Wang Wei-en , Duara Ranjan , DeKosky Steven T. , Vaillancourt David
Published: April 2023

Background:

Hippocampal atrophy in cerebral amyloid angiopathy (CAA) has been reported to be similar to that in Alzheimer’s disease (AD).

Objective:

To evaluate if CAA pathology partly mediates reduced hippocampal volume in patients with AD.

Methods:

Patients with a clinical diagnosis of AD and neuropathological confirmation of AD+/-CAA in the National Alzheimer’s Coordinating Center database were included in the study. The volumes of temporal lobe structures were calculated on T1-weighted imaging (T1-MRI) using automated FreeSurfer software, from images acquired on average 5 years prior to death. Multivariate regression analysis was performed to compare brain volumes in four CAA groups. The hippocampal volume on T1-MRI was correlated with Clinical Dementia Rating sum of boxes (CDRsb) score, apolipoprotein E ( APOE) genotype, and hippocampal atrophy at autopsy.

Results:

The study included 231 patients with no ( n = 45), mild ( n = 70), moderate ( n = 67), and severe ( n = 49) CAA. Among the four CAA groups, patients with severe CAA had a smaller mean left hippocampal volume ( p = 0.023) but this was not significant when adjusted for APOE ɛ4 ( p = 0.07). The left hippocampal volume on MRI correlated significantly with the hippocampal atrophy grading on neuropathology ( p = 0.0003). Among patients with severe CAA, the left hippocampal volume on T1-MRI: (a) decreased with an increase in the number of APOE ɛ4 alleles ( p = 0.04); but (b) had no evidence of correlation with CDRsb score ( p = 0.57).

Conclusion:

Severe CAA was associated with smaller left hippocampal volume on T1-MRI up to five years prior to death among patients with neuropathologically confirmed AD. This relationship was dependent on APOE ɛ4 genotype.
Emotional Dysregulation in Mild Behavioral Impairment Is Associated with Reduced Cortical Thickness in the Right Supramarginal Gyrus
Authors: Imai Ayu , Matsuoka Teruyuki , Narumoto Jin
Published: April 2023

Background:

Mild behavioral impairment (MBI) has attracted attention as a possible precursor symptom of dementia, but its neural basis has not been fully investigated.

Objective:

We aimed to investigate the relationship between MBI and surface area, cortical thickness, and volume in the temporal and parietal lobes, which are strongly associated with dementia and emotional disorders.

Methods:

This retrospective study evaluated 123 participants: 90 with mild cognitive impairment (MCI), 13 with subjective cognitive decline (SCD), and 20 cognitively healthy (CH). Using analysis of covariance (ANCOVA) with sex, age, and MMSE score as covariates, cortical thickness, surface area, and volume in 10 regions were compared between groups with and without MBI. Groups with MBI emotional dysregulation were also compared with groups without MBI.

Results:

ANCOVA revealed significantly smaller cortical thickness in the MBI group’s right parahippocampal ( p = 0.01) and supramarginal gyri ( p = 0.002). After multiple comparison correction, only the right supramarginal gyrus was significantly smaller ( p = 0.02). When considering only MBI emotional dysregulation, the right parahippocampal and supramarginal gyrus’ cortical thicknesses were significantly smaller in this MBI group ( p = 0.03, 0.01). However, multiple comparison correction identified no significant differences ( p = 0.14, 0.11).

Conclusion:

Overall MBI and the emotional dysregulation domains were associated with reduced cortical thickness in the right parahippocampal and supramarginal gyri. Since neurodegeneration in the medial temporal and parietal lobe precedes early Alzheimer’s disease (AD), MBI, particularly emotion dysregulation, may predict early AD below the diagnostic threshold.
Potentially Inappropriate Medication Use in Community-Dwelling Older Adults Living with Dementia
Authors: Bae-Shaaw Yuna H. , Shier Victoria , Sood Neeraj , Seabury Seth A. , Joyce Geoffrey
Published: April 2023

Background:

The Beers Criteria identifies potentially inappropriate medications (PIMs) that should be avoided in older adults living with dementia.

Objective:

The aim of this study was to provide estimates of the prevalence and persistence of PIM use among community-dwelling older adults living with dementia in 2011-2017.

Methods:

Medicare claims data were used to create an analytic dataset spanning from 2011 to 2017. The analysis included community-dwelling Medicare fee-for-service beneficiaries aged 65 and older who were enrolled in Medicare Part D plans, had diagnosis for dementia, and were alive for at least one calendar year. Dementia status was determined using Medicare Chronic Conditions Date Warehouse (CCW) Chronic Condition categories and Charlson Comorbidity Index. PIM use was defined as 2 or more prescription fills with at least 90 days of total days-supply in a calendar year. Descriptive statistics were used to report the prevalence and persistence of PIM use.

Results:

Of 1.6 million person-year observations included in the sample, 32.7% used one or more PIMs during a calendar year in 2011-2017. Breakdown by drug classes showed that 14.9% of the sample used anticholinergics, 14.0% used benzodiazepines, and 11.0% used antipsychotics. Conditional on any use, mean annual days-supply for all PIMs was 270.6 days (SD = 102.7). The mean annual days-supply for antipsychotic use was 302.7 days (SD = 131.2).

Conclusion:

Significant proportion of community-dwelling older adults with dementia used one or more PIMs, often for extended periods of time. The antipsychotic use in the community-dwelling older adults with dementia remains as a significant problem.
A Novel c.800G>C Variant of the ITM2B Gene in Familial Korean Dementia
Authors: Rhyu Jee-Min , Park Joonhong , Shin Byoung-Soo , Kim Young-Eun , Kim Eun-Joo , Kim Ko Woon , Cho Yong Gon
Published: April 2023

Mutations in ITM2B have been reported to be associated with several familial dementias, such as Familial British dementia and familial Danish dementia. These are autosomal dominant disorders characterized by progressive dementia with an onset at around the fifth decade of life. We describe a family with cognitive impairment caused by a novel ITM2B p.*267Serext*11 mutation. The probands presented with cognitive impairment and cerebral infarction. MRI revealed diffuse white matter hyperintensity and microbleeds. Amyloid deposition was not observed on amyloid positron emission tomography. Our case suggests that the BRI2 mutation impacts cognition regardless of amyloid-β accumulation.
Tongue image segmentation algorithm based on deep convolutional neural network and attention mechanism
Authors: Tian Chang , Liu Yanjung , Li Meng , Fen Chaofan
Published: April 2023

The key step in the intelligence of tongue diagnosis is the segmentation of the tongue image, and the accuracy of the segmented edges has a significant impact on the subsequent medical judgment. Deep learning can predict the class of pixel points to achieve pixel-level segmentation of images, so it can be used to handle tongue segmentation tasks. However, different models have different segmentation effects, and they did not learn the connection between space and channels, resulting in inaccurate tongue segmentation. This paper first discussed the choice of model and loss function and then compared the results of different options to find the better model. Associating the red feature of the tongue is very conducive to segmentation as a feature, this paper tested many methods to try to get the color features of the original image to be paid attention to. Finally, this paper proposed an improved Encoder-Decoder network model to solve the problem based on the results. Start with Resnet as the backbone network, then introduce the U-Net model, and then we fused the attention layer, obtained from the source image through convolution and CBAM attention mechanism, and the feature layer obtained from the last upsampling in U-Net. Experimental results show that: The new, improved algorithm results are 2-3 percentage points higher than the popular algorithm, making it more suitable for tongue segmentation tasks.
Few-shot fake news detection via prompt-based tuning
Authors: Gao Wang , Ni Mingyuan , Deng Hongtao , Zhu Xun , Zeng Peng , Hu Xi
Published: April 2023

As people increasingly use social media to read news, fake news has become a major problem for the public and government. One of the main challenges in fake news detection is how to identify them in the early stage of propagation. Another challenge is that detection model training requires large amounts of labeled data, which are often unavailable or expensive to acquire. To address these challenges, we propose a novel Fake News Detection model based on Prompt Tuning (FNDPT). FNDPT first designs a prompt-based template for early fake news detection. This mechanism incorporates contextual information into textual content and extracts relevant knowledge from pre-trained language models. Furthermore, our model utilizes prompt-based tuning to enhance the performance in a few-shot setting. Experimental results on two real-world datasets verify the effectiveness of FNDPT.
An efficient deep neural network to segment lung nodule using optimized HDCCARUNet model
Authors: Arul King J. , Helen Sulochana C.
Published: April 2023

Lung cancer is a severe disease that may lead to death if left undiagnosed and untreated. Lung cancer recognition and segmentation is a difficult task in medical image processing. The study of Computed Tomography (CT) is an important phase for detecting abnormal tissues in the lung. The size of a nodule as well as the fine details of nodule can be varied for various images. Radiologists face a difficult task in diagnosing nodules from multiple images. Deep learning approaches outperform traditional learning algorithms when the data amount is large. One of the most common deep learning architectures is convolutional neural networks. Convolutional Neural Networks use pre-trained models like LeNet, AlexNet, GoogleNet, VGG16, VGG19, Resnet50, and others for learning features. This study proposes an optimized HDCCARUNet (Hybrid Dilated Convolutional Channel Attention Res-UNet) architecture, which combines an improved U-Net with a modified channel attention (MCA) block, and a HDAC (hybrid dilated attention convolutional) layer to accurately and effectively do medical image segmentation for various tasks. The attention mechanism aids in focusing on the desired outcome. The ability to dynamically allot input weights to neurons allows it to focus only on the most important information. In order to gather key details about different object features and infer a finer channel-wise attention, the proposed system uses a modified channel attention (MCA) block. The experiment is conducted on LIDC-IDRI dataset. The noises present in the dataset images are denoised by enhanced DWT filter and the performance is analysed at various noise levels. The proposed method achieves an accuracy rate of 99.58 % . Performance measures like accuracy, sensitivity, specificity, and ROC curves are evaluated and the system significantly outperforms other state-of-the-art systems.
Research and improvement of C-means clustering algorithm based on Image segmentation application
Authors: Wang Chunying , Zhang Jiahui , Yang Qi
Published: April 2023

The traditional fuzzy C-means clustering technology only considers one performance Angle of image segmentation process when processing data, resulting in low accuracy of image segmentation. In this paper, the traditional FCM algorithm is analyzed, and the low clustering accuracy, noise interference and lack of flexibility and other problems are fully considered from the relationship between parameter components, non-local spatial information elements and noise sensitivity. Firstly, a distance calculation method based on robust statistics theory is proposed, which can deal with abnormal noise stably. Secondly, based on the extreme learning machine theory, the non-local spatial information coefficient is introduced to improve the identification ability of the influence factors. This method not only guarantees the anti-noise performance of the algorithm, but also preserves the image data, improving the iteration efficiency and segmentation accuracy of the algorithm. The test results show that the accuracy of the improved C-means clustering algorithm for image segmentation is 95.5%, which is compared with the traditional C-means clustering technique and other optimization algorithms.
A self-learning interior search algorithm based on reinforcement learning for energy-aware job shop scheduling problem with outsourcing option
Authors: Liu Xinyu , Liu Lu , Jiang Tianhua
Published: April 2023

Energy-aware scheduling has been viewed as a feasible way to reduce energy consumption during the production process. Recently, energy-aware job shop scheduling problems (EAJSPs) have received wide attention in the manufacturing area. However, the majority of previous literature about EAJSPs supposed that all jobs are fabricated in the in-house workshop, while the outsourcing of jobs to some available subcontractors is neglected. To get close to practical production, the outsourcing and scheduling are simultaneously determined in an energy-aware job shop problem with outsourcing option (EAJSP-OO). To formulate the considered problem, a novel mathematical model is constructed to minimize the sum of completion time cost, outsourcing cost and energy consumption cost. Considering the strong complexity, a self-learning interior search algorithm (SLISA) is developed based on reinforcement learning. In the SLISA, a new Q-learning algorithm is embedded to dynamically select search strategies to prevent blind search in the iteration process. Extensive experiments are carried out to evaluate the performance of the proposed algorithm. Simulation results indicate that the SLISA is superior to the compared existing algorithms in more than 50% of the instances of the considered EAFJSP-OO problem.
Radiomics model of diffusion-weighted whole-body imaging with background signal suppression (DWIBS) for predicting axillary lymph node status in breast cancer
Authors: Haraguchi Takafumi , Kobayashi Yasuyuki , Hirahara Daisuke , Kobayashi Tatsuaki , Takaya Eichi , Nagai Mariko Takishita , Tomita Hayato , Okamoto Jun , Kanemaki Yoshihide , Tsugawa Koichiro
Published: April 2023

BACKGROUND: In breast cancer diagnosis and treatment, non-invasive prediction of axillary lymph node (ALN) metastasis can help avoid complications related to sentinel lymph node biopsy. OBJECTIVE: This study aims to develop and evaluate machine learning models using radiomics features extracted from diffusion-weighted whole-body imaging with background signal suppression (DWIBS) examination for predicting the ALN status. METHODS: A total of 100 patients with histologically proven, invasive, clinically N0 breast cancer who underwent DWIBS examination consisting of short tau inversion recovery (STIR) and DWIBS sequences before surgery were enrolled. Radiomic features were calculated using segmented primary lesions in DWIBS and STIR sequences and were divided into training (n = 75) and test (n = 25) datasets based on the examination date. Using the training dataset, optimal feature selection was performed using the least absolute shrinkage and selection operator algorithm, and the logistic regression model and support vector machine (SVM) classifier model were constructed with DWIBS, STIR, or a combination of DWIBS and STIR sequences to predict ALN status. Receiver operating characteristic curves were used to assess the prediction performance of radiomics models. RESULTS: For the test dataset, the logistic regression model using DWIBS, STIR, and a combination of both sequences yielded an area under the curve (AUC) of 0.765 (95% confidence interval: 0.548–0.982), 0.801 (0.597–1.000), and 0.779 (0.567–0.992), respectively, whereas the SVM classifier model using DWIBS, STIR, and a combination of both sequences yielded an AUC of 0.765 (0.548–0.982), 0.757 (0.538–0.977), and 0.779 (0.567–0.992), respectively. CONCLUSIONS: Use of machine learning models incorporating with the quantitative radiomic features derived from the DWIBS and STIR sequences can potentially predict ALN status.
DFR-U-Net: Dual residual and feature fusion network for ulna and radius segmentation on dual-energy X-ray absorptiometry images
Authors: Yang Fan , Weng Xin , Wu Yuhui , Miao Yuehong , Lei Pinggui , Hu Zuquan
Published: April 2023

BACKGROUND: Ulna and radius segmentation of dual-energy X-ray absorptiometry (DXA) images is essential for measuring bone mineral density (BMD). OBJECTIVE: To develop and test a novel deep learning network architecture for robust and efficient ulna and radius segmentation on DXA images. METHODS: This study used two datasets including 360 cases. The first dataset included 300 cases that were randomly divided into five groups for five-fold cross-validation. The second dataset including 60 cases was used for independent testing. A deep learning network architecture with dual residual dilated convolution module and feature fusion block based on residual U-Net (DFR-U-Net) to enhance segmentation accuracy of ulna and radius regions on DXA images was developed. The Dice similarity coefficient (DSC), Jaccard, and Hausdorff distance (HD) were used to evaluate the segmentation performance. A one-tailed paired t-test was used to assert the statistical significance of our method and the other deep learning-based methods (P < 0.05 indicates a statistical significance). RESULTS: The results demonstrated our method achieved the promising segmentation performance, with DSC of 98.56±0.40% and 98.86±0.25%, Jaccard of 97.14±0.75% and 97.73±0.48%, and HD of 6.41±11.67 pixels and 8.23±7.82 pixels for segmentation of ulna and radius, respectively. According to statistics data analysis results, our method yielded significantly higher performance than other deep learning-based methods. CONCLUSIONS: The proposed DFR-U-Net achieved higher segmentation performance for ulna and radius on DXA images than the previous work and other deep learning approaches. This methodology has potential to be applied to ulna and radius segmentation to help doctors measure BMD more accurately in the future
The “Africa Rising”: An Empirical Analysis of the Determinants of Per-Capita Growth
Authors: Scalamonti Francesco
Published: April 2023

In developing economies, the race toward inclusive development has prompted researchers to reconsider the drivers of growth in view of achieving the Sustainable Development Goals (SDGs). This research note has the purpose to explore the determinants of African growth after analysing reference literature to select the explanatory variables.

We examine growth in a panel of 54 African countries using the generalised method of moment system estimators (GMM-sys), by controlling idiosyncratic unobserved individual effects of countries with fixed-effects models. Using GMM-sys to estimate growth models is not novel, and many previous studies have used this appropriate approach for growth analyses. As far as we know, we have not found any studies analysing the determinants of growth in a panel of 54 African countries and/or using GMM estimators with data referring to the last decade. We have used the real per-capita GDP as a dependent variable. As a data source, the main international organisations (UN, WB, IMF) have been considered, and the analysed timeframe is 2010-2019, where we have more data completeness.

Based on the time-series data collected and estimation methodology used, the findings show determinants of African growth, and our findings can have an implication for managers and decision-makers with an interest in African emerging markets. The real per-capita GDP with one order of lags is the variable with the highest magnitude in all models and it is expected of us. Furthermore, we have found confirmations for the variables of the macroeconomic framework highlighted in the recent reference literature on growth, and we have found new evidence for the variables of the African merchandise trade across developed and developing countries.

In the conclusion, we highlight the importance of a sound governance and business environment in African countries for achieving the SDGs.
An Optimal Control for Ebola Virus Disease with a Convex Incidence Rate: Imputing from the Outbreak in Uganda
Authors: Kamugisha Mbabazi Fulgensia , Osman Shaibu , Kweyunga Eliab Horub , Abubakar Mwasa , Asaph Keikara Muhumuza , Muhumuza Nalule Rebecca , Rebecca Nekaka , Olupot-Olupot Peter , Luboobi Livingstone S.
Published: April 2023

Ebola Virus disease (EVD) is an emerging and re-emerging zoonotic disease which mostly occur in Africa. Both prediction of the next EVD and controlling an ongoing outbreak remain challenging to disease prone countries. Depending on previous experiences to curb an outbreak is subjective and often inadequate as temporal socioeconomic advances are dynamic and complex at each disease. We hypothesize that a scientific model would predict EVD disease outbreak control. In this work, a mathematical model with a convex incidence rate for an optimal control model of Ebola Virus Disease is formulated and analyzed. An optimal control strategy which aims at reducing the number of infected individuals in the population and increasing the number of recovered through treatment is evaluated. Three control measures: tracing of contacts, lock-down and treatment have been considered. A qualitative analysis and numerical experiments are performed on the model and the findings reveal that the most expensive strategy involved imposing lock-down and contact tracing of the infected while the cheapest alternative was lock-down and treatment of the infected. Hence, policy makers should concentrate on treatment and lock down to combat the disease.
Kurikulum K 13 Dan Efek Pergaulan Terhadap Perilaku Bulliying Siswa
Authors: Qolbiyah Shofwatal , Agusmad Agusmad , Rouf Abdul
Published: April 2023

The objectives of this study are as follows: to find out the effect of implementing the 2013 curriculum, the effect of association on bullying behavior at MAN 4 Jombang with a quantitative explanatory research approach. The population is 136 students with Proportional Random Sampling sampling obtained 100 samples from class XII A and XII B with quantitative data analysis techniques obtained there is a partial positive effect between the implementation of the 2013 curriculum on bullying behavior at MAN 4 Jombang with an explanation that the level of bullying behavior of students can be predicted from the high and low implementation of the K-13 curriculum. That there is no partially significant effect between the effects of student association on bullying behavior at MAN 4 Jombang with the explanation that the level of student bullying behavior cannot be predicted from the high and low effects of student association. That there is an effect of implementing the K-13 curriculum and student association on behavior bullying at MAN 4 Jombang with a low proportion and other variables that can explain it is possible that the main objectives of the school are less focused on character building, infrastructure that is less supportive, and environmental role models that are less supportive.
PERBANDINGAN NILAI EOSINOFIL ANTARA PENDERITA RINITIS ALERGI DAN PENDERITA ASMA BRONKIAL
Authors: Utama Budi , Dwiryanti Rizki , Rohani Siti , Raisha Zafira Ananda
Published: April 2023

Rinitis alergi dan asma bronkial merupakan penyakit saluran pernapasan yang memiliki kesamaan dalam hal anatomi dan mediator inflamasi. Inflamasi  pada rinitis alergi dan asma bronkial melibatkan akumulasi eosinofil dalam membran mukosa. Penelitian ini bertujuan untuk menganalisis perbedaan nilai eosinofil antara penderita rinitis alergi dan penderita asma bronkial. Jenis penelitian ini adalah observasional analitik desain cross sectional. Pemeriksaan nilai eosinofil menggunakan Automated Hematology Analyzer Sysmex XS-800i di Balai Besar Laboratorium Kesehatan Palembang. Populasi pada penelitian ini adalah mahasiswa Fakultas Kedokteran Universitas Muhammadiyah Palembang. Teknik consecutive sampling sebanyak 31 penderita rinitis alergi dan 31 penderita asma bronkial yang telah memenuhi kriteria inklusi dan eksklusi. Hasil penelitian memperlihatkan rata-rata nilai eosinofil penderita rinitis alergi 2,26% dan penderita asma bronkial 5,74% dengan hasil Mann-Whitney test diperoleh p=0,000 (p<0,05). Kesimpulan, terdapat perbedaan yang bermakna antara nilai eosinofil antara penderita rinitis alergi dan penderita asma bronkial.
Phytochemical Evaluation of different Extracts of Sida Spinosa by various Chemical Test
Authors: Kumar Soni Rakesh , M Kewatkar Shailesh , Jain Vidhi , Saluja Manmeet Singh
Published: April 2023

Plants offer a large range of natural compounds belonging to different molecular families which have various properties to humans. These molecules  possess  interesting biological activities which attracted several researchers to their  elucidation  to  provide  knowledge  that will  lead  to  advancement  medicine.  The present paper deals with the screening of various phytochemical present in various extract viz., Aqueous, Ethanolic, Ethyl Acetate, chloroform and petroleum ether of Sida Spinosa
A Review of Hydrogels, with its Properties and Applications in Medicine
Authors: Kumar Soni Rakesh , M Kewatkar Shailesh , Jain Vidhi , Singh Saluja Manmeet
Published: April 2023

A hydrogel is an insoluble in water three-dimensional polymer network that has the potential to absorb bodily fluids in a biological setting. Hydrogels may also be used in medical applications. Chemical crosslinking mechanisms such as optical polymerization, enzymatic reactions, and physical crosslinking mechanisms such as temperature- and pH-dependent processes, as well as ionic crosslinking, are all responsible for the formation of a network of polymers of this kind. Both physical and chemical hydrogels may be generated by the application of weak secondary forces, but chemical hydrogels can also be formed through the application of covalent forces. Hydrogels may be made from a wide variety of polymers derived from either natural or synthetic sources. The swelling, the mechanical characteristics, and the biological properties of hydrogels are some of the most essential aspects of hydrogels, and each of these qualities may have an effect on the structure and morphology of the hydrogel. Hydrogen is utilized in a variety of medical applications, including tissue engineering, contact lenses, wound dressings, and the release of therapeutic agents. This is possible because hydrogen has a structure that is analogous to the extracellular matrix (ECM), and it also has the ability to absorb water. There is a discussion on hydrogels, the many varieties of hydrogels, the characteristics of hydrogels, and their uses in medicine.
Peran Perawat Melaksanakan Kolaborasi Interprofesional (IPC) dalam Asuhan Keperawatan pada Pasien Covid-19
Authors: Mulidan Mulidan , Syaftriani Afina Muharani
Published: April 2023

Wabah COVID-19 menyerang dunia secara global pada akhir tahun 2019 dan menjadi pandemi global yang menelan banyak korban di seluruh dunia, dalam hal ini perawat menjadi garda terdepan menangani kasus dengan resiko serius bahkan kematian saat memberikan asuhan keperawatan pasien COVID-19. Perawat dituntut memberikan pelayanan terbaik dengan melakukan kolaborasi antar tim kesehatan lainnya untuk memenuhi kebutuhan klinis pasien. Pentingya peran dan sikap perawat dalam melakukan koordinasi dan kolaborasi menjadi prioritas dalam pemberian asuhan keperawatan pasien COVID-19 untuk meningkatkan keselamatan pasien. Tujuan penelitian ini untuk mengeksplorasi bagaimana peran dan pengalaman perawat melaksanakan kolaborasi interprofesional dalam merawat pasien Covid-19. Metode yang digunakan metode kualitatif dengan pendekatan fenomenologi deskriptif, pengumpulan data dilakukan dengan FGD (Focus Group Discussion) dan wawancara mendalam (in depth interview). Partisipan penelitian berjumlah 16 partisipan yang terdiri dari 10 orang perawat pelaksana ruang rawat inap covid 19,2 orang kepala ruangan,2 orang katim dan 2 orang kepala keperawatan. Mayoritas lama jam kerja partisipan 8 jam per shift, mayoritas pendidikan partisipan jenjang  D3 keperawatan (14 orang), dan jenjang profesi Ners (2 orang). Hasil dan simpulan analisis tematik yang dilakukan oleh peneliti ditemukan 4 tema, yaitu : 1. Strategi meningkatkan sikap Interprofesional multiprofesi, 2. Perspektif perawat pelaksanaan komunikasi interprofesional, 3. Meningkatkan kerjasama tin dan mendalami peran tanggung jawab, 4.Tantangan praktik kolaborasi interprofesional dalam asuhan keperawatan.
Design of information and measurement systems within the Industry 4.0 paradigm
Authors: Vasylenko Olga , Ivchenko Sergii , Snizhnoi Hennadii
Published: April 2023

The subject of the study is the process of Information and Measuring System (IMS) designing as a component of the Cyber-Physical System (CPS) in the paradigm of Industry 4.0 (I4.0). The aim of the study is to develop methodological support for the design of IMS and Automatic Control Systems (ACS) as components of production CPS (CPPS), in particular, for Digital Factory. Objectives: to determine the conceptual model of IMS; choose the IoT model for structural synthesis, select the appropriate regulatory (sets of standards and implementation models) and hardware; perform R&D of IMS based on NB-IoT sensors; formalize the procedure for integrating components into the CPPS, develop the Asset Administration Shell. The methods used are: heuristic synthesis methods, experimental planning theory. The following results were obtained. The key role of optimally designed IMS level 4.0 in increased decision-making accuracy in CPPS management and control processes is demonstrated. The quality of control is improved both by quickly obtaining accurate information for updating models in cyber add-ons, and at the physical level, in ACS. The universal model of IMS implementation in CPPS was proposed. The stages of choosing the concept, structure, hardware and communication protocols of the IIoT ecosystem IMS + ACS have been performed. The methodology was tested during the development of the NB-IoT Tech remote monitoring system, which has a decentralized structure for collection data on resources consumed. The integration of the ecosystem as a component of CPPS at the appropriate levels of the architectural model RAMI 4.0 has been performed. Regulatory support has been formed and the functional aspect of the Asset Administrative Shell for CPPS integration has been developed. Conclusions. Scientific novelty: it is proposed to design the IMS as a component of the AAS of the cyber-physical system, according to the implementation methodology of its subsystems at the corresponding levels of RAMI4.0 and the selected IoT model. The new approach, called "soft digitalization", combines the approaches of Industry 3.0 and 4.0, it is designed for the sustainable development of automated systems to the level of cyber-physical systems and is relevant for the recovery of the economy of Ukraine. Practical significance of the results: the IoT-Tech system based on Smart sensors has been developed and tested. This information and measurement system is non-volatile and adapted to measure any parameters in automated systems of various levels of digitization.
Supervised identification and equalization of transmission channel using reproducing kernel Hilbert space
Authors: Badi Imad , Badi Hassan , Khamjane Aziz , El Moutaouakil Karim , Bahri Abdelkhalek
Published: April 2023

The subject matter of the article is to identify and equalize the parameters of telecommunication channels. The goal is to develop a new mathematical approach based on positive definite kernels on a Hilbert space. The tasks to be solved are: (a) to formulate a mathematical procedure based on a kernel; a kernel is a function that maps pairs of data points to a scalar value, and positive definite kernels are widely used in machine learning and signal processing applications; (b) to identify the channel parameters using the proposed method; and (c) to apply the Zero Forcing and MMSE equalizer to measure the performance of the proposed system. This article introduces a new method to address the problem of supervised identification of transmission channel parameters based on the positive definite kernel on Hilbert space, which implements Gaussian kernels. The input sequence, used as an input for a system or process, is assumed to be independent, have a zero mean, a non-Gaussian distribution, and be identically distributed. These assumptions are made to simplify the analysis and modeling. The proposed method for estimating the parameters of the channel impulse response yields promising results, indicating that the estimated parameters are close to the measured parameters of the model for various channels. The convergence of the estimated parameters toward the measured parameters of the model is particularly noticeable for BRAN A (indoor) and BRAN E (outdoor) channels. The method has been tested with different channel models, and the results remain consistent. Overall, the proposed method appears to be a reliable and effective approach for estimating channel impulse response parameters. The accuracy of the estimated parameters is particularly noteworthy considering the challenges inherent in modeling wireless channels, which can be influenced by various factors such as obstacles and interference. These findings have important implications for the design and optimization of wireless communication systems. Accurate estimates of channel impulse response parameters are essential for predicting and mitigating the effects of channel distortion and interference, and the proposed method represents a promising tool for achieving this goal. Further research and testing are needed to validate and refine the method and to explore its potential applications in different settings and scenarios. We evaluated the performance of the system using the estimated parameters obtained from the proposed method. Two equalizers, MMSE and ZF, were used, and the results show that MMSE outperforms ZF. Both equalizers produced highly satisfactory outcomes.
Syntactical method for reconstructing highly fragmented OOXML files
Authors: Boiko Maksym , Moskalenko Viacheslav
Published: April 2023

A common task in computer forensics is to recover files that lack file system metadata. In the case of searching for file fragments in unallocated space, file carving is the most often used method, which is ideal for unfragmented data. However, such methods and the tools based on them are ineffective for recovering OOXML files with a high fragmentation level. These methods do not provide reliable determination of the correct order of fragments. Techniques for reconstructing documents based on the analysis of words and phrases are also ineffective in fragmented OOXML documents. The main reason is that OOXML files are ZIP archives and, as a result, store data on disk space in a compressed form. This paper proposes a syntactical method for reconstructing OOXML documents based on knowledge about the internal structure of this file type, regardless of their content. The details of the implementation of the reconstruction algorithm and the peculiarities of restoring certain types of local elements of the document were considered. The efficiency of the algorithm was tested on the Govdocs1 and NapierOne datasets. The proposed method was applied to 4096-byte data blocks, which correspond to the standard cluster size in different file systems. The experimental results confirmed the method's suitability for practical use with 82.97 % of recovered files, including 34.38 % reconstructed completely, 0.43 % excluding the last 21 bytes at most, and another 48.16 % excluding embeddings that require other approaches. In the latter case, obtaining a fully working document without displaying graphic images and other contents of different embeddings is possible. The presence in OOXML files of CRC-32 hashes of the uncompressed data stream of each local element allows us to confirm the correctness of information recovery and its integrity unambiguously. Simultaneously, the method's effectiveness depends mainly on data verification methods during the reconstruction of local elements that occupy at least three clusters in the file. Therefore, this method is supposed to be improved by developing new mechanisms for verifying XML elements.