bluuebunny/crossref_metadata_2025 huggingface dataset:
Sosialisasi Peningkatan Ketahanan Kesehatan di Era Transisi Pandemi Ke Endemi Covid-19 Melalui Revitalisasi Posbindu “Mugi Waras” Kelurahan Rejasari Kecamatan Purwokerto Barat
Authors: Wibowo Yudhi , Roestijawati Nendyah , Mulyanto Joko , Krisnansari Diah , Munfiah Siti , Saraswati Metta
Published: January 2025

Meskipun status telah menjadi endemik, namun covid-19 masih ada dan akan tetap menjadi risiko tinggi bagi masyarakat yang memiliki komorbid. Kelompok paling rentan adalah kelompok usia lanjut dan penderita penyakit komorbid seperti hipertensi, kanker, diabetes dan penyakit paru kronik yang merupakan penyakit tidak menular (PTM). Pandemi covid-19 telah menyebabkan 159.379 kematian dan 83% yang meninggal berusia >45 tahun dan komorbid per tanggal 21 November 2022. Pos Pembinaan Terpadu (Posbindu) PTM merupakan bentuk peran serta masyarakat dalam upaya pengendalian faktor risiko secara mandiri dan berkesinambungan. Metode palaksanaan dengan penyuluhan dan dilakukan pre dan pos tes. Rerata usia peserta pengabdian adalah 42 tahun, 72,7% berjenis kelamin perempuan dan 63,6% merupakan kader. Uji Wilcoxon menunjukkan nilai p = 0,038 artinya terdapat perbedaan signifikan secara statistik antara rerata skor pengetahuan sebelum dan setelah penyuluhan dan menunjukkan nilai p = 0,004 artinya terdapat perbedaan signifikan secara statistik antara rerata skor sikap sebelum dan setelah penyuluhan. Penyuluhan terbukti meningkatkan pengetahuan tentang program posbindu dan sikap positif untuk melakukan upaya pencegahan dengan deteksi dini.
STUDI EFEKTIVITAS HERBA TEMPUYUNG (Sonchus arvensis), KAYU SECANG (Caesalpinia sappan L.), DAN DAUN KEPEL (Stelechocarpus burahol), SEBAGAI ANTIHIPERURISEMIA: SYSTEMATIC REVIEW
Authors: Putri Natalie Wiguna , Ramadani Jasmine Athaya , Maulana Hamzah , Adawiyah Nurul Robiah Al , Putri Dwi Nuranggia Apriliani , Wibowo Rafif Dzaky , Deviriandra Yoana Rizki , Pribadi Fajar Wahyu
Published: January 2025

Hiperurisemia adalah kadar asam urat yang melebihi batas normal. Terapi konvensional yang terutama mengandalkan obat-obatan penghambat xantin oksidase yang tidak lepas dari adanya efek samping. Sebagai alternatif pengobatan, digunakanlah obat tradisional yang memiliki efek yang mirip dalam kemampuan menurunkan kadar asam urat seperti Cesalpinia sappan L. (daun tempuyung), Sonchus arvensis (kayu secang), dan Stelechocarpus burahol (daun kepel) yang memerlukan pengkajian lebih lanjut. Penelitian ini dilakukan untuk mengkaji efektivitas tiga tanaman herbal untuk menangani hiperurisemia. Pengkajian didasarkan pada metode preferred reporting items for systematic reviews and meta-analyses guideline. Daun tempuyung memiliki potensi penurunan kadar asam urat 52-64,267% dan konsentrasi penghambatan 50 terhadap xantin oksida pada 119.02 ppm, kayu secang dapat menghambat xantin oksida sebesar 58,922% dan konsentrasi penghambatan 50 pada 9.236 μg/ml, serta daun kepel memiliki konsentrasi penghambatan 50 terhadap xantin oksida pada 28,39±20,21 μg/ml. Berdasarkan hasil yang didapatkan, diketahui bahwa daun kepel tempuyung dan kayu secang memiliki kemampuan menghambat enzim xantin oksidase yang signifikan sehingga dapat digunakan sebagai jamu antihiperurisemia.
POTENSI EKSTRAK JINTAN HITAM (Nigella sativa) SEBAGAI KANDIDAT TERAPI KOMPLEMENTER DIABETES MELITUS
Authors: Fary Kevin Zufaris , Simanjuntak Evi Christina Madalena , Simanjuntak Evi Christina Madalena , Marhadhani Syifa Fatimatuzzahra , Azzahra Aurora , Oktaviana Husnul Afifah Rizky , Putri Iqfani Amalia , Daffa Mochamad Zidqi , Setiawati Setiawati
Published: January 2025

Diabetes melitus merupakan penyakit metabolik dengan peningkatan glukosa dalam darah dengan ketidakmampuan sel β pankreas menghasilkan insulin maupun penurunan efektivitas penggunaan insulin. Perawatan utama Diabetes Melitus (DM) adalah terapi konvensional dengan menggunakan obat hipoglikemik oral dan terapi injeksi insulin. Masyarakat juga menggunakan pengobatan tradisional karena dianggap memiliki efek samping minimal. Jintan hitam (Nigella sativa) merupakan salah satu tanaman herbal yang dapat digunakan untuk menurunkan kadar gula darah. Penelitian ini bertujuan untuk mengetahui potensi manfaat dan dosis yang digunakan pada pengobatan diabetes melitus. Literature review ini diperoleh dengan hasil pencarian literatur melalui Pubmed dan Sciencedirect didapatkan 7(tujuh) jurnal penelitian eksperimental. Pemberian ekstrak jintan hitam (Nigella sativa) pada pasien diabetes melitus dapat menurunkan gula darah dengan efek hipoglikemik yang dimiliki dan sebanding dengan penggunaan metformin.
 
Kata kunci: diabetes melitus, metformin, Nigella sativa
HIPERTENSI PADA ANAK: TINJAUAN PUSTAKA
Authors: Pratama Radya Putra , Alexandra Florence , Saad Nursan
Published: January 2025

Hipertensi pada anak merupakan masalah kesehatan yang perlu mendapat perhatian serius. Meskipun angka kejadiannya tidak setinggi pada orang dewasa, hipertensi pada anak dapat meningkatkan risiko penyakit kardiovaskuler dan ginjal di kemudian hari. Artikel ini bertujuan untuk mengkaji tentang definisi, epidemiologi, etiologi, patogenesis, manifestasi klinis, penegakan diagnosis, serta tatalaksana hipertensi pada anak. Penyebab hipertensi pada anak dapat dibagi menjadi hipertensi primer yang umumnya berkaitan dengan obesitas dan sindrom metabolik, serta hipertensi sekunder yang paling sering disebabkan oleh kelainan ginjal. Diagnosis hipertensi pada anak ditegakkan melalui anamnesis, pemeriksaan fisik, dan pemeriksaan penunjang. Tatalaksana hipertensi pada anak meliputi modifikasi gaya hidup dan pemberian obat anti-hipertensi, tergantung tingkat keparahan dan ada tidaknya kerusakan organ target. Pemeriksaan tekanan darah secara berkala setelah usia 3 tahun penting dilakukan agar hipertensi pada anak dapat dideteksi dan ditangani secara dini untuk mencegah komplikasi jangka panjang.
Fishing season estimation for tuna troll line fishing fleet at Palabuhanratu Nusantara Fishing Port, Sukabumi, Indonesia
Authors: Annida Shafira Bilqis , Baihaqi Faqih
Published: January 2025

Indonesia covers over 15% of global tuna production. This has placed Indonesia as the top tuna-producing country in the world. Troll line is one of the fishing fleets that targets tuna as main catch, operates in many areas in Indonesia, including those bases at Palabuhanratu Nusantara Fishing Port in Sukabumi. Fishermen complained about uncertain changes of the fishing season in recent years. This causes fishermen unable to determine the right time for fishing. Some trolling fishermen suffer losses and even bankruptcy. This study tried to estimate fishing season for trolling fleets based on three main catch commodities: skipjack tuna, yellowfin tuna, and bigeye tuna. Data on catches and fishing trips are collected monthly from 2018 to 2022 from the Sukabumi Regency Central Capture Fisheries Statistics Agency. Data was processed using a catch-per-unit effort approach followed by a fishing season index analysis. Results show that the three main catch commodities have similar fishing season patterns. Peak fishing season is from the beginning of the east season in June to the middle of the second transition season in October. Productivity of the three commodities reached its lowest point in the lean season in January and February, in the middle and end of the west season.Keywords: catch composition, fishing season, productivity, tuna
Strategic Culture Cult of Defense: Dominant Culture in China's Salami Slicing Geopolitical Strategy
Authors: Paramesvari Nyoman Sekar Ayu
Published: January 2025

China continues to try to establish norms in international dynamics, especially in Asia, with a "salami-slicing" strategy to expand its influence without triggering major conflicts. This strategy is seen in how China responds to its two territorial disputes; the occupation of islands in the South China Sea and the Indian border. The salami-slicing strategy helps China gradually occupy disputed territories and offers a fait accompli that the other party must accept. This paper will then use Bloomfield's strategic culture framework which emphasizes the existence of dominant and subordinate subcultures. The author finds that in influencing China's policies in territorial disputes, the dominant strategic culture is the Cult of Defense coined by Scobell. This strategic culture has three (3) implications for policy, namely projecting strength when on the verge of crisis and the tendency to take risks, and in the outline of its actions, China will then justify using its power.
Distributed Identity for Zero Trust and Segmented Access Control: A Novel Approach to Securing Network Infrastructure
Authors: Ahmadi Sina
Published: January 2025

“Distributed Identity” refers to the transition from centralized identity systems using Decentralized Identifiers (DID) and Verifiable Credentials (VC) for secure and privacy-preserving authentications. With distributed identity, control of identity data is returned to the user, making credential-based attacks impossible due to the lack of a single point of failure. This study assesses the security improvements achieved when distributed identity is employed with the ZTA principle, particularly concerning lateral movements within segmented networks. It also considers areas such as the implementation specifications of the framework, the advantages and disadvantages of the method to organizations, and the issues of compatibility and generalizability. Furthermore, the study highlights privacy and regulatory compliance, including the General Data Protection Regulation (GDPR) and California Consumer Data Privacy Act (CCPA), analyzing potential solutions to these problems. The study implies that adopting distributed identities can enhance overall security postures by an order of magnitude, providing contextual and least-privilege authorization and user privacy. The research recommends refining technical standards, expanding the use of distributed identity in practice, and discussing its applications for the contemporary digital security landscape.
The Continue Fail of African Countries: Evidence of Some Countries Stuck in Underdevelopment
Authors: Sandambi Nerhum
Published: January 2025

The study analyses African countries in particular, in order to understand why these countries are still trapped in underdevelopment. In fact, a large number of developing countries are still trapped in underdevelopment due, on the one hand, to institutional ignorance in these countries, especially political institutions, in line with the inefficiency of economic institutions which, from the outset, have not been able to ensure that they function as they should in order to guarantee the greatest possible prosperity for their societies. The lack of a set of institutions that allow African countries to transform their economies, however, represents a plausible reason for the continuation of these countries trapped in underdevelopment in particular. On the other hand, there seems to be an unbreakable vicious circle in African countries. These cycles help to consolidate economic failure and, above all, to consolidate the prison of underdevelopment in particular, in other words, a large part of these cycles promote dual societies that are characterised by the lack of a better distribution of income for their populations in particular.
A Predictive Model for Venous Thromboembolism Based on Multi-View Clustering
Authors: Li Baiya
Published: January 2025

Background: Venous thromboembolism (VTE) is a common chronic disease with a considerable risk of recurrence, and early screening for VTE risk assessment and comprehensive data decision-making analyses can reduce its incidence and harm to a certain extent. We proposed a clustering model based on multi-view learning for VTE incidence risk prediction. Methods: This is a retrospective study of 15,856 orthopedic surgical patients who met the diagnosis in a hospital information system between 1992-2017. The risk of developing venous thromboembolism was analyzed and predicted from multiple views using multi-view learning to improve the accuracy of disease prediction. Results: Five multi-view clustering algorithms were selected as comparison algorithms and the performance of these models was evaluated using metrics such as accuracy, purity, and F-score. After comparing the ACC values for each algorithm, it was found that the proposed algorithm had a significantly higher ACC value (0.9172) than the other comparison algorithms (0.6481, 0.6242, 0.7740, 0.8306, and 0.7844, respectively). Conclusions: The proposed algorithm has high effectiveness for VTE risk prediction. The model can assist healthcare professionals to improve the accuracy and timeliness of VTE risk assessment and identify the risk of VTE in patients as early as possible.
Acute and Chronic Training Load Effects on Elite Volleyball Players
Authors: Pisa Marcel Frezza , Norberto Matheus Silva , Zecchin Arthur , Bisam Filho Ricardo , Puggina Enrico Fuini
Published: January 2025

This study investigated the acute and chronic effects of training load on vertical jump height, agility, and recovery status in elite male volleyball players. Eleven athletes' internal load was monitored over two 3-week periods, with external load quantification and recovery status assessed in weeks 3 and 7. Countermovement jump and agility tests were conducted pre- and post-training sessions. Results showed higher training load in the first period compared to the second, influencing motor performance, with better outcomes observed in the second period. No negative acute effects on motor performance were found; two sessions showed post-session CMJ improvement, potentially influenced by jump frequency and quantity within those sessions. Periods of high or reduced TL impacted motor performance, specifically vertical jump height and agility. Vertical jump results proved sensitive to TL fluctuations, providing insights into training status, recovery, and motor performance. Reducing TL strategically during the season facilitated physical recovery and positive motor performance adaptations. This suggests managing training load strategically can optimize performance and recovery in elite volleyball players.
Improved YOLOv10 Lightweight Bearing Surface Defect Detection Algorithm
Authors: Ding Lei , Tong Yuhua , Cui Yunhao , Sun Zhijie
Published: January 2025

Aiming at the problems of insufficient lightweight degree and incompatibility between accuracy and model lightweight of existing rolling bearing surface defect detection algorithms, based on the study of previous related algorithms, the YOLOv10 model algorithm was improved and the optimized model algorithm YOLO-ARE was obtained. The undersampling module ADown is innovatively introduced in Backbone and Head, which enables the model to capture the features of bearing defect images at a higher level, while reducing the amount of computation and optimizing the accuracy of target detection. In the backbone network, the fusion C2f-RVB-EMA module is inserted to optimize the model to extract the image defect features of the rolling bearing, which further improves the lightweight level of the model. The experiment was carried out on the self-built rolling bearing defect data set. Compared with the original YOLOv10n model, GFLOPs of the improved model decreased by 9.2%, Parameters decreased by 13.6%, model size decreased by 8.6%, accuracy P and mAP50 increased by 0.6% and 0.3% respectively. The detection speed is 158.1f/s, and the model can be arranged on the edge end to meet the requirements of industrial batch detection of bearing appearance defects.
COVID-19, Infection Inhibitors and Medicines
Authors: Hanai Toshihiko
Published: January 2025

The fast mutation of COVID-19 viruses still confuses us, and the mRNA vaccines do not inhibit the infection and may protect against the heavy disease. The infection mechanism is described with the protein-protein binding stereo structure; therefore, the infection strength of variants has been estimated from the protein-protein (S-RBD binding with ACE-2) interaction energy values calculated using a molecular mechanics program. The binding strength order was Alfa < Lambda < WT < FE.1 < XBB1.5 < EG.5 ≈ BQ.1 ≈ Alpha+E484K ≈ Omicron XBB.1.16 ≈ Epsilon, Iota < EG.5 < Delta plus ≈ Beta, Kappa B.1.621 ≈ KP.3 ≈ Kappa B.1.617.1 ≈ Delta B.1.517.2 < KP.2 < BA.2.86 ≈ JN.1 ≈ HV.1 ≈ BA.1 < BA.2. The mutation from acidic amino acid to basic amino acid strength the binding. The substitute size of amino acids causes the steric hindrance for the binding. The affinity level supports the infection strength. Various proposed infection inhibitors are quantitatively analyzed. TCA acids and natural polyphenols inhibit the binding of S-RBD to ACE-2. The cocktail dose of known medicines may enhance their performance. The inhibiting multiplication may be achieved using glycated compounds that bind glycoproteins and reduce glycoprotein activities.
Reevaluating the Potential of a Vanilla Transformer Encoder for Unsupervised Time Series Anomaly Detection
Authors: Han Chan Sik , Kim HyungWon , Lee Keon Myung
Published: January 2025

Transformer encoders are widely used in deep learning-based time series anomaly detection due to their ability to capture temporal dependencies. Recently, many studies have attempted to improve a vanilla Transformer encoder by incorporating their novel techniques into the vanilla Transformer encoder. These advanced models have shown remarkable performance in various time series anomaly detection benchmarks. However, unlike these approaches, we argue that vanilla Transformer encoders remain undervalued. To support this claim, Through extensive experiments, we evaluated the proposed framework against advanced models on widely recognized time series anomaly detection benchmarks. The results demonstrate that our simple framework delivers performance comparable to that of the advanced models. The code for the proposed framework, along with its pre-trained weights for the benchmark datasets, is publicly available at https://github.com/chatterboy/revisitVanillaTransEncUnsupTSAD.
The Emerging Role of CD8+ T Cells in Shaping Treatment Outcomes of Patients with MDS and AML
Authors: Tasis Athanasios , Spyropoulos Theodoros , Mitroulis Ioannis
Published: January 2025

CD8+ T cells are critical players in anti-tumor immunity against solid tumors, targeted by immunotherapies. Emerging evidence suggests that CD8+ T cells also play a crucial role in anti-tumor responses and determining treatment outcomes in hematologic malignancies like myelodysplastic neoplasms (MDS) and acute myeloid leukemia (AML). In this review, we focus on the implication of CD8+ T cells in treatment response of patients with MDS and AML. First, we review reported studies of aberrant functionality and clonality of CD8+ T cells in MDS and AML, often driven by the immunosuppressive bone marrow microenvironment, which can hinder effective antitumor immunity. Additionally, we discuss the potential use of CD8+ T cell subpopulations, including memory and senescent-like subsets, as predictive biomarkers for treatment response to a variety of treatment regimens, such as hypomethylating agents which is the standard of care for patients with higher-risk MDS, and chemotherapy which is the main treatment of patients with AML. Understanding the multifaceted role of CD8+ T cells and their interaction with malignant cells in MDS and AML, will provide useful insights into their potential as prognostic/predictive biomarkers, but also uncover alternative approaches to novel treatment strategies that could reshape the therapeutic landscape, thus improving treatment efficacy, aiding in overcoming treatment resistance and improving patient survival in these challenging myeloid neoplasms.
Development and Validation of a qPCR Assay for Quantitative Evaluation of Residual Host Cell DNA for Rabies Vaccines Produced in Vero Cells
Authors: Zhao Danhua , Zong Weiying , Wu Wanxin , Li Yuhua , Wu Zongsong , Yang Zhixing , Cao Shouchun
Published: January 2025

Residual host cell DNA in biological products poses potential risks of tumorigenicity and infectivity. Therefore, current regulatory authorities including the World Health Organization (WHO), the Chinese Pharmacopoeia, and the U.S. Food and Drug Administration (FDA) have
limited the acceptance amounts of residual DNA (less than 10 ng or 3 ng/dose). Among various detection methods for residual DNA, quantitative PCR (qPCR) is recognized as the most practical method for its high sensitivity, accuracy, precision, and efficiency. This study aimed to develop a qPCR method for detecting residual Vero cell DNA to facilitate downstream purification and ensure quality control during vaccine manufacturing. We selected two highly repetitive Vero genomic DNA sequences as amplification targets: the "172 bp" sequence and the Alu repetitive sequence. The qPCR method was meticulously optimized and validated for linearity, quantitation limit, specificity, accuracy, repeatability, intermediate precision, and robustness. The "172 bp" sequence qPCR assay showed excellent linearity and amplification efficiency, establishing a dependable approach for quantifying residual Vero DNA in pharmaceuticals and for regulatory compliance monitoring.
Evaluation of the Florida Newborn Screening Program Education Campaign
Authors: Richey Mirine , Wilson Cynthia B. , Jia Minna , Galbraith Travis
Published: January 2025

Florida’s Newborn Screening Program campaign aims to increase awareness and participation of birthing facilities, providers, and parents. This evaluation aimed to determine the effectiveness and reach of the Newborn Screening Program (NBS) Statewide Educational Campaign to pregnant women through surveys and focus groups. The online survey, conducted throughout Florida in English, Spanish, and Haitian Creole, evaluated the reach and effectiveness of educational materials such as paid advertisements and brochures. The surveys also served to recruit participants for in-person focus groups throughout the state. The findings showed that 85.3% of the mothers had discussions with health professionals about the screening program, while others did not hear about if from health professionals. More than 50% of the respondents learned about the program through health facilities, with additional exposure from media platforms such as television, radio, and friends. The study shows the need for increased outreach of the campaign and better communication and education from medical professionals to increase awareness.
A Framework for Generating Counterfactual Explanations to Explain Black-Box Models
Authors: Parmar Joshan , Liò Pietro , Banerjee Soumya
Published: January 2025

Automated decision-making systems are becoming more prevalent across a range of fields. For example, they are used across the finance industry to decide whether customers are entitled to loans, and in medicine to support clinical decisions. With decisions that substantially impact individual lives, users will often want explanations of why these decisions were made. Indeed, current and in-progress legal frameworks have begun to require these. For example, GDPR entitles users to explanations about decisions made by these automated systems. Similarly, the Algorithmic Accountability Act would require companies to conduct impact assessments for bias, effectiveness and other factors. One way of producing such explanations for the decisions of machine learning models is to ask the question “What must I change in the input to change the output of a model”. This is known as a counterfactual explanation. Our methodology begins by fitting simple models to the variation of the output caused by the change of one input feature. We then score each of these models using user-tuneable scoring functions. By selecting the best possible change and then repeating, we chain together these simple models into sequential counterfactuals. Our method is modular, which means that it is inherently extensible. Each component of out methodology can be easily modified to make it more useful for specific situations. We examine how well our methodology performs on multiple data formats: images and tabular data. We compare our framework to other methodologies. We demonstrate that our method can, in some cases, produce more interpretative counterfactuals that change fewer features than some existing methodologies. Finally, we make a Python implementation of our code available so that it can be used by the machine learning community.
Alternative Proof of the Ribbonness on Classical Link
Authors: Kawauchi Akio
Published: January 2025

Alternative proof is given for an earlier presented result that if a link in 3-space bounds a proper oriented surface (without closed component) in the upper half 4-space, then the link bounds a proper oriented ribbon surface in the upper half 4-space which is a renewal embedding of the original surface.
Using Synthetic and Pseudosynthetic Data to Enhance Polyp Detection in Future AI-Assisted Endoscopy Frameworks. Is it the Right Time?
Authors: Ioanovici Andrei-Constantin , Mărușteri Marius-Ștefan , Feier Andrei Marian , Ioanovici Irina , Dobru Daniela-Ecaterina
Published: January 2025

Background/Objectives: Colorectal cancer (CRC) is a widespread malignancy that can be mitigated through early detection and removal of precancerous polyps. Artificial intelligence (AI), particularly deep learning, enhances polyp detection during colonoscopy but faces challenges due to limited medical imaging datasets. This study evaluates whether synthetic and pseudosynthetic data—augmented data generated from original datasets—can improve AI accuracy for polyp detection. Methods: We used multiple real and synthetic polyp datasets, applying data augmentation techniques to create pseudosynthetic data, and employing a modified U-Net architecture for polyp segmentation. The model was trained and evaluated across ten experiments using different combinations of real, pseudosynthetic, and synthetic data. Performance metrics included accuracy, precision, recall, Dice coefficient, Intersection over Union (IoU), and F1 score. External validation used the CVC-Colon-DB dataset with 612 image-mask pairs. Results: Combining real and pseudosynthetic data achieved the best performance, with a Dice score of 0.7638, precision of 0.7638, recall of 0.6774, and F1 score of 0.8979. Models trained solely on CycleGAN-generated data performed poorly, while diffusion-based synthetic data offered better generalization (precision: 0.7488, recall: 0.6695, F1: 0.8987). Conclusions: U-Net models trained with synthetic and pseudosynthetic data outperform those trained solely on real data, effectively addressing data scarcity, diversity, and ethical concerns. Models benefit more from pseudosynthetic data alone compared to mixed sources. Diffusion-generated synthetic data leads to better model performance than GAN-generated data. These findings confirm that synthetic and pseudosynthetic data are effective tools to improve model generalization and address ethical concerns in AI-assisted diagnostic environments.
Fish Stock Assessment Models with Emphasis on the use of the Classic Gordon Schaefer Model: A Review
Authors: Chamera Francisco , Kamndaya Mphatso , Kadaleka Solomoni , Phepa Patrick , Mwamtobe Peter Mpasho , Soko Alpha
Published: January 2025

The main objective of stock assessment is to assure, in the long run, of the self-sustainability of the stock. In this paper, we discuss fish stock assessment methods. We present the advantages and disadvantages of each method discussed. Morden approaches to fish stock assessment include multispecies, multi-gear and ecosystem approaches. However, most of these approaches are expensive to apply in developing countries. Simple methods sometimes may perform as well as, or even better than complex methods. We discuss the Gordon Schaefer (GS) model as an alternative method for assessing fishery stocks in developing countries, with Malawi as an example of a developing country. We review how the GS model has been applied globally in general and in Malawi in particular. The review shows that most studies have concentrated on the calculation of maximum sustainable yield or maximum economic yield leaving out other reference points. The use of all reference points, including open access yield and optimum sustainable yield, is important in making correct management decisions. Bifurcation analysis, calculation of annual sustainable production and calculation of depletion are missing in most studies. Future research could focus on integrating the use of all four reference points, bifurcation analysis and calculation of depletion as well as annual sustainable production.
The Effectiveness of the Use of the Ultrasound Methodology (Applied to Live Animals) to Assess the Quality of the Meat
Authors: Meškinytė Edita , Jukna Vigilijus , Zigmantaitė Vilma , Ilina Oksana , Kučinskas Audrius
Published: January 2025

The use of non-invasive technologies is among the most accurate methods for assessing meat quality parameters. Beef tenderness and palatability are strongly influenced by the amount of intramuscular fat, commonly referred to as marbling. Marbling is widely used in breeding programs to select animals with superior meat quality. In this study, we analyzed 236 Angus bulls and 22 Angus heifers from various farms in Lithuania, all aged 450±112 days. Ultrasound examinations were performed using a linear transducer with a silicone standoff to evaluate the loin eye area. Key meat quality indicators such as fat thickness, loin area and thickness, and intramuscular fat were found to increase with the weight of bulls, ranging from 365±12.70 kg to 825.57 ± 11.75 kg (p < 0.001), and heifers, ranging from 273±20.71 kg to 767±41.01 kg (p < 0.001). The highest intramuscular fat values were observed at 7.50± 0.75% (p < 0.05) to 6.9±1.36% (p < 0.1) in bulls weighing 431.33–825.57 kg and 7.98 ± 0.28% (p <0.001) in heifers weighing 603 kg. These findings demonstrate the feasibility of selecting Angus cattle for improved meat quality traits across different weight categories.
Casting Light on Dependency Structures in Ensemble Forecasts with the 2-D Rank Histogram
Authors: Ben Bouallègue Zied
Published: January 2025

A forecast is reliable if it is statistically indistinguishable from the observation in a distributional sense. In probabilistic forecasting, reliability is a necessary (but not sufficient) condition for optimal decision-making. In ensemble forecasting, reliability is the sign of a well-designed system. Tools for assessing reliability in the univariate case exist and have proved to be popular. One well known example of a tool for ensemble forecasts is the rank histogram. Although univariate probabilistic forecasts are historically the most commonly used, multivariate multivariate forecasting is fundamental when multiple variables that influence each other play a role in a decision-making process. The simplest of the multivariate cases is the bivariate one where only two interdependent variables are forecast. Here, we discuss how assessing reliability of bivariate ensemble forecasts can be performed using generalisations of univariate diagnostic tools. We introduce the 2-D rank histogram, a simple and non-restrictive generalisation of the univariate rank histogram. A summary statistics of the ensemble reliability in the bivariate space is also suggested together with a strategy to disentangle marginal and dependency contributions. The interpretation of 2-D rank histograms is illustrated with synthetic data and ECMWF ensemble forecasts. Toy-model experiments are used to help associate histogram patterns with typical reliability misspecifications in a fully controlled environment while an application to the ECMWF ensemble shows how reliability issues can be diagnosed with this versatile tool.
The Role of AI and Living Intelligence in Higher Education: Potential Applications and Challenges
Authors: Allam Hesham
Published: January 2025

The integration of artificial intelligence (AI) and the emerging concept of living intelligence in higher education is transforming the educational landscape by improving teaching, learning, and administrative processes. Living intelligence represents a dynamic partnership between human cognition and AI, emphasizing adaptability, co-evolution, and continuous learning. AI technologies offer numerous advantages, such as personalized learning experiences, enhanced efficiency, and innovative pedagogical methods. However, adopting AI and living intelligence also introduces challenges, especially in maintaining academic integrity and addressing ethical concerns. This paper explores the multifaceted impact of AI and living intelligence in higher education, focusing on their applications, benefits, challenges, and ethical implications. Through a literature survey, this study contributes to existing knowledge while highlighting pressing challenges that higher education professionals must address.
AFAR-WQS: A Quick and Simple Toolbox for Water Quality Simulation
Authors: Rogéliz-Prada Carlos Andrés , Nogales Jonathan
Published: January 2025

Effective water quality management in large-scale river networks is critical for maintaining ecosystem health and ensuring sustainable water resources. Given the complexities of developing detailed water quality models at the macro-watershed scale, there is an increasing need for preliminary approaches that provide rapid, practical insights, especially in data-limited or resource-constrained contexts. This paper presents AFAR-WQS (Assimilation Factor Analysis of Rivers for Water Quality Simulation), a MATLAB-based tool designed to simulate 13 key water quality determinants using the steady-state assimilation factors framework. The tool integrates graph theory and a Depth-First Search (DFS) algorithm to model assimilation factors, concentrations, and loads for each water quality determinant, incorporating both diffuse and point-source pollution. AFAR-WQS enables large-scale simulations with minimal computational demands and low data requirements, providing researchers, managers, and policymakers with an accessible tool for optimizing intervention strategies and resource allocation in river basin management. The DFS algorithm employed by AFAR-WQS demonstrates efficient computational performance, resolving river networks with up to 100,000 segments within minutes, making it well-suited for macro-basin scale applications.
Dietary Habits and Their Impact on Health Metrics Among University Students: Challenges and Opportunities for Intervention
Authors: Abdurasulova Aisanam
Published: January 2025

Dietary habits among university students are a critical area of concern due to their long-term implications for health and well-being. This study explores the dietary behaviors of university students and their relationship with key health metrics such as BMI, blood pressure, and cholesterol levels. The findings highlight a prevalence of unhealthy dietary choices influenced by barriers such as time constraints, stress, and limited access to affordable, nutritious foods. Poor dietary adherence was associated with higher BMI, elevated blood pressure, and increased cholesterol levels, underscoring the importance of healthy eating behaviors. Adherence to the Dietary Approaches to Stop Hypertension (DASH) diet was linked to improved health outcomes, including lower blood pressure and better mental health. However, barriers to healthy eating remain a significant challenge. The results emphasize the need for interventions to improve the university food environment, enhance nutrition education, and support students in adopting healthier eating habits. Addressing these issues is essential for promoting both physical and mental health among university students.
Clinical Reasoning Processes in Emergency: A Grounded Theory Study
Authors: Mendonça Susana
Published: January 2025

Background: Nurses' clinical reasoning is increasingly recognized as a core competency that enhances the quality and safety of care across various healthcare con-texts. Nurses with refined clinical reasoning skills significantly contribute to improving patient health outcomes and achieving broader health gains. In emergency services, this competency is essential for nurses to assess complex problems rapidly, anticipate com-plications, and prevent the deterioration of patients' conditions. This expertise enables them to discern the severity of clinical situations within a short timeframe and intervene effectively. Objectives: To analyze the clinical reasoning process of nurses; to develop a theory to explain the clinical reasoning process in emergency. Methods: This qualitative study explores the research question: "How is the clinical reasoning process of nurses working in emergency services developed?" The methodology used was grounded theory, with theoreti-cal sampling including 20 expert nurses. Data collection methods included in-depth in-terviews, participant observation, and field notes. Results: The theoretical analysis identi-fied Clinical Reasoning as a substantive theory, comprising two subprocesses: Diagnostic Evaluation and Nursing Therapeutic Intervention. A nurses' clinical reasoning deter-mines two levels of patient severity: Level I: Life-threatening situations (Risk of Life) and Level II: Non-immediate life-threatening situations (Expressed Problems). Interventions are tailored to the identified severity levels. Conclusions: The Nursing Clinical Reasoning Model is a dynamic and continuous process involving diagnostic evaluation and thera-peutic intervention. It is deeply embedded in the relationship between nurse, patient, and family, within a specific context of care. The care context influences the nurse's evaluation, intervention, and the patient's behaviour.
Investigating the Effects of Economic Sanctions on Small Businesses
Authors: Roberts Olivia
Published: January 2025

This study investigates the impact of economic sanctions on small businesses, focusing on the financial, operational, and psychological effects experienced by business owners. Through qualitative research methods, including in-depth interviews with 32 small business owners, the study uncovers the multifaceted challenges posed by sanctions and the strategies employed by businesses to cope with them. Findings indicate that sanctions significantly disrupt financial stability, increase costs, limit access to credit, and reduce profit margins, leading to severe financial strain for small businesses. Operational disruptions, such as supply chain interruptions and delays in product availability, further exacerbate these challenges, making it difficult for businesses to meet customer demand. Additionally, business owners experienced heightened levels of stress and anxiety, which affected decision-making and overall motivation. Despite these difficulties, small businesses demonstrated remarkable resilience, employing coping strategies such as diversifying suppliers, shifting to local sourcing, implementing cost-cutting measures, and embracing digital tools to maintain operations. The study also highlights sector-specific impacts, with manufacturing and retail facing the greatest challenges, while agriculture and service sectors showed more adaptability. Government support was found to be limited and often inadequate, leaving many businesses without the necessary resources to navigate the challenges of sanctions. This research emphasizes the need for more targeted support and policy interventions to help small businesses cope with the economic hardships imposed by sanctions.
ANALISIS SENTIMEN DAN KATEGORISASI REVIEW PELANGGAN PADA CAFE KOPI PASTE DENGAN METODE NAIVE BAYES DAN K-NEAREST NEIGHBOR
Authors: Prasetyo Vincentius Riandaru , Ryanda Ihza Akbar , Prima Delta Ardy
Published: January 2025

Minum kopi telah menjadi budaya di kalangan masyarakat Indonesia, budaya minum kopi ini sudah ada sejak zaman penjajahan Belanda. Seiring berkembangnya bangsa Indonesia, penikmat kopi di Indonesia pun semakin meningkat. Untuk memenuhi kebutuhan masyarakat Indonesia, maka lahirlah banyak tempat minum kopi (coffee shop) atau biasa disebut cafe. Karena banyaknya cafe yang ada, maka setiap cafe harus berlomba-lomba meningkatkan kualitasnya agar bisnisnya tetap terus berjalan. Kopi Paste merupakan cafe yang berdiri sejak 2017 di Sidoarjo. Kopi Paste harus tetap dapat bersaing dengan cafe lainnya yang ada di Sidoarjo dengan terus meningkatkan kualitas dari produk, tempat dan pelayanan. Hal inilah yang menjadi latar belakang pembuatan sistem customer relationship management pada cafe Kopi Paste. Tujuan dari pembuatan sistem ini untuk meningkatkan loyalitas pelanggan sehingga dapat meningkatkan penjualan dan dapat terus bersaing dengan cafe lainnya yang ada di Sidoarjo. Metode yang digunakan dalam pembuatan sistem ini menggunakan metode K-nearest neighbor (KNN) dalam melakukan kategorisasi data review yang diberikan oleh pelanggan. Lalu sistem juga menggunakan metode Naive Bayes Classifier untuk melakukan analisis sentimen dari data yang sudah dikategorikan sebelumnya. Data review yang digunakan berasal dari sosial media dan juga dari google review Kopi Paste yang telah diberikan oleh pelanggan sebelumnya. Metode Naive Bayes Classifier berhasil menganalisis sentimen dengan akurasi sebesar 93,33% dan metode K-Nearest Neighbor berhasil melakukan kategorisasi data dengan akurasi sebesar 77%.Kata kunci: Analisis Sentimen, Kategorisasi, Naive Bayes, K-Nearest Neighbor
ANALISA PERHITUNGAN PERFORMANCE MAINTENANCE HEAD TRUCK MENGGUNAKAN METODE TOTAL PRODUCTIVE MAINTENANCE (TPM)
Authors: Ardianzah Calvin Diaz , Darujati Cahyo , Gumelar Agustinus Bimo
Published: January 2025

Penelitian ini bertujuan buat mengetahui penyebab kerusakan atau kurangnya ketersediaan Head truck pada pelabuhan Indonesia menggunakan metode Total Productive Maintenance (TPM). Jumlah Head truck pada Pelabuhan Indonesia sebesar 140 Head truck. Dengan menggunakan metode yakni mengambil dan mengumpulkan data dalam penulisan ini adalah Sistem Literatur. Data dikumpulkan menurut data utama pembugaran truk menurut bulan agustus hingga oktober, lalu data tersebut diolah memakai performance maintenance dan analisis memakai fishbone diagram dan langkah-langkah pemugaran menggunakan pelaksanaan memakai TPM. Pada akhir penulisan ini, bisa digaris bawahi jika penulis sedang menjelaskan sebab dan dampak menurut kegagalan alat/mesin tunggangan timbal. Untuk menerima mesin yang bisa menjaga kehandalannya, diharapkan desain yang benar. Total Productive Maintenance (TPM) adalah rancangan yang benar buat mencapai permasalahan ini. Bahan yang dipakai adalah data waktu operasional, kerusakan, waktu menunggu / alat berhenti dan keseringan kerusakan pada 3 bulan agustus - oktober 2020 menggunakan memperhitungkan nilai Mean Time Beetwen Failure (MTBF) yang didapatkan Head truck menggunakan memakai data rekam Head truck Volvo D9B 380 dan Volvo D11A 370.Kata kunci: Maintenance, Volvo, Head truck, Total Productive Maintenance (TPM), Mean Time Beetwen Failure (MTBF), Availability
PERHITUNGAN KOLONI BAKTERI SUSU SEGAR PADA RUANG WARNA YCBCR
Authors: Fitri Zilvanhisna Emka , Sahenda Lalitya Nindita , Holili Rexy Solehudin Abdi , Rukmi Dyah Laksito
Published: January 2025

The problem with fresh milk on the SPR farm is the manual milking process, which causes the milk to be less hygienic and becomes an ideal growing medium for microbes. Therefore, it is necessary to carry out a procedure for checking the microbiological status as an indicator of food safety. To test for microbial contamination in fresh milk, namely the Total Plate Count (TPC) test, but in this study the focus is on the calculation of bacterial colonies using digital image processing techniques. The stages of the research carried out are the preprocessing process (cropping and color conversion to YCbCr space), image enhancement (addition of brightness and inverse image), the segmentation combination process (gray degree and channel area thresholding) and colony calculation using labeling based on the proximity of 8 neighbors to the feature area. From the results of the study, it was found that bacterial colonies had a wide area range of 150 ≤ area ≤ 8000. A comparison of manual TPC calculations with the system has been carried out on 5 test samples and obtained an average error difference of 0.176.Keywords : channel area thresholding, bacterial colonies, fresh milk, TPC, YCbCr
ANALISIS KEAMANAN WEBSITE DENGAN INFORMATION SYSTEM SECURITY ASSESSMENT FRAMEWORK (ISSAF) DAN OPEN WEB APPLICATION SECURITY PROJECT (OWASP)
Authors: Nugroho Verseveranda Setyo , Christanto Febrian Wahyu
Published: January 2025

Meningkatnya penggunaan platform digital dalam setiap kegiatan manusia memunculkan kembali perhatian yang pada dasarnya diperlukan di setiap aktifitas atau hal yang dikerjakan manusia, seperti keamanan. Keamanan juga tentu saja hadir dalam kegiatan yang sudah terdigitalisasi atau dapat disebut juga sebagai Cybersecurity. Keamanan dalam Cybersecurity penting karena berkaitan dengan data pribadi (Privacy), integritas (Integrity), akses atau verifikasi (Authentication), kerahasiaan (Confidentiality) dan ketersediaan (Availability).  DiamantePro Digital Creative bertempat di Jakarta adalah perusahaan platform digital yang menyediakan jasa undangan atau Invitation seperti undangan pernikahan, meeting internal maupun seminar. Pada dasarnya undangan memerlukan adanya identitas pribadi dari individu yang akan diundang. Maka dari itu untuk melindungi data pengguna dari kebocoran data identitas, DiamantePro Digital Creative memerlukan adanya pengujian penetrasi melalui website mereka. Metode penetrasi tingkat keamanan website dapat menggunakan Information Systems Security Assessment Framework (ISSAF) dan Open Web Application Security Project (OWASP). Hasil pengujian membuktikan bahwa website diaundangkamu.com tidak dapat ditembus karena memiliki fitur security yang mumpuni seperti Naga Cyber Defense dari hostingan rumahweb.com.Kata kunci: Pengujian Penetrasi, ISSAF, OWASP
IMPLEMENTASI QSVM DALAM KLASIFIKASI BINER PADA KASUS KANKER PROSTAT
Authors: Hilmy Nur Amalina Rahmaputri , Akrom Muhamad
Published: January 2025

Quantum Machine Learning (QML) is increasingly attracting attention as a potential solution to improve computational performance, especially in handling complex and big data-driven classification tasks. In this study, the Quantum Support Vector Machine (QSVM) algorithm is applied to prostate cancer classification, with the results compared to the classical Support Vector Machine (SVM) model. QSVM shows superiority in accuracy, reaching 0.93, compared to the classical SVM which has an accuracy of 0.91. In addition, QSVM produces precision, recall, and F1-score values of 0.83, 0.95, and 0.88, respectively, higher than the precision of 0.82, recall of 0.93, and F1-score of 0.87 of the classical SVM. These findings indicate that QSVM is more effective in handling high-dimensional data and complex classification, thus demonstrating the great potential of QML in medical applications, especially in cancer classification and biomarker discovery.Keywords: Quantum Machine Learning, Quantum Support Vector Machine, Klasifikasi, Kanker Prostat