bluuebunny/crossref_metadata_2025 huggingface dataset:
Sistem Informasi Eksekutif Performa Televisi PT. Jawa Pos Media Televisi Menggunakan Metode Drill Down
Authors: Wajendra Dewi Marylda Salma , Windy Fadhilah Susanti  , Safitri Pradhistya Suwandi  , Eka Dyar Wahyuni  , Agung Brastama Putra  , Siti Mukaromah
Published: August 2024

Dalam era digital, industri media mengalami transformasi cepat yang didorong oleh perkembangan teknologi informasi dan perubahan perilaku konsumen. PT. Jawa Pos Media Televisi (JTV) sebagai penyedia konten media terkemuka di Indonesia menghadapi tantangan dalam memahami performa acara televisi mereka untuk mempertahankan daya saing. Penelitian ini bertujuan untuk mengembangkan Sistem Informasi Eksekutif berbasis metode drill-down yang dapat membantu JTV dalam menganalisis data performa televisi secara lebih mendalam dan real-time. Metode drill-down memungkinkan pengguna untuk melakukan analisis hierarkis, dari level tinggi hingga detail, sehingga memberikan wawasan yang lebih dalam mengenai perilaku pemirsa dan performa acara televisi. Penelitian ini menggunakan pendekatan Waterfall dalam pengembangan perangkat lunak, yang meliputi studi literatur, pengumpulan data, proses ETL (Extract, Transform, Load), dan pengujian sistem. Hasil penelitian menunjukkan bahwa sistem informasi eksekutif ini efektif dalam membantu eksekutif JTV membuat keputusan strategis yang lebih cerdas dan responsif. Dengan visualisasi data yang mendalam, sistem ini juga meningkatkan efisiensi operasional JTV dalam menghadapi persaingan di industri media modern.
Peramalan Rating Program Televisi Menggunakan Metode ARIMA Studi Kasus : PT Jawa Pos Media Televisi
Authors: Wajendra Dewi Marylda Salma , Windy Fadhilah Susanti  , Safitri Pradhistya Suwandi  , Eka Dyar Wahyuni  , Asif Faroqi  , Siti Mukaromah
Published: August 2024

Persaingan di industri media dan penyiaran semakin intensif dengan stasiun televisi berlomba-lomba menarik perhatian penonton melalui program-program yang menarik dan berkualitas. PT Jawa Pos Media Televisi (JTV) sebagai stasiun televisi regional terus berupaya meningkatkan kualitas dan popularitas programnya. Salah satu indikator keberhasilan program televisi adalah rating, yang tidak hanya mencerminkan popularitas program tetapi juga berpengaruh langsung terhadap pendapatan iklan dan daya saing stasiun televisi. Penelitian ini bertujuan untuk meramalkan rating program televisi di JTV menggunakan metode Autoregressive Moving Average (ARIMA), yang dikembangkan oleh George Box dan Gwilym Jenkins  Metode ini dipilih karena kemampuannya dalam mengidentifikasi pola dan tren dalam data historis untuk peramalan di masa depan. Data yang digunakan dalam penelitian ini adalah data time series penayangan program dari tahun 2019. Hasil penelitian menunjukkan bahwa model ARIMA dapat memberikan gambaran yang jelas mengenai perilaku penonton untuk lima program terpopuler di JTV, yaitu “Stasiun Dangdut”. “Ocydia”, “Aneh-Aneh E Jagad”, “Inspirasi”, dan “Pojok Pitu”. Program “Stasiun Dangdut” menunjukkan peningkatan rating pada beberapa hari, sedangkan “Ocydia” memiliki rating yang stabil di tingkat rendah. “Aneh-Aneh E Jagad” menunjukkan variasi harian yang besar, dan “Inspirasi” serta “Pojok Pitu” memiliki rating yang stabil. Temuan ini dapat membantu JTV dalam mengoptimalkan program-programnya dan mengambil keputusan strategis terkait penjadwalan program, produksi konten, dan strategi pemasaran.
RANCANG BANGUN SISTEM E-ARSIP BERBASIS WEBSITE MENGGUNAKAN METODE ENKRIPSI AES (Advanced Encryption Standard) STUDI KASUS KPU SIDOARJO
Authors: Ravi Octian Nafis  , Sidqon Mochamad
Published: August 2024

Abstrak ini menjelaskan perancangan dan implementasi sistem pengendalian persediaan dengan menggunakan metode lot sizing "Lot for Lot" di ZulfahCollection.id, sebuah usaha mikro, kecil, dan menengah (UMKM), yang memanfaatkan framework Laravel. Penelitian ini bertujuan untuk meningkatkan efisiensi operasional dan pengelolaan persediaan secara optimal sesuai dengan kebutuhan bisnis. Proses dimulai dengan analisis kebutuhan yang mendalam untuk memahami proses operasional dan kebutuhan persediaan di UMKM tersebut, yang kemudian digunakan sebagai dasar untuk merancang sistem pengendalian persediaan. Fokus utama perancangan ini meliputi keamanan data, antarmuka pengguna yang intuitif, alur kerja gudang yang efisien, dan penerapan metode lot sizing "Lot for Lot". Setelah dirancang, sistem ini diimplementasikan sebagai aplikasi web menggunakan framework Laravel dan diuji secara menyeluruh untuk memastikan kualitas serta kinerjanya. Uji coba mencakup pengujian fungsionalitas, performa, dan keamanan data untuk memastikan sistem dapat diandalkan dalam operasi sehari-hari. Pelatihan intensif diberikan kepada pengguna UMKM untuk memastikan pemahaman yang baik dalam penggunaan sistem dan metode lot sizing yang diterapkan. Evaluasi dilakukan untuk menilai efektivitas sistem dalam pengelolaan persediaan dan pengurangan biaya gudang. Hasilnya menunjukkan bahwa sistem ini mampu membantu ZulfahCollection.id meningkatkan efisiensi operasional dan optimalisasi pengelolaan persediaan mereka. Penelitian ini tidak hanya bermanfaat bagi ZulfahCollection.id tetapi juga memberikan wawasan berharga bagi UMKM lainnya dalam meningkatkan manajemen persediaan mereka melalui penerapan teknologi berbasis web. Diharapkan bahwa penelitian ini dapat memberikan kontribusi nyata dalam bidang manajemen persediaan dan teknologi informasi bagi UMKM, serta membantu mereka mencapai efisiensi operasional yang lebih baik dan pengurangan biaya yang signifikan.
Urgensi Etika Dan Kode Etik Bagi Guru Sejarah Dalam Meningkatkan Kualitas Pendidikan
Authors: Mardiyah A.S. Taulama  , Moh. Imron Rosidi
Published: August 2024

Tujuan penelitian ini untuk mengetahui pentingnya etika dan kode etik guru terhadap siswa. Penelitian ini menggunakan metode kualitatif dengan pendekatan studi literatur. Data ini diperoleh dari berbagai sumber ilmiah, misalnya buku dan artikel dari jurnal yang terbit 2017 hingga 2024. Hasil penelitian mununjukkan bahwa pendidikan memainkan peran penting dalam membentuk generasi masa depan yang beretika dan beradab. Guru memiliki peran kunci dalam membantu siswa mengembangkan nilai-nilai etika dan moral yang diperlukan untuk hidup yang adil dan jujur. Oleh karena itu, pentingnya etika dan kode etik guru terhadap siswa tidak dapat dipandang remeh. Guru yang memiliki etika dan kode etik yang baik dapat membantu siswa mengembangkan kejujuran, kesadaran diri, dan kemampuan berkomunikasi yang efektif. Mereka juga dapat membantu siswa mengembangkan kemampuan berpikir kritis dan membuat keputusan yang bijak. Dengan demikian, siswa dapat menjadi individu yang beretika dan beradab, serta memiliki kemampuan untuk menjadi bagian dari masyarakat yang lebih baik. Selain itu, etika dan kode etik guru juga dapat membantu siswa mengembangkan potensi diri mereka, kepercayaan diri dan rasa aman. Ketika guru memiliki etika dan kode etik yang baik. Jadi guru harus meningkatkan kualitas dan martabat profesinya. Dalam proses pengajaran di sekolah etika juga sangat penting dan guru juga harus memiliki sifat-sifat yang baik, seperti berpikir terbuka, kritis, inovatif, dan percaya diri, serta mampu menerapkan nilai-nilai humaniora yang sesuai dengan bidang keahliannya.
Gut-to-brain neuromodulation by synthetic butyrate-producing commensal bacteria
Authors: Wang Siyao , Zhou Xuwei , Ma Yanchang , Zhang Shuyi , Gong Xu , Zhang Baoyan , Zhou Jiarui , Chen Guo-Qiang , Shi Wei , Yang Yun
Published: August 2024

Previous studies have revealed the existence of gut epithelial-neuronal synapses and an array of neuroactive bacterial metabolites, highlighting the potential of gut chemicals in stimulating gut-to-brain neurotransmission. However, bacterial metabolite-focused studies in murine models frequently apply systemic administration of the chemicals, and the illustrated gut-to-brain signals are generally through humoral pathways, probably distinct from the physiological working mechanism, since many bacterial metabolites could not cross the blood-brain barrier in primates. Limited by delivery approach, research on gut-to-brain neurotransmission pathway regulated by gut bacterial metabolites is sparse. To address this challenge, engineered commensal bacteria were harnessed for gut delivery of bacterial metabolites with physiological biogeography. In murine model of depression, the synthetic butyrate-overproducing Escherichia coli Nissle 1917 (EcN) significantly attenuates depressive-like syndromes. The aberrantly activated paraventricular thalamus (PVT) is modulated by gut butyrate via a gut-to-brain neurotransmission route, which is illuminated for the first time. We provide a paradigm for dissecting gut-to-brain neurotransmission pathways regulated by gut bacterial metabolites, and point out a new avenue for non-invasive gut-to-brain neuromodulation by oral administration of metabolically engineered commensal bacteria, without the dependence on external devices or surgery.
Quantitative understanding of the initial stage of liquid to crystalline or amorphous phase transitions
Authors: Dong Hao-De , Zhang Peng , Qin Ming-Yang , Hui Jian , Wang Hong , Liu Zi-Kui , Xiang X.-D.
Published: August 2024

In 2005, Science magazine listed the “nature of a glassy substance” as one of the 125 most challenging scientific questions of the century. A quantitative understanding of the time-temperature transition (TTT) curve for critical nucleation of amorphous materials is crucial to answering this question. Despite extensive efforts over the past 70 years, a quantitative model for the TTT curve remains elusive due to a lack of understanding of physical properties such as the interfacial energy at the incubation time t* for critical nucleation. In this study, a relationship between the critical nucleation viscosity and the interfacial energy as a function of t* is established and a quantitative TTT model is developed. The model demonstrates excellent agreement with experimental TTT data for various amorphous materials. Most importantly, it allows the accurate and definitive determination of T0, the true minimum crystallization temperature at the lower end-point of the TTT curve, as well as the temperature below which the amorphous liquid-to-solid state transition occurs. This offers an unambiguous answer to the nature of glassy substances: Above T0, a liquid with constant amorphous structure relaxation; and below T0, a solid with stable amorphous structure.
Pemanfaatan Dana Bagi Hasil Cukai Hasil Tembakau Oleh Satuan Polisi Pamong Praja Kabupaten Lima Puluh Kota Dalam Penegakan Hukum Di Bidang Cukai Hasil Tembakau
Authors: Sri Novita Endang , Helen Zennis
Published: August 2024

Dalam Undang Undang Nomor 23 Tahun 2014 tentang Pemerintahan Daerah dikenal azas desentralisasi yaitu penyerahan urusan pemerintahan oleh Pemerintah Pusat kepada daerah otonom. Penyerahan urusan oleh Pemerintah Pusat kepada Pemerintah Daerah dengan sendirinya disertai dengan pelimpahan keuangan. Dalam Undang Undang Nomor 1 Tahun 2022 tentang Hubungan Keuangan Antara Pemerintah Pusat dan Pemerintah Daerah dikenal istilah Transfer ke Daerah (TKD). Salah satu bentuk dari TKD tersebut adalah Dana Bagi Hasil (DBH) yang diantaranya adalah Dana Bagi Hasil Cukai Hasil Tembakau (DBH CHT) yang diberikan kepada provinsi penghasil cukai tembakau dan penghasil tembakau. Peraturan Menteri Keuangan Republik Indonesia Nomor 215/PMK.07/2021 tentang Penggunaan, Pemantauan dan Evaluasi Dana Bagi Hasil Cukai Hasil Tembakau, memperuntukan DBH CHT salah satunya untuk bidang penegakan hukum. Selanjutnya DBH CHT bidang penegakan hukum dialokasikan pada rekening Satuan Polisi Pamong Praja. Spesifikasi penelitian ini adalah penelitian yang bersifat deskriptif analitis. Pendekatan yang digunakan adalah yuridis normatif didukung dengan pendekatan yuridis empiris. Data yang digunakan dalam penelitian adalah data sekunder sebagai data utama dan data primer sebagai data pendukung yang diperoleh melalui studi kepustakaan dan studi lapangan lapangan dengan teknik wawancara. Data tersebut kemudian dianalisis secara kualitatif dan disajikan dalam bentuk deskriptif kualitatif. Berdasarkan hasil penelitian, pembahasan dan analisis disimpulkan bahwa: Pertama, Pemanfaaatan DBH CHT tersebut merupakan tuntutan dari pemerintah pusat kepada pemerintah daerah untuk berkontribusi membantu, mendukung pemerintah pusat dalam program Pemberantasan Barang Kena Cukai (BKC) Ilegal. Ditinjau dari teori kemanfaatan, kegiatan penegakan hukum oleh Satuan Polisi Pamong Praja Kabupaten Lima Puluh Kota dalam rangka pemanfaatan Dana Bagi Hasil Cukai Hasil Tembakau memberikan pleasure (kemanfaatan, kebahagian yang sebesar-besarnya) bagi seluruh pihak baik itu pemerintah pusat, pemerintah daerah dan masyarakat. Kedua, kendala yang dihadapi oleh Satuan Polisi Pamong Praja dalam penegakan hukum di bidang cukai hasil tembakau terkait faktor sarana dan fasilitas hukum terkait kebijakan pemerintah menaikan tarif cukai hasil tembakau serta faktor masyarakat, dimana kenaikan tarif cukai tembakau tidak berbanding lurus dengan pendapatan masyarakat.
MEMBENTUK SUMBER DAYA MANUSIA MILENIAL MENGHADAPI REVOLUSI INDUSTRI 4.0
Authors: Ardianto Hendri , Meyliana Henny , Nagatirtha Partama , Mudjiarto Hari , Gusnadi Gunawan
Published: August 2024

Tujuan  Kegiatan  pengabdian  kepada  masyarakat  ini  untuk meningkatkan sumber daya manusia milenial menghadapi 4.0 bagi muda-mudi Vihara Buddhagaya Pejambon-Pesawaran.   Kegiatan  pengabdian dilaksanakan dengan webinar  langsung. Metode mulai dari survei, focus group discussion, demonstrasi dan unjuk kerja. Materi  pelatihan yang diberikan meliputi kiat-kiat meningkatkan SDM guna menghadapi tantangan dan peluang di era 4.0 untuk pemuda. Hasil kegiatan  pengabdian menunjukkan keberhasilan dengan indikasi berupa tingginya antusiasme dan motivasi untuk  mengikuti webinar serta pada setiap tahapan kegiatan pelatihan.
Two Applications of the Concepts of Pole and Polar with Respect to a Circle
Authors: Patrascu Ion , Smarandache Florentin
Published: August 2024

In this article, we present in a unified form the theoretical results regarding the concepts of pole and polar with respect to a circle, and as applications of these, we offer a demonstration of Newton’s theorem related to the circumscribed quadrilateral and a demonstration of a theorem usually derived as a particular case of Pascal’s theorem related to the inscribed hexagon.
Harnessing Machine Learning for Accurate Cardiovascular Disease Prediction
Authors: Abdelhafeez Ahmed , Ashraf Abdullah , Elbehiery Hussam
Published: August 2024

Cardiovascular disease (CVD) is a life-threatening disease rising considerably in the world. Early detection and prediction of CVD as well as other heart diseases might protect many lives. This requires tact clinical data analysis. The potential of predictive machine learning algorithms to develop the doctor’s perception is essential to all stakeholders in the health sector since it can augment the efforts of doctors to have a healthier climate for patient diagnosis and treatment. We used the machine learning (ML) algorithm to carry out a significant explanation for accurate prediction and decision-making for CVD patients. Simple random sampling was used to select heart disease patients from the Khyber Teaching Hospital and Lady Reading Hospital, Pakistan. ML methods such as decision tree (DT), random forest (RF), logistic regression (LR), Naïve Bayes (NB), and support vector machine (SVM) were implemented for classification and prediction purposes for CVD patients in Pakistan. We performed exploratory analysis and experimental output analysis for all algorithms. We also estimated the confusion matrix and recursive operating characteristic curve for all algorithms. performance of the proposed ML algorithm was estimated using numerous conditions to recognize the most suitable machine learning algorithm in the class of models. RF algorithm had the highest accuracy of prediction, sensitivity, and recursive operative characteristic curve of 85.01%, 92.11%, and 87.73%, respectively, for CVD. It also had the least specificity and misclassification errors of 43.48% and 8.70%, respectively, for CVD. These results indicated that the RF algorithm is the most appropriate algorithm for CVD classification and prediction. Our proposed model can be implemented in all settings worldwide in the health sector for disease classification and prediction.
Revisiting Machine Learning for Predictive Modeling for Stroke from Electronic Health Records
Authors: Abdellah Mohamed Refaat , Owaidah Rahma Elsayed , Selem Sarah Mohammed , Abdel-aziem Ayman H.
Published: August 2024

Stroke is a big worldwide threat with serious health and economic implications. To solve this, researchers are developing automated stroke prediction algorithms, allowing for early intervention and perhaps saving lives. Machine learning (ML) techniques have emerged as powerful tools for predicting stroke risk based on patient data. This study explores the application of various ML algorithms, including logistic regression, decision trees, support vector machines, and k-nearest neighbors, for stroke prediction. The models are trained on historical data from patients with known stroke outcomes and evaluated on their ability to classify individuals into stroke-risk categories accurately. ML techniques are being increasingly adapted for use in the medical field because of their high accuracy. The main contribution of this study is a stacking method that achieves a high performance that is validated by various metrics, such as AUC, precision, recall, F-measure, and accuracy. This study also shows how different ML models can predict strokes by analyzing factors such as age, gender, accompanying habits, and personal lifestyle. It is quite essential to understand the risk factors that make a patient more susceptible to strokes, thus some factors make stroke prediction much easier. This research offers an analysis of the factors that enhance the stroke prediction process based on electronic health records.
Anticipating Diabetes using Fusion-Ensemble Machine Learning Techniques
Authors: Aziz Alber S. , Ibrahim Khaled , Elsharkawy Ahmed , Khaliel Nariman
Published: August 2024

Diagnosis of diabetes is still a complex task for most of the already existing machine learning methods. The objective of the study is to develop a decision support system for predicting the probability of diabetes disease. By using machine learning algorithms and considering fuzzy logic to handle uncertainty, a decision support system can be developed for predicting the probability of having diabetes in the given dataset. Diabetes patients will increase day by day; In current research, several algorithms have been used to predict diabetes. These are major issues for diabetes. Using these technologies, A model will be implemented to predict diabetes using different algorithms with proper comparison and find the best algorithm to predict diabetes. The prediction model is made using 11 classification algorithms from Ski learn, and their accuracies are compared. The expected result is that one of the best algorithms can be obtained for making a diabetes detection model. The fusion (ensemble) model is used for diabetes prediction, intended to improve the accuracy of classification. and use two algorithms to apply the fusion (ensemble) model, which picks the maximum accuracy of the list of classifiers with the rest of the classifiers. The source code is publicly accessible at https://github.com/diabetes-mellitus-implementation.
Prediction of COVID-19 Patients using Machine Learning Algorithms
Authors: Ali Ahmed M. , Esmail Shimaa A.
Published: August 2024

Coronavirus disease (COVID-19), also known as severe acute respiratory syndrome (SARS-COV2), has caused widespread concern for public health worldwide. Based on its rapid spread among those exposed to the wet animal market in Wuhan, China, the city was identified as its origin. The symptoms, reactions, and rates of recovery observed in coronavirus patients around the world have varied. The number of sufferers continues to grow at an exponential rate, and some countries are currently dealing with the third wave. Since the most effective treatment for this disease has yet to be established, early discovery of probable COVID-19 patients can help isolate them socially, slowing the spread and flattening the curve This study examines current research on coronavirus disease and its impact across age groups. We evaluate the effectiveness of Decision Tree (DT), and Logistic Regression (LR) in detecting COVID-19 in patients based on symptoms. A dataset from a public repository was pre-processed before applying Machine Learning (ML) techniques to it. The results show that all ML algorithms are effective in identifying COVID-19 in potential patients. DT classifiers have the highest accuracy of 98.70%, while SVM, KNN, and LR algorithms achieve 93.60%, 93.50%, and 92.80%, respectively.
Resolving the Paradoxism with the Logic of "Not" in Javanese Puppetry: A Re-reading of Wrekodoro and Dewaruci’s Mythology
Authors: Christianto Victor , Smarandache Florentin
Published: August 2024

There are a large number of text collections in Indonesia, related to wayang theme, cf Zoetmulder (1971). And the text collections do not consist only of wayang purwa but also variations of this theme, including wayang wahyu etc. In the mean time, a Japanese scholar Shoshichi Nagatomo proposed the concept of "Asian Logic" which differs from Western logic. In Western thought, things are often seen as black or white, good or bad. However, Asian logic embraces certain degree of ambiguity. Here, "good" characters can exhibit flaws, and "bad" characters can possess redeeming qualities; especially among Javanese people preference is given more to harmony with others and with Nature (called “serasi” or “cocok”). This concept resonates with the Javanese philosophy of "ngono yo ngono neng ojo ngono" (note: the phrase can be translated: “you can do what you like, but you should not to” – as in paradoxism). This philosophy can be considered as embodiment of the logic of "not," acknowledging the contradictory multifaceted nature of reality. This review article is a follow up to our previous articles [1, 12, 13].
The Politicization of Religion in Indonesia (The Use of Hadith on Jihad and Hijra in General Election in Indonesia)
Authors: . Alamsyah , Mahmudah Siti , Asnawi Habib Shulton , Larasati Yuniar Galuh , Pane Erina
Published: August 2024

Religion was often used as a political media to win the battle for power, including politics in Indonesia. The terms "Jihad" and "Hijrah" contained in the Qur'an and Hadith are also used by Islamist groups as a campaign tool to win the presidential or governorial elections, as happened on Jakarta’s governorial election 2017 and Indonesia’s presidential election 2019. Jihad which was originally meaningful generally interpreted by this group only as a hard effort to defeat the party or potential leader of the opponents who was considered un-Islamic or not in favor of Islamic interests. Hijrah is also interpreted as a move from a lifestyle that have not been "shar'i" to "shar'i life", Islamic, halal, and good life. The concept of understanding and practice like this will certainly have an impact on the life of the nation and religion  in  the pluralistic Indonesia.  This  study  used    qualitative research,    the Critical Discourse Analysis (CDA) theory to be developed in critical hermeneutics. The finding is that the concepts  of  jihad  and  Hijrah  were misused  on  the presidential and  governorial elections in Indonesia, especially on 2017 and 2019. The motive is to win the group and defeat opponents who are considered un-Islamic,  shar'i,  and siding  with  the interests of Muslims. Identity politics based on narrow and one-sided perceptions has proven to cause unrest and endanger the life of the nation and religion, because it will be cause disharmony and disintegration.
A Study of Mystical Thinking in Ancient Chinese Expressions: Focusing on “Chen Wei”
Authors: Hao Wang Shih , Wang Pei-Jung
Published: August 2024

Ancient Chinese have expressed culture that communicated with gods and spirits through sound and language. This culture has contained mystical thinking that expressed orally, through spoken words, presenting a form of sound. This paper comprehensively examined and summarized the materials of mystical thinking in ancient Chinese from the texts of “Chen Wei”, analyzing the forms of mystical consciousness and the linguistic cultural features that favor cautious speech and silence over verbosity. The study explores the origins of mystical thinking in Chinese culture, tracing it back to the ancient people's blessings and declarations to spirits through shamans and historians, as well as court and folk poetry and proverbs. It then discussed how this was inherited by the texts of “Chen Wei” in Han dynasty, and how the mode of linguistic expression gradually shifted from sound to writing, examining the evolution of the mystical thinking content in ancient Chinese culture.
The Effectiveness of Music Education in Psychomotor Development, Communication and Social Skills in Children with Autism Spectrum Disorders and Intellectual Disabilities-A Trend Study
Authors: Lici Eliona , Avdulaj Entela , Zhapaj Arbana , Lamaj Ilva
Published: August 2024

The study aims to investigate the use of music education in inclusive classes in ordinary schools to show substantial evidence on how musical experiences may impact the various forms of development of children with Autism Spectrum Disorders and Intellectual Disability. The research synthesizes the advantages of music in education by examining several musical applications found useful for special needs students. However, presents some applied examples to fulfill this vision based on our dialogue about different music applications in the field of special education in Albania and highlights the efficiency to use music education as much as possible in inclusive classes.  The study was conducted in a school in Vlora, Albania with five children attending this school (mean age 8-13 years) who presented ASD and ID or other related disorders. A structured checklist utilized to evaluate their progress over a three-month period, facilitated by music education sessions. Findings showed that children in the music group exhibited larger enhancement over time on rhythm performance, communication skills, and social behavior. The results raise an important subject about the value of integrating music within educational programs designed to help special needs children as it can serve as an effective tool in supporting all developmental domains. The findings from this paper suggest musical interventions may have further potential to support educational processes and development of children. More research is needed to uncover additional advantages and enhance music education instructional strategies for students with special needs.
Spiritual Impact of Theocentric Outdoor Environmental Education on Affective Disposition among Malaysian Pre-service Science Teachers
Authors: Shaharil Najwa Huda , Syed-Abdullah Sharifah Intan Sharina , Fakhruddin Fathiyah Mohd , Rahman Norshariani Abd , Ramlan Mohd Aswad
Published: August 2024

This study utilizes  theocentric outdoor environmental education (TOEE) approach in a one-day program for 20 pre-service science teachers to explore how the spirituality aspects in TOEE approach impacts participants' environmental affective disposition. Participants’ experiences in the program were interpreted through photo elicitation focus group discussions. The findings suggest that the TOEE approach nurtures environmental disposition among pre-service science teachers. TOEE approach enhances aspects of environmental disposition in three key ways: (i) by fostering environmental sensitivity through a profound sense of reverence and gratitude for nature as a divine creation; (ii) by instilling a sense of personal responsibility through the recognition of one's role as khalifah or steward on Earth; and (iii) by motivating individuals through the belief in divine rewards and punishments, which encourages pro-environmental actions and discourages environmental harm. The insights serve as a practical strategy for enhancing the current sustainability curriculum to better fit the Malaysian religious context.
The use of aqueous ozone solution in the treatment of mine blast injury with extensive soft tissue defects: A case study
Authors: Pyatkovskyy Taras , Pokryshko Olena , Bilyk Oleksandr , Danylkov Serhii
Published: August 2024

Mine explosions frequently result in severe and complex injuries, presenting challenges in wound management and infection control. This study aimed to examine the efficacy, safety, and practical implications of ozonated water in wound care for mine explosion injuries. A 37-year-old male soldier presented with extensive lacerations of both legs sustained during a mine explosion. Following initial stabilisation, foreign bodies removal and debridement, the patient was hospitalised where he underwent surgical intervention, and negative pressure wound therapy to repair tissue damage. Microbial cultures obtained from wound samples revealed the presence of multi-drug resistant strains of Pseudomonas aeruginosa and Staphylococcus aureus. Antibiotic susceptibility testing indicated limited treatment options due to resistance of the isolated strains. Given the severity of the injuries and microbial colonisation, ozonated water was introduced as part of the wound care regimen. Topical application of ozonated water was initiated on admission and repeated with each dressing change. Over the course of treatment, the patient demonstrated significant improvement in wound healing, reduction in microbial burden, and resolution of infection signs. The use of ozonated water facilitated expedited wound closure and minimised the need for systemic antibiotics. This case highlights the potential of ozonated water as an effective adjunct therapy in the management of mine explosion wounds, particularly in cases of multi-drug resistant microbial colonisation. The findings underscore the practical value of ozonated water in improving wound care outcomes and reducing reliance on systemic antibiotics in complex trauma cases
TINGKAT PENGETAHUAN TENTANG KEPUTIHAN PADA REMAJA PUTRI SMA NEGERI 3 PALANGKARAYA
Authors: Halida Suryadini  , Syahrida Dian Ardani  , Egi Melia
Published: August 2024

Keputihan merupakan gejala berupa cairan yang dikeluarkan dari alat-alat genitalia yang tidak berupa darah, pengeluaran cairan ini sebagai keadaan normal dari saluran kelamin wanita. Tujuan dalam penelitian ini adalah untuk mengetahui Tingkat Pengetahuan Tentang Keputihan Pada Remaja Putri Di SMA Negeri 3 Palangkaraya. Metode penelitian yang digunakan ini adalah penelitian kuantitatif dengan metode penelitian survei deskriptif untuk mengukur persentase tingkat pengetahuan tentang keputihan pada remaja putri di SMA Negeri 3 Palangkaraya yang diukur dengan tiga tingkatan yaitu kategori baik 76-100%, cukup 56%-75% dan kurang <56%. Hasil penelitian pada tingkat pengetahuan pada remaja putri di SMA Negeri 3 Palangkaraya adalah termasuk tingkat kriteria kategori “Baik” dengan persentase 84,33%.
Comparative Analysis of Hybrid A* and RRT* Algorithms and Optimizing the RRT* Algorithm in Autonomous Maze Navigation
Authors: Li Yanfeng
Published: August 2024

The path planning problem of autonomous mobile robots is a very important problem in today's robotics field, and after decades of development, many path planning algorithms have been derived, among which the A* algorithm and the Rapidly Exploring Random Trees (RRT) algorithm are more widely used. These two methods use completely different basic principles, the A* algorithm relies on global search, while the RRT algorithm uses random search, in order to explore the advantages and disadvantages of the two algorithms, this paper mainly analyzes the principles of the A* algorithm and the RRT* algorithm and compares their performances in the maze navigation of robots. After that, the RRT* algorithm is also improved to make up for the shortcomings of the lower efficiency of the RRT* algorithm by utilizing a bidirectional search strategy, and then finally curve fitting is used to obtain a path that is more suitable for the robot's motivation. After comparative analysis, the A* algorithm is more suitable for precise path planning given a global map, and it can calculate the best path quickly. The RRT* algorithm is more suitable for the path planning problem of an unknown map, and due to the principle of stochastic search, it can make the algorithm calculate a reachable path faster.
A Dual Role Study of Lampreys in Ecosystems Based on Multi-Model Analysis
Authors: Xia Yurun , He Yujie
Published: August 2024

With environmental changes and increased human activities, changes in the sex ratio of Lampreys, a key secondary consumer, have a significant impact on the health and stability of aquatic ecosystems. To address this, this study created a seven-gill eel population prediction model based on the Lotka-Volterra Model incorporating a Bayesian Hierarchical Model suitable for changes in the seven-gill eel population. The model predicted the population density of Lampreys based on the sex ratio of Lampreys, analyzed the stability of the ecosystem based on the food web of Lampreys and concluded that the ecosystem was unstable when the sex ratio of Lampreys was close to 1:1, and the ecosystem became stable when the sex ratio of Lampreys was far away from 1:1. At the same time, this study also determined that Lampreys have the advantages of high survival rate, high life span, and high resource utilization in the ecosystem through the Topsis principle. The study of Lampreys populations is essential for understanding and protecting aquatic ecosystems, as well as helping to monitor water quality and environmental change. Thus, the study of their population size, distribution, and behavior contributes to the understanding and protection of aquatic ecosystems.
Research on the Influencing Factors of Book Star Ratings -Taking Amazon Platform as an Example
Authors: Song Rong
Published: August 2024

Books are one of the main ways for people to acquire knowledge, so usually reading books is very important. Reading books can not only enrich people's knowledge base but also make them more cheerful and help people resist loneliness. With the development of the times, reading has become an indispensable part of people's lives, people have gradually shifted from buying books from bookstores to buying books from online shopping platforms. This study utilized data set information from Amazon Kindle books found on the Kaggle website and filtered out 3933 suitable data. Then, this article will use frequency analysis and linear regression analysis to analyze Price, Reviews, Editor's Pick, Kindle Unlimited, and Good Reads Choice. The results of this research are Editor's Pick, Kindle Unlimited, and Good Reads Choice, recommended by editors, have a substantial positive impact on star ratings. The price will have a significant negative effect on star ratings. However, Reviews will not have an impact on Star ratings Based on the analysis obtained from research, some suggestions can be made for people to choose high-star-rated books.
Review of the Development of Input Word Prediction
Authors: Ke Qianwei
Published: August 2024

As one of the important applications in the field of human-computer interaction, input word prediction has made great progress in recent years.This paper reviews and summarizes the development process of input word prediction technology, from the early statistical model-based method to the current deep learning-based technology application, describes its development trajectory and the representative algorithms of each technology.In addition, this paper also analyzes the current problems and challenges faced by input method word prediction, such as language model modeling, user personalized needs, multi-language input and other aspects of the problem, and discusses the future development trend, including the combination of multi-modal information, fusion reinforcement learning and other new technologies.Finally, this paper also looks forward to the extensive application prospects of input word prediction technology in many fields, and its potential contribution to improving user input efficiency, improving user experience and promoting the development of natural language technology.
Quantitative Study of Tennis Match Momentum and Flow Based on Machine Learning
Authors: Yang Dishen , Zhang Qingyang , Luan Baihe
Published: August 2024

Momentum is a critical factor influencing the dynamics and outcomes of tennis matches. To enhance the predictive accuracy of these fluctuations, this study utilizes machine learning techniques and big data analytics to improve the prediction of these fluctuations. The study conducts a comprehensive analysis to identify the correlation between a player's momentum and 14 key features in a tennis match. An optimized BP neural network model, based on Levenberg-Marquardt theory, was developed to predict match flow and quantify the stalemate degree. The model is evaluated using a confusion matrix and ROC curve, affirming its predictive validity, where the results revealed an F1 Score and an AUC, both exceeding 0.5. With big data, this approach not only enhances the spectator experience by visualizing match dynamics but also aids in strategy development and training optimization for competitors. This research highlights the practical applications of quantitative modeling in understanding and forecasting the pivotal moments in tennis.
A study of sports scores based on correlation analysis and logistic regression
Authors: Chen Shaohang , Chen Jiacheng , Jiang Ruizhe , Xuan Yian , Liang Siming
Published: August 2024

This paper examines the relationship between "Momentum" and victory in the imbledon 2023 men's tournament, particularly between the semi-finals and the final. The definition and ormula of the Pearson's correlation coefficient are presented, and the Pearson's correlation oefficient is analysed based on the match data between Carlos Alcaraz and Novak Djokovic. The results show that there is a positive correlation between "Momentum" and winning. Then the logistic regression model was used to analyse the factors affecting the variation of "kinetic energy", such as onsecutive points, decisive points, key points, fatigue level and double faults. By training and testing the data set, a logistic regression model was developed for predicting the changes in "kinetic energy", and some suggestions based on the differences in "Momentum" were made to help players and coaches better cope with the changes in "Momentum" in the game. This paper also proposes some suggestions based on "Momentum" differences to help players and coaches better cope with "Momentum" changes in matches.
Advancements in Pedestrian Re-Identification
Authors: Ren Ziqi
Published: August 2024

The realm of pedestrian re-identification (Re-ID) technology, a vital component in areas such as video surveillance and intelligent security, has witnessed significant advancements, particularly in the context of deep learning. This domain, despite its nascent start in the broader image processing landscape, has seen rapid evolution with the advent of robust deep learning techniques. The present article delves into a range of methodologies pertinent to pedestrian Re-ID, emphasizing both the integral loss functions in metric learning and the necessary datasets for effective Re-ID implementation. Additionally, it explores the diverse applications of pedestrian Re-ID technology, shedding light on its multifaceted utility. The discourse extends to an analysis of the impending research challenges and potential directions in this field, encapsulating the dynamic and evolving nature of pedestrian re-identification technology. In conclusion, this article offers a comprehensive overview of the current state and forward-looking insights into pedestrian Re-ID, marking a pivotal contribution to the understanding of this transformative technology.
Momentum Evaluation and Situation Prediction Model Based on Integrated Machine Learning Model - a case of tennis match
Authors: Peng Yadi , Du Xinzhou
Published: August 2024

The quantitative analysis of momentum is of great guiding significance to the adjustment of competition strategy and state of coaches and athletes in the field of sports. However, the existing momentum research is mainly explained from the perspective of economics, psychology and other theories, without quantitative analysis. Or after quantitative analysis, the model can only predict the results of the whole game, and cannot accurately predict the changes during the game. Therefore, using data from the 2023 Wimbledon men's singles final as a data set, we propose a momentum evaluation model, a state prediction model and an integrated machine learning model composed of XGBoost, LightGBM, GBDT, to resume the match flow, identify which player perform better at a specific time, and predict state fluctuation. Finally, the 2023 Wimbledon men's singles final match is perfectly visualized and the state fluctuation at each time in this match is accurately predicted. The results suggest that models we established have high prediction accuracy and high stability.
Navigating Complexity in Collaborative Environments through Innovations in Multi-Agent Multi-Armed Bandit Algorithms
Authors: Zhu Siqi
Published: August 2024

As Multi-Armed Bandit (MAB) applications grow increasingly complex, particularly when multiple agents collaborate or compete, traditional bandit algorithms face fresh challenges, underscoring the rising importance of research in multi-agent multi-armed bandits (MAMAB). Developments in MAMAB algorithms have spurred significant advances across a variety of fields, addressing challenges in dynamic and uncertain environments. This paper offers an exhaustive review of recent progress in MAMAB algorithms, emphasizing major strides in enhancing cooperative decision-making and operational efficiency. Our focus is particularly on the contributions of Filippo Vannella et al., who have explored sample complexity within the MAMAB framework. Their research signals a shift towards optimizing global actions by minimizing sample complexity and harnesses mean field techniques in contexts such as optimizing wireless networks. Additionally, this paper addresses communication complexity, a crucial aspect of MAMAB systems, where numerous novel algorithms have been developed. These algorithms strike a balance between performance and communication overhead, diminishing the need for frequent and costly interactions among agents. In application terms, the incorporation of MAMAB algorithms in sectors like clinical trials and wireless network spectrum management showcases their potential to revolutionize conventional approaches. Through a detailed examination of current research trends and prospective future directions, this article contributes to the broader discourse on harnessing MAMAB algorithms to navigate the complexities of collaborative environments effectively.
Wearable Emotion Recognition Technology Based on Heart Rate Variability and EEG Rhythm
Authors: Lin Jiahao
Published: August 2024

This paper explores the utilization of physiological signals, including heart rate variability (HRV) and electroencephalography (EEG), in emotion recognition through wearable devices. Heart Rate Variability (HRV) is closely linked to emotional arousal. HRV can detect subtle changes in heart rate patterns, which are indicative of different emotional states. By analyzing these patterns, researchers can identify and differentiate between various emotions someone may be experiencing. The integration of heart sound signals alongside traditional ECG signals presents an innovative approach, enhancing the accuracy of emotion recognition systems. Similarly, EEG rhythms are investigated for their association with cognitive and emotional states. This involves using brain rhythm sequences in classification tasks. The study underscores the significance of single-channel selection in EEG-based emotion recognition, demonstrating notable improvements in accuracy. Furthermore, wearable emotion recognition devices offer potential benefits for personalized emotion management and mental health intervention, catering to individuals with affective disorders and aiding in medical diagnosis and treatment. The Smartex S.R.L. platform exemplifies the advancement in wearable monitoring technology, facilitating data acquisition and interpretation for emotion recognition, particularly in patients with bipolar disorder. Overall, the paper highlights the evolving landscape of emotion recognition technology, with HRV and EEG emerging as prominent techniques alongside advancements in wearable device design and signal processing methodologies.
Research on Image Recognition of Pandas and Bears Based on Deep Learning
Authors: Zhao Xinbu
Published: August 2024

As deep learning technology has rapidly developed in recent years, image recognition and classification have become a hotspot research direction in artificial intelligence. Accurate identification of target objects in images has great significance for practical applications like autonomous driving, medical image analysis, and biodiversity conservation. Pandas and brown bears are both endangered species of worldwide conservation concern, but manually classifying large volumes of images can be challenging, especially for applications requiring high speed and precision. This study aims to leverage the powerful capabilities of convolutional neural networks (CNNs) to automatically and accurately distinguish between pandas and brown bears. CNNs can learn discriminative visual features from large labeled image datasets and have demonstrated state-of-the-art performance on various image recognition benchmarks. Applying these deep learning techniques has the potential to provide a new technological solution for the panda-brown bear identification challenge, which is crucial for effective wildlife monitoring and conservation strategies. The proposed approach involves training a CNN model using a comprehensive dataset of panda and brown bear images. The model's classification accuracy and inference speed are thoroughly evaluated. The research findings are expected to contribute to enhanced scale and efficiency of field data collection for protecting these vulnerable species.
Machine Learning-based Voting Classifier for Improving Sentiment Analysis on Twitter Data
Authors: Li Huatao
Published: August 2024

As the number of individuals sharing their thoughts on Twitter continues to grow, comprehending the underlying sentiment behind these tweets becomes increasingly crucial for researchers. To identify the optimal model capable of accurately distinguishing tweet sentiment, the author uses a dataset published in 2022, containing tweet texts annotated with corresponding sentiments. Six basic machine learning classification methods are used for model training: Logistic Regression, Naïve Bayes Classifier, Support Vector Classifier, Decision Tree Classifier, Random Forest Classifier, and K-Nearest Neighbors Classifier. Subsequently, the author assesses the trained models. Through the validation, the author finds that the Logistic Regression, Support Vector Classifier, and Random Forest Classifier perform the highest accuracy and F1-score, and the differences between these three models are small. To improve the model, the author votes the best three models together to build a new model. This model’s accuracy and F1-score are better than all the basic models, and the accuracy and F1-score have all reached 71.6%. The research shows the differences between each model and the best model when distinguishing between positive tweets, neutral tweets, and negative tweets.