Urena lobata is medicinal plant used to cure Diabetes mellitus traditionally. However, the phytoconstituent regulating blood glucose levels and their mechanism remain unexlpored. The study aim to evaluate antidiabetic potency of U.lobata leaf fractions. Ethanol solvent is used to extract U. lobata leaf by kinetic maceration methods therefore the concentrated extract was fractionated using n-hexane (Fraction A), n-butanol (Fraction B) and aqueous solvent (Fraction C) respectively. The anti-diabetic potency U.lobata was examined by oral glucose tolerance test. Blood glucose level of rat were measured after loading of glucose at the certain time and Area Under Curve (AUC) integrated were calculated using trape zoidal methods. Data was analyzed using one way ANOVA and continued with LSD test (p<0.05). Molecular docking was conducted by the PyRx 0.8 program after phytoconstituent identification of U.lobata extract using LC-HRMS. Ethanolic extract, fraction A, B and C decreased glucose AUC about 16 %, 8 %, 11% and 23% respectively compared to control group (p<0.05). Meanwhile, glibenclamid as reference drugs decreased glucosa AUC about 30 % and stronger than extract of U. lobata and its fractions. (+)-ar-Turmerone is predicted as potential compound in U.lobata leaf extract as anti-diabetic by binding on SUR1 based on in silico approach.
Ariful Islam, Nathan D. Rock, Kh. Aaditta Arnab
et al.
P-type doped CdTe free surfaces Schottky contacts, and even interfaces with isostructural p-ZnTe frequently exhibit downward band bending and moderate to high recombination velocities. Fermi level pinning by donor-like states can explain these band diagram features, as well as device response characteristics such as 1st quadrant rollover in current-voltage (JV) versus temperature (JVT). Parasitic downward band bending also produces voltage-dependent photocurrent collection, producing fill factor (FF) efficiency losses, JV dark/light non-superposition (or JV take-off), and irregularities in Jsc-Voc and Suns-Voc measurements. Herein, we develop a device physics model of state-of-the-art CdSeTe/CdTe solar cells consistent with known characterization of materials and devices, including the optical, thermalization, and trapping effects of band tail states and isolated defects. We use this model to demonstrate that Fermi-level pinning at the p-ZnTe/p-CdSeTe hole contact by donor-like defects reproduces the aforementioned observables, and conclude that (for contemporary few-um absorber thicknesses and low mobilities) it primarily affects FF rather than Voc. We investigate the performance gains possible from hypothetical passivated, hole-selective layers at the ZnTe/CdTe interface, which eliminate the downwards band bending caused by donor-like defects. For thinner devices and larger minority carrier diffusion lengths, these strategies will become more important for continued efficiency improvements.
Taminul Islam, Abdellah Lakhssassi, Toqi Tahamid Sarker
et al.
Quantifying exhaled CO2 from free-roaming cattle is both a direct indicator of rumen metabolic state and a prerequisite for farm-scale carbon accounting, yet no existing system can deliver continuous, spatially resolved measurements without physical confinement or contact. We present TRACE (Thermal Recognition Attentive-Framework for CO2 Emissions from Livestock), the first unified framework to jointly address per-frame CO2 plume segmentation and clip-level emission flux classification from mid-wave infrared (MWIR) thermal video. TRACE contributes three domain-specific advances: a Thermal Gas-Aware Attention (TGAA) encoder that incorporates per-pixel gas intensity as a spatial supervisory signal to direct self-attention toward high-emission regions at each encoder stage; an Attention-based Temporal Fusion (ATF) module that captures breath-cycle dynamics through structured cross-frame attention for sequence-level flux classification; and a four-stage progressive training curriculum that couples both objectives while preventing gradient interference. Benchmarked against fifteen state-of-the-art models on the CO2 Farm Thermal Gas Dataset, TRACE achieves an mIoU of 0.998 and the best result on every segmentation and classification metric simultaneously, outperforming domain-specific gas segmenters with several times more parameters and surpassing all baselines in flux classification. Ablation studies confirm that each component is individually essential: gas-conditioned attention alone determines precise plume boundary localization, and temporal reasoning is indispensable for flux-level discrimination. TRACE establishes a practical path toward non-invasive, continuous, per-animal CO2 monitoring from overhead thermal cameras at commercial scale. Codes are available at https://github.com/taminulislam/trace.
This research focuses on the impact of the flipped classroom model on second grade elementary school students' critical thinking skills in understanding multiplication. In the 21st century learning era, critical thinking is no longer just a plus; it is an essential ability that students must have in order to succeed and adapt amidst the flood of information. This study adopted a quantitative approach and used a quasi-experimental design. The research sample was selected using cluster random sampling technique, where class IIA served as the control class and class IIB as the experimental class. Data were collected through tests, observations, and documentation studies, then analyzed using descriptive and inferential statistics. The results showed that the flipped classroom model could be fully implemented up to 100%, starting from the preparation stage of the material at home to the active learning process in the classroom. The average pretest score for the control class was 43.15, while the experimental class had an average of 47.44. After the treatment, the average posttest score increased. The control class reached 70.00 and the experimental class reached 84.85. The independent sample t-test showed a significance value of 0.000 (below 0.05), which means there is a significant difference between the two groups. In addition, the calculation of an effect size value of 1.52 shows that the flipped classroom model has a huge impact on improving students' critical thinking skills.
Bu çalışma, İslâm düşünce geleneğinde önemli bir yere sahip olan reddiye literatürü çerçevesinde, Hıristiyanlık eleştirilerini “tahrif” ve “tebşîrat” kavramları üzerinden değerlendirmekte ve bu bağlamda Abdullah b. Abdullah et-Tercüman’ın (ö. 832/1429) katkılarını incelemektedir. Katolik bir rahipken İslâm’ı kabul eden ve Anselmo Turmeda adıyla bilinen Tercüman, Tuḥfetü’l-erîb fi’r-red ʿalâ ehli’ṣ-ṣalîb adlı eseriyle Hıristiyanlık eleştirisini hem bilgi hem yöntem açısından farklı bir düzleme taşımıştır. Reddiye geleneği, İslâm dünyasında Hıristiyanlık ve Yahudilik gibi dinlerin teolojik öğretilerine karşı geliştirilen eleştirileri içermektedir. Bu geleneğin temel kavramlarından biri olan “tahrif”, ilahî kitapların zamanla insanlar eliyle değiştirildiğini ve orijinal mesajdan saptığını ifade eder. “Tebşîrat” ise, Hz. Muhammed’in peygamberliğinin önceki kutsal metinlerde müjdelendiği inancına dayanır. Tercüman’ın çalışması, bu iki kavram etrafında şekillenmiş olup hem teolojik hem de metinsel açıdan özgün katkılar sunar. Üç ana bölümden oluşan eserin üçüncü ve en geniş kapsamlı bölümü Hıristiyan inançlarının eleştirisine ayrılır. Bu bölümde, dört İncil arasındaki metinsel çelişkiler, Hıristiyan mezheplerinin görüş ayrılıkları ve Pavlus’un Hıristiyanlık üzerindeki etkisi gibi konular detaylandırılır. Tercüman, mevcut dört İncil’in yazarlarının Hz. İsa ile doğrudan bağlantılarının bulunmadığını ve metinlerin birbirinden farklı ifadeler içermesinin, kutsal kitapların tahrif edildiğine delil olduğunu öne sürer. Yazar, Hıristiyanların Hz. İsa’ya tanrılık vasfı atfetmelerini, İncil metinlerindeki bazı ifadelerle çürütmeye çalışır. Ona göre Hz. İsa’nın bir insan ve peygamber olduğu yönündeki deliller bizzat İncil’in içinde bulunmaktadır. Tercüman ayrıca, Pavlus’un Hıristiyanlık inancına getirdiği felsefi etkilerle dini özünden uzaklaştırdığını ve hakikatin üzerinin bu sayede örtüldüğünü belirtir. Bu noktada, Tercüman’ın kullandığı delillerin çoğunlukla Hıristiyan kutsal metinlerinden seçilmiş olması dikkat çeker; bu yaklaşım onu klasik reddiye yazarlarından ayıran önemli bir özelliktir. Tebşîrat kısmında ise Hz. Muhammed’in peygamberliğinin Tevrat, Zebur ve İncil’de yer aldığı iddialarına odaklanılır. Tercüman, özellikle “Paraklit” kavramının Hz. Muhammed’i ifade ettiğini, “Farân dağlarından doğdu.” gibi ifadelerin Mekke ve Hicaz bölgesine işaret ettiğini savunur. Bu yorumlarıyla Tercüman, Hz. Muhammed’in peygamberliğinin önceki ilahî kitaplarda açık ya da örtük biçimde yer aldığını ifade eder. Özellikle Yeşaya ve Mika gibi metinlerdeki sembolik anlatımların İslâm peygamberiyle ilişkili olduğunu öne sürer. Tercüman’ın bu yorumları, kelâm geleneğindeki “beşâirü’n-nübüvve” anlayışıyla paralellik göstermekte ve nübüvvetin ispatı açısından önemli bir yöntem olarak değerlendirilmektedir. Bu bağlamda, Tercüman yalnızca teolojik bir savunma yapmamakta; aynı zamanda metin merkezli tarihsel bir analiz ortaya koymaktadır. Sonuç olarak Tercüman’ın reddiye anlayışı, klasik İslâm reddiye geleneğine özgün bir katkı sunmaktadır. Naklî delillere dayanarak yaptığı eleştiriler, onu aklî delillere ağırlık veren önceki yazarların ötesine taşımakta, Hıristiyan metinlerinin iç tutarsızlıklarını vurgulayarak İslâm’ın tevhîd anlayışını savunmaktadır. Bu yönüyle Tuḥfetü’l-erîb sadece bir teolojik metin değil aynı zamanda tarihsel, metinsel ve polemiksel bir inceleme niteliğini haizdir. Klasik ve çağdaş kaynaklara dayanarak hazırlanan çalışmada, öncelikle reddiye geleneğinde tahrif ve tebşîrat konusu incelenmiş ardından Tercüman’ın konumu tespit edilmiştir. Bu tespitten sonra onun eserini hem içerik hem yöntem açısından analiz etmek amaç ve hedef olarak belirlenmiştir. Tercüman’ın yaklaşımı, hem İslâm-Hıristiyan ilişkileri bağlamında hem de teolojik tartışmaların dönüşümü açısından önemli bir kaynak olmaya devam etmektedir.
Samah Mohamed Taha PhD, Elham Hassan Tawfik PhD,
Eman Sameh Abd Elhay PhD
et al.
Background Academic tripping, the phenomenon of students underperforming relative to their capabilities, presents a significant challenge in nursing education. Psychological factors such as self-control, fear of failure (atychiphobia), and learned helplessness are theorized to play a critical, yet underexplored, role in this process. Aim This study aimed to investigate the interrelationships between self-control, fear of failure, and learned helplessness among first-year nursing students who have experienced academic tripping. Methods A cross-sectional, exploratory design was employed with 370 first-year nursing students from two Egyptian universities, selected via convenience sampling. Participants completed validated Arabic versions of the Performance Failure Appraisal Inventory (PFAI), Learned Helplessness Scale (LHS), and Brief Self-Control Scale (BSCS). Pearson's correlation coefficient (r), ANOVA, and t-tests were used to analyze bivariate relationships and group differences. A path analysis was conducted to model the direct and indirect effects between the core variables. Results Participants reported moderate levels of learned helplessness (M = 44.12, SD = 5.32), self-control (M = 34.84, SD = 7.13), and fear of failure (M = 69.64, SD = 20.10). A significant negative correlation was found between self-control and learned helplessness ( r = −0.235, p < .001). Path analysis revealed that self-control exerts a significant direct effect on reducing learned helplessness (β = −0.097, p = .005) and a stronger indirect effect by mitigating negative failure appraisal (β = −0.533, p < .001), which in turn predicts helplessness (β = 0.085, p < .001). Study hours and the presence of an academic tripping plan were also significantly associated with better psychological outcomes. Conclusion The findings demonstrate that self-control is a pivotal protective factor against learned helplessness, operating both directly and indirectly through its reduction of catastrophic failure appraisal. This suggests that interventions designed to enhance self-regulatory capacities and reframe cognitive appraisals of failure may be effective in breaking the cycle of academic tripping and fostering resilience among novice nursing students.
Mohd Ruhul Ameen, Akif Islam, Abu Saleh Musa Miah
et al.
News media often shape the public mood not only by what they report but by how they frame it. The same event can appear calm in one outlet and alarming in another, reflecting subtle emotional bias in reporting. Negative or emotionally charged headlines tend to attract more attention and spread faster, which in turn encourages outlets to frame stories in ways that provoke stronger reactions. This research explores that tendency through large-scale emotion analysis of Bengali news. Using zero-shot inference with Gemma-3 4B, we analyzed 300000 Bengali news headlines and their content to identify the dominant emotion and overall tone of each. The findings reveal a clear dominance of negative emotions, particularly anger, fear, and disappointment, and significant variation in how similar stories are emotionally portrayed across outlets. Based on these insights, we propose design ideas for a human-centered news aggregator that visualizes emotional cues and helps readers recognize hidden affective framing in daily news.
Islam Mansour, Georg Fischer, Ronny Haensch
et al.
Digital elevation models derived from Interferometric Synthetic Aperture Radar (InSAR) data over glacial and snow-covered regions often exhibit systematic elevation errors, commonly termed "penetration bias." We leverage existing physics-based models and propose an integrated correction framework that combines parametric physical modeling with machine learning. We evaluate the approach across three distinct training scenarios - each defined by a different set of acquisition parameters - to assess overall performance and the model's ability to generalize. Our experiments on Greenland's ice sheet using TanDEM-X data show that the proposed hybrid model corrections significantly reduce the mean and standard deviation of DEM errors compared to a purely physical modeling baseline. The hybrid framework also achieves significantly improved generalization than a pure ML approach when trained on data with limited diversity in acquisition parameters.
Fahmida Islam, Adnan Mahmood, Noorain Mukhtiar
et al.
Federated Learning (FL) has been extensively employed for a number of applications in machine learning, i.e., primarily owing to its privacy preserving nature and efficiency in mitigating the communication overhead. Internet of Vehicles (IoV) is one of the promising applications, wherein FL can be utilized to train a model more efficiently. Since only a subset of the clients can participate in each FL training round, challenges arise pertinent to fairness in the client selection process. Over the years, a number of researchers from both academia and industry have proposed numerous FL frameworks. However, to the best of our knowledge, none of them have employed fairness for FL-based client selection in a dynamic and heterogeneous IoV environment. Accordingly, in this paper, we envisage a FairEquityFL framework to ensure an equitable opportunity for all the clients to participate in the FL training process. In particular, we have introduced a sampling equalizer module within the selector component for ensuring fairness in terms of fair collaboration opportunity for all the clients in the client selection process. The selector is additionally responsible for both monitoring and controlling the clients' participation in each FL training round. Moreover, an outlier detection mechanism is enforced for identifying malicious clients based on the model performance in terms of considerable fluctuation in either accuracy or loss minimization. The selector flags suspicious clients and temporarily suspend such clients from participating in the FL training process. We further evaluate the performance of FairEquityFL on a publicly available dataset, FEMNIST. Our simulation results depict that FairEquityFL outperforms baseline models to a considerable extent.
Saahil Islam, Venkatesh N. Murthy, Dominik Neumann
et al.
To restore proper blood flow in blocked coronary arteries via angioplasty procedure, accurate placement of devices such as catheters, balloons, and stents under live fluoroscopy or diagnostic angiography is crucial. Identified balloon markers help in enhancing stent visibility in X-ray sequences, while the catheter tip aids in precise navigation and co-registering vessel structures, reducing the need for contrast in angiography. However, accurate detection of these devices in interventional X-ray sequences faces significant challenges, particularly due to occlusions from contrasted vessels and other devices and distractions from surrounding, resulting in the failure to track such small objects. While most tracking methods rely on spatial correlation of past and current appearance, they often lack strong motion comprehension essential for navigating through these challenging conditions, and fail to effectively detect multiple instances in the scene. To overcome these limitations, we propose a self-supervised learning approach that enhances its spatio-temporal understanding by incorporating supplementary cues and learning across multiple representation spaces on a large dataset. Followed by that, we introduce a generic real-time tracking framework that effectively leverages the pretrained spatio-temporal network and also takes the historical appearance and trajectory data into account. This results in enhanced localization of multiple instances of device landmarks. Our method outperforms state-of-the-art methods in interventional X-ray device tracking, especially stability and robustness, achieving an 87% reduction in max error for balloon marker detection and a 61% reduction in max error for catheter tip detection.
Md. Shaheenur Islam Sumon, Khandaker Reajul Islam, Tanzila Rafique
et al.
Cephalometric analysis is essential for the diagnosis and treatment planning of orthodontics. In lateral cephalograms, however, the manual detection of anatomical landmarks is a time-consuming procedure. Deep learning solutions hold the potential to address the time constraints associated with certain tasks; however, concerns regarding their performance have been observed. To address this critical issue, we proposed an end-to-end cascaded deep learning framework (Self-CepahloNet) for the task, which demonstrated benchmark performance over the ISBI 2015 dataset in predicting 19 dental landmarks. Due to their adaptive nodal capabilities, Self-ONN (self-operational neural networks) demonstrate superior learning performance for complex feature spaces over conventional convolutional neural networks. To leverage this attribute, we introduced a novel self-bottleneck in the HRNetV2 (High Resolution Network) backbone, which has exhibited benchmark performance on the ISBI 2015 dataset for the dental landmark detection task. Our first-stage results surpassed previous studies, showcasing the efficacy of our singular end-to-end deep learning model, which achieved a remarkable 70.95% success rate in detecting cephalometric landmarks within a 2mm range for the Test1 and Test2 datasets. Moreover, the second stage significantly improved overall performance, yielding an impressive 82.25% average success rate for the datasets above within the same 2mm distance. Furthermore, external validation was conducted using the PKU cephalogram dataset. Our model demonstrated a commendable success rate of 75.95% within the 2mm range.
Education is a very important part of human life. In the world of Islamic education, there are many educational figures who have succeeded in giving birth to great ideas that are still being used today, one of which is Ibn Miskawaih. The discussion of Ibn Miskawaih's Islamic education thought is very important to be studied more deeply in order to add insight and knowledge. This research uses library research method. The data sources in this research are journals, articles, websites, books, and various other information media. The data analysis technique used is content and data analysis. The problems to be answered in this study are (1) Who is Ibn Miskawaih? And (2) How is Islamic Education Thought according to Ibn Miskawaih? The results showed that Ibn Miskawaih was the most famous Islamic scientist and the first to write moral philosophy, he was also the secretary of Amirul-Umara Adhud-Daulah of the Buwaihi daulat in Baghdad, concurrently the head of the Bait al-Hikam library, a moralist, the father of Educational Psychology, a Muslim philosopher who focused his attention on Islamic ethics, historian, physician, scientist and writer. The basis of Islamic education according to Ibn Miskawaih is sharia and soul. The purpose of education according to Ibn Miskawaih is the achievement of moral glory, as well as achieving goodness and happiness. The function of education according to Ibn Miskawaih is as a means of socializing individuals, as well as humanizing humans (humanization).
Azizah Putri Irmayanti, Zulheldi Zulheldi, Duski Samad
et al.
Pendidikan Agama Islam memiliki peran strategis dalam membentuk kepribadian dan karakter muslim seutuhnya. Hukum Islam tidak terlepas dari tujuan maqashid syari’ah itu sendiri. Penelitian ini bertujuan menguraikan urgensi pemahaman hukum Islam berlandaskan maqashid syari’ah dalam konteks pendidikan agama islam. Melalui lima elemen krusial yaitu menjaga agama, menjaga jiwa, menjaga akal, menjaga harta dan menjaga keturunan, diidentifikasi sebagai pilar utama yang harus dilindungi dan ditingkatkan. Metode penelitian adalah studi kepustakaan melalui kajian terhadap literatur terkait hukum Islam dan maqashid syari’ah. Hasil penelusuran pustaka menemukan konsep maqashid syari’ah sejalan dengan visi, misi, dan tujuan pendidikan agama Islam dalam membentuk kepribadian muslim paripurna. Pemahaman hukum Islam yang merefleksikan maqashid syari’ah sangat penting diintegrasikan dalam pendidikan agama Islam agar dapat membekali generasi muslim dengan kesadaran syari’ah yang komprehensif. Dengan memahami hukum islam dan maqashid syari’ah, diharapkan dapat memperkuat pondasi pendidikan agama islam sebagai sarana pembentukan karakter dan moral peserta didik sesuai dengan nilai-nilai Islam.
Mufidatun Nur Aini, Nuruddin Ghilman Halim, M Muhid
et al.
The Qur'an and hadith are the main guides for Muslims, but the temptation of lust sometimes leads humans to riya' behavior, which is doing good only for the praise of humans, not the pleasure of Allah. The book "Hidayatus Salikin" written by Abdul Samad al-Falimbani, is a phenomenal work that is quite calculated among Muslims. The figure with expertise in Sufism and philosophy more or less suggests practical applications in everyday life related to religious issues. Descriptive qualitative research method with content exploration in the book "Hidayatus Salikin". This article will present the concept of riya' from various scholars' perspectives and emphasize the importance of pure intention in worship. By focusing on the Hadiths of the Prophet Muhammad PBUH contained in the book "Hidayatus Salikin", this article provides an in-depth understanding of the dangers of riya' and the need to maintain sincere intentions, according to the teachings of the Qur'an and Hadith. Sheikh Abdul Samad al-Falimbani encourages Muslims to practice religion with purity of intention, avoiding riya', and following the guidance of the Qur'an and hadith. Riya' or performing acts of worship for human praise is considered shirk khafi (hidden shirk). This discourse underscores the necessity for Muslims to cultivate purity of intention, thereby fortifying their spiritual endeavors against the insidious influence of riya'.
Muhammad Khalidi, Muhammad Zaini, Danang Biyatmoko
Etnobotani merupakan ilmu yang mengkaji interaksi antara manusia dan tumbuhan di sekitarnya. Etnobotani bermanfaat bagi masyarakat khususnya daerah yang memiliki potensi lokal beragam. Fokus penelitian yakni pada kajian etnoekonomi masyarakat Dayak Bakumpai Kabupaten Barito Kuala yang dimana masyarakat masih memanfaatkan tumbuhan potensi lokal sebagai mata pencaharian atau untuk memenuhi kehidupan sehari-hari. Tujuan penelitian ini untuk mendeskripsikan etnoekonomi tumbuhan masyarakat Dayak Bakumpai Kabupaten Barito Kuala. Metode yang digunakan yakni deskriptif kualitatif dengan teknik pengumpulan data menggunakan snowball sampling. Subjek penelitian yakni tokoh adat, tokoh masyarakat, dan sesepuh desa. Hasil penelitian menunjukkan terdapat 20 spesies tumbuhan yang digunakan sebagai penunjang ekonomi warga Dayak Bakumpai dan hampir seluruh bagian tumbuhan dapat dimanfaatkan nilai ekonominya dengan hasil 3 spesies sebagai pewarna alami pembuatan makanan, 2 spesies untuk bahan “menginang” yang diperjualbelikan di pasaran, 2 spesies sebagai hiasan pekarangan bunga dan diperjualbelikan, 5 spesies untuk bumbu masakan yang dikonsumsi rumahan atau diperjualbelikan, 3 spesies sebagai perabot rumahan, tali/bakul, dan kayu bakar. 1 spesies untuk perawatan wajah “pupur sadingen”, 3 spesies untuk bahan makanan secara langsung yang diperjualbelikan, dan 1 spesies sebagai pewangi/parfum
Tousif Islam, Guglielmo Faggioli, Gaurav Khanna
et al.
We investigate the late-time tail behavior in gravitational waves from merging eccentric binary black holes (BBH) using black hole perturbation theory. For simplicity, we focus only on the dominant quadrupolar mode of the radiation. We demonstrate that such tails become more prominent as eccentricity increases. Exploring the phenomenology of the tails in both spinning and non-spinning eccentric binaries, with the spin magnitude varying from $χ=-0.6$ to $χ=+0.6$ and eccentricity as high as $e=0.98$, we find that these tails can be well approximated by a slowly decaying power law. We study the power law for varying systems and find that the power law exponent lies close to the theoretically expected value $-4$. Finally, using both plunge geodesic and radiation-reaction-driven orbits, we perform a series of numerical experiments to understand the origin of the tails in BBH simulations. Our results suggest that the late-time tails are strongly excited in eccentric BBH systems when the smaller black hole is in the neighborhood of the apocenter, as opposed to any structure in the strong field of the larger black hole. Our analysis framework is publicly available through the \texttt{gwtails} Python package.
Farhan Islam, Deborah Schlagel, Yongbin Lee
et al.
The addition of magnetic impurities in topological insulators can drive ferromagnetic order that leads to novel quantum anomalous Hall transport well below the Curie temperature. The fragility of the quantized regime has been ascribed to the random nature of the magnetic moment distribution. Here, we refine this hypothesis by using inelastic neutron scattering and density-functional theory calculations to show that two antagonistic components define the magnetism in Mn-substituted SnTe, thereby limiting the effectiveness of dilute magnetic TIs. One component is strongly bound antiferromagnetic dimers that compete with ferromagnetic order. The other component consists of undimerized moments where ferromagnetic order develops via long-range interactions.
This paper presents DEEGITS (Deep Learning Based Heterogeneous Traffic State Measurement), a comprehensive framework that leverages state-of-the-art convolutional neural network (CNN) techniques to accurately and rapidly detect vehicles and pedestrians, as well as to measure traffic states in challenging scenarios (i.e., congestion, occlusion). In this study, we enhance the training dataset through data fusion, enabling simultaneous detection of vehicles and pedestrians. Image preprocessing and augmentation are subsequently performed to improve the quality and quantity of the dataset. Transfer learning is applied on the YOLOv8 pretrained model to increase the model's capability to identify a diverse array of vehicles. Optimal hyperparameters are obtained using the Grid Search algorithm, with the Stochastic Gradient Descent (SGD) optimizer outperforming other optimizers under these settings. Extensive experimentation and evaluation demonstrate substantial accuracy within the detection framework, with the model achieving 0.794 mAP@0.5 on the validation set and 0.786 mAP@0.5 on the test set, surpassing previous benchmarks on similar datasets. The DeepSORT multi-object tracking algorithm is incorporated to track detected vehicles and pedestrians in this study. Finally, the framework is tested to measure heterogeneous traffic states in mixed traffic conditions. Two locations with differing traffic compositions and congestion levels are selected: one motorized-dominant location with moderate density and one non-motorized-dominant location with higher density. Errors are statistically insignificant for both cases, showing correlations from 0.99 to 0.88 and 0.91 to 0.97 for heterogeneous traffic flow and speed measurements, respectively.
Penelitian implementasi manajemen strategik lembaga pendidikan Islam untuk meningkatkan public interest ini dilakukan untuk mendapatkan gambaran terkait implementasi manajemen strategik di SMP Islam Sunan Gunung Jati Ngunut Tulungagung. Tujuan dari penelitian ini untuk mendeskripsikan implementasi manajemen strategi lembaga pendidikan Islam dalam peningkatan public interest yang difokuskan pada pengembangan program, anggaran, dan standar operasional prosedur di SMP Islam Sunan Gunung Jati Ngunut Tulungagung untuk meningkatkan public interest. Penelitian ini menggunakan jenis penelitian kualitatif dengan pendekatan fenomenologi yang memotret kejadian apa adanya sesuai kenyataan yang terjadi di lapangan. Sedangkan pengumpulan data menggunakan observasi, wawancara, dan dokumentasi. Analisis data dengan menggunakan metode analisis deskriptif. Hasil dari penelitian ini menunjukkan bahwa implementasi manajemen strategik yang meliputi pengembangan program, anggaran, dan standar operasional prosedur memberikan dampak positif dalam ranah pendidikan di SMP Islam Sunan Gunung Jati dalam meningkatkan public interest.
Ruzelan Khalid, Mohd Kamal Mohd Nawawi, Nurhanis Ishak
et al.
A speed-density model can be utilised to efficiently flow pedestrians in a network. However, how each model measures and optimises the performance of the network is rarely reported. Thus, this paper analyses and optimises the flow in a topological network using various speed-density models. Each model was first used to obtain the optimal arrival rates to all individual networks. The optimal value of each network was then set as a flow constraint in a network flow model. The network flow model was solved to find the optimal arrival rates to the source networks. The optimal values were then used to measure their effects on the performance of each available network. The performance results of the model were then compared with thatof other speed-density models. The analysis of the results can help decision-makers understand how arrival rates propagate through traffic and determine the level of the network throughputs. (original abstract)