Taminul Islam, Toqi Tahamid Sarker, Mohamed Embaby
et al.
Ruminal acidosis is a prevalent metabolic disorder in dairy cattle causing significant economic losses and animal welfare concerns. Current diagnostic methods rely on invasive pH measurement, limiting scalability for continuous monitoring. We present FUME (Fused Unified Multi-gas Emission Network), the first deep learning approach for rumen acidosis detection from dual-gas optical imaging under in vitro conditions. Our method leverages complementary carbon dioxide (CO2) and methane (CH4) emission patterns captured by infrared cameras to classify rumen health into Healthy, Transitional, and Acidotic states. FUME employs a lightweight dual-stream architecture with weight-shared encoders, modality-specific self-attention, and channel attention fusion, jointly optimizing gas plume segmentation and classification of dairy cattle health. We introduce the first dual-gas OGI dataset comprising 8,967 annotated frames across six pH levels with pixel-level segmentation masks. Experiments demonstrate that FUME achieves 80.99% mIoU and 98.82% classification accuracy while using only 1.28M parameters and 1.97G MACs--outperforming state-of-the-art methods in segmentation quality with 10x lower computational cost. Ablation studies reveal that CO2 provides the primary discriminative signal and dual-task learning is essential for optimal performance. Our work establishes the feasibility of gas emission-based livestock health monitoring, paving the way for practical, in vitro acidosis detection systems. Codes are available at https://github.com/taminulislam/fume.
We characterize twisted right-angled Artin groups whose finitely generated subgroups are also twisted right-angled Artin groups. Additionally, we give a classification of coherence within this class of groups in terms of the defining graph. Furthermore, we provide a solution to the isomorphism problem for a notable subclass of these groups.
Md. Shaheenur Islam Sumon, Md. Sakib Bin Islam, Md. Sohanur Rahman
et al.
The early detection and prediction of cardiovascular diseases are crucial for reducing the severe morbidity and mortality associated with these conditions worldwide. A multi-headed self-attention mechanism, widely used in natural language processing (NLP), is operated by Transformers to understand feature interactions in feature spaces. However, the relationships between various features within biological systems remain ambiguous in these spaces, highlighting the necessity of early detection and prediction of cardiovascular diseases to reduce the severe morbidity and mortality with these conditions worldwide. We handle this issue with CardioTabNet, which exploits the strength of tab transformer to extract feature space which carries strong understanding of clinical cardiovascular data and its feature ranking. As a result, performance of downstream classical models significantly showed outstanding result. Our study utilizes the open-source dataset for heart disease prediction with 1190 instances and 11 features. In total, 11 features are divided into numerical (age, resting blood pressure, cholesterol, maximum heart rate, old peak, weight, and fasting blood sugar) and categorical (resting ECG, exercise angina, and ST slope). Tab transformer was used to extract important features and ranked them using random forest (RF) feature ranking algorithm. Ten machine-learning models were used to predict heart disease using selected features. After extracting high-quality features, the top downstream model (a hyper-tuned ExtraTree classifier) achieved an average accuracy rate of 94.1% and an average Area Under Curve (AUC) of 95.0%. Furthermore, a nomogram analysis was conducted to evaluate the model's effectiveness in cardiovascular risk assessment. A benchmarking study was conducted using state-of-the-art models to evaluate our transformer-driven framework.
Bangladesh is strongly seeking renewable energy sources to meet its increasing electricity demand as part of the worldwide shift to sustainable energy. Even in areas where the use of solar energy would be quite practical, many educational institutions nationwide still rely on the traditional power grid despite having significant solar potential. This paper fills a crucial research gap by providing a thorough technoeconomic analysis of a grid-tied solar photovoltaic (PV) system designed for an educational institution in Narail, Bangladesh, an application that is little documented in national literature. Essential design criteria, including the best system orientation, comprehensive loss analysis, precise component specifications, and energy yield projections, were all included in the system model, which was created using local meteorological data. According to the operational study, the suggested system can generate about 133 MWh of energy per year, of which 100 MWh is exported to the national grid and 33 MWh is set aside for self-consumption. A remarkably competitive levelized cost of energy (LCOE) of $0.0577/kWh is attained by the design. Additionally, the system is expected to lower carbon dioxide emissions by around 77.5 tonnes annually or more than 1900 tonnes throughout its 25-year operating lifespan, assuming a conservative 1% annual degradation rate. These findings highlight the economic feasibility and environmental sustainability of grid-connected solar PV systems for educational institutions in Bangladesh, which have the potential to improve energy security and significantly contribute to national carbon reduction efforts.
Niful Islam, Khan Md Hasib, Fahmida Akter Joti
et al.
Skin cancer is a major concern to public health, accounting for one-third of the reported cancers. If not detected early, the cancer has the potential for severe consequences. Recognizing the critical need for effective skin cancer classification, we address the limitations of existing models, which are often too large to deploy in areas with limited computational resources. In response, we present a knowledge distillation based approach for creating a lightweight yet high-performing classifier. The proposed solution involves fusing three models, namely ResNet152V2, ConvNeXtBase, and ViT Base, to create an effective teacher model. The teacher model is then employed to guide a lightweight student model of size 2.03 MB. This student model is further compressed to 469.77 KB using 16-bit quantization, enabling smooth incorporation into edge devices. With six-stage image preprocessing, data augmentation, and a rigorous ablation study, the model achieves an impressive accuracy of 98.75% on the HAM10000 dataset and 98.94% on the Kaggle dataset in classifying benign and malignant skin cancers. With its high accuracy and compact size, our model appears to be a potential choice for accurate skin cancer classification, particularly in resource-constrained settings.
Current state-of-the-art video object segmentation models have achieved great success using supervised learning with massive labeled training datasets. However, these models are trained using a single source domain and evaluated using videos sampled from the same source domain. When these models are evaluated using videos sampled from a different target domain, their performance degrades significantly due to poor domain generalization, i.e., their inability to learn from multi-domain sources simultaneously using traditional supervised learning. In this paper, We propose a dynamically growing tree of sub-networks (DGT) to learn effectively from multi-domain sources. DGT uses a novel lifelong learning technique that allows the model to continuously and effectively learn from new domains without forgetting the previously learned domains. Hence, the model can generalize to out-of-domain videos. The proposed work is evaluated using single-source in-domain (traditional video object segmentation), multi-source in-domain, and multi-source out-of-domain video object segmentation. The results of DGT show a single source in-domain performance gain of 0.2% and 3.5% on the DAVIS16 and DAVIS17 datasets, respectively. However, when DGT is evaluated using in-domain multi-sources, the results show superior performance compared to state-of-the-art video object segmentation and other lifelong learning techniques with an average performance increase in the F-score of 6.9% with minimal catastrophic forgetting. Finally, in the out-of-domain experiment, the performance of DGT is 2.7% and 4% better than state-of-the-art in 1 and 5-shots, respectively.
Thermodynamics is the most basic energy science. It is derived from the words thermal (heat) and dynamic (motion). Although its laws have existed since the beginning of the universe, the development of thermodynamics as a science was with the invention of steam engines in England. These laws, as known, are the Zeroth, First, Second and Third Laws of Thermodynamics. These are four macro laws, and they were determined based on observation. They are not the product of theoretical thought. Zeroth Law reveals the basic structure of temperature measurement, based on the principle that if two different systems are in thermal equilibrium separately with a third system, there must be thermal equilibrium between them. The First and Second Laws are the fundamental laws regarding energy. The first law, also known as the law of conservation of energy, and the second law, also known as the law of entropy, along with explaining the principles of energy conversion; also make a significant contribution to understand the functioning mechanism of the universe. The Third Law states that as chemically homogeneous and perfectly crystalline substances approach absolute zero temperature (273 C, 0 K), their entropy (or entropy changes) will also approach zero; in other words, it states that there can be no disorder or movement in the substances in question at this temperature. It is also important that thermodynamics science has a wide range of applications, from technical fields to philosophy, therefore it is known by large masses. Because its laws are among the most fundamental laws of the universe, in other words it is universal. Laws are noteworthy for the establishment, operation and analysis of engineering and many other systems, as well as for understanding the order of the universe. Additionally, some cosmologists strive to explain the order and functioning mechanism of the universe by making use of the laws of thermodynamics. Moreover, the laws of thermodynamics also give clues about the existence of the universe. Entropy, defined based on the second law of thermodynamics, is a phenomenon that gives the numerical magnitude of the disorder or complexity of a system. The more disordered or complex a system is, the greater its irreversibility and entropy will be. There is also a structure of the law that affects every system everywhere in the universe. It is not dependent on time and space. Time and space are literally subject to this law. The law has been in effect since the beginning of the universe, and it will continue to exist if the universe exists. Because universe means system; system means mechanism; mechanism means a structure that works according to the sovereignty of laws. Energy is needed to sustain life. Even though energy is not destroyed, the use of resources means that they move from a certain potential to a dead state (environment) because of the law of entropy. This process is valid for all systems in the universe. While all energies are processed through this process, living creatures in nature experience a similar process as they move from life to death. Eventually, all energy sources in the universe will go to the environment and become dead. This situation can be described as the entropy apocalypse of the universe. The concept of the apocalypse also reflects chaotic conditions such as noise and turmoil. Sources state that a cosmically stagnant situation will occur before the apocalypse. This is the cosmic dead state of the universe. In this study, an attempt was made to establish a relationship between entropy and doomsday by using the laws of Thermodynamics. In this context, the fact that the end of the universe is similar to the point where the entropy apocalypse will occur, as expressed in the doomsday scenario in religious literature (including the Religion of Islam), is tried to be explained on the plane of science and religion.
Penelitian ini mengeksplorasi citra tubuh perempuan melalui tafsir ayat Gaḍ al-Ḅasar and Ḥifz al-Furūj dalam perspektif psikologi sosial, menggunakan teori “Mind, Self, and Society” dari George Herbert Mead. Penelitian ini bertujuan untuk memahami bagaimana interpretasi ayat-ayat tersebut mempengaruhi persepsi diri dan citra tubuh perempuan dalam konteks sosial yang lebih luas. Melalui analisis dari kitab Tafsir al-Munir dan pendekatan psikologi sosial, penelitian ini mengungkapkan bahwa tafsir ayat-ayat tersebut seringkali mengarah pada konstruksi sosial yang membentuk norma dan perilaku yang diinternalisasi oleh norma agama. Teori Mead digunakan untuk menganalisis interaksi antara individu dan masyarakat dalam membentuk citra tubuh. Penelitian ini digolongkan kedalam kajian kepustakaan (library research) yang bersifat kualitatif dengan metode deskriptif maudu’i dan analisis konten. Hasil penelitian menunjukkan bahwa konsep kesucian tubuh perempuan menekankan pentingnya menjaga kesucian melalui perilaku dan penampilan dengan menggunakan pakaian yang tertutup (jilbab) dan selaras dengan ajaran agama. Termasuk dengan mejaga pandangan dan memelihara kehormatan. Interpretasi agama tidak hanya mempengaruhi persepsi individu tentang tubuh mereka, tetapi juga berperan dalam membentuk identitas sosial dan interaksi sosial. Penelitian ini memberikan wawasan tentang bagaimana nilai-nilai agama dan norma sosial berinteraksi dalam membentuk citra tubuh perempuan dan menawarkan perspektif baru dalam memahami dinamika ini melalui lensa psikologi sosial.
Bu çalışma Ali Kuşçu’nun Şerhu’t-Tecrîd başlıklı eseri ve Ta‘lîkât alâ mebâhisi’l-egâlîti’l-hissiyye min Şerhi’l- Mevâkıf adlı eserinde ele almış olduğu optiğe dair konuları problem odaklı bir listeleme faaliyetine tabi kılıp, optik problemleri tarihsel açıdan yorumlamayı, bunu yaparken de dav’ (الضوء), nûr (النور), şuâ (الشعاع) gibi farklı ışık terimlerinin kullanımını incelemeyi hedeflemektedir. Ali Kuşçu Şerhu’t-Tecrîd adlı eserinde; görme kuvvetini açıklar, görsel algıya dair matematikçilerin ve tabiatçıların görüşlerini sundukları deliller bağlamında tartışır, aynada görüntünün resmedilmesi, tek bir adet nesnenin iki adet görülmesi gibi konuları irdeler, görsel algıda dav’ ve rengi önkoşul olarak, renk algısını da dav’a bağlı olarak açıklar, dav’ın niçin cisim olamayacağından bahseder, birincil ve ikincil ışıklar terminolojisini dahil ettiği anlatısını karanlıkta görme problemiyle tamamlar. Şerhu’t-Tecrîd’de son olarak ele aldığı karanlık problemi, Ta‘lîkât alâ mebâhisi’l-egâlîti’l-hissiyye min Şerhi’l-Mevâkıf adlı eserinde tartıştığı ilk problemdir. Bu eserinde Ali Kuşçu, gemideki gözlemcinin göreceli hareketi, benzer üçgen geometrisiyle aynada görünen nesnenin büyüklüğünün belirlenmesi, farklı renklerin meydana gelmesi gibi problemlere ilgi duyar, mesaisini bu problemlerin geliştirilmesine ayırır. Optik problemleri tartışırken, hareket teorisi, göz fizyolojisi gibi farklı doğa felsefesi problemlerine kapı aralar, anlatılarını yer yer geometrik gösterimler ile destekler. İki eserinde de ışığa dair olguları tartışırken farklı ışık terimlerinin kullanımına dikkat eder.
Ahmad Febriyanto, Abdullah Azzam, Hanifah Ramadhani Kutia
et al.
The main objective of this study is to investigate the short- and long-term effects of Foreign Direct Investment (FDI) on CO2 in some ASEAN countries having high levels of FDI, including Singapore, Indonesia, Malaysia, and Vietnam. This study applied Autoregressive Distributed Lag (ARDL) analysis in estimating the short- and long-term relationship of FDI on CO2. The test results showed that the short-term positive impact of FDI on CO2 was shown in Singapore and Indonesia, while the negative effect was seen in Vietnam. In the long-term relationship, a positive impact was found in Vietnam and Singapore.
Alaul Islam, Tingying He, Anastasia Bezerianos
et al.
We present a systematic review and design space for visualizations on smartwatches and the context in which these visualizations are displayed--smartwatch faces. A smartwatch face is the main smartwatch screen that wearers see when checking the time. Smartwatch faces are small data dashboards that present a variety of data to wearers in a compact form. Yet, the usage context and form factor of smartwatch faces pose unique design challenges for visualization. In this paper, we present an in-depth review and analysis of visualization designs for popular premium smartwatch faces based on their design styles, amount and types of data, as well as visualization styles and encodings they included. From our analysis we derive a design space to provide an overview of the important considerations for new data displays for smartwatch faces and other small displays. Our design space can also serve as inspiration for design choices and grounding of empirical work on smartwatch visualization design. We end with a research agenda that points to open opportunities in this nascent research direction. Supplementary material is available at: https://osf.io/nwy2r/.
Prognostic and Health Management (PHM) has been widely applied to hardware systems in the electronics and non-electronics domains but has not been explored for software. While software does not decay over time, it can degrade over release cycles. Software health management is confined to diagnostic assessments that identify problems, whereas prognostic assessment potentially indicates when in the future a problem will become detrimental. Relevant research areas such as software defect prediction, software reliability prediction, predictive maintenance of software, software degradation, and software performance prediction, exist, but all of these represent diagnostic models built upon historical data, none of which can predict an RUL for software. This paper addresses the application of PHM concepts to software systems for fault predictions and RUL estimation. Specifically, this paper addresses how PHM can be used to make decisions for software systems such as version update and upgrade, module changes, system reengineering, rejuvenation, maintenance scheduling, budgeting, and total abandonment. This paper presents a method to prognostically and continuously predict the RUL of a software system based on usage parameters (e.g., the numbers and categories of releases) and performance parameters (e.g., response time). The model developed has been validated by comparing actual data, with the results that were generated by predictive models. Statistical validation (regression validation, and k-fold cross validation) has also been carried out. A case study, based on publicly available data for the Bugzilla application is presented. This case study demonstrates that PHM concepts can be applied to software systems and RUL can be calculated to make system management decisions.
Juli Iswanto, Wahyuddin Nur Nasution, Mesiono Mesiono
This research aims to analyze: (1) planning for the development of teaching staff resources; (2) the process of developing teaching staff; (3) educational staff development components; and (4) a model for developing teaching staff resources. This research uses a multisite qualitative method where data collection techniques employ interviews, observation, and document study techniques to enhance the validity of the data findings and the authenticity of the research. The results of this research conclude that: (1) planning for the development of teaching staff is led by the head of the madrasah, involving all elements of the madrasah and taking various forms in the short term, long term, and incidental; (2) the process of developing teaching staff is carried out using various steps, namely: problem identification; needs analysis; determination of funds; target setting; program determination; program implementation; and evaluation; (3) the teacher competency development components include the pedagogical component, professional component, social component, and personality component; and (4) a model for developing teaching staff takes the form of position rotation; training; guidance/counseling; job instructor training; demonstrations and examples; temporary assignments; workshops; seminars; comparative studies; teacher performance management; career development; improving welfare; subject teacher deliberation; education and training; supervision; and further studies.
Shifa Mohamed Saeed, Islam Abdeljawad, M. Hassan
et al.
Abstract The dependence of Islamic bank rates on the conventional bank rate violates the religiosity principle and the fundamentals of an efficient market due to the possibility of arbitrage profit from the rate differences. This study tests such dependency in a dual banking system by considering monthly data from January 2009 to April 2018 on Malaysia banks using several ARDL tests, supplemented by robustness tests using a 12-month correlation of the rolling standard deviation and causality models based on the Toda-Yamamoto approach to investigate the short- and long-run dependency of rates. The study finds that Malaysia's Islamic bank deposit and financing rates are influenced by both the conventional and Bank Negara Malaysia's policy rates. Results imply that Islamic banks do serve profit-driven customers. We suggest that Islamic banks are forced to benchmark their rates to conventional rates because of the trade-off between religious and economic fundamentals by profit-driven customers. The study demonstrates that the question of Islamic legitimacy of Islamic banks needs to be addressed considering the correct profiling of the customer-base and the regulatory environment in which Islamic banks operate in. We discuss implications for Islamic rate-setting behavior in the presence of monetary shocks in a dual banking system.
IS-LM terdiri dari IS dan LM. IS adalah Invesment Saving yang berarti pasar barang sedangkan LM adalah Liquidity Money yang berarti pasar uang. Model IS-LM menjelaskan interaksi antara dua pasar, yaitu pasar barang dan pasar uang.
Riashat Islam, Samarth Sinha, Homanga Bharadhwaj
et al.
Learning policies from fixed offline datasets is a key challenge to scale up reinforcement learning (RL) algorithms towards practical applications. This is often because off-policy RL algorithms suffer from distributional shift, due to mismatch between dataset and the target policy, leading to high variance and over-estimation of value functions. In this work, we propose variance regularization for offline RL algorithms, using stationary distribution corrections. We show that by using Fenchel duality, we can avoid double sampling issues for computing the gradient of the variance regularizer. The proposed algorithm for offline variance regularization (OVAR) can be used to augment any existing offline policy optimization algorithms. We show that the regularizer leads to a lower bound to the offline policy optimization objective, which can help avoid over-estimation errors, and explains the benefits of our approach across a range of continuous control domains when compared to existing state-of-the-art algorithms.
The objective of the study is to explore the knowledge of consumers on nutrition, food safety and hygiene of the sampled respondents in the Sylhet City Corporation in Bangladesh in terms of different demographic characteristics. This study includes the different types of consumers in the life cycle viz. baby, child, adolescent, young and old so that an overall awareness level can be measured in the urban area of Bangladesh. The study is confined to SCC area in which all types of respondents has been included and findings from this study will be used generally for Bangladesh in making policy In conducting the study the population has been divided into six group as, Baby, child, adolescent, parental, unmarried adult young and married adult matured. We find that the average score of awareness of food nutrition and hygiene of unmarried adult is higher than that of married adults. The study suggested it is needed to increase awareness in of the parents for feeding babies. The average awareness of parents to their childs eating behavior between 5 and 9 years is 3.36 out of 5. The awareness is around 67 percent so we should be more careful in this regard. The average awareness adolescent food habit is 1.89 on three points scales which about 63 percent only. Therefore, the consciousness of adolescent has been to increase in taking food. The average feeding styles of parents is 4.24 out of 5 to their children up to 9 years and in percentage it is 84 percent.
The objective of this paper is to assess the impact of micro credit on the livelihoods of the clients in the haor area of Sunamganj district, Sylhet, Bangladesh. The major findings of the study are that 66.2 percent respondents of borrowers and 98.7 non-borrowers are head of the family and an average 76.6 percent and among the borrowers 32 percent is husband/wife while 1.3 percent of non-borrowers and on average 22.2. In terms of sex 64.7 percent of borrowers and 92.5 percent of non-borrowers are male while 35.3 percent of borrowers and 7.5 percent of non-borrowers are female. The impact of micro-credit in terms of formal and informal credit receiving households based on DID method showed that total income, total expenditure and investment have been increased 13.57 percent, 10.39 percent and 26.17 percent. All the elements of total income have been increased except debt which has been decreased by 2.39 percent. But the decrease in debt is the good sign of positive impact of debt. Consumption of food has been increased but non-food has been decreased. All the elements of investment have been increased except some factors. The savings has been decreased due excess increase in investment. The study suggested that for breaking vicious cycle of poverty by micro-credit the duration of loans should be at least five year and the volume of loans must be minimum 500,000 and repayment should at not be less than monthly. The rate of interest should not be more than 5 percent.
Natasya Salsabilla Syarief, Andre an Pangestu, Hesti Klatina Putri
et al.
Every child is born in different conditions. There are children who are born with physical disabilities or are called physically disabled. Education for children with physical disabilities is no different from children who do not have physical disabilities. However, children with disabilities need special attention from teachers and parents. Children with physical disabilities have slightly different characteristics from normal children. In the learning process, special services are needed for children with disabilities. The purpose of this journal is to determine the characteristics of children with disabilities and education for children with disabilities. The method used in this journal is the library method which is sourced from articles, journals, books related to knowing the characteristics of children with disabilities and education for children with disabilities.
Nanofibers mats of chitosan (CTS)/polyvinyl alcohol (PVA)/halloysite nanoclay and drug loaded CTS/PVA/halloysite nanoclay//3-glycidyoxypropyl trimethoxysilane were fabricated using the electrospinning method. Electrospun nanofiber samples were characterized by Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and thermogravimetric analysis (TGA). FTIR confirmed the chemical and physical bonding among ingredients such as CTS, PVA, nanoclay and crosslinker in the nanofibers. SEM images showed the development of uniform nanofibers. The average nanofiber diameter was observed in the range of 50–200 nm. Antimicrobial activity was examined against E. coli and S. aurus bacteria. The results obtained indicated that all nanofiber samples showed significant antimicrobial activity against the Gram-positive as well as Gram-negative bacteria. TGA results indicated that the thermal stability of nanofibers increased with the addition of the crosslinker. The drug release was studied in phosphate buffer saline (PBS) solution (pH 7.4) at 37°C and was released from nanofibers in 2.5 h. Hence, these prepared nanofibers can be used in medication where the drug is required for a long duration.