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Adaptive Energy Balance Control System via State of Charge (SOC) for a Sustainable Solar-Powered Outdoor-Hydroponics in Tropical Islands
Full text linkEfficient energy management is essential for sustaining outdoor hydroponics systems powered by solar energy, particularly in tropical island environments where sunlight and rainfall vary throughout the day. To address this, a solar-powered hydroponics system was developed with an adaptive energy balance control strategy based on the State of Charge (SoC). The system requires reliable real-time monitoring and decision-making, achieved by integrating voltage, current (ACS712), light-dependent resistors (LDR), and flow sensors, along with an ESP32 microcontroller for data acquisition and control logic. The adaptive control method dynamically regulates power consumption by adjusting the water pump's operation in response to SoC levels, solar radiation, and rainfall. Experimental validation shows the system maintains the battery’s SoC above 55%, ensuring power availability while optimizing energy use. Pump operation is disabled during rainfall and minimized at night to prevent deep discharge, enhancing overall system stability. Daytime solar charging is complemented by controlled discharge during non-solar hours, improving energy sustainability. The results confirm the effectiveness of the proposed strategy in reducing unnecessary energy consumption, improving system reliability, and supporting continuous hydroponic cultivation under varying tropical conditions.
ABSTRAK: Pengurusan tenaga yang cekap amat penting bagi memastikan kelestarian sistem hidroponik luar yang menggunakan tenaga solar, terutama di kawasan pulau tropika yang mempunyai corak cahaya matahari dan perubahan hujan sepanjang hari. Bagi memenuhi keperluan ini, satu sistem hidroponik berkuasa solar telah dibangunkan dengan strategi kawalan imbangan tenaga adaptif berasaskan State of Charge (SoC). Sistem ini memerlukan pemantauan masa nyata dan keupayaan membuat keputusan terpercayai, dicapai melalui integrasi penderia voltan, arus (ACS712), LDR (Rintangan Peka Cahaya), dan aliran, serta mikropengawal ESP32 bagi pemerolehan data dan logik kawalan. Kaedah kawalan adaptif ini mengatur penggunaan tenaga secara dinamik dengan melaras operasi pam air berdasarkan tahap SoC, intensiti cahaya matahari, dan keadaan hujan. Dapatan kajian menunjukkan sistem ini mampu mengekalkan SoC bateri melebihi 55%, sekaligus memastikan bekalan kuasa yang stabil sambil mengoptimum penggunaan tenaga. Operasi pam dihentikan semasa hujan dan dikurangkan pada waktu malam bagi mengelakkan nyahcas bateri berlebihan, seterusnya meningkatkan kestabilan sistem. Pengecasan bateri pada waktu siang dilengkapi dengan penyahcasan terkawal semasa tanpa cahaya matahari, sekaligus memperkukuh kemampanan tenaga. Dapatan kajian membuktikan bahawa strategi kawalan ini berkesan dalam mengurangkan penggunaan tenaga tidak diperlukan, meningkatkan kebolehpercayaan sistem, dan menyokong penanaman hidroponik berterusan dalam persekitaran tropika yang dinamik
Surrogate Model-based Optimization of a Biplane Unmanned Aerial Vehicle Wing Using Genetic Algorithm and Response Surface Methodology
Full text linkMulti-wing aerial vehicle designs have existed since the early days of aviation; however, they are not commonly employed in modern designs, as advances in materials technology have enabled the structurally feasible high-aspect-ratio monoplane configurations. Nevertheless, the application has the potential to serve as a solution in the field of mini unmanned aerial vehicle (UAV) design, offering the advantage of a smaller wing span. The most popular multi-wing application, the biplane, features two wings mounted in parallel and is typically characterized by the geometric terms decalage, stagger, and gap, which should be optimized during the design process. This study focuses on aerodynamic optimization of a biplane wing to maximize the lift-to-drag ratio (CL/CD) and the CL3/2/CD by optimizing the aforementioned geometric parameters. To this end, the aerodynamic analysis method was initially validated by comparing it with wind-tunnel data for a monoplane wing reported in the literature. The Response Surface Method (RSM) was used to assess the effects of the design parameters and to generate surrogate models for CL/CD and CL3/2/CD. The optimal design was determined using the genetic algorithm. The results indicated that decalage and gap distance primarily determine range improvement, whereas gap and stagger are the primary parameters for improving endurance. The applied optimization approach resulted in improvements of 10.93% in range and 46.63% in endurance relative to the base biplane configuration.
ABSTRAK: Reka bentuk kenderaan udara berbilang sayap telah wujud sejak awal perkembangan penerbangan; namun, ia jarang digunakan dalam reka bentuk moden berikutan kemajuan teknologi bahan yang membolehkan nisbah-aspek-tinggi konfigurasi monosayap lebih berdaya struktur. Namun, aplikasi berbilang sayap berpotensi menjadi penyelesaian reka bentuk kenderaan udara tanpa pemandu bersaiz kecil (mini UAV), khususnya dengan kelebihan bentangan sayap lebih kecil. Konfigurasi berbilang sayap iaitu paling lazim, sayap berkembar (biplane), merujuk kepada dua sayap yang disusun selari dan dicirikan oleh parameter geometri utama seperti sela, aturan, dan jurang, perlu ditentukan secara optimum dalam proses reka bentuk. Kajian ini memfokuskan kepada pengoptimuman aerodinamik sayap berkembar bagi memaksimumkan nisbah angkat kepada seretan (CL/CD) dan CL3/2/CD melalui pemilihan optimum parameter geometri tersebut. Kaedah analisis aerodinamik terlebih dahulu disahkan melalui perbandingan data terowong angin bagi monosayap yang dilaporkan dalam literatur. Kaedah Permukaan Tindak Balas (Response Surface Method, RSM) digunakan bagi menilai kesan parameter reka bentuk dan model pengganti bagi CL/CD dan CL3/2/CD, manakala reka bentuk optimum ditentukan menggunakan algoritma genetik. Keputusan menunjukkan bahawa sela dan jurang merupakan parameter utama dalam peningkatan jarak terbang, manakala jurang dan aturan lebih dominan dalam meningkatkan daya tahan penerbangan. Pendekatan pengoptimuman yang dicadangkan menghasilkan peningkatan sebanyak 10.93% dari segi jarak dan 46.63% dari segi daya tahan berbanding konfigurasi sayap berkembar asas
Deep Learning Framework for Sentiment Prediction using Residual Connections in Bidirectional – Gated Recurrent Unit
Full text linkSentiment analysis plays an essential role in Natural Language Processing (NLP) for differentiating emotions and opinions expressed in various pieces of text. However, existing algorithms face challenges in handling complex language patterns and capturing long-term dependencies, thereby increasing overall computational cost. This research aims to design an improved sentiment analysis model that enhances accuracy and efficiency while addressing gradient-related limitations in deep networks. This research proposes a Residual Bidirectional Gated Recurrent Unit (RBi-GRU) algorithm for effective sentiment analysis, leveraging residual connections to improve accuracy and efficiency. Residual connections are incorporated into the Bi-GRU network to facilitate gradient flow across layers and mitigate the vanishing gradient problem during training. It also enables deeper networks by protecting data from earlier layers, which further enhances feature representation. Additionally, tokenization, stemming, and global vector-based word representations (GloVe) are employed during preprocessing to capture the semantic relationships and meanings of words, thereby improving contextual understanding in sentiment analysis. The developed RBi-GRU algorithm achieves 98.74% accuracy, 98.99% precision, 98.32% sensitivity, and 98.64% F1-score on the Sentiment140 dataset, compared with the Rectified Linear Unit-based Gated Recurrent Unit (ReLU-GRU).
ABSTRAK: Analisis sentimen memainkan peranan penting dalam Pemprosesan Bahasa Semula Jadi (NLP) bagi membezakan emosi dan pendapat yang dizahirkan dalam teks; namun, algoritma sedia ada menghadapi cabaran pengendalian corak bahasa yang kompleks serta kebergantungan jangka panjang, sekaligus meningkatkan masa pemprosesan. Kajian ini bertujuan mereka bentuk model analisis sentimen berketepatan tinggi dan cekap sambil menangani kekangan berkaitan kecerunan rangkaian mendalam. Sebuah algoritma Unit Kawalan Berulang Baki Dua Arah (RBi-GRU) dicadangkan dengan gabungan baki ke dalam rangkaian Bi-GRU bagi memudahkan aliran kecerunan antara lapisan dan mengurangkan masalah lenyap kecerunan semasa latihan, di samping membolehkan pembinaan rangkaian lebih mendalam dan meningkatkan perwakilan ciri. Selain itu, teknik prapemprosesan seperti penandaan token, pengakaran (stemming), serta penggunaan Representasi Kata Vektor Global (GloVe) diaplikasi bagi menangkap hubungan semantik dan makna kontekstual perkataan dengan lebih berkesan. Dapatan kajian eksperimen menunjukkan bahawa algoritma RBi-GRU mencapai ketepatan 98.74%, kejituan 98.99%, kepekaan 98.32%, dan skor F1 sebanyak 98.64% pada set data Sentimen140, sekaligus mengatasi prestasi model Unit Kawalan Berulang berasaskan Unit Pembetulan Linear (ReLU-GRU)
ToxoSegFusion: Attention-enhanced Dual-backbone Neural Architecture for Retinal Lesion Segmentation
Full text linkOcular toxoplasmosis (OT) often presents a diagnostic dilemma in clinics, with retinal lesions that are not only varied in appearance but also frequently subtle and underrepresented in fundus images. Current automated segmentation tools, though promising, are often hampered by class imbalance and a lack of robust testing across real-world scenarios. To address these gaps, we developed ToxoSegFusion, a dual-backbone deep learning framework that capitalizes on the complementary strengths of DenseNet121 and ResNet101, enhanced with attention modules. Unlike typical single-backbone models, this hybrid approach was specifically tuned for the intricate challenges of OT lesion segmentation, using a combined Dice and binary cross-entropy loss to better balance rare lesion pixels. We trained and validated on 149 image-mask pairs from the OTFID-Version 3 dataset, achieving an intersection over union of 0.858 and a Dice coefficient of 0.795, both exceeding the current MobileNetV2/U-Net baseline. The model also demonstrated reliable performance on the DRIVE dataset for vessel segmentation, indicating practical flexibility. By facilitating accurate lesion localization, ToxoSegFusion enables more timely interventions in ophthalmology. Future directions include larger multi-center trials and streamlined models for routine deployment.
ABSTRAK: Toksoplasmosis okular (OT) sering menimbulkan cabaran diagnostik di klinik, dengan lesi retina halus pelbagai rupa dan kurang terwakili pada imej fundus. Alat segmentasi automatik semasa, walaupun memberi harapan, sering terhad pada ketidakseimbangan kelas dan kekurangan ujian di peringkat perubatan. Bagi mengatasi kekurangan ini, kajian ini membangunkan ToxoSegFusion, sebuah rangka kerja pembelajaran mendalam berkomponen dua yang memanfaatkan kekuatan saling melengkapi DenseNet121 dan ResNet101, diperkaya dengan mekanisme perhatian. Tidak seperti model komponen tunggal biasa, pendekatan hibrid ini dirancang khusus bagi cabaran kompleks segmentasi lesi OT, menggunakan kehilangan Dice dan entropi silang binari gabungan bagi keseimbangan terbaik antara piksel lesi yang jarang. Kajian ini melatih dan mengesahkan 149 pasangan imej-topeng dari set data OTFID-Versi 3, mencapai persilangan atas kesatuan 0.858 dan pekali Dice 0.795, keduanya melebihi garis dasar MobileNetV2/U-Net semasa. Model juga menunjukkan prestasi terbaik pada DRIVE bagi segmentasi salur darah, mencadangkan fleksibiliti praktis. Melalui pengesanan lokasi lesi yang tepat, ToxoSegFusion membuka jalan bagi intervensi lebih tepat pada masa oftalmologi. Pada masa hadapan, cadangan bagi penyebaran rutin adalah melalui ujian berbilang pusat yang lebih besar dan perkemasan model
Enhanced Beach Photo Translation using Modified Unsupervised GAN with Regularization
Full text linkTo optimize time and cost, tourists often require tools to modify the sky background and atmosphere in beach photos, such as replacing blue-sky views with sunsets or vice versa. Independent modification of the sky and sea is difficult because their color palettes are similar. Another problem that often occurs in image translation is the scarcity of paired datasets, and beach photo datasets are particularly limited in lighting conditions, weather variations, and viewing perspectives. This limitation can cause Generative Adversarial Networks (GAN) models to lose their generalization ability, become prone to overfitting, and produce visual artifacts in the outputs. Therefore, this study proposes an unsupervised GAN approach using a modified CycleGAN and improves its performance for beach image translation by integrating identity mapping, -parameter optimization, a multiscale kernel, and regularization techniques. CycleGAN consists of two generators and two discriminators. The sunset generator translates a blue sky into a sunset sky; the generated output is then passed to the sunset discriminator to determine whether it is real or fake. The generator input image is resized and normalized through preprocessing. The generator architecture is structured to enhance image reconstruction and feature extraction. The details of the translation results are fine-tuned using a 30x30 PatchGAN discriminator and a multiscale kernel convolutional layer. The effect of the hyperparameter , which strikes a balance between cycle consistency, structural preservation, and color fidelity, is also investigated in this work. The findings indicate that while higher values increase generator loss, they also improve consistency, making it harder to handle dark objects and white clothing. To overcome this issue, regularization techniques, namely photometric augmentation and spectral normalization (SN), together with multiscale kernel convolutional (MSCov), have been applied. Photometric augmentation and MSCov are used to enhance the model's robustness to photographic variations, while SN improves its efficiency and stability. The results of the study show that the proposed method improves image translation accuracy as measured by Mean Squared Error (MSE), Structural Similarity Index (SSIM), and Learned Perceptual Image Patch Similarity (LPIPS).
ABSTRAK: Bagi mengoptimum masa dan kos, pelancong sering memerlukan alat menukar foto latar belakang langit dan suasana pantai, seperti mengganti pemandangan langit biru dengan matahari terbenam atau sebaliknya. Pengubahsuaian bebas langit dan laut sukar dilakukan kerana palet warna langit dan laut adalah serupa. Masalah lain sering berlaku dalam menterjemah imej adalah ketiadaan set data foto berpasangan dan foto pantai sering mengalami kepelbagaian terhad, terutama dari segi pencahayaan, variasi cuaca dan perspektif tontonan. Had ini boleh menyebabkan model Rangkain Generatif Adversari (GAN) ??kehilangan keupayaan generalisasi, terdedah kepada terlebih muat dan penghasilan artifak visual dalam hasil terjemahan. Oleh itu, kajian ini mencadangkan pendekatan GAN tanpa pengawasan menggunakan CycleGAN yang diubah suai bagi meningkatkan prestasi terjemahan imej pantai melalui penyepaduan pemetaan identiti, pengoptimuman parameter , kernel berbilang skala dan menggunakan teknik regularisasi. CycleGAN terdiri daripada dua generator dan dua rangkaian neural diskriminator. Generator matahari terbenam digunakan dalam menterjemah langit biru kepada langit matahari terbenam, kemudian imej terhasil dimajukan kepada diskriminator matahari terbenam bagi menentukan sama ada imej terhasil dikelaskan sebagai imej sebenar atau imej palsu. Imej input generator diubah saiz dan dinormalkan melalui prapemprosesan. Seni bina generator distrukturkan dengan meningkatkan pembinaan semula imej dan pengekstrakan ciri. Butiran hasil terjemahan diperhalusi menggunakan diskriminator PatchGAN 30x30 dan lapisan konvolusi kernel berbilang skala. Kesan hiper parameter turut dikaji bagi mencapai keseimbangan antara ketekalan kitaran, pemeliharaan struktur, dan kesetiaan warna. Dapatan kajian menunjukkan bahawa walaupun nilai lebih tinggi ianya meningkatkan kehilangan generator dan konsistensi, menjadikannya lebih sukar dalam mengendali objek gelap dan pakaian putih. Bagi mengatasi isu ini, teknik regularization iaitu pembesaran fotometrik dan normalisasi spektral (SN) bersama Konvulasi Kernel Skala Berbilang (MSCov) telah digunakan. Pembesaran fotometrik dan MSCov dilaksanakan dalam meningkatkan keteguhan model pada variasi fotografi, manakala SN digunakan bagi meningkatkan kecekapan dan kestabilan model. Hasil kajian menunjukkan kaedah ini mampu meningkatkan ketepatan hasil terjemahan imej berdasarkan Ralat Purata Kuasa Dua (MSE), Indeks Persamaan Struktur (SSIM) dan Persamaan Tampalan Perseptual Imej Terpelajar (LPIPS)
Inertial Sensor Self-Calibration Module Using Attitude Heading And Reference System For Autonomous Underwater Vehicle Navigation
Full text linkThis research addresses the complex task of enhancing navigation accuracy in Autonomous Underwater Vehicles (AUVs), a self-propelled robotic system used for ocean exploration, environmental monitoring, and underwater interventions. A core component of AUV navigation is an Inertial Measurement Unit (IMU), a sensor suite that tracks orientation and motion by measuring accelerations and angular rates. However, the IMU is highly susceptible to noise interference, which degrades accuracy and reliability. To address these challenges, this study introduces an innovative Inertial Sensor Self-Calibration Module that dynamically adjusts calibration parameters in real time, thereby compensating for sensor drift and inaccuracies. The research further conducts a comparative analysis of several calibration and filtering techniques integrated into the AUV's Inertial Navigation System (INS), including Magnetic Calibration, the Extended Kalman Filter (EKF), the EKF with Measurement Noise, the EKF with Process Noise, and the Attitude and Heading Reference System (AHRS) Filter. Among these, the AHRS filter demonstrated superior precision, achieving the lowest average error of 1.06 degrees with a standard deviation of 0.345 degrees in angle measurements, an improvement of up to 97.81% compared to raw data. These findings highlight the effectiveness of the AHRS filter in improving navigation accuracy in complex underwater environments. The insights gained from this research not only deepen the understanding of noise impact and sensor calibration in AUV systems but also pave the way for future innovations in oceanic exploration, environmental monitoring, and underwater interventions.
ABSTRAK: Kajian ini adalah berkenaan menangani tugas kompleks meningkatkan ketepatan navigasi Kenderaan Bawah Air Autonomi (AUV), iaitu sistem robotik berkuasa sendiri digunakan bagi penerokaan lautan, pemantauan alam sekitar, dan intervensi bawah air. Komponen utama navigasi AUV bergantung pada Unit Pengukuran Inersia (IMU), iaitu rangkaian pengesan orientasi dan pergerakan dengan mengukur pecutan dan kadar sudut. Namun, IMU sangat terdedah kepada gangguan bunyi, di mana ianya mengurangkan ketepatan dan kebolehpercayaan. Oleh itu, kajian ini memperkenalkan Modul Kalibrasi Diri Pengesan Inersia yang inovatif, iaitu secara dinamik menyesuaikan parameter kalibrasi pada masa nyata, secara efektif mengimbangi hanyutan sensor dan ketidaktepatan. Kajian ini juga membuat analisis perbandingan beberapa teknik kalibrasi dan penapisan yang diintegrasikan pada Sistem Navigasi Inersia (INS) AUV, termasuk Kalibrasi Magnetik, Penapis Kalman Lanjutan (EKF), EKF dengan Bunyi Pengukuran, EKF dengan Bunyi Proses, dan Penapis Sistem Rujukan Sikap dan Haluan (AHRS). Antara teknik-teknik ini, penapis AHRS menunjukkan ketepatan terbaik, dengan ralat purata terendah iaitu 1.06 darjah dan sisihan piawai pengukuran sudut 0.345 darjah dan peningkatan sehingga 97.81% berbanding data mentah. Penemuan ini menunjukkan keberkesanan penapis AHRS dalam meningkatkan ketepatan navigasi pada persekitaran bawah air yang kompleks. Dapatan kajian ini bukan sahaja memperdalam pemahaman tentang kesan bunyi dan kalibrasi pengesan dalam sistem AUV, tetapi turut membuka ruang terhadap inovasi masa depan dalam penerokaan lautan, pemantauan alam sekitar, dan intervensi bawah air
GNN-Based Skyline Query Processing for Large-Scale and Incomplete Graphs
Full text linkSkyline queries are crucial in database management, selecting optimal points from multi-dimensional datasets based on dominance relationships. They are widely used in decision-making, recommendation systems, and data filtering. However, traditional skyline algorithms struggle with large volumes and missing data, leading to high computational costs and inefficiencies. This research proposes a hybrid approach that integrates the ISkyline dominance graph technique with Graph Neural Networks (GNNs) to improve skyline query performance under such conditions. The GNN component is utilized to predict skyline tuples in the presence of missing or incomplete data. Evaluation on both synthetic and real-world datasets demonstrates improved accuracy and efficiency compared with established methods such as ISkyline, SIDS, and OIS. This research demonstrates the potential to improve query processing efficiency and to support applications in e-commerce, finance, and smart data systems.
ABSTRAK: Pertanyaan latar langit adalah penting dalam pengurusan pangkalan data, iaitu dengan memilih titik optimum daripada set data berbilang dimensi berdasarkan hubungan dominasi. Ia digunakan secara meluas dalam membuat keputusan, sistem pengesyoran, dan penapisan data. Walau bagaimanapun, algoritma latar langit tradisional bergelut dengan kuantiti data yang besar dan data menghilang, membawa kepada peningkatan kos pengiraan dan ketidakcekapan. Kajian ini mencadangkan pendekatan hibrid yang mengintegrasi teknik graf penguasaan ISkyline dengan Rangkaian Graf Neural (GNNs) bagi meningkatkan prestasi pertanyaan latar langit berkeadaan sedemikian. Komponen GNN digunakan bagi meramalkan tupel latar langit dengan kehadiran data menghilang atau tidak lengkap. Penilaian pada kedua-dua set data sintetik dan dunia nyata menunjukkan peningkatan ketepatan dan kecekapan jika dibandingkan dengan kaedah sedia ada seperti ISkyline, SIDS dan OIS. Kajian ini menunjukkan potensi bagi mencipta pemprosesan pertanyaan yang lebih cekap, menyokong aplikasi e-dagang, kewangan dan sistem data pintar
Advanced Groundwater Level Forecasting using QSO-based Vision Transformer Model for Sustainable Water Resource Management
Full text linkThe reliable predictions of groundwater levels are crucial for long-term management of water resources, and they are an excellent source for human well-being. While conventional ML approaches work well for low-dimensional data, they are optimized for hyperparameters on high-dimensional inputs and also capture complex temporal correlations. To address these restrictions, this research presents a new framework for predicting groundwater-level changes up to five months in advance, built on the Vision Transformer (ViT) and optimised with Quokka Swarm Optimisation (QSO). ViT enables strong global feature extraction and long-range dependency modelling by processing time-series data as sequential image-like patches, in contrast to traditional neural networks. Drawing on quokka’s adaptive survival behaviour, the QSO procedure optimises the transformer's hyperparameters, such as patch size, attention heads, and depth, in real time to enhance prediction accuracy. The ViT+QSO outperformed baseline deep learning methods on groundwater datasets from Southern Africa in terms of RMSE, MAE, correlation coefficient (R), and Nash-Sutcliffe Efficiency (NSE). Quantile regression uncertainty quantification further improves the model's reliability for water resource planning. Hydrological variables, in addition to climate indices, influence groundwater fluctuations, as confirmed by ablation research. The proposed ViT-QSO achieved 93% R and 0.118 MAE, whereas the basic ViT achieved only 89.1% R and 0.152 MAE for groundwater-level prediction. Scalability, interpretability, and suitability for areas with limited monitoring infrastructure are hallmarks of the proposed methodology. This research provides valuable insights into how to better withstand the effects of climate change, in addition to human activities, on groundwater supplies.
ABSTRAK: Ramalan paras air bawah tanah yang boleh dipercayai adalah penting bagi pengurusan jangka panjang sumber air dan kesejahteraan manusia; namun, pendekatan pembelajaran mesin konvensional berhadapan kekangan pengendalian input berdimensi tinggi serta model korelasi temporal kompleks. Kajian ini mencadangkan satu rangka kerja baharu berasaskan Pengubah Visi (ViT) yang dioptimum menggunakan Optimisasi Kawanan Quokka (QSO) dalam meramal perubahan paras air bawah tanah pada lima bulan lebih awal. ViT memproses data siri masa sebagai tampalan jujukan menyerupai imej bagi membolehkan pengekstrakan ciri global dan model kebergantungan jarak jauh, manakala QSO mengoptimumkan hiperparameter pengubah secara adaptif dalam meningkatkan ketepatan ramalan. Model ViT-QSO menunjukkan prestasi unggul berbanding kaedah pembelajaran mendalam asas pada set data air bawah tanah di Afrika Selatan, dengan peningkatan ketara dari segi RMSE, MAE, pekali korelasi (R), dan Kecekapan Nash–Sutcliffe (NSE), serta mencapai nilai R sebanyak 93% dan MAE 0.118 berbanding ViT asas masing-masing mencatatkan 89.1% dan 0.152. Pengkuantitian ketidakpastian melalui regresi kuantil meningkatkan kebolehpercayaan model bagi perancangan sumber air, manakala kajian ablasi mengesahkan peranan pembolehubah hidrologi dan indeks iklim terhadap turun naik paras air bawah tanah. Secara keseluruhan, metodologi yang dicadangkan adalah berskala, boleh ditafsir, dan sesuai pada kawasan infrastruktur pemantauan terhad, serta memberikan sumbangan penting dalam menangani kesan perubahan iklim dan aktiviti manusia terhadap sumber air bawah tanah
Growth Kinetics of Grey Oyster Mushroom (Pleurotus pulmonarius) Spawn Culture Revival from Spray Drying Application
Full text linkSpray drying represents a promising approach for preserving fungal mycelium liquid cultures, with the assurance of successful revival from powder form being a critical factor for its application. Although liquid culture of grey oyster mushroom (Pleurotus pulmonarius) has been well established, its potential for drying and subsequent revival remains underexplored, thereby limiting its use in long-term storage. In this study, the effect of various spray dryer inlet temperatures (80, 90, 100, 110, 120, 130, 140, and 150 °C) on the revival and growth kinetics of P. pulmonarius powder cultures was evaluated using maltodextrin and Arabic gum (10%) as protective agents. Growth kinetics were modelled using the Contois model, which revealed that powder prepared at 80 °C with maltodextrin exhibited the highest µmax value (6.0205), indicating superior revival and growth capacity. With Arabic gum, the highest µmax (5.7604) was observed at 90 °C. At higher inlet temperatures, revival and growth were still observed with both protectants, albeit at reduced rates. Metabolic activity, reflected by glucose consumption and citric acid production, further confirmed successful revival, with citric acid reaching the highest level of 4.958 ppm. Overall, this study demonstrates that spray-dried powder cultures of P. pulmonarius retain viability and metabolic activity, highlighting their potential for long-term preservation.
ABSTRAK: Teknik pengeringan sembur merupakan satu inovasi berguna untuk kultur cecair miselium kulat, dan jaminan bahawa bentuk serbuk kultur tersebut boleh dihidupkan semula merupakan faktor penting dalam pengeringan. Walaupun kultur cecair miselium cendawan tiram kelabu telah berjaya dibangunkan, potensinya untuk dikeringkan dan seterusnya dihidupkan semula masih kurang diterokai, sekaligus menhadkan aplikasinya dalam penyimpanan jangka panjang. Kesan suhu masuk pengering sembur yang berbeza-beza (80, 90, 100, 110, 120, 130, 140 dan 150°C) terhadap kebolehan hidup semula dan kinetik pertumbuhan kultur serbuk tiram kelabu menggunakan 10% maltodekstrin dan gam Arab sebagai agen pelindung telah dikaji. Model Cointois telah digunakan dan nilai yang diperoleh daripada persamaan tersebut menunjukkan bahawa kultur serbuk yang disediakan pada 80°C dengan perlindungan maltodekstrin mempunyai nilai µmax tertinggi iaitu 6.0205 dan menunjukkan kebolehan hidup semula serta pertumbuhan yang paling baik. Dengan penggunaan gam Arab, nilai µmax tertinggi (5.7604) dicapai pada suhu 90°C. Bagi kedua-dua agen pelindung, walaupun pada suhu yang lebih tinggi, kultur serbuk masih menunjukkan kebolehan untuk hidup semula dan berkembang tetapi pada kadar yang lebih rendah. Dari aspek metabolisme, penggunaan glukosa dan penghasilan asid sitrik yang tertinggi iaitu 4.958 ppm menunjukkan bahawa kultur serbuk mempunyai aktiviti metabolik dalam tempoh hidup semula . Kajian ini membuktikan bahawa kultur serbuk P. pulmonarius mampu dibangunkan dan berpotensi untuk dipelihara dalam tempoh yang lebih lama
Stability and Control of Humanoid Robots during Walking and Sudden Stops on Uneven Terrain using Inverted Pendulum Modeling and Fuzzy-enhanced Linear Quadratic Regulator
Full text linkHumanoid robots, designed to resemble human structure with a head, arms, and legs, stand out among various robot types due to their ability to interact with human-designed environments. Their kinematic structure, composed of links connected by joints, enables humanoid robots to perform tasks such as walking on uneven terrain, lifting objects, and opening doors. Walking is a critical function for humanoid robots, but it shifts their center of mass (CoM), which can lead to instability and falls. The inverted pendulum model is a widely used approach to humanoid robot walking, particularly on uneven surfaces, enabling CoM projection and balance adjustment. However, disturbances from floor unevenness, mechanical issues, and other factors can still cause the robot to fall. This underscores the need for effective control systems to stabilize movement and maintain balance. The Linear Quadratic Regulator (LQR) is a suitable control approach for humanoid robots, particularly in multiple-input, multiple-output systems. It stabilizes the robot while managing computational load, generating complex humanoid movements through linear approximations. To address uncertainties and adapt control gains, fuzzy control can be integrated, thereby enabling smoother transitions and improved disturbance handling. When walking on uneven terrain, a humanoid robot's body orientation may shift, causing the CoM to exceed tolerance limits and leading to falls. Thus, a control system that adjusts the walking pattern to maintain CoM stability is essential. In conclusion, humanoid robots, with their human-like structure, can perform complex tasks in human environments. Control systems, such as LQR and fuzzy control, are critical for maintaining balance and stability, even under challenging conditions, thereby enabling humanoid robots to keep their trajectory and prevent falls.
ABSTRAK: Robot seperti manusia (humanoid), direka menyerupai struktur manusia dengan kepala, lengan, dan kaki, menonjol dalam pelbagai jenis robot kerana keupayaannya berinteraksi dengan persekitaran yang direka oleh manusia. Struktur kinematiknya, terdiri daripada pautan yang dihubungkan oleh sendi, membolehkan robot humanoid melakukan tugas seperti berjalan di permukaan tidak rata, mengangkat objek, dan membuka pintu. Berjalan adalah fungsi penting robot humanoid, tetapi pergerakan mengubah pusat jisim (CoM), menyebabkan ketidakstabilan dan jatuh. Model bandul terbalik adalah pendekatan biasa digunakan bagi robot humanoid berjalan, terutama pada permukaan tidak rata, membolehkan unjuran CoM dan pelarasan keseimbangan. Namun, gangguan ketidaksamaan permukaan lantai, masalah mekanikal, dan faktor lain masih boleh menyebabkan robot jatuh. Ini menunjukkan keperluan pada sistem kawalan berkesan yang menstabilkan pergerakan dan mengekalkan keseimbangan. Regulator Kuadratik Linear (LQR) adalah pendekatan kawalan sesuai untuk robot humanoid, terutama dalam sistem masukan dan keluaran berganda. Ia menstabilkan robot sambil menguruskan beban pengiraan, menghasilkan pergerakan humanoid yang kompleks melalui pendekatan linear. Bagi menangani ketidakpastian dan menyesuai keuntungan kawalan, kawalan kabur boleh diintegrasikan, menjadikan peralihan lebih lancar dan pengendalian gangguan lebih baik. Apabila berjalan di permukaan tidak rata, orientasi badan robot humanoid mungkin berubah, menyebabkan CoM bergerak melebihi had toleransi, yang mengakibatkan jatuh. Oleh itu, sistem kawalan penyesuaian corak berjalan bagi mengekalkan kestabilan CoM adalah penting. Kesimpulannya, robot humanoid, dengan struktur seperti manusia, dapat melakukan tugas kompleks dalam persekitaran manusia. Sistem kawalan seperti LQR dan kawalan kabur adalah penting bagi memastikan keseimbangan dan kestabilan, walaupun dalam keadaan mencabar, membolehkan robot humanoid mengekalkan trajektori mereka dan mencegah jatuh