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Produk hiliran rambutan: meningkatkan nilai pasaran dan inovasi tempatan
Get PDFRambutan (Nephelium lappaceum) ialah sejenis tanaman buah tropika bermusim. Musim utama rambutan biasanya berlangsung dari bulan Jun hingga Ogos, manakala musim kedua jatuh pada bulan Disember hingga Januari. Namun begitu, masalah lambakan buah rambutan sering menjadi isu besar setiap kali musim tiba
Do investors get an advantage from corporate green bond issuance? A cross-country study
Get PDFThis study examines the stock's response to corporate green bond issuance announcements. Analyzing a dataset of 230 global corporate green bond issuers from 38 countries between 2013 and 2022 through an event study, the findings reveal a positive market reaction, especially within the non-financial corporate sector. Green bonds in this sector are primarily used to fund their own eco-friendly projects, signaling a commitment to environmental sustainability, and generating investor confidence. Variation in market reactions across countries is noted, with developed countries exhibiting a significantly more positive response. This suggests that environmental initiatives hold greater value in these regions, highlighting the alignment between sustainable practices and investor sentiment. These results emphasize the potential advantages of integrating green bonds and their environmental commitments into investment strategies, particularly for portfolio diversification and attracting investors seeking sustainable opportunities
Improving YoloPX using YoloP and Yolov8 for panoptic driving perception
Get PDFAutonomous driving technology (ADS) has seen significant advancements over the past decade, with car manufacturers investing heavily in its development to meet the growing demand for safer, more efficient, and eco-friendly transportation solutions. The panoptic driving perception system is central to ADS, essential for accurately interpreting the driving environment. This system requires high precision, lightweight design, and real-time responsiveness to detect surrounding vehicles, lane lines, and drivable areas effectively. This study introduces an enhanced YOLOPX model that combines YOLOP and YOLOv8 to create an adaptive multi-task learning network capable of traffic object detection, drivable area segmentation, and lane detection. The model integrates YOLOP's detection head with YOLOPX's anchor-free detection head to improve generalization, incorporates YOLOv8's advanced backbone structure to enhance feature extraction accuracy, and retains YOLOP's three-neck architecture to optimize multi-task processing. The improved model employs a mode loss function for segmentation tasks, enhancing generalization and improving lane detection accuracy. Experiments conducted using the BDD100k dataset demonstrated the model's effectiveness: achieving 98.8% accuracy and 27.6% IoU for lane line detection, 90.4% mIoU for drivable area segmentation, and 85.9% recall and 76.9% mAP50 for traffic object detection. This model represents a significant advancement in ADS, enhancing both the safety and reliability of autonomous vehicles
Advancing 5G communication systems: a novel method for OOBE suppression in Filter-OFDM waveforms
Get PDFToday’s 5th Generation (5G) wireless communications systems count heavily on Orthogonal Frequency Division Multiplexing (OFDM) waveforms. Numerous concerns regarding OFDM-based Long-Term Evolution (LTE) communication limitations have not been thoroughly explored. In contrast, Out-of-Band Emissions (OOBE) occur when a signal is transmitted or received outside the accessible frequency range and poses a problem. This situation is a significant obstacle to creating state-of-theart 5G communication technology. Some thoughts have been given to modifying the OFDM filters for 5G communications due to their resistance to Inter Carrier Interference (ICI) and Inter Symbol Interference (ISI). To reduce spectral leakage into neighbouring sub-bands, OFDM-based waveforms require pulse windowing. This paper proposes a Kaiser-Blackman A (KBA) filter that shows Key Performance Indicators (KPIs) in the simulation concerning OOBE with minimal complexity. The filter was observed in the Power Spectrum Density (PSD) of the Adjacent Channel Leakage Ratio (ACLR) in the time domain, yielding a result of –170 dBm, which was then measured in the frequency domain at –180 dBm. Comparatively, –220 dBm shows an improvement in performance in the hybrid domain, unlike conventional filters’ baseline performance. As a result, low latency has been achieved, which is considered a critical challenge of 5G communication and beyond
Kemenangan Nurul Izzah Isyarat Ahli PKR berfikiran realistik
Get PDFRencana mengenai kemenangan NI dalam pemilihan PKR pada pilihan pemelihan 202
Enhanced tunability of a multiwavelength Brillouin-Erbium laser using cascaded photonic crystal and large effective area fibers
No full textThis work demonstrates a wideband multiwavelength laser using bismuth-based erbium-doped fiber with fiber coils of photonic crystal fiber (PCF) and a large effective area fiber (LEAF) in cascaded configuration. The generation of the multiwavelength laser was based on the stimulated Brillouin scattering effect. The tunability of the laser-based PCF that was initially limited due to four-wave mixing induced spectral broadening was enhanced by adding a coil of LEAF. A flawless tuning range in the L-band spanning 53 nm was achieved with a set of six laser lines. This multiwavelength laser could be tuned from 1567 to 1620 nm at 350 mW pump power and 5 mW Brillouin pump power. The generated output channels were very stable, showing a maximum peak power fluctuation of 0.75 dB over the duration of 60 min. This work showcases a technique to enhance the tuning range of Brillouin-based lasers for photonics applications in general
Optimizing dual training approaches for goat face recognition
Get PDFLivestock management faces a significant challenge in ensuring effective traceability and monitoring of food-producing animals. Advances in human biometric technologies have prompted the use of face recognition technology for goat identification and verification. This research project aims to enhance goat face recognition accuracy through the utilization of two versions of labeled images and video frame images. The primary challenge lies in determining the optimal type of training data to use. Regular validation on diverse datasets encompassing various goat face recognition scenarios is crucial to ensure the model's generalization capabilities. Furthermore, the dataset utilized reflects the complexities associated with livestock surveillance, including diverse settings and lighting conditions, posing significant challenges to accurate goat detection and recognition. The objectives of this study are to develop a robust system capable of effectively addressing these challenges and to strike a balance between training data inclusion and model generalization. The methodology employed involves leveraging Roboflow to extract frames from video data, label the images, preprocess them, and apply augmentation techniques to enhance dataset diversity. Frames from test videos, initially treated as "unseen" data, have been pivotal in improving the model's recognition capabilities by exposing it to realistic conditions. The project's methodology highlights the dynamic nature of model development and refinement in addressing real-world challenges in livestock management. Overall, the project aims to contribute to the advancement of goat detection and recognition systems, with promising results expected in improving livestock management practices. Ongoing experimentation and adaptation of techniques, such as adjustments to model architecture and hyperparameters, are conducted to achieve this
