Compared with visible-light remote sensing, multispectral remote sensing provides multi-band land surface information and enhances spectral separability through data fusion, thereby enabling more accurate surface representation. However, spectral redundancy, resolution discrepancies, and highly complex urban environments impose greater challenges on existing methods. Deep learning approaches based on convolutional neural network (CNN) offer superior capabilities in extracting and integrating multispectral features, enabling more accurate urban land cover segmentation. This review focuses on pixel-level urban land cover segmentation and systematically summarizes recent advances in deep learning for multispectral remote sensing. First, we emphasize that the rich spectral information and spatial complementarity of multispectral data effectively enhance segmentation performance and alleviate ambiguities caused by the ‘same spectrum-different objects’ and ‘same object-different spectra’. Second, we review 19 publicly available multispectral datasets, highlighting differences in spectral bands, spatial resolution, and application scenarios, and summarize a standardized preprocessing pipeline including radiometric calibration, geometric correction, band normalization, and spectral dimensionality reduction to support reproducibility. Third, we discuss representative spectral-spatial feature extraction and cross-scale context modeling strategies, covering dilated convolution, 3D-2D hybrid structures, dual-branch architectures, and multi-scale enhancement modules. Extensive comparative experiments on ISPRS Potsdam and GID datasets further demonstrate the applicability and performance differences of representative models. Finally, future research trends and directions are discussed, encompassing multi-temporal and multi-scale temporal learning, cross-modal fusion, and the lightweight design of complex models.

Zinc oxide (ZnO) is a promising photoanode material for photoelectrochemical (PEC) applications; however, its limitations, including a wide band gap and a high electron–hole recombination rate, hinder its performance. This study addresses these challenges by growing ZnO on graphene/nickel foam (Gr/Ni-foam) using a hydrothermal method, varying reaction parameters (time: 2, 4, 6, 8, 10 h; temperature: 150, 180 and 200 ºC). Results demonstrate significantly enhanced PEC activity via hydrothermal reaction temperature at 200 ºC for 8 h, attributed to the porous Ni-foam substrate, which improves electrolyte accessibility and light harvesting. Gr further reduces by about 50% the photocurrent onset potential, which in turn boosts charge transfer. Interestingly, ZnO on Gr/Ni-foam exhibits outstanding PEC activity (24.93 mA cm-2 at 1.23 V vs RHE) compared to other tested substrates, primarily due to the advantages of the porous structure of Ni-foam. This research highlights the critical role of substrate selection, particularly the use of porous Ni-foam, in overcoming the intrinsic limitations of ZnO to enabling the development of high-performance photoelectrodes for efficient PEC water splitting.

Tapak geologi, umpamanya batu kapur sering dilihat sebagai sumber ekonomi dari sudut pengkuarian. Namun begitu, sumber ini yang kaya dari segi nilai saintifik boleh menjana pendapatan untuk pelancongan dan menjadi tempat pembelajaran berkenaan sejarah dan proses bumi. Gua batu kapur bukan saja penting dari aspek geologi tetapi juga berfungsi sebagai habitat organisma tertentu dan juga akuifer, menjadikannya penting untuk dipelihara bagi tujuan pendidikan, penyelidikan dan pelancongan. Walau bagaimanapun, satu cabaran utama dalam menilai kepentingan geologi gua batu kapur adalah kurangnya set parameter yang jelas untuk pencirian saintifik geologi, yang sering menyebabkan pendekatan penilaian menjadi terlalu umum. Oleh itu, kajian ini membangunkan satu set parameter yang sesuai bagi mengenal pasti dan menilai nilai saintifik gua batu kapur berdasarkan pemetaan geologi. Set penilaian ini mengandungi 13 parameter yang merangkumi: kehadiran mineral, bilangan jenis batuan, fosil, struktur geologi, hidrogeologi, kepelbagaian speleotem, kepelbagaian speleogen, bilangan keunikan speleotem & speleogen, bilangan proses pembentukan speleotem, bentuk laluan gua, corak gua, bilangan ruang untuk dipelihara dan integriti. Parameter ini diuji di Gua Bilah dan Gua Persit, Merapoh, Pahang dan didapati parameter yang digunakan mampu mengenal pasti keistimewaan gua dari aspek saintifik geologi dan keunikan setiap gua.

Artocarpus elasticus is a plant notable for its high concentration of flavonoid compounds, specifically prenylated flavonoids. These flavonoids are recognized for their significant biological activities, including antioxidant, anti-inflammatory, and antidiabetic properties. Furthermore, flavonoids are known to function as acetylcholinesterase inhibitors, which are relevant in the context of neurodegenerative disorders such as Alzheimer's disease. To the best of our knowledge, the potential of prenylated flavonoids from A. elasticus has not been previously explored. Consequently, this study aims to investigate the potential of prenylated flavonoids from A. elasticus against acetylcholinesterase. The isolation process yielded four prenylated flavonoids, such as racemic cyclomourisin (1), cycloartocarpin (2), artocarpin (3), and cudraflavone C (4). Molecular docking results indicated binding affinity values of (R)-cyclomourisin _2.5 kcal/mol, (S)-cyclomourisin _4.8 kcal/mol, cycloartocarpin _2.8 kcal/mol, artocarpin _4.0 kcal/mol, and cudraflavone C _3.9 kcal/mol. Moreover, in vitro results demonstrated that the inhibition of the four prenylated flavonoids were 27.52% (racemic cyclomourisin), 0% (cycloartocarpin), 48.32% (artocarpin), and 37.19% (cudraflavone C). Based on these findings, artocarpin shows potential as an acetylcholinesterase inhibitor. Thus, further research of artocarpin as a lead compound targeting acetylcholinesterase can be conducted to obtain the optimized structure.

This study presents a novel flood monitoring and early warning system leveraging multi-walled carbon nanotube (MWCNT)-embedded polydimethylsiloxane (PDMS) flexible strain sensors for real-time water-level detection. Traditional systems, such as acoustic Doppler profilers, ultrasonic sensors, and IoT-based networks, face limitations related to cost, environmental adaptability, and accuracy. In contrast, the proposed system integrates stretchable MWCNT-PDMS sensors with an Arduino Nano microcontroller and Bluetooth-enabled data transmission to achieve high sensitivity (detecting deformations as small as 0.1 mm), low power consumption, and robust performance in harsh environments. The sensors detect the structural strain caused by rising water levels, translating mechanical deformation into resistance changes. A hardware-software framework processes these signals to trigger three-tier alerts: safe (blue LED), cautious (yellow LED), and dangerous (red LED), with real-time updates via an LCD and wireless Bluetooth communication (HC-05). Experimental results demonstrate a gauge factor exceeding 5 for optimized sensors, enabling precise threshold-based warnings. The system’s non-intrusive design, durability, and adaptability to irregular surfaces such as riverbanks and infrastructure offer significant advancements in disaster resilience. By addressing critical gaps in cost, accuracy, and deployment flexibility, this work provides a scalable solution for mitigating flood risks in vulnerable regions.

In this study, YBa2Cu3O7-δ (Y-123) high-temperature superconductors were synthesised via a modified thermal decomposition method using metal acetate precursors, with the sintering temperature varied between 920 °C and 980 °C. Phase formation, microstructure, and superconducting properties were systematically studied. Thermogravimetric analysis (TGA/DTG) showed a multi-step decomposition process, with final oxide formation occurring at temperatures above 500 °C. X-ray diffraction (XRD) confirmed Y-123 as the dominant phase, accompanied by impurity phases such as Y2BaCuO5 (Y-211) and BaCuO2. Scanning electron microscopy (SEM) showed significant grain growth from 0.85 µm to 1.36 µm as the sintering temperature increased from 920 °C to 980 °C, which enhanced the grain connectivity in the sample. Electrical resistivity measurements showed that all samples exhibited a consistent onset critical temperature (Tc-onset) of 93.1 K, while the zero-resistance temperature (Tc-zero) increased from 75.1 K to 88.1 K as the sintering temperature increased from 920 °C to 960 °C. The improvement in superconducting performance is due to enhanced phase formation and increased grain connectivity. These findings indicate that optimisation of sintering temperatures is a key factor in improving the microstructure and superconducting properties of Y-123 samples.

Rendang is a traditional food from West Sumatera, Indonesia, made from beef, coconut milk, and spices cooked for 6-7 h. Commercial rendang products have a long shelf-life at room temperatures, but they are not always processed according to commercial sterilization designed to reduce 12 log cycles of Clostridium botulinum. It is not known whether the rendang seasoning could assist the thermal process due to its antimicrobial activity. This study used a metabolomics approach to profile the compounds acting as antimicrobials in rendang seasoning. Due to safety concerns, Clostridium sporogenes was utilized as a surrogate for C. botulinum. Three formulas of rendang seasoning with and without coconut milk were heated for 60 or 120 min, respectively. Each seasoning was macerated in n-hexane and the extracts were tested for their antimicrobial activity toward C. sporogenes using agar well diffusion method. The extracts were analyzed using gas chromatography mass spectrometry to identify the compounds and a multivariate data analysis was conducted. The study showed that rendang seasoning A, B, and C with or without coconut milk inhibited C. sporogenes with diameters of inhibition zone ranging from 4.71 mm to 18.92 mm. The metabolomics showed a well-defined clustering of Principal Component Analysis model (Q2 = 0.654, R2X = 0.853) and Orthogonal Projection to Latent Structure model (Q2 = 0.865, R2Y = 0.95). Garlic and great galangal were identified as the leading antimicrobial spices in rendang seasoning against C. sporogenes, as OPLS showed their key compounds, 2-vinyl-1,3-dithiane, 2-vinyl-4H-1,3-dithiine, and (S)-4-(1-acetoxyallyl)phenyl acetate, as well as an unknown compound.

Amphiphilic polymers derived from quaternized 2-(N, N-dimethylamino)ethyl methacrylate (DMAEMA) were synthesized via radical polymerization to achieve a balance between hydrophilic and hydrophobic segments for antibacterial applications. Quaternization with bromoalkane compounds introduced alkyl side chains, thereby enhancing amphiphilicity and enabling tunable physicochemical properties. The polymers obtained exhibited molecular weights ranging from 1600 to 50600 Da, providing a wide distribution suitable for correlating chain length with performance characteristics. Thermogravimetric analysis confirmed that all samples maintained stability up to 400 °C, indicating robust thermal resistance. Enzymatic degradation studies demonstrated time-dependent surface erosion, with pore formation observed on polymer surfaces beginning at day 5, accompanied by progressive weight reduction. Structural and morphological analyses were performed using Fourier-transform infrared spectroscopy (FTIR), which verified successful functional group modification; meanwhile, scanning electron microscopy (SEM) showed distinct topographical changes associated with amphiphilic behavior and biodegradability of the polymer. Collectively, the findings highlight the successful design of amphiphilic DMAEMA-based polymers with promising thermal stability, controlled degradability, and structural versatility, underscoring their potential utility in antibacterial material development.

Kaedah ultrasonik amat sensitif untuk mengesan peranan fonon dalam mekanisme superkonduktor. Dalam kajian ini, halaju bunyi bujur, vL dan ricih, vs bagi superkonduktor NdBa₂Cu₃-xFexO₇-δ dengan x = 0, 0.02 dan 0.05 telah diukur daripada 80 K hingga sekitar 220 K dan frekuensi 10 MHz. NdBa₂Cu₃O7 menunjukkan suhu genting mula, Tc-mula 93 K dan sampel x = 0.02 menunjukkan Tc-mula 58 K, manakala sampel x = 0.05 tidak menunjukkan sifat superkonduktor. Halaju bunyi bujur dan ricih meningkat secara monotonik apabila suhu diturunkan hingga ke 80 K. Suhu Debye, θD menurun apabila Fe didopkan ke dalam NdBa₂Cu₃O7. θD adalah 370 K bagi NdBa₂Cu₃O7 dan 314 K dan 265 K masing-masing bagi x = 0.02 dan 0.05. Ini menunjukkan pelembutan kekisi berlaku apabila sifat mensuperkonduksi bahan berkurangan. Pemalar gandingan elektron-fonon bagi superkonduktor NdBa₂Cu₃O₇ mengikut teori BCS tiga dimensi dalam had gandingan lemah ialah λBCS = 0.67, manakala dalam senario van Hove dua dimensi, λvH2 = 0.023. Bagi sampel x = 0.02, λBCS = 0.55 dan λvH2 = 0.020. Nilai λvH2 ini adalah selaras dengan nilai yang diukur dan dilaporkan dalam superkonduktor oleh penyelidik lain. Kajian ini menunjukkan bahawa gandingan elektron-fonon serta ketumpatan keadaan yang tidak terhingga pada aras Fermi seperti yang dicadangkan oleh model van Hove dua dimensi dapat menerangkan mekanisme superkonduktor suhu tinggi dalam NdBa₂Cu₃O7.

In the study, a crucial research gap concerning the estimation of the coefficient of variation in an asymmetric distribution was addressed, specifically focusing on the three-parameter lognormal (3PLN) model, which exhibits large variation. The purpose of this research was to determine the effectiveness of four statistical approaches: likelihood-based, parametric bootstrap, profile likelihood, and Bayesian inference in formulating confidence intervals for reparameterizing the coefficient of variation (CV) within the 3PLN model. To evaluate the performance of the confidence intervals, we utilize specific performance measures such as the coverage rate and mean length. The results of our simulation study provide insights into how the profile likelihood method performs in estimating the CV, showing its effectiveness compared to alternative methods, particularly in ensuring accurate coverage rates. Nevertheless, when utilizing Adam’s optimization algorithm to derive point estimates, the Bayesian approach stands out as a dependable option for establishing confidence intervals, especially when dealing with a wide range of variances, from small to large. To demonstrate the practical application of our research, we utilize all proposed confidence intervals in estimating rainfall kinetic energy data. This real-world application serves as validation and showcases the utility of confidence intervals in practical scenarios.