Search Results (459)

Digital services companies help in the digitalization and transformation of the industry in driving Malaysia by advancing the economy of the country. However, digital services companies often face financial risks in terms of liquidity, solvency, efficiency, profitability, and operational risks. These risks increase the chances of failure and financial volatility, which put the companies at a serious disadvantage. This paper proposes an integrated Entropy-Weight TOPSIS model to analyze the financial risks of the listed digital services companies within Malaysia. The entropy method helps to prevent subjective weights by reflecting on information obtained from the financial reports of the companies. This study also provides an analysis to show possible improvements for the companies. The interest coverage ratio (ICR), which measures the capability to settle interest on debt, shows the highest weight followed by the basic indicator approach (BIA) and return on asset (ROA) based on the entropy weighting method. In addition, CLOUDPT, ITMAX, and MSNIAGA are ranked as the top three digital services companies that give the highest relative closeness to the ideal solution. The results help the risk managers to identify the criteria that caused the greatest financial risk in digital services companies to formulate targeted strategies to improve the companies’ financial health. Full article

Background/Objectives: Nurses working in closed psychiatric wards experience substantial psychosocial and spiritual burdens, emotional strain, and ethical tension due to continuous exposure to patients in crisis. As formal caregivers, nurses’ health and multidimensional well-being are essential for sustaining compassionate, dignity-preserving practice. However, the lived meaning of self-care within highly restrictive psychiatric environments remains insufficiently understood. This study explores how psychiatric nurses in South Korea experience and interpret self-care. Methods: A qualitative phenomenological design was used. Eight psychiatric nurses with more than three years of experience in closed psychiatric wards participated in in-depth, face-to-face interviews conducted between August 2018 and January 2019. Data were analysed using Colaizzi’s method to identify and synthesise essential themes. Results: Five categories captured the essence of nurses’ self-care experiences: (1) struggling to establish therapeutic roles as a psychiatric nurse; (2) conflating professional identity with ideals of good nursing; (3) recognising a gradual loss of motivation and hope to continue psychiatric nursing; (4) acknowledging the need to care for oneself and refocus on inner vitality; and (5) engaging in self-care through interactions with patients. Self-care was understood as a reflective, relational, and transformative process rather than as a set of stress-relief activities. Conclusions: Psychiatric nurses perceived self-care as an existential journey involving vulnerability, self-reflection, and renewal, which fostered both personal and professional growth. By framing self-care as an ethically grounded, relational practice that sustains therapeutic presence and safeguards moral and professional integrity, this study extends existing self-care literature beyond behavioural strategies. Full article

Accurate segmentation of adhered (sticky) corn kernels and reliable damage detection are critical for quality control in corn processing and kernel selection. Traditional watershed algorithms suffer from over-segmentation, whereas deep learning methods require large annotated datasets that are impractical in most industrial settings. This study proposes W&C-SVM, a hybrid computer vision method that integrates an improved watershed algorithm (Sobel gradient and Euclidean distance transform), convex hull defect detection and an SVM classifier trained on only 50 images. On an independent test set, W&C-SVM achieved the highest damage detection accuracy of 94.3%, significantly outperforming traditional watershed SVM (TW + SVM) (74.6%), GrabCut (84.5%) and U-Net trained on the same 50 images (85.7%). The method effectively separates severely adhered kernels and identifies mechanical damage, supporting the selection of intact kernels for quality control. W&C-SVM offers a low-cost, small-sample solution ideally suited for small-to-medium food enterprises and breeding laboratories. Full article

Normalization is a critical step in Multiple-Criteria Decision Analysis (MCDA) because it transforms heterogeneous criterion values into comparable information. This study examines normalization techniques through the lens of entropy, highlighting how criterion data structure shapes normalization behavior and ranking stability within TOPSIS (Technique for Order Preference by Similarity to Ideal Solution). Seven widely used normalization procedures are analyzed regarding mathematical properties, sensitivity to extreme values, treatment of benefit and cost criteria, and rank reversal. Normalization is treated as a source of uncertainty in MCDA outcomes, as different schemes can produce divergent rankings under identical decision settings. Shannon entropy is employed as a descriptive measure of information dispersion and structural uncertainty, capturing the heterogeneity and discriminatory potential of criteria rather than serving as a weighting mechanism. An illustrative experiment with ten alternatives and four criteria (two high-entropy, two low-entropy) demonstrates how entropy mediates normalization effects. Seven normalization schemes are examined, including vector, max, linear Sum, and max–min procedures. For vector, max, and linear sum, cost-type criteria are treated using either linear inversion or reciprocal transformation, whereas max–min is implemented as a single method. This design separates the choice of normalization form from the choice of cost-criteria transformation, allowing a cleaner identification of their respective contributions to ranking variability. The analysis shows that normalization choice alone can cause substantial differences in preference values and rankings. High-entropy criteria tend to yield stable rankings, whereas low-entropy criteria amplify sensitivity, especially with extreme or cost-type data. These findings position entropy as a key mediator linking data structure with normalization-induced ranking variability and highlight the need to consider entropy explicitly when selecting normalization procedures. Finally, a practical entropy-based method for choosing normalization techniques is introduced to enhance methodological transparency and ranking robustness in MCDA. Full article

Digital fringe projection profilometry (DFPP) is a widely used technique for full-field, non-contact 3D surface measurement, offering precision from the sub-micrometer-to-millimeter scale depending on system geometry and fringe design. This review provides a consolidated synthesis of advances reported between 2022 and 2025, covering projection and imaging architectures, phase formation and unwrapping strategies, calibration approaches, high-speed implementations, and learning-based reconstruction methods. A central contribution of this review is the integration of these developments within a metrological perspective, explicitly relating phase–height transformation, fringe parameters, system geometry, and calibration to dominant uncertainty sources and error propagation. Recent progress highlights trade-offs between sensitivity, robustness, computational complexity, and applicability to non-ideal surfaces, while learning-based and hybrid optical–computational approaches demonstrate substantial improvements in reconstruction reliability under challenging conditions. Remaining challenges include measurements on reflective or transparent surfaces, dynamic scenes, environmental instability, and real-time operation. The review outlines emerging research directions such as physics-informed learning, digital twins, programmable optics, and autonomous calibration, providing guidance for the development of next-generation DFPP systems for precision metrology. Full article

Beamforming is highly significant for the physical layer of wireless communication systems, for multi-antenna systems such as multiple input multiple output (MIMO) and massive MIMO, since it improves spectral efficiency and reduces interference. Traditional linear beamforming methods such as zero-forcing beamforming (ZFBF) and minimum mean square error (MMSE) beamforming provide closed-form solutions. Yet, their performance drops when they face non-ideal conditions such as imperfect channel state information (CSI), dynamic propagation environment, or high-dimensional system configurations, primarily due to static assumptions and computational limitations. These limitations have led to the rise of deep learning-based beamforming, where data-driven models derive beamforming solutions directly from CSI. By leveraging the representational capabilities of cutting-edge deep learning architectures, along with the increasing availability of data and computational resources, deep learning presents an adaptive and potentially scalable alternative to traditional methodologies. In this work, we unify and systematically compare our two unsupervised learning architectures for uplink receive beamforming: a simple neural network beamforming (NNBF) model, composed of convolutional and fully connected layers, and a transformer-based NNBF model that integrates grouped convolutions for feature extraction and transformer blocks to capture long-range channel dependencies. They are evaluated in a common multi-user single input multiple output (MU-SIMO) system model to maximize sum-rate across single-antenna user equipments (UEs) under 3GPP-compliant channel models, namely TDL-A and UMa. Furthermore, we present a FLOPs-based asymptotic computational complexity analysis for the NNBF architectures alongside baseline methods, namely ZFBF and MMSE beamforming, explicitly characterizing inference-time scaling behavior. Experiments for the simple NNBF are performed under simplified assumptions such as stationary UEs and perfect CSI across varying antenna configurations in the TDL-A channel. On the other hand, transformer-based NNBF is evaluated in more realistic conditions, including urban macro environments with imperfect CSI, diverse UE mobilities, coding rates, and modulation schemes. Results show that the transformer-based NNBF achieves superior performance under realistic conditions at the cost of increased computational complexity, while the simple NNBF presents comparable or better performance than baseline methods with significantly lower complexity under simplified assumptions. Full article

Few-shot classification aims to generalize from very limited samples, providing an effective solution for data-scarce scenarios. From a symmetry viewpoint, an ideal Few-Shot classifier should be invariant to class permutations and treat support and query features in a balanced manner, preserving intra-class cohesion while enlarging inter-class separation in the embedding space. However, existing methods often violate this symmetry because prototypes are estimated from few noisy samples, which induces asymmetric representations and task-dependent biases under complex inter-class relations. To address this, we propose FC-SBAAT, feature collaboration, and Sparse Bias-Aware Attention Transformer, a framework that explicitly leverages symmetry in feature collaboration and prototype construction. First, we enhance symmetric interactions between support and query samples in both attention and contrastive subspaces and adaptively fuse these complementary representations via learned weights. Second, we refine prototypes by symmetrically aggregating intra-class features with learned importance weights, improving prototype quality while maintaining intra-class symmetry and increasing inter-class discrepancy. For matching, we introduce a Sparse Bias-Aware Attention Transformer that corrects asymmetric task bias through bias-aware attention with a low computational overhead. Extensive experiments show that FC-SBAAT achieves 55.71% and 73.87% accuracy for 1-shot and 5-shot tasks on MiniImageNet and 70.37% and 83.86% on CUB, outperforming prior methods. Full article

Background/Objectives: Tuberculosis remains a significant global health problem, particularly in resource-limited environments. Its mortality and spread can be considerably decreased by early and precise detection via chest X-ray imaging. This study introduces a novel approach based on hybrid deep learning for Tuberculosis detection from chest X-ray images. Methods: The introduced approach combines GhostNet, a lightweight convolutional neural network tuned for computational efficiency, and MobileViT, a transformer-based model that can capture both local spatial patterns and global contextual dependencies. Through such integration, the model attains a balanced trade-off between classification accuracy and computational efficiency. The architecture employs feature fusion, where spatial features from GhostNet and contextual representations from MobileViT are globally pooled and concatenated, which allows the model to learn discriminative and robust feature representations. Results: The suggested model was assessed on two publicly available chest X-ray datasets and contrasted against several cutting-edge convolutional neural network architectures. Findings showed that the introduced hybrid model surpasses individual baselines, attaining 99.52% accuracy on dataset 1 and 99.17% on dataset 2, while keeping low computational cost (7.73M parameters, 282.11M Floating Point Operations). Conclusions: These outcomes verify the efficacy of feature-level fusion between a convolutional neural network and transformer branches, allowing robust tuberculosis detection with low inference overhead. The model is ideal for clinical deployment and resource-constrained contexts due to its high accuracy and lightweight design. Full article

Elevation Digital Beamforming (DBF) technology is key to achieving high-resolution wide-swath (HRWS) imaging in spaceborne Synthetic Aperture Radar (SAR) systems. However, multi-channel DBF-SAR systems face a prominent conflict between the need for real-time channel error calibration and the constraints of limited on-board hardware resources. To address this bottleneck, this paper proposes a real-time channel error calibration method based on Fast Fourier Transform (FFT) pulse compression and introduces a “calibration-operation” dual-mode control with a parameter-persistence architecture. This scheme decouples high-complexity computations by confining them to the system initialization phase, enabling on-board, real-time, closed-loop compensation for multi-channel signals with low resource overhead. Test results from a high-performance Field-Programmable Gate Array (FPGA) platform demonstrate that the system achieves high-precision compensation for inter-channel amplitude, phase, and time-delay errors. In the 4-channel system validation, the DBF synthesized signal-to-noise ratio (SNR) improved by 5.93 dB, reaching a final SNR of 44.26 dB. This performance approaches the theoretical ideal gain and significantly enhances the coherent integration gain of multi-channel signals. This research fully validates the feasibility of on-board, real-time calibration with low resource consumption, providing key technical support for the engineering robustness and efficient data processing of new-generation SAR systems. Full article

Objectives: Burn being a major traumatic issue worldwide impacts millions of lives annually. Herein, a novel xanthan dialdehyde/sodium alginate/copper-doped mesoporous bioactive glass nanoparticle (XDA/Na-ALG/Cu-MBGN) hydrogel is presented in this study. Methods: The hydrogel was fabricated by a casting method, followed by its characterization in terms of its morphology, surface topography, and in vitro biochemical and physical interactions. Results: Scanning electron microscopy images revealed the rough surface of the hydrogel, ideal for cell attachment and proliferation. The nanoporous structure revealed by BET enabled it to hold moisture for an extended span. The nanopores were developed because of the ether linkage developed between XDA and Na-ALG, as evident from Fourier Transform Infrared Spectroscopy. The loading of Cu-MBGNs was also confirmed by FTIR. The release of copper ions was sustained throughout the 7 days, and it is accounting for about 22 µg/mL in 330 h, which follows the degradation kinetics of XDA/Na-ALG/Cu-MBGN hydrogels. The released copper ions promoted angiogenesis, as confirmed by the enhanced release of vascular endothelial growth factor (VEGF) for the XDA/Na-ALG/Cu-MBGN hydrogel (275 ng/mL) in comparison to 200 ng/mL of the bare TCP. The hydrogel, despite being bactericidal against Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus) did not show toxicity towards human dermal fibroblasts confirmed via a Water-Soluble Tetrazolium 8 assay. Conclusions: Hence, the developed XDA/Na-ALG/Cu-MBGN hydrogel possesses potential to be investigated further in terms of in vivo interactions. Full article

Amid escalating climate change and delayed political measures to prevent them, questions of how individuals negotiate the tension between collective climate protection and personal disaster preparedness have become increasingly urgent. This study explores these dynamics by examining the biography of ‘Lukas Sandner’, a sustainability activist whose trajectory reflects a shift from collective climate action to personal adaptation. Using a reconstructive biographical analysis based on a biographical narrative interview and the documentary method, the study reconstructs the interpretive frameworks and orientations that shape his actions and that situate him within this tension. The analysis shows that transformative learning was triggered by a disorienting event—particularly a severe heavy rainfall event—which redirected his focus from collective prevention efforts towards individual preparedness. His strategies include stockpiling, technical measures, and gardening understood as a hybrid practice linking ecological ideals with precautionary foresight. These shifts are dialectical, shaped by earlier experiences of concealment and reframing. The findings illustrate how personal trajectories intersect with broader social dynamics, showing how biographical experiences, structural conditions, and collective discourses converge to shape preparedness, highlighting the interplay between collective responsibility and private resilience. The reconstruction of Sandner’s biography may provide clues to underlying societal trends toward individualised adaptation and risk prevention strategies in industrialised societies in response to growing disillusionment about the possibility of preventing climate change. Full article

Natural Product Synthesis (NPS) is a cornerstone of organic chemistry, historically rooted in the dual goals of structure elucidation and synthetic strategy development for bioactive compounds. Initially focused on identifying the structures of medicinally relevant natural products, NPS has evolved into a dynamic field with applications in drug discovery, immunotherapy, and smart materials. This evolution has been propelled by advances in reaction design, mechanistic insight, and the integration of green chemistry principles. A particularly promising development in NPS is the use of photochemistry, which harnesses light—a renewable energy source—to drive chemical transformations. Photochemical reactions offer unique excited-state reactivity, enabling synthetic pathways that are often inaccessible through thermal methods. Their precision and sustainability make them ideal for modern synthetic challenges. This review explores a wide range of photochemical reactions, from classical to contemporary, emphasizing their role in total synthesis. By showcasing their potential, the review aims to encourage broader adoption of photochemical strategies in the synthesis of complex natural products, promoting innovation at the intersection of molecular complexity, sustainability, and synthetic efficiency. Full article

Asphalt binders that perform exceptionally well in resisting both rutting and cracking are highly desirable for withstanding the combined effects of extreme low temperatures and heavy vehicle loads. This work highlights the benefits of softening oils on the cracking performance of styrene–butadiene–styrene-modified asphalt (SBSMA). Additionally, the inherent correlations between cracking-performance parameters of binders and mixtures were thoroughly analyzed. A bio-based oil (bio-oil) and a petroleum-based oil (re-refined engine oil bottom, REOB) were selected as the softening oils. The benefit provided by softening oils was evaluated using various rheological indices, while the adverse effects of oxidative aging on cracking resistance were also considered. The cracking properties at intermediate temperatures were characterized by the modified Glover–Rowe (M G–R) parameter, δ_{8967 kPa}, and fatigue life (N_f). The low-temperature cracking properties of binders were evaluated by stiffness and m-value. The indirect tensile asphalt cracking (IDEAL-CT) test was conducted utilizing the CT-index and post-peak slope to estimate the fracture properties of the mixtures. The oxidative aging of binder and mixture samples was simulated and carried out based on lab aging methods; meanwhile, the carbonyl index obtained from the Fourier transform infrared (FTIR) scanning was used to track and evaluate the aging level of binders. The results show that the cracking performance could be greatly improved by the application of softening oils. Meanwhile, the bio-oils were found to operate with much higher efficiency than REOB, since the oil modification index (OMI) result showed that bio-oils exhibited four to six times the efficiency of REOB, in terms of improving the stress relaxation property. The correlations proved that the cracking-related parameters shared an inherent relationship with R² above 0.85, while these parameters consistently declined as the binder aged. The cracking performance of the mixtures at intermediate temperatures was mainly governed by the fatigue life of the binder, whereas thermal cracking performance was highly associated with the binder’s relaxation property. Full article

Deep learning (DL) methods have revolutionized natural language processing (NLP), enabling industrial documentation systems to process and generate text with high accuracy and fluency. Modern deep learning models, such as transformers and recurrent neural networks (RNNs), learn contextual relationships in text, making them ideal for analyzing and creating complex industrial documentation. Transformer-based architectures, such as BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer), are ideally suited for tasks such as text summarization, content generation, and question answering, which are crucial for documentation systems. Pre-trained language models, tuned to specific industrial datasets, support domain-specific vocabulary, ensuring the generated documentation complies with industry standards. Deep learning-based systems can use sequential models, such as those used in machine translation, to generate documentation in multiple languages, promoting accessibility, and global collaboration. Using attention mechanisms, these models identify and highlight critical sections of input data, resulting in the generation of accurate and concise documentation. Integration with optical character recognition (OCR) tools enables DL-based NLP systems to digitize and interpret legacy documents, streamlining the transition to automated workflows. Reinforcement learning and human feedback loops can enhance a system’s ability to generate consistent and contextually relevant text over time. These approaches are particularly effective in creating dynamic documentation that is automatically updated based on data from sensors, registers, or other sources in real time. The scalability of DL techniques enables industrial organizations to efficiently produce massive amounts of documentation, reducing manual effort and improving overall efficiency. NLP has become a fundamental technology for automating the generation, maintenance, and personalization of industrial documentation within the Industry 4.0, 5.0, and emerging Industry 6.0 paradigms. Recent advances in large language models, search-assisted generation, and multimodal architectures have significantly improved the accuracy and contextualization of technical manuals, maintenance reports, and compliance documents. However, persistent challenges such as domain-specific terminology, data scarcity, and the risk of hallucinations highlight the limitations of current approaches in safety-critical manufacturing environments. This review synthesizes state-of-the-art methods, comparing rule-based, neural, and hybrid systems while assessing their effectiveness in addressing industrial requirements for reliability, traceability, and real-time adaptation. Human–AI collaboration and the integration of knowledge graphs are transforming documentation workflows as factories evolve toward cognitive and autonomous systems. The review included 32 articles published between 2018 and 2025. The implications of these bibliometric findings suggest that a high percentage of conference papers (69.6%) may indicate a field still in its conceptual phase, which contextualizes the article’s emphasis on proposed architecture rather than their industrial validation. Most research was conducted in computer science, suggesting early stages of technological maturity. The leading countries were China and India, but these countries did not have large publication counts, nor were leading researchers or affiliations observed, suggesting significant research dispersion. However, the most frequently observed SDGs indicate a clear health context, focusing on “industry innovation and infrastructure” and “good health and well-being”. Full article

The first-principles method using density functional theory (DFT) reveals the mechanics, electronic structure, and magnetic properties of six Zr-based equiatomic quaternary Heusler compounds and their transformation under hydrostatic pressure. The results show that these compounds maintain mechanical stability under hydrostatic pressures of 0–100 GPa, and the ductility of all the alloys is improved except ZrCrFeGe. In the ground state structure, ZrVFeAl and ZrCrFeGe are half metals, ZrVCoAl and ZrCrFeAl are spin gapless semiconductors, while ZrCrMnAl and ZrMnFeAl are regarded as nearly half metals. ZrVFeAl, ZrVCoAl, ZrCrFeAl, and ZrCrFeGe have high spin polarization and satisfy the Slater–Pauling rule, and their spin-flip band gaps are 0.43 eV, 0.35 eV, 0.14 eV, and 0.11 eV, respectively. These half-metallic compounds maintain half-metallicity within a certain pressure range, while spin gapless semiconductors (SGS) complete the SGS~half-metal~near-half-metal transition under hydrostatic pressure. These half-metallic compounds and spin gapless semiconductors are ideal candidates for spintronic applications. Full article