Explore the vast range of titles available.

This study employs ab initio calculations based on density functional theory (DFT) to investigate the structural properties, 1 H-NMR spectra, and vibrational spectra of methane sulfonic acid (MSA) at low degree of hydration. The findings reveal that energetically stable structures are formed by small clusters consisting of one or two MSA molecules ( m  = 1 and 2) and one or two water molecules in (MSA) m ·(H 2 O) n (m = 1–2 and n = 1–5).These stable structures arise from the formation of strong cyclic hydrogen bonds between the proton of the hydroxyl (OH) group in MSA and the water molecules. However, clusters containing three or more water molecules ( n  > 2) exhibit proton transfer from MSA to water, resulting in the formation of ion-pairs composed of CH 3 SO 3 − and H 3 O + species. The measured 1 H-NMR spectra demonstrate the presence of hydrogen-bonded interactions between MSA and water, with a single MSA molecule interacting with water molecules. This interaction model accurately represents the hydrogen bonding network, as supported by the agreement between the experimental and calculated NMR chemical shift results.

Multiple system atrophy (MSA) is a fatal, rapidly progressing neurodegenerative disease of uncertain etiology, clinically characterized by various combinations of Levodopa unresponsive parkinsonism, cerebellar, autonomic and motor dysfunctions. The morphological hallmark of this α-synucleinopathy is the deposition of aberrant α-synuclein in both glia, mainly oligodendroglia (glial cytoplasmic inclusions /GCIs/) and neurons, associated with glioneuronal degeneration of the striatonigral, olivopontocerebellar and many other neuronal systems. Typical phenotypes are MSA with predominant parkinsonism (MSA-P) and a cerebellar variant (MSA-C) with olivocerebellar atrophy. However, MSA can present with a wider range of clinical and pathological features than previously thought. In addition to rare combined or “mixed” MSA, there is a broad spectrum of atypical MSA variants, such as those with a different age at onset and disease duration, “minimal change” or prodromal forms, MSA variants with Lewy body disease or severe hippocampal pathology, rare forms with an unusual tau pathology or spinal myoclonus, an increasing number of MSA cases with cognitive impairment/dementia, rare familial forms, and questionable conjugal MSA. These variants that do not fit into the current classification of MSA are a major challenge for the diagnosis of this unique proteinopathy. Although the clinical diagnostic accuracy and differential diagnosis of MSA have improved by using combined biomarkers, its distinction from clinically similar extrapyramidal disorders with other pathologies and etiologies may be difficult. These aspects should be taken into consideration when revising the current diagnostic criteria. This appears important given that disease-modifying treatment strategies for this hitherto incurable disorder are under investigation.

Background Skin cancer is one of the highly occurring diseases in human life. Early detection and treatment are the prime and necessary points to reduce the malignancy of infections. Deep learning techniques are supplementary tools to assist clinical experts in detecting and localizing skin lesions. Vision transformers (ViT) based on image segmentation classification using multiple classes provide fairly accurate detection and are gaining more popularity due to legitimate multiclass prediction capabilities. Materials and methods In this research, we propose a new ViT Gradient‐Weighted Class Activation Mapping (GradCAM) based architecture named ViT‐GradCAM for detecting and classifying skin lesions by spreading ratio on the lesion's surface area. The proposed system is trained and validated using a HAM 10000 dataset by studying seven skin lesions. The database comprises 10 015 dermatoscopic images of varied sizes. The data preprocessing and data augmentation techniques are applied to overcome the class imbalance issues and improve the model's performance. Result The proposed algorithm is based on ViT models that classify the dermatoscopic images into seven classes with an accuracy of 97.28%, precision of 98.51, recall of 95.2%, and an F1 score of 94.6, respectively. The proposed ViT‐GradCAM obtains better and more accurate detection and classification than other state‐of‐the‐art deep learning‐based skin lesion detection models. The architecture of ViT‐GradCAM is extensively visualized to highlight the actual pixels in essential regions associated with skin‐specific pathologies. Conclusion This research proposes an alternate solution to overcome the challenges of detecting and classifying skin lesions using ViTs and GradCAM, which play a significant role in detecting and classifying skin lesions accurately rather than relying solely on deep learning models.

Arabic is recognised as the 4th most used language of the Internet. Arabic has three main varieties: (1) classical Arabic (CA), (2) Modern Standard Arabic (MSA), (3) Arabic Dialect (AD). MSA and AD could be written either in Arabic or in Roman script (Arabizi), which corresponds to Arabic written with Latin letters, numerals and punctuation. Due to the complexity of this language and the number of corresponding challenges for NLP, many surveys have been conducted, in order to synthesise the work done on Arabic. However these surveys principally focus on two varieties of Arabic (MSA and AD, written in Arabic letters only), they are slightly old (no such survey since 2015) and therefore do not cover recent resources and tools. To bridge the gap, we propose a survey focusing on 90 recent research papers (74% of which were published after 2015). Our study presents and classifies the work done on the three varieties of Arabic, by concentrating on both Arabic and Arabizi, and associates each work to its publicly available resources whenever available.

As COVID-19 cases rise in one of Southeast Asia’s worst-hit nations, low public trust in vaccines will be a huge block in the government’s vaccination program. In a recent survey in January 2021 which was conducted by Pulse Asia, nearly half of Filipinos said that they would not get vaccinated against COVID-19 due to safety concerns. A proposed solution would be a Multisectoral Approach (MSA). MSA refers to the deliberate collaboration among various stakeholder groups (e.g. government, civil society and private and religious sectors) and sectors (e.g. health, environment and economy) to achieve a policy outcome. By engaging multiple sectors, such as interfaith collaborations, governments can leverage knowledge, expertise, reach and resources, benefiting from their combined and varied strengths as they work toward the shared goal of building public trust on vaccines.

Agriculture and climate change are internally correlated with each other in various aspects, as climate change is the main cause of biotic and abiotic stresses, which have adverse effects on the agriculture of a region. The land and its agriculture are being affected by climate changes in different ways, e.g., variations in annual rainfall, average temperature, heat waves, modifications in weeds, pests or microbes, global change of atmospheric CO2 or ozone level, and fluctuations in sea level. The threat of varying global climate has greatly driven the attention of scientists, as these variations are imparting negative impact on global crop production and compromising food security worldwide. According to some predicted reports, agriculture is considered the most endangered activity adversely affected by climate changes. To date, food security and ecosystem resilience are the most concerning subjects worldwide. Climate-smart agriculture is the only way to lower the negative impact of climate variations on crop adaptation, before it might affect global crop production drastically. In this review paper, we summarize the causes of climate change, stresses produced due to climate change, impacts on crops, modern breeding technologies, and biotechnological strategies to cope with climate change, in order to develop climate resilient crops. Revolutions in genetic engineering techniques can also aid in overcoming food security issues against extreme environmental conditions, by producing transgenic plants.

Objective: To delineate the frequency and nature of dystonia in multiple system atrophy (MSA). Methods: a cohort of 24 patients with clinically probable MSA over the past 10 years were prospectively followed up. Motor features were either dominated by parkinsonism (MSA-P subtype, n=18) or cerebellar ataxia (MSA-C, n=6). Classification of dystonic features and their changes with time was based on clinical observation during 6–12 monthly follow up visits. Parkinsonian features and complications of drug therapy were assessed. Most patients (22/24) died during the observation period. Neuropathological examination was confirmatory in all of the five necropsied patients. Results: At first neurological visit dystonia was present in 11 (46%) patients all of whom had been levodopa naive at this time point. Six patients (25%) exhibited cervical dystonia (antecollis) (MSA-P n=4, MSA-C n=2), five patients (21%) showed unilateral limb dystonia (MSA-P n=4; MSA-C n=1). A definite initial response to levodopa treatment was seen in 15/18 patients with MSA-P, but in none of the six patients with MSA-C. A subgroup of 12 patients with MSA-P developed levodopa induced dyskinesias 2.3 years (range 0.5–4) after initiation of levodopa therapy. Most patients had peak dose craniocervical dystonia; however, some patients experienced limb or generalised dystonia. Isolated peak dose limb chorea occurred in only one patient. Conclusion: The prospective clinical study suggests that dystonia is common in untreated MSA-P. This finding may reflect younger age at disease onset and putaminal pathology in MSA-P. Levodopa induced dyskinesias were almost exclusively dystonic affecting predominantly craniocervical musculature. Future studies are required to elucidate the underlying pathophysiology of dystonia in MSA.

Bearings, as core components of mechanical equipment, play a critical role in ensuring equipment safety and reliability. Early fault detection holds significant importance. Addressing the challenges of insufficient robustness in bearing fault diagnosis under industrial high-noise conditions and the difficulty of extracting fault features from a single modality, this study proposes a three-channel multimodal fault diagnosis method that integrates a Convolutional Auto-Encoder (CAE) with a dual attention mechanism (M-CNNBiAM). This approach provides an effective technical solution for the precise diagnosis of bearing faults in high-noise environments. To suppress substantial noise interference, a CAE denoising module was designed to filter out intense noise, providing high-quality input for subsequent diagnostic networks. To address the limitations of single-modal feature extraction and restricted generalization capabilities, a three-channel time–frequency signal joint diagnosis model combining the Continuous Wavelet Transform (CWT) with an attention mechanism was proposed. This approach enables deep mining and efficient fusion of multi-domain features, thereby enhancing fault diagnosis accuracy and generalization capabilities. Experimental results demonstrate that the designed CAE module maintains excellent noise reduction performance even under −10 dB strong noise conditions. When combined with the proposed diagnostic model, it achieves an average diagnostic accuracy of 98% across both the CWRU and self-test datasets, demonstrating outstanding diagnostic precision. Furthermore, under −4 dB noise conditions, it achieves a 94% diagnostic accuracy even without relying on the CAE denoising module. With a single training cycle taking only 6.8 s, it balances training efficiency and diagnostic performance, making it well-suited for real-time, reliable bearing fault diagnosis in industrial environments with high noise levels.

Maritime activity is expected to increase, and therefore also the need for maritime surveillance and safety. Most ships are obligated to identify themselves with a transponder system like the Automatic Identification System (AIS) and ships that do not, intentionally or unintentionally, are referred to as dark ships and must be observed by other means. Knowing the future location of ships can not only help with ship/ship collision avoidance, but also with determining the identity of these dark ships found in, e.g., satellite images. However, predicting the future location of ships is inherently probabilistic and the variety of possible routes is almost limitless. We therefore introduce a Bidirectional Long-Short-Term-Memory Mixture Density Network (BLSTM-MDN) deep learning model capable of characterising the underlying distribution of ship trajectories. It is consequently possible to predict a probabilistic future location as opposed to a deterministic location. AIS data from 3631 different cargo ships are acquired from a region west of Norway spanning 320,000 sqkm. Our implemented BLSTM-MDN model characterizes the conditional probability of the target, conditioned on an input trajectory using an 11-dimensional Gaussian distribution and by inferring a single target from the distribution, we can predict several probable trajectories from the same input trajectory with a test Negative Log Likelihood loss of −9.96 corresponding to a mean distance error of 2.53 km 50 min into the future. We compare our model to both a standard BLSTM and a state-of-the-art multi-headed self-attention BLSTM model and the BLSTM-MDN performs similarly to the two deterministic deep learning models on straight trajectories, but produced better results in complex scenarios.