Water resources planners and policymakers often ask questions about the future projections of drought characteristics (events, intensity, severity, duration, and peak) under different climatic scenarios. This study focused on quantifying the historical (1951-2005) and future (2026-2100) drought characteristics using the Standardized precipitation index (SPI) under RCP 4.5 and RCP 8.5 climate scenarios for the Balochistan province of Pakistan, an arid and drought-vulnerable region. Precipitation data of MPI-ESM-LR_RCA4 RCM was obtained from the Coordinated Regional Climate Downscaling Experiment South Asia (CORDEX-SA). The CORDEX-SA data was interpolated at 12-gauge stations and bias-corrected by the distribution mapping method using Asian Precipitation - Highly-Resolved Observational Data Integration towards Evaluation (APHRODITE) data. The drought characteristics were calculated based on standardized precipitation index (SPI), and intercompared between northern Balochistan (NB) and southern Balochistan (SB). It was found that the northern Balochistan (NB) region has suffered more droughts in the historical period and is also projected to have more severe and intense droughts than SB region. It was also found that with the increase of drought events, the duration reduces, which means that the higher the drought events at a station, the lower the drought duration. Government officials should focus more on managing the already few freshwater resources sustainably, given the increased likelihood of droughts in Balochistan due to climate change.

The assessment of aridity conditions is a key factor for water management and the implementation of mitigation and adaptation policies in agroforestry systems. Towards this aim three aridity indices were computed for the Iberian Peninsula (IP): the De Martonne Index (DMI), the Pinna Combinative Index (PCI), and the Erinç Aridity Index (EAI). These three indices were first computed for the baseline period 1961‒1990, using a gridded observational data (E-OBS), and, subsequently, for the periods 2011‒2040 (short-range) and 2041‒2070 (medium-range) using an ensemble of six Regional Climate Models (RCMs) experiments generated by the EURO-CORDEX project. Two Representative Concentration Pathways (RCPs) were analyzed, an intermediate anthropogenic radiative forcing scenario (RCP4.5) and a fossil-intensive emission scenario (RCP8.5). Overall, the three indices disclose a strengthening of aridity and dry conditions in central and southern Iberia until 2070, mainly under RCP8.5. Strong(weak) statistically significant correlations were found between these indices and the total mean precipitation (mean temperature) along with projected significant decreasing(increasing) trends for precipitation(temperature). The prevalence of years with arid conditions (above 70% for 2041‒2070 under both RCPs) are projected to have major impacts in some regions, such as southern Portugal, Extremadura, Castilla-La Mancha, Comunidad de Madrid, Andalucía, Región de Murcia, Comunidad Valenciana, and certain regions within the Aragón province. The projected increase in both the intensity and persistence of aridity conditions in a broader southern half of Iberia will exacerbate the exposure and vulnerability of this region to climate change, while the risk of multi-level desertification should be thoroughly integrated into regional and national water management and planning.

With increasing demand from unmanned driving and robotics, more attention has been paid to point cloud-based 3D object accurate detection technology. However, due to the sparseness and irregularity of the point cloud, the most critical problem is how to utilize the relevant features more efficiently. In this paper, we proposed a point-based object detection enhancement network to improve the detection accuracy in the 3D scenes understanding based on the distance features. Firstly, the distance features are extracted from the raw point sets and fused with the raw features about reflectivity of the point cloud to maximizing the use of information in point cloud. Secondly, we enhanced the distance features and raw features that we collectively refer to them as self-features of the key points in Set Abstraction (SA) layers with the self-attention mechanism, so that the foreground points can be better distinguished from the background points. Finally, we revised the group aggregation module in SA layers to enhance the feature aggregation effect of key points. We conducted experiments on the KITTI dataset and nuScenes dataset and the results show the enhancement method proposed in this paper has excellent performance.

This study analyzes the potential response of the seasonal cycle of heatwave (HWDI), dry (CDD) and wet (CWD) spells indices over West Africa for the near (2031-2060) and the far (2071-2100) future periods, under RCP4.5 and RCP8.5 scenarios using CORDEX simulations. Although some relative biases during the historical period (1976-2005), the CORDEX simulations and their ensemble mean outperform the seasonal variability of the above indices over three defined sub-regions of West Africa (i.e., Guinea gulf, west and east Sahel). They have shown significant correlation coefficients and less RMSE. They project an increase in heatwave days for both near and far future periods over whole west Africa region under both RCP scenarios. In addition, the Sahel regions will face to a decrease in wet spells days from March to November, whereas, the Gulf of Guinea will face to a decrease during all the year, except CCCLM simulation which indicates an increase during the retreat phase of the monsoon (October to December). The results also have shown an increase in dry spells over Sahel regions, more pronounced during March-November period, whereas, over Guinea gulf, the increase is observed over the entire year. On the other hand, the months of increasing dry spells and decreasing wet spells coincide, suggesting that countries in these regions could be exposed simultaneously to dry season associated with a high risk of drought and heatwave under future climate conditions.

In the search of potent α-amylase inhibitors, we have synthesized seventeen derivatives of 2-mercaptobenzimidazole bearing sulfonamide (1-17) and evaluated for their α-amylase inhibitory potential. All compounds display a variable degree of α-amylase activity having IC50 values ranging between 0.90 ± 0.05 to 11.20 ± 0.30 µM when compared with the standard drug acarbose having IC50 value 1.70 ± 0.10 µM. Compound 1, 2, 11, 12 and 14 having IC50 values 1.40 ± 0.10, 1.30 ± 0.05, 0.90 ± 0.05, 1.60 ± 0.05 and 1.60 ± 0.10 µM respectively were found many folds better than the standard drug acarbose. The remaining analogs showed good inhibitory potentials. All the synthesized compounds were characterized by HREI-MS, 1H and 13C-NMR. Structure activity relationship (SAR) has been recognized for all newly synthesized analogs. Through molecular docking study, binding mode of active analogs with α-amylase enzyme was confirmed.

This study focusses on identifying a set of representative future climate projections for the Upper Indus Basin (UIB). Although a large number of GCM’s predictor sets are nowadays available in the CMIP5 archive, the issue of their reliability for specific regions must still be confronted. This situation makes it imperative to sort out the most appropriate, single or small-ensemble set of GCMs for the assessment of climate change impacts in a region. Here a set of different approaches is adopted and applied for a step-wise shortlist and selection of appropriate climate models for the UIB under two RCPs: RCP 4.5 and RCP 8.5, based on, a) range of projected mean changes, b) range of projected extreme changes, and c) skill in reproducing the past climate. Furthermore, because of higher uncertainties in climate projection for high mountainous regions like the UIB, a wider range of future GCM climate projections is considered by using all possible future extreme scenarios (wet-warm, wet-cold, dry-warm, dry-cold). Based on this two-fold procedure, a limited number of climate models is pre-selected, out of which the final selection is done by assigning ranks to the weighted score for each of the mentioned selection criteria. The dynamically downscaled climate projections from the Coordinated Regional Downscaling Experiment (CORDEX) available for the top-ranked GCMs are further statistically downscaled (bias-corrected) over the UIB. The downscaled projections up to year 2100 indicate temperature increases ranging between 2.3 °C and 9.0 °C and precipitation changes that range, from a slight annual increase of 2.2% under the drier scenarios, to as high as 15.9% for the wet scenarios. Moreover, for all scenarios, the future precipitation will be more extreme, as the probability of wet days will decrease, while, at the same time, the precipitation intensities will increase. The spatial distribution of the downscaled predictors across the UIB also shows similar patterns for all scenarios, with a distinct precipitation decrease over the south-eastern parts of the basin, but an increase in the northeastern parts. These two features are particularly intense for the “Dry-Warm” and the “Median” scenarios over the late 21st century.

This paper proposes a parameter coordination optimization design of Power System Stabilizer (PSS) based on an improved Quasi-Affine Transformation Evolutionary (QUATRE) algorithm to suppress low-frequency oscillation and improve the dynamic stability of power system. To begin, the Simulated Annealing (SA) algorithm randomly updates the globally optimal solution of each QUATRE iteration and matches the inferior solution with a certain probability to escape the local extreme point. This new algorithm is first applied to power system. Second, Since damping ratio is one of the criteria to measure the dynamic stability of power system, this paper sets the objective function according to the principle of maximization of damping coefficient of electromechanical mode, and uses SA-QUATRE to search a group of global optimal PSS parameter combinations to improve the stability margin of the system as much as possible. Finally, the method's rationality and validity were validated by applying it to the simulation examples of IEEE 4-machine 2-area system with different operation states. The comparison with the traditional optimization algorithm shows that the proposed method has more advantages for multi-machine PSS parameter coordination optimization, and can restrain the low-frequency oscillation of power system more effectively and enhance the system stability.

In recent years, the need to make the built environment more resilient and adaptable to climate change has become increasingly evident. In Europe, this aspect concerns the vast majority of existing buildings, which present several deficiencies from the energy-efficiency point of view, considering they were designed before the introduction of modern energy codes. Nowadays, it is possible to retrofit existing buildings using advanced and high-efficient technologies such as Double Skin Façades (DSFs). The research aims to evaluate the use of properly designed DSFs for the energy restoration of existing buildings. In detail, various DSF configurations are applied to a residential building located in central Italy and investigated under present and future climate conditions, estimated through regional climate models. The results underline that all the analysed Double Skin Façades confirm to be a useful option, and, in particular, the Multi-Storey typology allows drastic energy needs decrement. Moreover, the general increase in temperatures and solar radiations could affect the building energy performance, and the insertion of DSF can mitigate the climate change effects, reducing the predicted energy consumption and ensuring better behaviours than the building in its original state.

In recent times, models like the Transformer have showcased remarkable prowess in tasks related to natural language processing. However, these models tend to be excessively intricate and demand extensive training. Additionally, while the multi-head self-attention mechanism in the Transformer model aims to capture semantic connections between words in a sequence, it encounters limitations when handling short sequences, thereby limiting its effectiveness in 5-polarity Arabic sentiment analysis tasks. The switch-transformer model has recently emerged as a high-performing alternative. Nevertheless, when these models are trained using single-task learning, they often fall short of achieving exceptional performance and struggle to generate robust latent feature representations, especially when working with compact datasets. This challenge is particularly pronounced in the case of the Arabic dialect, which is considered a low-resource language. Given these constraints, this research introduces a novel approach to sentiment analysis in Arabic text. This method leverages multitask learning in tandem with the switch-transformer shared encoder to enhance model adaptability and refine sentence representation. By introducing a mixture of expert (MoE) mechanism that break down the problem into smaller, more manageable sub-problems, the model becomes adept at handling lengthy sequences and intricate input-output relationships, benefiting both five-point and three-polarity Arabic sentiment analysis tasks. This proposed model effectively discerns sentiment in Arabic dialect sentences. The empirical results highlight the outstanding performance of the suggested model, as evidenced in evaluations on the Hotel Arabic-Reviews Dataset, the Book Reviews Arabic Dataset, and the LARB dataset.

Vaccination is one of the best approaches to control and eradicate foot-and-mouth disease (FMD). To achieve this goal, vaccines with inactivated FMD virus antigen in suitable adjuvants are being used in addition with other control measures. However, only a limited number of vaccine strains are commercially available which often have a restricted spectrum of activity against the different FMD virus strains in circulation. As a result, when new strains emerge, it is important to measure the efficacy of the current vaccine strains against these new variants. This is important for countries where FMD is endemic but also for countries that hold an FMD vaccine bank to be prepared for emergency vaccination. The emergence and spread of the O/ME-SA/Ind-2001 lineage of viruses posed a serious threat to countries which had OIE-endorsed FMD control plans and had not reported FMD for many years. In vitro vaccine matching results showed a poor match (r1-value <0.3) to the more widely used vaccine strain O Manisa and less protection in a challenge test. This paper describes the use of O3039 vaccine strain as an alternative either alone or in combination with the O Manisa vaccine strain with virulent challenge by a O/ME-SA/Ind-2001d sub-lineage virus from Algeria (O/ALG/3/2014). The experiment included challenge at 7 days post vaccination (to study protection and emergency use) and 21 days post vaccination (as would be done in standard potency studies). The results indicated that the O3039 vaccine strain alone as well as the combination with O Manisa is effective against this strain of the O/ME-SA/Ind/2001d lineage offering protection from clinical disease even after 7 days post vaccination and with reduction in viraemia and virus excretion.

Transformer models have emerged as frontrunners in the field of natural language processing, primarily due to their adept use of self-attention mechanisms to grasp the semantic linkages between words in sequences. Despite their strengths, these models often face challenges in single-task learning scenarios, particularly when it comes to delivering top-notch performance and crafting strong latent feature representations. This challenge is more pronounced in the context of smaller datasets and is particularly acute for under-resourced languages such as Arabic. In light of these challenges, this study introduces a novel methodology for text classification of Arabic texts. This method harnesses the newly developed Reverse Positional Encoding (RPE) technique. It adopts inductive-transfer learning (ITL) framework combined with a switching self-attention shared encoder, thereby increasing the model's adaptability and improving its sentence representation accuracy. The integration of Mixture of Experts (MoE) and Reverse Positional Encoding (RPE) techniques empowers the model to process longer sequences more effectively. This enhancement is notably beneficial for Arabic text classification, adeptly supporting both the intricate five-point and the simpler ternary classification tasks. The empirical evidence points to its outstanding performance, achieving accuracy rates of 87.20% for the HARD dataset, 72.17% for the BRAD dataset, and 86.89% for the LABR dataset, as evidenced by the assessments conducted on these datasets.

The use of unmanned aerial aircrafts (UAVs) is governed by strict regulatory frameworks that prioritize safety. To guarantee safety, it is necessary to acquire and maintain situational awareness (SA) throughout the operation. Existing Canadian regulations require pilots to operate their aircrafts in visual line-of-sight. Therefore, the task of acquiring and maintaining SA primary falls to the pilots. However, the development of aerial transport is entering a new era with the adoption of a highly dynamic and complex system known as advanced air mobility (AAM), which involves UAV operating autonomously beyond visual line-of-sight. SA must therefore be acquired and maintained primarily by each UAV through specific technologies and procedures. In this paper, we review these technologies and procedures in order to decompose the SA of the UAV in the AAM. We then use the system modeling language to provide a high-level structural and behavioral representation of the AAM as a system having UAV as its main entity. In a case study, we analyze one of the flagships UAVs of our industrial partner. Results show that this UAV doesn’t have all the technologies and methodologies necessary to achieve all the identified SA goals identified for the safety of the AAM. This work is a theoretical framework intended to contribute to the realization of the AAM, and we also expect to impact the future design and utilization of UAVs.

It is known that complexes based on natural polysaccharides are able to eliminate bone defects. Prolonged hyperglycemia means low bone regeneration and a chronic inflammatory response. The purpose of the study was to increase the efficiency of early bone formation in a cavity of critical size in diabetes mellitus in the experiment. The polyelectrolyte complex contains high molecular ascorbate of chitosan, chondroitin sulfate, sodium hyaluronate, heparin, adgelon serum growth factor, sodium alginate and amorphous nanohydroxyapatite (CH-SA-HA). Studies were conducted on 5 groups of white female Wistar rats: group 1 - regeneration of a bone defect in healthy animals under a blood clot; group 2 - regeneration of a bone defect under a blood clot in animals with diabetes mellitus; group 3 - bone regeneration in animals with diabetes mellitus after filling the bone cavity with a collagen sponge; group 4 - filling of a bone defect with a CH-SA-HA construct in healthy animals; Group 5 - filling of a bone defect CH-SA-HA in animals with diabetes mellitus. Implantation of the CH-SA-HA construct into bone cavities in type I diabetic rats can accelerate the rate of bone tissue repair. The inclusion of modifying polysaccharides and apatite agents in the construction may be a prospect for further improvement of the properties of implants.