This study delves into analyzing drought patterns in Baluchistan by applying copula-based bivariate probabilistic models complemented by Severity Duration Frequency (SDF) curves. The calculation of the Standardized Precipitation Index (SPI) hinges on monthly aggregate precipitation data from ten distinct sites compiled over six-month periods. After evaluating various parametric distributions, the Log-Normal distribution emerges as suitable for modeling drought severity and duration. A range of bivariate copulas is employed to simulate the characteristics of drought severity and duration, which are then compared against observed data. Remarkably, the Gumbel copula classified as an extreme value copula-outperforms its counterparts according to diverse statistical benchmarks. By utilizing the dependence function, we derive the conditional distribution of drought variables: severity and duration. These conditional distributions subsequently inform the calculation of return periods, forming the basis for constructing SDF diagrams at fixed recurrence levels across the study region. The study\'s finding indicates that a severe drought could occur over the region with higher return periods for a specific duration. The implications of this research are significant, showcasing the potential of copula-based joint modeling techniques to generate frequency curves for drought severity and duration. This development holds promise for effective water resource management and the formulation of strategies to mitigate the impact of drought in vulnerable regions.

In most arid and semi-arid environments, groundwater is one of the precious resources threatened by water table decline and desiccation, thus it must be constantly monitored. Identifying the causes influencing the variations of the subsurface water level, such as meteorological drought, is one approach for monitoring these fluctuations. In the present study, the effect of two meteorological drought indices SPI and SPEI on the fluctuations of the underground water level was evaluated, as was their relationship with the drought index of the subsurface water level (SWI) using multivariate linear regression and M5 decision tree regression. After calculating climatic and hydrological drought indices in a 6-month time window for a long-term statistical period (1989-2018), the semi-deep aquifers of Golestan province, which is located in northern Iran, were considered as a research location for this purpose. The results demonstrated that the effect of meteorological drought does not immediately manifest in the changes of the subsurface water table and the hydrological drought index. By adding the meteorological drought index with a 6-month lag step, the average air temperature, and the total rainfall from the previous 6 months as new variables, the correlation with the SWI index increases, so that in the best-case scenario, the M5 decision tree model provides the best result in predicting the SWI index. The second half of the year yielded a coefficient of determination of 0.92 and an error value of RMSE = 0.27 for the SPEI index. Among the meteorological drought indices, the SPEI index, which is based on precipitation and evapotranspiration, created a stronger link with the SWI index, which highlights the significance of potential evapotranspiration. It is a warning that, as a result of global warming, subsurface water tables in this region may fall in the future.

Droughts frequently occurring in India have significant societal, economic, and environmental effects. The lack of direct measurements of groundwater in location and time hinders quantitative methods to analyse the intricate nature of groundwater drought. This work used the datasets derived from the Gravity and Climate Experiment (GRACE and GRACE-FO) and Global Land Data Assimilation System (GLDAS) to extensively analyse Groundwater Storage changes in the Lower Gangetic Basin (LGB) using unique hydrological parameters between the years 2003 and 2022. The analysis highlights that the GRACE-derived terrestrial water storage anomaly in the LGB decreased significantly (-12.12 mm/yr), and the amount of Groundwater Storage Anomaly (GWSA) decreased similarly (-10.80 mm/yr), while in the GRACE-FO period, a positive trend has been noticed in TWSA (33.96 mm/yr) and GWSA (64.8 mm/yr) respectively. A drought indicator called the GRACE-derived groundwater drought index (GGDI) has been computed for the entire LGB region. A traditional drought study viz. Standardised Precipitation Index (SPI) was performed over LGB to justify the results of the GGDI. The results from GGDI study effectively matched the periods of significant drought occurrences with the 12-month SPI time series. From the GGDI, this study examined groundwater drought\'s spatial distribution, temporal evolution, and trend (Modified Mann Kendall trend) aspects. According to research findings, the LGB experienced three major drought periods between 2009-2010, 2019 (moderate), and 2015-2016 (severe). The study offers reliable quantitative data on the evolution of GRACE-derived groundwater drought, which may add a new perspective to additional drought research in the densely populated study area, which depends majorly on agriculture, livestock and less skilled water-intensive industries such as leather and textile industries in a sub-tropical climate. This paradigm incorporates changes in groundwater resources caused by human activities and climate change, paving the way for measuring progress towards sustainable use and water security.

Drought as a natural phenomenon has always been a serious threat to regions with hot and dry climates. One of the major effects of drought is the drop in groundwater level. This paper focused on the SPI (Standardized Precipitation Index) and SWI (Standardized Water-Level Index) to assess meteorological and hydrological drought, respectively. In the first part, we used different time frames of SPI (3, 6, 12, and 24 months) to investigate drought in Yazd, a dry province in the center of Iran for 29 years (1990-2018). Then, in the second part, the relationship between SPI and SWI was investigated in the three aquifers of Yazd by some rain gauge stations and the closest observation wells to them. In addition to using SPI and SWI, we also used different machine learning (ML) algorithms to predict drought conditions including linear model and six non-linear models of K_Nearest_Neighbors, Gradient_Boosting, Decision_Tree, XGBoost, Random_Forest, and Neural_Net. To evaluate the accuracy of the mentioned models, three statistical indicators including Score, RMSE, and MAE were used. Based on the results of the first part, Yazd province has changed from mild wet to mild drought in terms of meteorological drought (the amount of rainfall according to SPI), and this condition can worsen due to climate change. The models used in ML showed that SPI-6 (score ave = 0.977), SPI-3 (score ave = 0.936), SPI-24 (score ave = 0.571), and SPI-12 (score ave = 0.413) indices had the highest accuracy, respectively. The models of Neural_Net (score ave = 0.964-RMSE ave = 0.020-MAE ave = 0.077) and Gradient_Boosting (score ave = 0.551-RMSE ave = 0.124-MAE ave = 0.248) had the highest and lowest accuracy in prediction of the SPI in all four-time scales. Based on the results of the second part, about the SWI, Random_Forest model (score = 0.929-RMSE = 0.052-MAE = 0.150) and model of Neural_Net (score = 0.755-RMSE = 0.235-MAE = 0.456) had the highest and lowest accuracy, respectively. Also, hydrological drought (reduction of the groundwater level) of the region has been much more severe, and according to the low correlation coefficient of average SPI and SWI (R2 = 0.14), we found that the uncontrolled pumping wells, as a main factor than a shortage of rainfall, have aggravated the hydrological drought, and this region is at risk of becoming a more arid region in the future.

The Algerian Saharan rangelands are an arid ecosystem characterized by limited soil, water, and vegetation resources, which make it very susceptible to degradation. This research focuses on the diachronic assessment and multi-temporal mapping of the degradation of steppe vegetation in the south of Biskra during the period 1987-2019, using remote sensing data (MSAVI index), for extracting spatiotemporal data to monitor the rangeland vegetation dynamics. We examined demographic evolution, number of livestock, and land use from quantitative data. The results show that during this period, the landscape of the region changed considerably. The area of rangelands decreased from 19,939 ha (1987) to 3605 ha (2019), where 58% of the pre-existing vegetation was transformed into bare soil. This study confirmed that the rangeland vegetation health is closely related to climate, and its degradation is mainly due to the recurrence, duration, severity, and magnitude of drought events. Manmade activities were also a determinant factor of long-term degradation of the rangeland, such as the expansion of new land development areas that increased from 3754 ha (1987) to 24,410 ha (2019). This trend was found throughout the region, including predominantly pastoral regions such as Oumache and El Haouch, leading to overgrazing with a loss of about 2% of vegetation cover. All these factors have led to a severe and continuous degradation of pastoral resources in a vulnerable environment. The preservation of these limited resources requires appropriate management of the ecosystem and a rational exploitation of its vegetation, soil, and water resources.

This is a study of concept on the initial application for wood adhesives totally biosourced from the covalent reaction between soy protein isolate (SPI) and a commercial flavonoid tannin, namely quebracho tannin. The adhesive is composed exclusively of the two vegetable biomaterials mentioned and thus is totally biosourced and non-toxic, as tannin has been classified as being not at all toxic by the European Commission REACH program. The pre-reaction between the two yielded the best plywood bonding results when limited to a temperature of 40 °C, final cross-linking being achieved during the plywood higher temperature hot pressing procedure, as for any other thermosetting adhesive. Pre-reaction at higher temperatures, namely 60 °C and 80 °C, achieved extensive premature cross-linking that lost any activity to cross-link further when hot pressed for preparing plywood. The reaction was followed by thermomechanical analysis, by matrix assisted laser desorption ionization time of flight (MALDI ToF) mass spectrometry, and by plywood shear strength tested dry, after a 24 h cold water soak and 1 h in boiling water. The adhesive of this approach lends itself to be further reinforced by the multitude of approaches on soy resins already developed by several other research groups.

The purpose of this study is to determine the feasibility of using a single scan ramped hybrid-encoding (RHE) method for rapid bicomponent T2* analysis of the human knee joint. The proposed method utilizes RHE to acquire ultrashort echo time (UTE) and subsequent gradient echo images at 16 different echo times ranging between 40 μs and 30 ms in a single scan. In the proposed RHE technique, UTE imaging was followed by acquisition of 14 gradient recalled echo images, where an additional UTE image was obtained within the first readout by oversampling single point imaging (SPI) encoding. The single scan RHE method with a 9-minute scan time was performed on human cadaveric knee joints from six donors and in vivo knee joints from four healthy volunteers at 3 T. A bicomponent signal model was used to characterize the short T2* and long T2* water components. Mean bicomponent T2* parameters for patellar tendon, anterior cruciate ligament (ACL), posterior cruciate ligament (PCL) and meniscus were calculated. In the experimental results, the RHE technique provided bicomponent T2* parameter estimations of tendon, ACL, PCL and meniscus, which were similar to previously reported values in the literature. In conclusion, the proposed single scan RHE technique provides rapid bicomponent T2* analysis of the human knee joint with a total scan time of less than 9 minutes.

In this study, we explore the feasibility of a new imaging scheme for quantitative susceptibility mapping (QSM): continuous single-point imaging (CSPI), which uses a pure phase encoding strategy to achieve true phase imaging and improve QSM accuracy.
The proposed CSPI is a modification of conventional SPI to allow acquisition of multiple echoes in a single scan. Immediately following a phase encoding gradient, the free induction decay is continuously sampled with extremely high temporal resolution to obtain k-space data at a fixed spatial frequency (i.e., at a fixed k-space coordinate). By having near-0 readout duration, CSPI results in a true snapshot of the transverse magnetization at each TE. Additionally, parallel imaging with autocalibration is utilized to reduce scan time, and an optional temporal averaging strategy is presented to improve signal-to-noise ratio for objects with low proton density or short T2* decay. The reconstructed CSPI images were input to a QSM framework based on morphology enabled dipole inversion.
In an experiment performed using iron phantoms, susceptibility estimated using CSPI showed high linearity (R2 = 0.9948) with iron concentration. Additionally, reconstructed CSPI phase images showed much reduced ringing artifact compared with phase images obtained using a frequency encoding strategy. In an ex vivo experiment performed using human tibia samples, estimated susceptibilities ranged from -1.6 to -2.1 ppm, in agreement with values reported in the literature (ranging from -1.2 to -2.2 ppm).
We have demonstrated the feasibility of using CSPI to obtain true phase images for QSM.

Robotic thyroid surgery using the da Vinci surgical system has certain cosmetic advantages; however, the invasiveness of robotic thyroid surgery is still a concern to many surgeons. Previous research has not directly compared the surgical stress of robotic thyroidectomy with that of conventional open surgery. The aim of the present study was to evaluate surgical stress using postsurgical measurements of several clinical markers.
A pilot study was performed to evaluate surgical stress following robotic and open thyroid surgery. A total of 29 papillary thyroid cancer patients from July to November 2014 were enrolled. Fourteen patients underwent conventional open surgery, and fifteen underwent robotic thyroidectomy. IL-6 levels, serum WBC counts, CRP levels, surgical plethysmographic index (SPI), and visual analogue scale (VAS) score were measured to compare surgical stress between the robotic and the open surgery groups.
No significant differences were seen between the two groups in IL-6 level, WBC count or CRP level (p = 0.380, 0.374, 0.360, respectively). Mean SPI level during the surgery was 41.9 ± 4.7 in open group compared to 39.5 ± 2.2 in robotic group, though this finding showed borderline significance (p = 0.095). VAS score after open surgery was significantly higher than after robotic operation (p = 0.048).
The results of this study suggest that robotic thyroidectomy can result in a less than equivocal systemic stress response than is seen in open thyroidectomy. However, further investigation including large-scale, prospective, multicenter studies is warranted for non-inferiority trials.

Soy protein isolate (SPI) nanoparticles (28-179 nm) were prepared by employing a cold gelation method with a slight modification. The obtained nanoparticles exhibited uniform size distribution and spherical shape with a unique honeycomb-like core structure. Nanoparticle characteristics including size, surface charge and hydrophobicity could be adjustable by changing calcium concentration and environmental pH. Generally, higher calcium concentration and lower pH led to formation of nanoparticles with larger size, lower surface charge and hydrophobicity. Both protein conformation and nanoparticle dissociation studies indicated that calcium likely shielded negative charges on the SPI polypeptide chains, and functioned as a salt-bridge to permit polypeptide chains to approach one another. In this process, calcium favoured the development of β-sheet structures to form SPI aggregates stabilised by hydrogen bonding. These aggregates were then associated to build SPI nano-networks through hydrophobic interactions. In vitro study indicated that the SPI nanoparticles were non-toxic and mainly located in the cytoplasm when uptaken into Caco-2 cells.