A machine learning framework for spatio-temporal vulnerability mapping of groundwaters to nitrate in a data scarce region in Lenjanat Plain, Iran.

The temporal aspect of groundwater vulnerability to contaminants such as nitrate is often overlooked, assuming vulnerability has a static nature. This study bridges this gap by employing machine learning with Detecting Breakpoints and Estimating Segments in Trend (DBEST) algorithm to reveal the underlying relationship between nitrate, water table, vegetation cover, and precipitation time series, that are related to agricultural activities and groundwater demand in a semi-arid region. The contamination probability of Lenjanat Plain has been mapped by comparing random forest (RF), support vector machine (SVM), and K-nearest-neighbors (KNN) models, fed with 32 input variables (dem-derived factors, physiography, distance and density maps, time series data). Also, imbalanced learning and feature selection techniques were investigated as supplementary methods, adding up to four scenarios. Results showed that the RF model, integrated with forward sequential feature selection (SFS) and SMOTE-Tomek resampling method, outperformed the other models (F1-score: 0.94, MCC: 0.83). The SFS techniques outperformed other feature selection methods in enhancing the accuracy of the models with the cost of computational expenses, and the cost-sensitive function proved more efficient in tackling imbalanced data issues than the other investigated methods. The DBEST method identified significant breakpoints within each time series dataset, revealing a clear association between agricultural practices along the Zayandehrood River and substantial nitrate contamination within the Lenjanat region. Additionally, the groundwater vulnerability maps created using the candid RF model and an ensemble of the best RF, SVM, and KNN models predicted mid to high levels of vulnerability in the central parts and the downhills in the southwest.

Adaptive beam forming across temperature variation in optical phased array enabled with deep neural network.

Integrated optical phased arrays (OPA) require calibration to account for mismatches amongst the channels. Furthermore, beams emitted from an OPA tend to distort when the chip's temperature changes. We propose to utilize a deep neural network (DNN) to adaptively control the phase modulator voltages of the OPA and create a desired beam pattern in the presence of process mismatches and temperature changes. As a proof of concept, adaptive beam forming was demonstrated with an integrated 128-channel OPA realized in a commercial foundry silicon photonics (SiP) process. Beam forming within 50° field of view (FoV) is demonstrated, while accuracy of 0.025° is achieved when the beam is swept in 0.1° step at a fixed temperature. The DNN is also used to create beams with multiple peaks at desired spatial angles. The DNN is shown to properly adjust the phase modulator voltages to keep the beam nearly intact as temperature changes within 20°C range.

Transport and retention of functionalized graphene oxide nanoparticles in saturated/unsaturated porous media: Effects of flow velocity, ionic strength and initial particle concentration.

The widespread use of nanomaterials has raised the threat of nanoparticles (NPs) infection of soils and groundwater resources. This research aims to investigate three parameters including flow velocity, ionic strength (IS), and initial particle concentration effects on transport behavior and retention mechanism of functionalization form of graphene oxide with polyvinylpyrrolidone (GO-PVP). The transport of GO-PVP was investigated in a laboratory-scale study through saturated/unsaturated (Saturation Degree = 0.91) sand columns. Experiments were conducted on flow velocity from 1.20 to 2.04 cm min-1, initial particle concentration from 10 to 50 mg L-1, and IS of 5-20 mM. The retention of GO-PVP was best described using the one-site kinetic attachment model in HYDRUS-1D, which accounted for the time and depth-dependent retention. According to breakthrough curves (BTCs), the lower transport related to the rate of mass recovery of GO-PVP was obtained by decreasing flow velocity and initial particle concentration and increasing IS through the sand columns. Increasing IS could improve the GO-PVP retention (based on katt and Smax) in saturated/unsaturated media; katt increases from 2.81 × 10-3 to 3.54 × 10-3 s-1 and Smax increases from 0.37 to 0.42 mg g-1 in saturated/unsaturated conditions, respectively. Our findings showed that the increasing retention of GO-PVP through the sand column under unsaturated condition could be recommended for the reduction of nanoparticles danger of ecosystem exposure.

Optically biased and controlled signal processing in silicon photonics.

Optically biased and controlled signal processing is demonstrated in a commercial foundry silicon photonics integrated circuit process. Data and control signals are carried by different wavelengths in a WDM format. Optical signals on bias and control channels are converted to electrical voltages using series stacked photodiodes operating in photoconductive mode. Two examples of this scheme, namely, an amplitude modulator and a two-tap sequence detector capable of supporting different modulation formats, are experimentally demonstrated. The amplitude modulator requires 0.25 mW of optical control signal power to tune its optical output power by 15 dB. The two-tap sequence detector maps the consecutive symbols of a modulated signal such as OOK, PAM-3, and PAM-4, to distinct levels. A maximum control signal power of 5 mW is needed to calibrate and bias the sequence detector. This latter scheme may be extended to detect longer sequences and other modulation formats.

Can river flow prevent land subsidence in urban areas?

Land subsidence, a silent death, occurs due to various factors like significant reduction in groundwater (GW) levels. It is a widespread phenomenon with irreparable consequences on buildings, infrastructures, and, in severe cases, groundwater aquifers. This study aims to assess the impact of river flow on the acceleration and control of land subsidence in an arid and semi-arid region. To achieve this goal, we analyze the interconnection between GW and SW and investigate the role of the Zayandeh-Rud River's drying up on land subsidence in the Isfahan-Borkhar aquifer in Iran's central plateau. To facilitate this assessment, we utilize the Interferometric Synthetic Aperture Radar (InSAR) technique to estimate the vertical deformation velocity of the aquifer (average land subsidence rate). The results show that the Isfahan-Borkhar aquifer has experienced a significant annual decline of more than 25 m, with an alarming rate exceeding 0.8 m/year. Our analysis of 31 piezometric wells (P-Wells) from 2000 to 2022 reveals a downward monotonic (in 16 P-Wells) and nonmonotonic (in 12 P-Wells) trend in groundwater table changes. Moreover, the GW table in the P-Wells near the river depends entirely on river flow. Furthermore, our findings indicate that river regulation exerts a dominant role in the control of land subsidence. Consequently, when water flows in the Zayandeh-Rud River, the rate of land subsidence declines significantly, particularly in urban regions. Therefore, maintaining a constant flow of water in the river can prevent or reduce ongoing land subsidence in Isfahan.

Hydro-meteorological droughts across the Baltic Region: The role of the accumulation periods.

Based on the physical and geographical conditions, the Baltic Region is categorised as a humid climate zone. This means that, there is usually more precipitation than evaporation throughout the year, suggesting that droughts should not occur frequently in this region. Despite the humid climate in the region, the study focused on assessing the spatio-temporal patterns of droughts. The drought events were analysed across the Baltic Region, including Sweden, Finland, Lithuania, Latvia, and Estonia. This analysis included two drought indices, the Standardized Precipitation Index (SPI) and the Streamflow Drought Index (SDI), for different accumulation periods. Daily data series of precipitation and river discharge were used. The spatial and temporal analyses of selected drought indices were carried out for the Baltic Region. In addition, the decadal distribution of drought classes was analysed to disclose the temporal changes and spatial extent of drought patterns. The Pearson correlation between SPI and SDI was applied to investigate the relationship between meteorological and hydrological droughts. The analysis showed that stations with more short-duration SPI or SDI cases had fewer long-duration cases and vice versa. The number of SDI cases (SDI ≤ -1) increased in the Western Baltic States and some WGSs in Sweden and Finland from 1991 to 2020 compared to 1961-1990. The SPI showed no such tendencies except in Central Estonia and Southern Finland. The 6-month accumulation period played a crucial role in both the meteorological and hydrological drought analyses, as it revealed prolonged and widespread drought events. Furthermore, the 9- and 12-month accumulation periods showed similar trends in terms of drought duration and spatial extent. The highest number of correlation links between different months was found between SPI12-SDI9 and SPI12-SDI12. The results obtained have deepened our understanding of drought patterns and their potential impacts in the Baltic Region.

COVID-19 research in management: An updated bibliometric analysis.

The unprecedented impact of COVID-19 on the global economy as well as on the academic literature. Since early 2020, management researchers have made exceptional efforts to extend our understanding of the pandemic's effect on consumption, sourcing, the workplace, and corporate strategies. The present study uses a bibliometric design to analyze the extensive database of COVID-19 studies in management literature generated over a 2-year period. The analysis focused on the performance of research constituents, thematic analysis of the literature, categorization of the themes at a societal, organizational, and individual level, and finally, a deep analysis of future research calls in the body of literature.

Human-induced arsenic pollution modeling in surface waters - An integrated approach using machine learning algorithms and environmental factors.

In recent years, assessment of sediment contamination by heavy metals, i.e., arsenic, has attracted the interest of scientists worldwide. The present study provides a new methodology to better understand the factors influencing surface water vulnerability to arsenic pollution by two advanced machine learning algorithms including boosted regression trees (BRT) and random forest (RF). Based on the sediment quality guidelines (Effects range low) polluted and non-polluted arsenic sediment samples were defined with concentrations >8 ppm and <8 ppm, respectively. Different conditioning factors such as topographical, lithology, erosion, hydrological, and anthropogenic factors were acquired to model surface waters' vulnerability to arsenic. We trained and validated the models using 70 and 30% of both polluted and non-polluted samples, respectively, and generated surface vulnerability maps. To verify the maps to arsenic pollution, the receiver operating characteristics (ROC) curve was implemented. The results approved the acceptable performance of the RF and BRT algorithms with an area under ROC values of 85% and 75.6%, respectively. Further, the findings showed higher importance of precipitation, slope aspect, distance from residential areas, and slope length in arsenic pollution in the modeling process. Erosion, lithology, and land use maps were introduced as the least important factors. The introduced methodology can be used to define the most vulnerable areas to arsenic pollution in advance and implement proper remediation actions to reduce the damages.

A Bidirectional Neural Interface SoC With Adaptive IIR Stimulation Artifact Cancelers.

We present a 180-nm CMOS bidirectional neural interface system-on-chip that enables simultaneous recording and stimulation with on-chip stimulus artifact cancelers. The front-end cancellation scheme incorporates a least-mean-square engine that adapts the coefficients of a 2-tap infinite-impulse-response filter to replicate the stimulation artifact waveform and subtract it at the front-end. Measurements demonstrate the efficacy of the canceler in mitigating artifacts up to 700 mVpp and reducing the front-end amplifier saturation recovery time in response to a 2.5 Vpp artifact. Each recording channel houses a pair of adaptive infinite-impulse-response filters, which enable cancellation of the artifacts generated by the simultaneous operation of the 2 on-chip stimulators. The analog front-end consumes 2.5 µW of power per channel, has a maximum gain of 50 dB and a bandwidth of 9.0 kHz with 6.2 µVrms integrated input-referred noise.

Experimental demonstration of remotely powered, controlled, and monitored optical switching based on laser-delivered signals.

We experimentally demonstrate remotely powered, controlled, and monitored optical switching. The control signal of the switch is modulated on an optical wave and sent from a transmitter. At the switch location, the control signal is converted from an optical to an electrical signal to drive the switch. In addition, to provide electrical power at the switch location, optical power is sent from a distance and converted to electrical power using a series of photodiodes. We experimentally demonstrate (a) 1 Gb/s on-off keying data channel transmission and switching with a 1 MHz optically delivered control signal, and (b) 40 Gb/s quadrature phase-shift keying data channel transmission and remotely monitoring switch state and bias drift. The switching function is demonstrated without using any local electrical power supply. Moreover, the monitoring tones are transmitted to the remote switch and fed back to the transmitter to realize a switch state and detect the bias drift.

Soil moisture change analysis under watershed management practice using in situ and remote sensing data in a paired watershed.

Soil moisture, vegetation cover, and land surface temperature are vital variables in water-energy balance, eco-hydrological processes, and water resources management, which can be influenced by watershed management activities. This research focused on the spatial and temporal variability of soil moisture, vegetation cover, land surface temperature, and Temperature-Vegetation Dryness Index (TVDI) under a biological watershed management practice in the Taleghan paired watershed, namely, treated (TW) and control watersheds (CW), in Alborz province, Iran. In this research, along with the remote sensing techniques, the soil moisture and vegetation cover data were measured and statistically analyzed in the three aspects of both TW and CW during a growth period from May to October 2017. The results indicated that soil moisture, vegetation cover, and land surface temperature values in the paired watershed were significantly different at the 0.01 level during the study period. The increased vegetation cover in the TW had an inverse effect on the land surface temperature and TVDI, while directly impacted the soil moisture content. The average TVDI in the CW was 0.83, while this index was found to be 0.69 in the TW. Unlike the vegetation cover and soil moisture, the results revealed that the southern aspects had the highest TVDI and land surface temperature compared to the northern and eastern aspects of both watersheds. However, the increased vegetation cover as a biological watershed management activity in the steep terrain and mountainous areas of TW led to an increased soil moisture and a decreased land surface temperature and soil dryness. As a result, decreasing soil dryness in the TW can exert vital controls on the water resources and increasing water availability. In the arid and semiarid countries such as Iran, a proper watershed management activity can effectively increase soil moisture and water availability in the watersheds. In particular, the vegetation cover protection and biological practices can be considered as practical solutions in the rehabilitation of exhausted watersheds in arid and semiarid environments.

Assessment of hydro-geochemical properties of groundwater under the effect of desalination wastewater discharge in an arid area.

Desalination to increase irrigation water supply for agricultural production is becoming important in water-scarce regions. While desalination has positive effects on the potential irrigation water quantity and quality, the technique may also be a considered potential source of groundwater pollution. The present study investigated the effects of desalination wastewater discharge on groundwater quality in an arid area in southern Iran for the 2012-2017 period. The chemical composition of the groundwater samples was evaluated considering pH, EC, Na+, Ca2+, Mg2+, SO42+, Cl-, and HCO3-. The suitability of groundwater for drinking and irrigation purposes as well as spatial pattern of groundwater pollution was analyzed. The results showed that mean concentration of Na+, Ca2+, Mg2+, SO42-, and Cl- in all investigated wells increased from 148, 94, 46, 247, and 257 mg/L in 2012 to 282, 146, 71, 319, and 582 mg/L in 2017, respectively. Using Gibb's diagram, it was shown that the groundwater quality is slightly alkaline and primarily controlled by evaporation. Based on our findings, about 78% of the study aquifer displayed groundwater with good to excellent water quality that can be used for drinking and irrigation purposes. However, the eastern part of the aquifer was classified as unsuitable for use due to the disposal of desalination plant wastewater. The spatial distribution of WQI and other indices such as SAR, TDS, and TH showed that groundwater in the eastern part of the aquifer has deteriorated since the establishment of the desalination plants. To reverse this trend, it is important to implement regulations against wastewater discharge from desalination plants.

Carbon nanomaterials against pathogens; the antimicrobial activity of carbon nanotubes, graphene/graphene oxide, fullerenes, and their nanocomposites.

Recently, antibiotic resistance of pathogens has grown given the excessive and inappropriate usage of common antimicrobial agents. Hence, producing novel antimicrobial compounds is a necessity. Carbon nanomaterials (CNMs) such as carbon nanotubes, graphene/graphene oxide, and fullerenes, as an emerging class of novel materials, can exhibit a considerable antimicrobial activity, especially in the nanocomposite forms suitable for different fields including biomedical and food applications. These nanomaterials have attracted a great deal of interest due to their broad efficiency and novel features. The most important factor affecting the antimicrobial activity of CNMs is their size. Smaller particles with a higher surface to volume ratio can easily attach onto the microbial cells and affect their cell membrane integrity, metabolic procedures, and structural components. As these unique characteristics are found in CNMs, a wide range of possibilities have raised in terms of antimicrobial applications. This study aims to cover the antimicrobial activities of CNMs (both as individual forms and in nanocomposites) and comprehensively explain their mechanisms of action. The results of this review will present a broad perspective, summarizes the most remarkable findings, and provides an outlook regarding the antimicrobial properties of CNMs and their potential applications.

Evaluation of a new X-band weather radar for operational use in south Sweden.

The performance of a new type of X-band weather radar (WR) for Sweden during a pilot run is studied. Compared to the conventional C-band WRs, the X-band WR covers a smaller area but with a higher spatiotemporal resolution, making it suitable for urban hydrological applications. Rainfall estimations from different elevation angles of the radar (levels) are compared at one-minute and single-event timescales with the observations of several rain gauges at different ranges using hyetographs. In general, the estimations aligned well with observations and the best match appeared for ranges as long as 5-10 km. Seemingly, radar estimations suffered from overshooting of lower lying showers by higher level scans in longer ranges (19-30 km) and from the reflectivity contamination due to moving objects in short ranges (<1 km). Also, the effective range of the radar dropped sharply for the moments when a cloudburst was located over the radar. Although various sources of error could affect the X-band WR rainfall estimates, higher resolution spatiotemporal rainfall monitoring for wider areas will benefit from an integration of data from a network of X-band WRs.

Very high resolution, altitude-corrected, TMPA-based monthly satellite precipitation product over the CONUS.

The Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) product provided over 17 years of gridded precipitation datasets. However, the accuracy and spatial resolution of TMPA limits the applicability in hydrometeorological applications. We present a dataset that enhances the accuracy and spatial resolution of the TMPA monthly product (3B43). We resample the TMPA data to a 1 km grid and apply a correction function derived from the Parameter-elevation Regressions on Independent Slopes Model (PRISM) to reduce bias in the data. We confirm a linear relationship between bias and elevation above 1,500 meters where TMPA underestimates measured precipitation, providing a proof-of-concept of how simple linear scaling can be used to augment existing satellite datasets. The result of the correction is the High-Resolution Altitude-Corrected Precipitation product (HRAC-Precip) for the CONUS. Using 9,200 precipitation stations from the Global Historical Climatology Network (GHCN), we compare the accuracy of TMPA 3B43 versus the new HRAC-Precip product. The results show an improvement of the mean absolute error of 12.98% on average.

Rapid differential diagnosis of vaginal infections using gold nanoparticles coated with specific antibodies.

Vaginal infections caused by bacteria, Candida and Trichomonas vaginalis, affect millions of women annually worldwide. Symptoms and signs have limited value in differential diagnosis of three causes of vaginitis. Current laboratory methods for differential diagnosis are either expensive or time consuming. Therefore, in this work, development of a method based on gold nanoparticles has been investigated for rapid diagnosis of vaginal infections. Specific antibodies against three main causes of vaginal infections were raised in rabbits. The antibodies were then purified and conjugated to gold nanoparticles and used in an agglutination test for detection of vaginal infections. Finally, sensitivity and specificity of this test for diagnosis of vaginal infections were estimated using culture method as gold standard. Purification of antibodies from sera was confirmed by electrophoresis. Construction of nanoparticles was proved by TEM and FT-IR methods. Conjugation of antibodies to gold nanoparticles was confirmed using XPS method. Sensitivity and specificity of gold nanoparticles for diagnosis of Candida species were 100%, for Gardnerella were 100% and 93%, and for T. vaginalis was 53.3% and 100%, respectively. Gold nanoparticle-based method is a simple, rapid, accurate, and cost-effective test for differential laboratory diagnosis of vaginal infections.

Low-power thermo-optic silicon modulator for large-scale photonic integrated systems.

Silicon platform enables the monolithic realization of large-scale photonic integrated systems. Many emerging applications facilitated by silicon photonics such as optical biosensing, optical neurostimulation, optical phased arrays, holographic displays, 3D cameras, optical machine learning, and optical quantum information processing systems require the integration of a large number of optical phase modulators with modest modulation speed. Classical optical modulators are not suitable for such large-scale integration because of their inability to provide low optical loss, compact size, high efficiency, and wide optical bandwidth, all at the same time. We report a thermo-optic silicon modulator realized in a 0.0023-mm2 silicon footprint of a commercial foundry silicon photonics process. The optical modulator consumes 2.56 mW for 180° phase modulation over 100-nm optical bandwidth while achieving 1.23-dB optical loss without air-gap trench or silicon undercut post-processing. Geometrical design optimization, at the core of this demonstration, is applicable to the realization of compact thermo-optic devices for large-scale programmable photonic integrated systems, with a potential to reduce power consumption roughly by an order of magnitude without sacrificing scalability and optical modulation bandwidth.

A Chopper-Stabilized, Current Feedback, Neural Recording Amplifier.

Advanced neural prosthetics requires high density neural recording and stimulation electrodes interfacing with the tissue. For an implantable device, area, power consumption, and noise performance are the key design metrics. Due to the low-frequency nature of the recorded signals, chopping technique is inevitable to satisfy the noise requirement while maintaining a small area and low power consumption. However, chopping leads to a significant drop in input impedance, which leads to potential attenuation of neural signals recorded from high impedance miniature electrodes, and an unacceptable large input current drawn from the tissue. This work presents a chopper stabilized, current feedback amplifier (CFA) with input impedance boosted to 3.0 GΩ. The amplifier has an adjustable voltage gain of 40-60 dB, and an adjustable high-pass cut-off frequency of 0.5 - 5 Hz, with a power consumption of 2.6 µW and noise efficiency factor (NEF) of 3.2.

Iran's Land Suitability for Agriculture.

Increasing population has posed insurmountable challenges to agriculture in the provision of future food security, particularly in the Middle East and North Africa (MENA) region where biophysical conditions are not well-suited for agriculture. Iran, as a major agricultural country in the MENA region, has long been in the quest for food self-sufficiency, however, the capability of its land and water resources to realize this goal is largely unknown. Using very high-resolution spatial data sets, we evaluated the capacity of Iran's land for sustainable crop production based on the soil properties, topography, and climate conditions. We classified Iran's land suitability for cropping as (million ha): very good 0.4% (0.6), good 2.2% (3.6), medium 7.9% (12.8), poor 11.4% (18.5), very poor 6.3% (10.2), unsuitable 60.0% (97.4), and excluded areas 11.9% (19.3). In addition to overarching limitations caused by low precipitation, low soil organic carbon, steep slope, and high soil sodium content were the predominant soil and terrain factors limiting the agricultural land suitability in Iran. About 50% of the Iran's existing croplands are located in low-quality lands, representing an unsustainable practice. There is little room for cropland expansion to increase production but redistribution of cropland to more suitable areas may improve sustainability and reduce pressure on water resources, land, and ecosystem in Iran.

The effects of extended conjugation length of purely organic phosphors on their phosphorescence emission properties.

We synthesized a series of purely organic phosphors, bromobenzaldehyde derivatives, with varying conjugation length to investigate the effects of conjugation length on their phosphorescence emission properties. As the conjugation length increases phosphorescence efficiency decreases with a redshift in the emission color at 77 K. Our computational results imply that this correlation is related to the intersystem crossing rate and that the rate is determined by spin-orbit coupling strength rather than by simply the energy difference between the lowest lying singlet and triplet states. TD-DFT calculations show that the S1 → T1 transition occurs more dominantly than the S1 → T2 transition for all cases. Moreover, singlet excited states are localized on the aldehyde functional group, regardless of the conjugation length, while triplet excited states are evenly distributed over the conjugated backbone. Consequently, as the conjugation length increases, the larger spatial separation between singlet and triplet states diminishes the spin-orbit coupling efficiency, resulting in reduced phosphorescence.