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Cover crops (CCs) offer ecosystem benefits, yet their impact on subsequent crop yields varies with climate, soil, and management practices. Using the Decision Support System for Agrotechnology Transfer (DSSAT) at the University of Nebraska-Lincoln’s Eastern Nebraska Research, Education, and Extension Center (ENREEC), we identified optimal cereal rye management strategies focusing on planting, termination, and the intervals between CC termination and corn planting. Results showed minimal impact of CC management variations on corn yield, underscoring corn’s resilience to management changes. Delayed planting notably decreased CC biomass, nitrogen uptake, and biomass nitrogen content on average by 8.8%, 11%, and 9.2% for every five-day delay from 25 September. Every 5-day increase in the interval between CC termination and corn planting reduced biomass by 19.3%. Conversely, each 5-day delay in CC termination from 10 September to 10 October increased biomass by 30%, enhancing SOC accumulation. SOC changes over the 30-year simulation ranged from 5.8% to 7.7%, peaking with late May terminations. The earliest termination showed the highest nitrogen content in biomass (3.4%), with the lowest (0.69%) in mid-May. Our results demonstrate that strategic CC management supports soil health without negatively impacting corn yield in Eastern Nebraska, providing valuable insights for farmers and practitioners aiming to implement sustainable CC practices while preserving crop productivity.

Extreme precipitation has become more frequent and intense with time and space. Infrastructure design tools such as Intensity-Duration-Frequency (IDF) curves still rely on historical precipitation and stationary assumptions, risking current and future urban infrastructure. This study developed IDF curves by incorporating non-stationarity trends in precipitation annual maximum series (AMS) for Dallas–Fort Worth, the fourth-largest metropolitan region in the United States. A Pro-NEVA tool was used to develop non-stationary IDF curves, taking historical precipitation AMS for seven stations that showed a non-stationary trend with time as a covariate. Four statistical indices—the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Root Mean Square Error (RMSE), and Nash–Sutcliffe Efficiency (NSE)—were used as the model goodness of fit evaluation. The lower AIC, BIC, and RMSE values and higher NSE values for non-stationary models indicated a better performance compared to the stationary models. Compared to the traditional stationary assumption, the non-stationary IDF curves showed an increase (up to 75%) in the 24 h precipitation intensity for the 100-year return period. Using the climate change adaptive non-stationary IDF tool for the DFW metroplex and similar urban regions could enable decision makers to make climate-informed choices about infrastructure investments, emergency preparedness measures, and long-term urban development and water resource management planning.

The strain of red microalgae Galdieria sulphuraria CCMEE 5587.1 was evaluated in a controlled laboratory environment for its ability to tolerate and remove two heavy metal (HM) ions: cadmium [Cd(II)] and lead [Pb(II)] in aqueous solutions as a single metal species. Various concentrations (0 mg L−1 to 5 mg L−1) of Cd and Pb ions were added to the Cyanidium medium in which the chosen microalgae strain G. sulphuraria CCMEE 5587.1 was grown at an acidic pH of 2.5. The effectiveness of G. sulphuraria CCMEE 5587.1 in tolerating and removing these two metal ions was measured by analyzing its growth profile, growth rate, nutrient removal, and metal ion removal efficiency. The growth of G. sulphuraria CCMEE 5587.1 was inhibited during the initial days of incubation, and the growth rate decreased when the HM concentration in the media was increased. Nutrient removal in the HM-containing media is comparable to that in the control media at low metal concentrations but decreases as the metal concentration rises. G. sulphuraria CCMEE 5587.1 has the highest removal efficiency for Cd and Pb in a medium containing 2.5 mg L−1 of metal ions, which is 49.80% and 25.10%, and the corresponding sorption capacity is 1.45 mg g−1 and 0.53 mg g−1 of dry biomass, respectively. These findings suggest that G. sulphuraria CCMEE 5587.1 holds potential as a viable bioremediation solution for extracting Cd and Pb from wastewater, alongside its capacity to remove nutrients concurrently. The study underscores the dual advantage of G. sulphuraria CCMEE 5587.1, making it a promising candidate for addressing heavy metal pollution in wastewater treatment processes.

We reported that gamma-hydroxybutyrate (GHB) is released upon Herpes Simplex Virus Type-1 (HSV-1) acute infection. However, the cellular biochemical processes involved in the production of GHB in infected cells are unclear. This study aims to shed light on the biochemical pathway and the stage within the viral life cycle responsible for the release of GHB in infected cells. UV-inactivation, acyclovir (ACV), and cycloheximide (CHX) treatments were used to inhibit HSV-1 replication at various stages. Vero cells treated with UV-inactivated HSV-1 significantly decreased GHB production. However, ACV or CHX treatments did not affect GHB production. We also showed that inhibition of glycolytic enzyme enolase by sodium fluoride (NaF) significantly reduces GHB production upon infection. This finding suggests that suppression of glycolytic activity negatively affects cellular GHB production. Our data also indicated that succinic semialdehyde dehydrogenase, an enzyme involved in the shunt of the tricarboxylic acid (TCA) cycle to generate succinic acid, was decreased upon infection, suggesting that infection may trigger the accumulation of succinic semialdehyde, causing the production of GHB. Although the precise mechanism has yet to be defined, our results suggest that early events following infection modulates the release of GHB, which is generated through the metabolic pathways of glycolysis and TCA cycle.

DC-biased optical orthogonal frequency division multiplexing (DCO-OFDM) has been introduced to visible light networking framework for connected vehicles (LiNEV) systems as a modulation and multiplexing scheme. This is to overcome the light-emitting diode (LED) bandwidth limitation, as well as to reduce the inter-symbol interference caused by the multipath road fading. Due to the implementation of the inverse fast Fourier transform, DC-OFDM suffers from its large peak-to-average power ratio (PAPR), which degrades the performance in LiNEV systems, as the LEDs used in the vehicles’ headlights have a limited optical power-current linear range. To tackle this issue, discrete Fourier transform spread-optical pulse amplitude modulation (DFTS-OPAM) has been proposed as an alternative modulation scheme for LiNEV systems instead of DCO-OFDM. In this paper, we investigate the system performance of both schemes considering the light-emitting diode linear dynamic range and LED 3 dB modulation bandwidth limitations. The simulation results indicate that DCO-OFDM has a 9 dB higher PAPR value compared with DFTS-OPAM. Additionally, it is demonstrated that DCO-OFDM requires an LED with a linear range that is twice the one required by DFTS-OPAM for the same high quadrature amplitude modulation (QAM) order. Furthermore, the findings illustrate that when the signal bandwidth of both schemes significantly exceeds the LED modulation bandwidth, DCO-OFDM outperforms DFTS-OPAM, as it requires a lower signal-to-noise ratio at a high QAM order.

An operational research study was conducted in 2019 to assess the quality of data submitted by antimicrobial resistance (AMR) surveillance sites in the Bagmati Province of Nepal to the National Public Health Laboratory for Global Antimicrobial Resistance and Use Surveillance System (GLASS). Measures were implemented to enhance the quality of AMR surveillance by strengthening capacity, improving infrastructure, implementing data sharing guidelines, and supervision. The current study examined reports submitted by surveillance sites in the same province in 2022 to assess whether the data quality had improved since 2019. The availability of infrastructure at the sites was assessed. Of the nine surveillance sites in the province, seven submitted reports in 2022 versus five in 2019. Completeness in reporting improved significantly from 19% in 2019 to 100% in 2022 (p < 0.001). Timely reports were received from two sites in 2019 and only one site in 2022. Specimen–pathogen consistency in accordance with the GLASS guidelines for urine, feces, and genital swab specimens improved, with ≥90% consistency at all sites. Overall, the pathogen–antibacterial consistency improved significantly for each GLASS priority pathogen. The study highlights the importance of dedicated infrastructure and institutional arrangements for AMR surveillance. Similar assessments covering all provinces of the country can provide a more complete country-wide picture.

With growing urbanization and ongoing development activities, the consumption of heavy metals has been increasing globally. Although heavy metals are vital for the survival of living beings, they can become hazardous when they surpass the permissible limit. The effect of heavy metals varies from normal to acute depending on the individual, so it is necessary to treat the heavy metals before releasing them into the environment. Various conventional treatment technologies have been used based on physical, chemical, and biological methods. However, due to technical and economic constraints and poor sustainability towards the environment, the use of these technologies has been limited. Microalgal-based heavy metal removal has been explored for the past few decades and has been seen as an effective, environment-friendly, and inexpensive method compared to conventional treatment technology. Cyanidiales that belong to red algae have the potential for remediation of heavy metals as they can withstand and tolerate extreme stresses of heat, acid salts, and heavy metals. Cyanidiales are the only photosynthetic organisms that can survive and thrive in acidic mine drainage, where heavy metal contamination is often prevalent. This review focuses on the algal species belonging to three genera of Cyanidiales: Cyanidioschyzon, Cyanidium, and Galdieria. Papers published after 2015 were considered in order to examine these species’ efficiency in heavy metal removal. The result is summarized as maximum removal efficiency at the optimum experimental conditions and based on the parameters affecting the metal ion removal efficiency. This study finds that pH, initial metal concentration, initial algal biomass concentration, algal strains, and growth temperature are the major parameters that affect the heavy metal removal efficiency of Cyanidiales.

Semiarid pasture management strategies can affect soil hydraulic and thermal properties that determine water fluxes and storage, and heat flow in unsaturated soils. We evaluated long-term (>10 years) perennial and annual semiarid pasture system effects on saturated hydraulic conductivity (ks), soil water retention curves (SWRCs), soil water thresholds (i.e., volumetric water content (θv) at saturation, field capacity (FC), and permanent wilting point (PWP); plant available water (PAW)), thermal conductivity (λ), and diffusivity (Dt) within the 0–20 cm soil depth. Forage systems included: Old World bluestem (Bothriochloa bladhii) + legumes (predominantly alfalfa (Medicago sativa)) (OWB-legume), native grass-mix (native), alfalfa + tall wheatgrass (Thinopyrum ponticum) (alfalfa-TW), and annual grass-mix (annual) pastures on a clay loam soil; and native, teff (Eragrostis tef), OWB-grazed, and OWB-ungrazed pastures on a sandy clay loam soil. The perennial OWB-legume and native pastures had increased soil organic matter (SOM) and reduced bulk density (ρb), improving ks, soil water thresholds, λ, and Dt, compared to annual teff and alfalfa-TW (P < 0.05). Soil λ, but not Dt, increased with increasing θv. Grazed pastures decreased ks and water retention compared to other treatments (P < 0.05), yet did not affect λ and Dt (P > 0.05), likely due to higher ρb and contact between particles. Greater λ and Dt at saturation and PWP in perennial versus annual pastures may be attributed to differing SOM and ρb, and some a priori differences in soil texture. Overall, our results suggest that perennial pasture systems are more beneficial than annual systems for soil water storage and heat movement in semiarid regions.

Broad-base agricultural terraces can be difficult to delineate in flat landscapes, particularly when covered by crops, due to subtle changes in elevation over relatively wide distances. In northeastern Oklahoma, these terraces are usually less than half a meter high and 15 to 20 m wide. The objective of this research was to develop and test a technique for identifying and classifying terraces using computer vision applied to terrain derivatives calculated from digital elevation models at five sites. We tested 38 terrain-derivative grid combinations or sets that represented 19 terrain characteristics, calculated from elevation models after two Gaussian smoothing strategies to provide some degree of generalization and a removal of excess noise. The best subsets achieved a 98% classification accuracy (kappa 0.96) and consisted of derivatives representing hydrology, morphometry, and visibility categories. Inaccuracies occurred primarily at the edges of some of the study sites, where agricultural fields bordered incised drainage areas where changes in elevation were similar to those for the terraces. Further study will elucidate the relationships between terrace “borrow” and “deposition” areas in the terrace areas and their relationships to yield and salinity issues. This work seeks to automate terrace identification for digital soil mapping on terraced fields for the improved delivery of soil information for resource conservation and land use.

Cover crop biomass is helpful for weed and pest control, soil erosion control, nutrient recycling, and overall soil health and crop productivity improvement. These benefits may vary based on cover crop species and their biomass. There is growing interest in the agricultural sector of using remotely sensed imagery to estimate cover crop biomass. Four small plot study sites located at the United States Department of Agriculture Agricultural Research Service, Crop Production Systems Research Unit farm, Stoneville, MS with different cereals, legumes, and their mixture as fall-seeded cover crops were selected for this analysis. A randomized complete block design with four replications was used at all four study sites. Cover crop biomass and canopy-level hyperspectral data were collected at the end of April, just before cover crop termination. High-resolution (3 m) PlanetScope imagery (Dove satellite constellation with PS2.SD and PSB.SD sensors) was collected throughout the cover crop season from November to April in the 2021 and 2022 study cycles. Results showed that mixed cover crop increased biomass production up to 24% higher compared to single species rye. Reflectance bands (blue, green, red and near infrared) and vegetation indices derived from imagery collected during March were more strongly correlated with biomass (r = 0–0.74) compared to imagery from November (r = 0.01–0.41) and April (r = 0.03–0.57), suggesting that the timing of imagery acquisition is important for biomass estimation. The highest correlation was observed with the near-infrared band (r = 0.74) during March. The R2 for biomass prediction with the random forest model improved from 0.25 to 0.61 when cover crop species/mix information was added along with Planet imagery bands and vegetation indices as biomass predictors. More study with multiple timepoint biomass, hyperspectral, and imagery collection is needed to choose appropriate bands and estimate the biomass of mix cover crop species.

Woody plant encroachment is an ongoing global issue. In the Southern Great Plains of the United States, the rapid encroachment and coalescence of woody plants are transforming herbaceous-dominated rangelands into woodlands with a detrimental impact on water quality and quantity. In this study, we conducted modeling simulations to assess how converting juniper (Juniperus virginiana) woodland and low to moderately productive grassland into switchgrass (Panicum virgatum) biomass production system would affect streamflow and sediment yields in the Lower Cimarron River, Oklahoma. First, the grassland areas in the basin were divided into productivity classes suitable for rangeland activities based on the soil productivity index. Next, the Soil and Water Assessment Tool was used to develop the basin hydrologic model, calibrated and validated for streamflow in five gaging stations with a percent bias of 0.76, and R2 of >0.77. Then, the model was used to simulate evapotranspiration (ET), streamflow, groundwater recharge, and sediment loads under different land use conversion scenarios. Results showed that converting existing juniper woodlands, ~4% of the basin, to switchgrass had limited impacts on the water budget and sediment yield. A hypothetical scenario of converting low to moderately productive rangeland to switchgrass increased annual ET by 2.6%, with a decrease in streamflow by 10.8% and a reduction in sediment yield by 39.2% compared to the baseline model. Results indicated that switchgrass could be considered a potential land use alternative to address the juniper encroached grassland with minimal loss in streamflow but a substantial reduction in sediment yield in the southcentral region of the Great Plains.

This study aims to evaluate the potential of using a thermophilic acidophilic red alga, Galdieria sulphuraria for effective on-site treatment of municipal landfill leachate (LL). This study focused on evaluating the effects of LL dilution, nitrogen loading, and initial algal biomass density on the overall treatment efficiency, and evaluated the long-term performance of the system using 5-day growth cycles. This study confirmed that optimal conditions for G. sulphuraria biomass production are 20% strength LL, a lower initial biomass concentration of 0.25 g L−1, and the addition of N at twice the level of initial media. Furthermore, the results indicated G. sulphuraria’s ability to grow in elevated NH4-N concentration (>950 mg L−1) and provide nitrogen removal rates of up to 40 mg L−1 d−1. In addition, the long-term running experiment showed that the proposed algal-based system could be applied in semi-continuous mode to achieve bioremediation. Overall, the results obtained from this study can be used to develop the necessary process parameters to implement large-scale algal-based systems for landfill leachate treatment.

Evidence-based decision-making to combat antimicrobial resistance (AMR) mandates a well-built community-based surveillance system for assessing resistance patterns among commensals and pathogenic organisms. As there is no such surveillance system in Nepal, we attempted to describe the antimicrobial resistance pattern in E. coli isolated from the fecal samples of apparently healthy individuals in Dhulikhel municipality and also explored the local drivers of AMR. We used a mixed-method design with a cross-sectional quantitative component and a descriptive qualitative component, with focus group discussion and key informant interviews as the data collection method. Fecal samples were collected from 424 individuals randomly selected for the study. E. coli was isolated from 85.9% of human fecal samples, of which 14% were resistant to ≥3 class of antimicrobials (multidrug resistant). Of the 368 isolates, resistance to ampicillin (40.0%), tetracycline (20.7%) and cefotaxime (15.5%) were most prevalent. The major drivers of AMR were: lack of awareness of AMR, weak regulations on sales of antimicrobials, poor adherence to prescribed medications, and incomplete dosage due to financial constraints. These findings indicate the need for strict implementation of a national drug act to limit the over-the-counter sales of antimicrobials. Additionally, awareness campaigns with a multimedia mix are essential for educating people on AMR.

Chikungunya virus (CHIKV) is a re-emerging arbovirus in the alphavirus genus. Upon infection, it can cause severe joint pain that can last years in some patients, significantly affecting their quality of life. Currently, there are no vaccines or anti-viral therapies available against CHIKV. Its spread to the Americas from the eastern continents has substantially increased the count of the infected by millions. Thus, there is an urgent need to identify therapeutic targets for CHIKV treatment. A potential point of intervention is the sphingosine-1-phosphate (S1P) pathway. Conversion of sphingosine to S1P is catalyzed by Sphingosine kinases (SKs), which we previously showed to be crucial pro-viral host factor during CHIKV infection. In this study, we screened inhibitors of SKs and identified a novel potent inhibitor of CHIKV infection—SLL3071511. We showed that the pre-treatment of cells with SLL3071511 in vitro effectively inhibited CHIKV infection with an EC50 value of 2.91 µM under both prophylactic and therapeutic modes, significantly decreasing the viral gene expression and release of viral particles. Our studies suggest that targeting SKs is a viable approach for controlling CHIKV replication.

Combining data into a centralized, searchable, and linked platform will provide a data exploration platform to agricultural stakeholders and researchers for better agricultural decision making, thus fully utilizing existing data and preventing redundant research. Such a data repository requires readiness to share data, knowledge, and skillsets and working with Big Data infrastructures. With the adoption of new technologies and increased data collection, agricultural workforces need to update their knowledge, skills, and abilities. The partnerships for data innovation (PDI) effort integrates agricultural data by efficiently capturing them from field, lab, and greenhouse studies using a variety of sensors, tools, and apps and provides a quick visualization and summary of statistics for real-time decision making. This paper aims to evaluate and provide examples of case studies currently using PDI and use its long-term continental US database (18 locations and 24 years) to test the cover crop and grazing effects on soil organic carbon (SOC) storage. The results show that legume and rye (Secale cereale L.) cover crops increased SOC storage by 36% and 50%, respectively, compared with oat (Avena sativa L.) and rye mixtures and low and high grazing intensities improving the upper SOC by 69–72% compared with a medium grazing intensity. This was likely due to legumes providing a more favorable substrate for SOC formation and high grazing intensity systems having continuous manure deposition. Overall, PDI can be used to democratize data regionally and nationally and therefore can address large-scale research questions aimed at addressing agricultural grand challenges.

This paper focuses on edge-enabled cloud radio access network architecture to achieve ultra-reliable communication, a crucial enabler for supporting mission-critical machine-type communication networks. We propose coordinated multi-point transmission schemes taking advantage of diversity mechanisms in interference-limited downlink cellular networks. The network scenario comprises spatially distributed multiple remote radio heads (RRHs) that may cooperate through silencing, or by using more elaborated diversity strategies such as maximum ratio transmission or transmit antenna selection to serve user equipment in the ultra-reliable operation regime. We derive an exact closed-form expression for the outage probabilities and expected values of signal-to-interference ratio for silencing, transmit antenna selection and maximum ratio transmission schemes. We formulate rate control and energy efficiency under reliability constraints to test the performance and resource usage of the proposed schemes. Furthermore, we study the impact on average system sum throughput with throughput-reliability trade-off under cooperative communication. Extensive numerical analysis shows the feasibility of ultra-reliable communication by implementing diversity schemes with RRHs cooperation.

On-farm experimentation (OFE) allows farmers to improve crop management over time. The randomized complete blocks design (RCBD) with field-length strips as individual plots is commonly used, but it requires advanced planning and has limited statistical power when only three to four replications are implemented. Harvester-mounted yield monitor systems generate high resolution data (1-s intervals), allowing for development of more meaningful, easily implementable OFE designs. Here we explored statistical frameworks to quantify the effect of a single treatment strip using georeferenced yield monitor data and yield stability-based management zones. Nitrogen-rich single treatment strips per field were implemented in 2018 and 2019 on three fields each on two farms in central New York. Least squares and generalized least squares approaches were evaluated for estimating treatment effects (assuming independence) versus spatial covariance for estimating standard errors. The analysis showed that estimates of treatment effects using the generalized least squares approach are unstable due to over-emphasis on certain data points, while assuming independence leads to underestimation of standard errors. We concluded that the least squares approach should be used to estimate treatment effects, while spatial covariance should be assumed when estimating standard errors for evaluation of zone-based treatment effects using the single-strip spatial evaluation approach.

Citrus tristeza virus (CTV), the largest non-segmented plant RNA virus, has several peculiar features, among which is the production of a 5′-terminal long non-coding RNA (lncRNA) termed low-molecular-weight tristeza 1 (LMT1). In this study, we found that p33, a unique viral protein that performs multiple functions in the virus infection cycle, specifically binds LMT1, both in vivo and in vitro. These results were obtained through the expression of p33 under the context of the wild type virus infection or along with a mutant CTV variant that does not produce LMT1 as well as via ectopic co-expression of p33 with LMT1 in Nicotiana benthamiana leaves followed by RNA immunoprecipitation and rapid amplification of cDNA ends assays. Further experiments in which a recombinant p33 protein and an in vitro transcribed full-length LMT1 RNA or its truncated fragments were subjected to an electrophoretic mobility shift assay demonstrated that p33 binds to at least two distinct regions within LMT1. To the best of our knowledge, this is the first report of a plant virus protein binding to a lncRNA produced by the same virus. The biological significance of the interaction between these two viral factors is discussed.

Silvopasture systems combine tree and livestock production to minimize market risk and enhance ecological services. Our objective was to explore and develop a method for identifying driving factors linked to productivity in a silvopastoral system using machine learning. A multi-variable approach was used to detect factors that affect system-level output (i.e., plant production (tree and forage), soil factors, and animal response based on grazing preference). Variables from a three-year (2017–2019) grazing study, including forage, tree, soil, and terrain attribute parameters, were analyzed. Hierarchical variable clustering and random forest model selected 10 important variables for each of four major clusters. A stepwise multiple linear regression and regression tree approach was used to predict cattle grazing hours per animal unit (h ha−1 AU−1) using 40 variables (10 per cluster) selected from 130 total variables. Overall, the variable ranking method selected more weighted variables for systems-level analysis. The regression tree performed better than stepwise linear regression for interpreting factor-level effects on animal grazing preference. Cattle were more likely to graze forage on soils with Cd levels −1 (126% greater grazing hours per AU), soil Cr −1 (108%), and a SAGA wetness index of Dactylis glomerata L.). The result shows water flow within the landscape position (wetness index), and associated metals distribution may be used as an indicator of animal grazing preference. Overall, soil nutrient distribution patterns drove grazing response, although animal grazing preference was also influenced by aboveground (forage and tree), soil, and landscape attributes. Machine learning approaches helped explain pasture use and overall drivers of grazing preference in a multifunctional system.

Pathogens and plants are in a constant battle with one another, the result of which is either the restriction of pathogen growth via constitutive or induced plant defense responses or the pathogen colonization of plant cells and tissues that cause disease. Elicitins are a group of highly conserved proteins produced by certain oomycete species, and their sterol binding ability is recognized as an important feature in sterol–auxotrophic oomycetes. Elicitins also orchestrate other aspects of the interactions of oomycetes with their plant hosts. The function of elicitins as avirulence or virulence factors is controversial and is dependent on the host species, and despite several decades of research, the function of these proteins remains elusive. We summarize here our current understanding of elicitins as either defense-promoting or defense-suppressing agents and propose that more recent approaches such as the use of ‘omics’ and gene editing can be used to unravel the role of elicitins in host–pathogen interactions. A better understanding of the role of elicitins is required and deciphering their role in host–pathogen interactions will expand the strategies that can be adopted to improve disease resistance and reduce crop losses.

In this paper, we propose a non-localization routing protocol for underwater wireless sensor networks (UWSNs), namely, the triangle metric based multi-layered routing protocol (TM2RP). The main idea of the proposed TM2RP is to utilize supernodes along with depth information and residual energy to balance the energy consumption between sensors. Moreover, TM2RP is the first multi-layered and multi-metric pressure routing protocol that considers link quality with residual energy to improve the selection of next forwarding nodes with more reliable and energy-efficient links. The aqua-sim package based on the ns-2 simulator was used to evaluate the performance of the proposed TM2RP. The obtained results were compared to other similar methods such as depth based routing (DBR) and multi-layered routing protocol (MRP). Simulation results showed that the proposed protocol (TM2RP) obtained better outcomes in terms of energy consumption, network lifetime, packet delivery ratio, and end-to-end delay.

The authors wish to make the following erratum to this paper [...]

To provide high-precision positioning for Internet of Things (IoT) scenarios, we optimize the indoor positioning technique based on Ultra-Wideband (UWB) Time Difference of Arrival (TDOA) equipment. This paper analyzes sources of positioning error and improves the time synchronization algorithm based on the synchronization packet. Then we use the labels of the known position to further optimize the time synchronization performance, and hence improve TDOA measurements. After time synchronization optimization, a Weighted Least Square (WLS) and Taylor coordination algorithm is derived. Experiments show that our optimization reduces the average positioning error from 54.8 cm to 12.6 cm.

The development of hybrid satellite-terrestrial relay networks (HSTRNs) is one of the driving forces for revolutionizing satellite communications in the modern era. Although there are many unique features of conventional satellite networks, their evolution pace is much slower than the terrestrial wireless networks. As a result, it is becoming more important to use HSTRNs for the seamless integration of terrestrial cellular and satellite communications. With this intent, this paper provides a comprehensive performance evaluation of HSTRNs employing non-orthogonal multiple access technique. The terrestrial relay is considered to be wireless-powered and harvests energy from the radio signal of the satellite. For the sake of comparison, both amplify-and-forward (AF) and decode-and-forward (DF) relaying protocols are considered. Subsequently, the closed-form expressions of outage probabilities and ergodic capacities are derived for each relaying protocol. Extensive simulations are performed to verify the accuracy of the obtained closed-form expressions. The results provided in this work characterize the outage and capacity performance of such a HSTRN.

Aerial ad-hoc networks have the potential to enable smart services while maintaining communication between the ground system and unmanned aerial vehicles (UAV). Previous research has focused on enabling aerial data-centric smart services while integrating the benefits of aerial objects such as UAVs in hostile and non-hostile environments. Quality of service (QoS) provisioning in UAV-assisted communication is a challenging research theme in aerial ad-hoc networks environments. Literature on aerial ad hoc networks lacks cooperative service-oriented modeling for distributed network environments, relying on costly static base station-oriented centralized network environments. Towards this end, this paper proposes a quality of service provisioning framework for a UAV-assisted aerial ad hoc network environment (QSPU) focusing on reliable aerial communication. The UAV’s aerial mobility and service parameters are modelled considering highly dynamic aerial ad-hoc environments. UAV-centric mobility models are utilized to develop a complete aerial routing framework. A comparative performance evaluation demonstrates the benefits of the proposed aerial communication framework. It is evident that QSPU outperforms the state-of-the-art techniques in terms of a number of service-oriented performance metrics in a UAV-assisted aerial ad-hoc network environment.

Cancer is associated with higher morbidity and mortality and is the second leading cause of death in the US. Further, in some nations, cancer has overtaken heart disease as the leading cause of mortality. Identification of molecular mechanisms by which cancerous cells evade T cell-mediated cytotoxic damage has led to the modern era of immunotherapy in cancer treatment. Agents that release these immune brakes have shown activity to recover dysfunctional T cells and regress various cancer. Both cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) and Programmed Death-1 (PD-1) play their role as physiologic brakes on unrestrained cytotoxic T effector function. CTLA-4 (CD 152) is a B7/CD28 family; it mediates immunosuppression by indirectly diminishing signaling through the co-stimulatory receptor CD28. Ipilimumab is the first and only FDA-approved CTLA-4 inhibitor; PD-1 is an inhibitory transmembrane protein expressed on T cells, B cells, Natural Killer cells (NKs), and Myeloid-Derived Suppressor Cells (MDSCs). Programmed Death-Ligand 1 (PD-L1) is expressed on the surface of multiple tissue types, including many tumor cells and hematopoietic cells. PD-L2 is more restricted to hematopoietic cells. Blockade of the PD-1 /PDL-1 pathway can enhance anti-tumor T cell reactivity and promotes immune control over the cancerous cells. Since the FDA approval of ipilimumab (human IgG1 k anti-CTLA-4 monoclonal antibody) in 2011, six more immune checkpoint inhibitors (ICIs) have been approved for cancer therapy. PD-1 inhibitors nivolumab, pembrolizumab, cemiplimab and PD-L1 inhibitors atezolizumab, avelumab, and durvalumab are in the current list of the approved agents in addition to ipilimumab. In this review paper, we discuss the role of each immune checkpoint inhibitor (ICI), the landmark trials which led to their FDA approval, and the strength of the evidence per National Comprehensive Cancer Network (NCCN), which is broadly utilized by medical oncologists and hematologists in their daily practice.

Perfect channel state information (CSI) is required in most of the classical physical-layer security techniques, while it is difficult to obtain the ideal CSI due to the time-varying wireless fading channel. Although imperfect CSI has a great impact on the security of MIMO communications, deep learning is becoming a promising solution to handle the negative effect of imperfect CSI. In this work, we propose two types of deep learning-based secure MIMO detectors for heterogeneous networks, where the macro base station (BS) chooses the null-space eigenvectors to prevent information leakage to the femto BS. Thus, the bit error rate of the associated user is adopted as the metric to evaluate the system performance. With the help of deep convolutional neural networks (CNNs), the macro BS obtains the refined version from the imperfect CSI. Simulation results are provided to validate the proposed algorithms. The impacts of system parameters, such as the correlation factor of imperfect CSI, the normalized doppler frequency, the number of antennas is investigated in different setup scenarios. The results show that considerable performance gains can be obtained from the deep learning-based detectors compared with the classical maximum likelihood algorithm.

Underwater sensor networks (UWSNs) have witnessed significant R&D attention in both academia and industry due to their growing application domains, such as border security, freight via sea or river, natural petroleum production and the fishing industry. Considering the deep underwater-oriented access constraints, energy-centric communication for the lifetime maximization of tiny sensor nodes in UWSNs is one of the key research themes in this domain. Existing literature on green UWSNs are majorly adapted from the existing techniques in traditional wireless sensor network relying on geolocation and the quality of service-centric underwater relay node selection, without paying much attention to the dynamic underwater network environments. To this end, this paper presents an adapted whale and wolf optimization-based energy and delay-centric green underwater networking framework (W-GUN). It focuses on exploiting dynamic underwater network characteristics by effectively utilizing underwater whale-centric optimization in relay node selection. Firstly, an underwater relay node optimization model is mathematically derived, focusing on underwater whale dynamics for incorporating realistic underwater characteristics in networking. Secondly, the optimization model is used to develop an adapted whale and grey wolf optimization algorithm for selecting optimal and stable relay nodes for centric underwater communication paths. Thirdly, a complete workflow of the W-GUN framework is presented with an optimization flowchart. The comparative performance evaluation attests to the benefits of the proposed framework and is compared to state-of-the-art techniques considering various metrics related to underwater network environments.

The meteorological droughts in the climate transition zone of the Great Plains of the USA are projected to intensify, potentially leading to major shifts in water provisioning services in rangelands. To understand how meteorological drought interacts with vegetation to regulate runoff response, we collected precipitation, root zone soil moisture, and runoff data from experimental grassland and juniper (Juniperus virginiana L., redcedar) woodland watersheds for five years encompassing a drought year to pluvial year cycle. We contrasted the frequency distribution of precipitation intensities and applied wavelet analysis to reveal the coherence between precipitation and root zone soil moisture patterns. Compared with grassland, the root zone soil moisture in woodland had a narrower range, with the peak frequency skewed to a lower soil moisture content. The conversion of herbaceous vegetation to evergreen juniper woodland results in a delayed response of runoff to precipitation due to reduced antecedent soil moisture. The reduction of streamflow from the woodland watershed was greater in the normal and pluvial years than in the drought year. Thus, conversion from grassland to evergreen woody vegetation prolongs the impact of meteorological drought on soil moisture and streamflow. Restoring prairie that is heavily encroached by woody species may serve as an adaptive measure to mitigate the climate change impact on water resources and other ecosystem services provided by rangeland.

Climate change impacts on agricultural watersheds are highly variable and uncertain across regions. This study estimated the potential impacts of the projected precipitation and temperature based on the downscaled Coupled Model Intercomparison Project 5 (CMIP-5) on hydrology and crop yield of a rural watershed in Oklahoma, USA. The Soil and Water Assessment Tool was used to model the watershed with 43 sub-basins and 15,217 combinations of land use, land cover, soil, and slope. The model was driven by the observed climate in the watershed and was first calibrated and validated against the monthly observed streamflow. Three statistical matrices, coefficient of determination (R2), Nash-Sutcliffe efficiency (NSE), and percentage bias (PB), were used to gauge the model performance with satisfactory values of R2 = 0.64, NS = 0.61, and PB = +5% in the calibration period, and R2 = 0.79, NSE = 0.62, and PB = −15% in the validation period for streamflow. The model parameterization for the yields of cotton (PB = −4.5%), grain sorghum (PB = −27.3%), and winter wheat (PB = −6.0%) resulted in an acceptable model performance. The CMIP-5 ensemble of three General Circulation Models under three Representative Concentration Pathways for the 2016–2040 period indicated an increase in both precipitation (+1.5%) and temperature (+1.8 °C) in the study area. This changed climate resulted in decreased evapotranspiration (−3.7%), increased water yield (23.9%), decreased wheat yield (−5.2%), decreased grain sorghum yield (−9.9%), and increased cotton yield (+54.2%) compared to the historical climate. The projected increase in water yield might provide opportunities for groundwater recharge and additional water to meet future water demand in the region. The projected decrease in winter wheat yield—the major crop in the state—due to climate change, may require attention for ways to mitigate these effects.

Woody plant encroachment has profound impacts on the sustainable management of water resources in water-limited ecosystems. However, our understanding of the effects of this global phenomenon on groundwater recharge at local and regional scales is limited. Here, we reviewed studies related to (i) recharge estimation methods; (ii) mechanisms by which woody plants impact groundwater recharge; (iii) impacts of woody plant on recharge across different soil and geology; (iv) hydrological repercussions of woody plant removal; and (v) research gaps and needs for groundwater studies. We identified six different methods: water balance, water table, isotopes, chloride mass balance, electrical geophysical imaging, and modeling were used to study the impact of woody encroachment on groundwater. Woody plant encroachment could alter soil infiltration rates, soil water storage, transpiration, interception, and subsurface pathways to affect groundwater recharge. The impact is highly variable, with the extent and the magnitude varying across the soil, substrate, plant cover, and topographic locations. Our review revealed mixed effects of woody plant removal on groundwater recharge. Studies of litter interception, root water uptake, soil moisture dynamics, and deep percolation along with the progression of woody plant encroachment are still limited, warranting further experimental studies focusing on groundwater recharge. Overall, information about woody plant encroachment impacts on groundwater resources across a range of scales is essential for long-range planning of water resources.

Recent technological advancement in wireless communication has led to the invention of wireless body area networks (WBANs), a cutting-edge technology in healthcare applications. WBANs interconnect with intelligent and miniaturized biomedical sensor nodes placed on human body to an unattended monitoring of physiological parameters of the patient. These sensors are equipped with limited resources in terms of computation, storage, and battery power. The data communication in WBANs is a resource hungry process, especially in terms of energy. One of the most significant challenges in this network is to design energy efficient next-hop node selection framework. Therefore, this paper presents a green communication framework focusing on an energy aware link efficient routing approach for WBANs (ELR-W). Firstly, a link efficiency-oriented network model is presented considering beaconing information and network initialization process. Secondly, a path cost calculation model is derived focusing on energy aware link efficiency. A complete operational framework ELR-W is developed considering energy aware next-hop link selection by utilizing the network and path cost model. The comparative performance evaluation attests the energy-oriented benefit of the proposed framework as compared to the state-of-the-art techniques. It reveals a significant enhancement in body area networking in terms of various energy-oriented metrics under medical environments.

Mid-range resonant coupling-based high efficient wireless power transfer (WPT) techniques have gained substantial research interest due to the number of potential applications in many industries. This paper presents a novel design of a resonant two-loop WPT technique including the design, fabrication and preliminary results of this proposal. This new design employs a compensation inductor which is combined with the transmitter and receiver loops in order to significantly scale down the size of the transmitter and receiver coils. This can improve the portability of the WPT transmitters in practical systems. Moreover, the benefits of the system enhancement are not only limited to the lessened magnitude of the T X & R X , simultaneously both the weight and the bill of materials are also minimised. The proposed system also demonstrates compatibility with the conventional electronic components such as capacitors hence the development of the T X & R X is simplified. The proposed system performance has been validated using the similarities between the experimental and simulation results. The power efficiency of the prototype circuit is found to be 93%, which is close to the efficiency reached by the conventional design. However, the weight of the transmitter and receiver inductors is now reduced by 78%, while the length of these inductors is reduced by 80%.

This paper proposes a hybrid circuit between a conformal strongly-coupled magnetic resonance (CSCMR) and a strongly-coupled magnetic resonance (SCMR), for better wireless power transmission (WPT). This combination promises to enhance the flexibility of the proposed four-loop WPT system. The maximum efficiency at various distances is achieved by combining coupling-matching between the source and transmitting coils along with the coupling factor between the transmitting and receiving coils. Furthermore, the distance between transmitting and receiving coils is investigated along with the distance relationship between the source loop and transmission coil, in order to achieve the maximum efficiency of the proposed hybrid WPT system. The results indicate that the proposed approach can be effectively employed at distances comparatively smaller than the maximum distance without frequency matching. The achievable efficiency can be as high as 84% for the whole working range of the transmitter. In addition, the proposed hybrid system allows more spatial freedom compared to existing chargers.