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Abstract The Russian invasion of Ukraine on February 24, 2022, has had devastating effects on the Ukrainian population and the global economy, environment, and political order. However, little is known about the psychological states surrounding the outbreak of war, particularly the mental well-being of individuals outside Ukraine. Here, we present a longitudinal experience-sampling study of a convenience sample from 17 European countries (total participants = 1,341, total assessments = 44,894, countries with >100 participants = 5) that allows us to track well-being levels across countries during the weeks surrounding the outbreak of war. Our data show a significant decline in well-being on the day of the Russian invasion. Recovery over the following weeks was associated with an individual’s personality but was not statistically significantly associated with their age, gender, subjective social status, and political orientation. In general, well-being was lower on days when the war was more salient on social media. Our results demonstrate the need to consider the psychological implications of the Russo-Ukrainian war next to its humanitarian, economic, and ecological consequences.

As the size of wind turbines (WTs) increase, an additional increase in the structural load on the WT components is to be expected. This will have an impact on operational safety in terms of damage and service life. Spatial and temporal fluctuations in wind speed are responsible for the fatigue load during power generation. To minimize the effects of varying stresses, advanced control systems that incorporate appropriate models of the disturbances are proposed. These controllers are usually developed based on reduced-order models of nonlinear WTs, hence are affected by uncertainties such as modeling errors. Although robust controllers are able to deal with uncertainties, they are still only developed for design situations. Therefore, their performance can deteriorate significantly under very uncertain operating conditions. On the other hand, adaptive controllers are designed to consider multiple operating points in the design. However, most of these methods do not consider the optimization of different objectives in the design for structural load reduction or speed control of WTs. In this paper, a novel adaptive robust observer-based control strategy for structural load reduction and rotor speed regulation in commercial WTs operating at high wind speed regime is proposed. To achieve this, a robust disturbance accommodating controller (RDAC) is combined with an adaptive pitch controller (aIPC), which adapts to changing operating points. The proposed control method is tested on a 1.5 MW reference WT (RWT) developed by the National Renewable Energy Laboratory (NREL). The simulation results show that, compared with the state of the art presented on a gain-scheduled proportional integral (GSPI) and RDAC controllers, the proposed control method reduces the structural load on the rotor blades by 10.7 % and 9.2 %, respectively, and on the tower by 36.2 % and 8.4 %, respectively. Therefore, it makes a key contribution to mitigating the structural dynamic loads on WTs by reducing the load on multiple components. This is achieved without any significant impact on the rotor speed and power regulation performance or the generated power under changing operating conditions.

Xylazine is in the unregulated drug supply at increasing rates, usually combined with fentanyl, necessitating understanding of its pharmacology. Despite commentary from politicians, and public health officials, it is unknown how xylazine impacts naloxone efficacy, and. few studies have examined it alone. Here, we examine the impact of xylazine alone and in combination with fentanyl on several behaviors in mice. Surprisingly, naloxone precipitates withdrawal from xylazine and fentanyl/xylazine coadministration, with enhanced sensitivity in females. Further, xylazine is a full agonist at kappa opioid receptors, a potential mechanism for its naloxone sensitivity. Finally, we demonstrate surprising effects of xylazine to kappa opioid antagonism, which are relevant for public health considerations. These data address an ongoing health crisis and will help inform critical policy and healthcare decisions. One-sentence summary: We present surprising new insights into xylazine and fentanyl pharmacology with immediate implications for clinical practice and frontline public health.

Introduction: Liberia has a significant obstacle in terms of restricted power accessibility, as only 26.7% of its populace have access to electrical energy. To tackle this issue, there is a collaborative effort between the government and private sector to undertake energy projects aimed at enhancing the power supply of the grid. The purpose of this study is to evaluate the techno-economic feasibility and analyze the performance of a standalone biomass/solar photovoltaic (PV) hybrid energy system for a rural Liberian community that mostly relies on traditional biomass.Methods: HOMER pro program was used to configure the system, model the community’s load over a year, and generate the resource data of the location.Results and Discussion: Four different configurations were obtained and analyzed both technically and economically to determine the most feasible configuration. This study has shown that a hybrid configuration incorporating Biomass Gasifier, Solar PV, and Battery storage is more feasible with annual output power of 77104 kWh/yr, LCOE ($/kwh) of 0.29 and NPC ($) of 0.3979 million. If implemented with external funding, it will enhance the education, economic and socio-economic status of rural settlements. The results will serve as a valuable resource for informing choices on the implementation of the hybrid energy solution and guaranteeing its sustained efficacy within the community.

Abstract Optimization of water pumping systems has been studied using various techniques which include classical, mathematical, and heuristics. Few studies have explored use of optimal controllers in agricultural water pumping applications. Some studies also ignore the interconnection between the water demand and energy used. Introduction of renewable energy sources such as photovoltaic (PV) necessitates different geographical studies as the intensity of the renewable energy varies widely with location. In this paper, an optimal controller for a batteryless grid-connected photovoltaic system to power water supply system for irrigation purposes was developed. The aim was to minimize the operational cost of grid energy by maximizing utilization of photovoltaic power and minimizing the utilization of grid power. A case study was done at a farm in Kajiado (−1.6033257 $$^{\circ }$$ ∘ latitude and 36.7863352 $$^{\circ }$$ ∘ longitude). The farm photovoltaic, grid power, water pumps (underground and booster pump), and storage tanks were modelled into a binary linear programming optimization problem and solved using intlinprog solver on MATLAB. Energy demand data was collected using a three-phase power logger, while water demand data was collected using onsite water meter and stopwatch timer. Photovoltaic power produced was estimated using Photovoltaic Geographical Information System (PVGIS). Simulation results obtained show that the use of an optimal controller results in reduced cost of energy by 44.4%. Better utilization of renewable photovoltaic energy by 24% was observed, and 3.6% more water was pumped.

Conventional type 1 dendritic cells (cDC1s) are superior in antigen cross-presentation and priming CD8+ T cell anti-tumor immunity and thus, are a target of high interest for cancer immunotherapy. Type I interferon (IFN) is a potent inducer of antigen cross-presentation, but, unfortunately, shows only modest results in the clinic given the short half-life and high toxicity of current type I IFN therapies, which limit IFN exposure in the tumor. CD8+ T cell immunity is dependent on IFN signaling in cDC1s and preclinical studies suggest targeting IFN directly to cDC1s may be sufficient to drive anti-tumor immunity. Here, we engineered an anti-XCR1 antibody (Ab) and IFN mutein (IFNmut) fusion protein (XCR1Ab-IFNmut) to determine whether systemic delivery could drive selective and sustained type I IFN signaling in cDC1s leading to anti-tumor activity and, in parallel, reduced systemic toxicity. We found that the XCR1Ab-IFNmut fusion specifically enhanced cDC1 activation in the tumor and spleen compared to an untargeted control IFN. However, multiple treatments with the XCR1Ab-IFNmut fusion resulted in robust anti-drug antibodies (ADA) and loss of drug exposure. Using other cDC1-targeting Ab-IFNmut fusions, we found that localizing IFN directly to cDC1s activates their ability to promote ADA responses, regardless of the cDC1 targeting antigen. The development of ADA remains a major hurdle in immunotherapy drug development and the cellular and molecular mechanisms governing the development of ADA responses in humans is not well understood. Our results reveal a role of cDC1s in ADA generation and highlight the potential ADA challenges with targeting immunostimulatory agents to this cellular compartment.

Abstract Background In December 2019, the first case of COVID-19 was described in Wuhan, China, and by July 2022, there were already 540 million confirmed cases. Due to the rapid spread of the virus, the scientific community has made efforts to develop techniques for the viral classification of SARS-CoV-2. Results In this context, we developed a new proposal for gene sequence representation with Genomic Signal Processing techniques for the work presented in this paper. First, we applied the mapping approach to samples of six viral species of the Coronaviridae family, which belongs SARS-CoV-2 Virus. We then used the sequence downsized obtained by the method proposed in a deep learning architecture for viral classification, achieving an accuracy of 98.35%, 99.08%, and 99.69% for the 64, 128, and 256 sizes of the viral signatures, respectively, and obtaining 99.95% precision for the vectors with size 256. Conclusions The classification results obtained, in comparison to the results produced using other state-of-the-art representation techniques, demonstrate that the proposed mapping can provide a satisfactory performance result with low computational memory and processing time costs.

Abstract The blood–brain barrier (BBB) plays a pivotal role in brain health and disease. In the BBB, brain microvascular endothelial cells (BMECs) are connected by tight junctions which regulate paracellular transport, and express specialized transporter systems which regulate transcellular transport. However, existing in vitro models of the BBB display variable accuracy across a wide range of characteristics including gene/protein expression and barrier function. Here, we use an isogenic family of fluorescently-labeled iPSC-derived BMEC-like cells (iBMECs) and brain pericyte-like cells (iPCs) within two-dimensional confluent monolayers (2D) and three-dimensional (3D) tissue-engineered microvessels to explore how 3D microenvironment regulates gene expression and function of the in vitro BBB. We show that 3D microenvironment (shear stress, cell-ECM interactions, and cylindrical geometry) increases BBB phenotype and endothelial identity, and alters angiogenic and cytokine responses in synergy with pericyte co-culture. Tissue-engineered microvessels incorporating junction-labeled iBMECs enable study of the real-time dynamics of tight junctions during homeostasis and in response to physical and chemical perturbations.

Diagnosing faults occurring in industrial equipment and monitoring its status is crucial for maintaining the continuous operation of the equipment. The conventional diagnostic approaches, such as model-based methods, need to model the dynamics of the physical process; however, obtaining an accurate dynamic model of complex interconnected industrial systems poses a significant challenge. Modern diagnostic methods based on data-driven using artificial intelligence models rely heavily on sensor data to perform fault detection and diagnosis. Conventional machine learning methods have low detection accuracy and rely on domain knowledge to extract meaningful features from data acquired from the equipment. On the other hand, deep learning models can automatically extract useful features from fault data with high diagnostic performance. This paper proposes a two-dimensional Convolutional Neural Network (2DCNN) diagnostic model to effectively improve the diagnostic accuracy of predicting faults on a tabular dataset with multiple fault classes. A simple sliding window approach is proposed to effectively transform and reshape the features in the dataset as inputs to the proposed model architecture. The method's feasibility was evaluated using data from an industrial robotic fuse quality test bench. Experimental results show high diagnostic performance with an average accuracy of 99.98% compared to conventional methods commonly used for diagnostics. Moreover, validation of the diagnostic model on the experimental dataset using raw multi-sensor fusion from a robot manipulator platform was carried out. The results demonstrate the model's outstanding diagnostic performance, with an average testing accuracy of 99.74% for four fault classes.

Background: Effective viral haemorrhagic fevers (VHFs) control demand adequate emergency preparedness among healthcare workers (HCWs). Objective: This study assessed the emergency preparedness for VHF control among HCWs at Nnamdi Azikiwe University Teaching Hospital (NAUTH), Nnewi, Anambra state, Nigeria. Materials and Methods: It was a cross-sectional study conducted among 345 HCW at the NAUTH, Nnewi, Nigeria. Data was analysed using statistical package for social sciences (SPSS, IBM, Chicago IL, USA) software version 21. Chi square and student’s t-test were used to test associations between categorical and continuous variables, respectively. A p-value of

Campos et al. study the genetic aetiology of antidepressant side effects. Using data from the Australian Genetics of Depression study, the authors show that polygenic risk scores for traits such as BMI, insomnia and headaches have a shared genetic basis with side effects to commonly used antidepressant drugs.

Veteran suicide is one of the most complex and pressing health issues in the United States. According to the 2020 National Veteran Suicide Prevention Annual Report, since 2018 an average of 17.2 Veterans died by suicide each day. Veteran suicide risk screening is currently limited to suicide hotlines, patient reporting, patient visits, and family or friend reporting. As a result of these limitations, innovative approaches in suicide screening are increasingly garnering attention. An essential feature of these innovative methods includes better incorporation of risk factors that might indicate higher risk for tracking suicidal ideation based on personal behavior. Digital technologies create a means through which measuring these risk factors more reliably, with higher fidelity, and more frequently throughout daily life is possible, with the capacity to identify potentially telling behavior patterns. In this review, digital predictive biomarkers are discussed as they pertain to suicide risk, such as sleep vital signs, sleep disturbance, sleep quality, and speech pattern recognition. Various digital predictive biomarkers are reviewed and evaluated as well as their potential utility in predicting and diagnosing Veteran suicidal ideation in real time. In the future, these digital biomarkers could be combined to generate further suicide screening for diagnosis and severity assessments, allowing healthcare providers and healthcare teams to intervene more optimally.

ERRATUM TO: USE OF A MAIT ACTIVATING LIGAND, 5-OP-RU, AS A MUCOSAL ADJUVANT IN A MURINE MODEL OF VIBRIO CHOLERAE O1 VACCINATION DOI: 10.20411/pai.v7i1.525 Reason: The authors sincerely regret the inadvertent omission of Jackson G. Cacioppo as coauthor of this work. He has no additional conflicts of interest. Corrected version: Owen Jensen1,2, Shubhanshi Trivedi1, Jackson G. Cacioppo3, Kelin Li3, Jeffrey Aubé3, J. Scott Hale2, Edward T. Ryan4,5,6, Daniel T. Leung1, 2*

Abstract The blood–brain barrier (BBB) plays a key role in regulating transport into and out of the brain. With increasing interest in the role of the BBB in health and disease, there have been significant advances in the development of in vitro models. The value of these models to the research community is critically dependent on recapitulating characteristics of the BBB in humans or animal models. However, benchmarking in vitro models is surprisingly difficult since much of our knowledge of the structure and function of the BBB comes from in vitro studies. Here we describe a set of parameters that we consider a starting point for benchmarking and validation. These parameters are associated with structure (ultrastructure, wall shear stress, geometry), microenvironment (basement membrane and extracellular matrix), barrier function (transendothelial electrical resistance, permeability, efflux transport), cell function (expression of BBB markers, turnover), and co-culture with other cell types (astrocytes and pericytes). In suggesting benchmarks, we rely primarily on imaging or direct measurements in humans and animal models.

Abstract In this article, the use of refined composite multiscale fuzzy entropy (RCMFE) for cross‐domain diagnosis of bearings is introduced and verified with two publicly available datasets of varying operating conditions, a factor that challenges the diagnostic ability of trained models. For classification, the self organizing fuzzy (SOF) classifier is used. The diagnostic framework which primarily only involves extracting RCMFE feature and training the SOF classifier, is able to detect and isolate faults with over 97% accuracy when the classes are comprised of a single fault type and size. Compared to related works, the proposed approach does not require deep learning for feature extraction nor any domain adaptation technique as the RCMFE feature is robust against changing operating conditions. Furthermore, the method does not need target domain data during training. With regard to fault isolation, when the classes in the training data contain all the available fault sizes instead of a single size, the classifier can distinguish inner race faults from outer race and ball fault with an average accuracy of 96%. However, the accuracy for differentiating ball and outer race faults falls slightly to an average of 86%. Thus even for the latter arrangement which poses a tougher transfer learning problem, the proposed approach still performs very well.

Drug discovery (DD) is a time-consuming and expensive process. Thus, the industry employs strategies such as drug repositioning and drug repurposing, which allows the application of already approved drugs to treat a different disease, as occurred in the first months of 2020, during the COVID-19 pandemic. The prediction of drug–target interactions is an essential part of the DD process because it can accelerate it and reduce the required costs. DTI prediction performed in silico have used approaches based on molecular docking simulations, including similarity-based and network- and graph-based ones. This paper presents MPS2IT-DTI, a DTI prediction model obtained from research conducted in the following steps: the definition of a new method for encoding molecule and protein sequences onto images; the definition of a deep-learning approach based on a convolutional neural network in order to create a new method for DTI prediction. Training results conducted with the Davis and KIBA datasets show that MPS2IT-DTI is viable compared to other state-of-the-art (SOTA) approaches in terms of performance and complexity of the neural network model. With the Davis dataset, we obtained 0.876 for the concordance index and 0.276 for the MSE; with the KIBA dataset, we obtained 0.836 and 0.226 for the concordance index and the MSE, respectively. Moreover, the MPS2IT-DTI model represents molecule and protein sequences as images, instead of treating them as an NLP task, and as such, does not employ an embedding layer, which is present in other models.

While birds and other vertebrates are well known hazards to aviation at airports, the threat posed by invertebrates is less well understood. Here we present an example of a serious risk to flight safety from the mud-nesting keyhole wasp (Pachodynerus nasidens) which views aircraft pitot probes as an attractive nesting opportunity at Brisbane Airport. Pitot probes measure airspeed, and obstructions can render measurements inaccurate, leading to serious and potentially catastrophic consequences. We undertook experiments over 39 months to determine rates of nesting in pitot probes and the associated risk of blocked probes. We also examined how this risk was reduced by covering probes. A bow-tie risk analysis was completed to assess the safety, reputation, one-off financial loss, and injury and illness costs of a range of incidents of increasing severity, and climate modelling was used to show the potential spread of the keyhole wasp in Australia. The reduction in risk from covering 33% to 75% of all probes on arrival is substantial and made more significant when the costs of incidents are set against those of wasp management. The prospects for eradicating the keyhole wasp and the prospect of it spreading to other parts of Australia with suitable climatic conditions are discussed in view of the substantial risk the species presents.

The keyhole wasp (Pachodynerus nasidens Latreille 1812), a mud-nesting wasp native to South and Central America and the Caribbean, is a relatively recent (2010) arrival in Australia. In its native range it is known to use man-made cavities to construct nests. A series of serious safety incidents Brisbane Airport related to the obstruction of vital airspeed measuring pitot probes on aircraft possibly caused by mud-nesting wasps, prompted an assessment of risk. An experiment was designed to determine the species responsible, the types of aircraft most affected, the seasonal pattern of potential risk and the spatial distribution of risk on the airport. A series of replica pitot probes were constructed using 3D-printing technology, representing aircraft with high numbers of movements (landings and take-offs), and mounted at four locations at the airport. Probes were monitored for 39 months. Probes blocked by mud nesting wasps were retrieved and incubated in mesh bags. Emerging wasps were identified to species. Results show that all nests in probes were made by P. nasidens, and peak nesting occurs in the summer months. Nesting success (as proportion of nests with live adult emergents) was optimal between 24 and 31°C and that probes with apertures of more than 3 mm diameter are preferred. Not all areas on the airport are affected equally, with the majority of nests constructed in one area. The proportion of grassed areas within 1000 m of probes was a significant predictor of nesting, and probe volume may determine the sex of emerging wasps.

Abstract Introduction Hearing loss has been identified as the potentially largest modifiable risk factor for Alzheimer's disease (AD), estimated to account for a similar increase in AD risk as the apolipoprotein E (APOE) gene. Methods We investigated the genetic relationship between hearing loss and AD, and sought evidence for a causal relationship. Results We found a significant genetic overlap between hearing impairment and AD and a polygenic risk score for AD was able to significantly predict hearing loss in an independent cohort. Additionally, regions of the genome involved in inflammation were identified to be shared between hearing difficulty and AD. However, causality tests found no significant evidence of a causal relationship between these traits in either direction. Discussion Overall, these results show that the relationship between hearing difficulty and AD may, in part, be due to shared genes and immune response pathways between the traits. However, currently available data do not support a causal relationship.

Abstract Background The endothelial cells that form the lumen of capillaries and microvessels are an important component of the blood–brain barrier. Cell phenotype is regulated by transducing a range of biomechanical and biochemical signals in the local microenvironment. Here we report on the role of shear stress in modulating the morphology, motility, proliferation, apoptosis, and protein and gene expression, of confluent monolayers of human brain microvascular endothelial cells derived from induced pluripotent stem cells. Methods To assess the response of derived human brain microvascular endothelial cells (dhBMECs) to shear stress, confluent monolayers were formed in a microfluidic device. Monolayers were subjected to a shear stress of 4 or 12 dyne cm−2 for 40 h. Static conditions were used as the control. Live cell imaging was used to assess cell morphology, cell speed, persistence, and the rates of proliferation and apoptosis as a function of time. In addition, immunofluorescence imaging and protein and gene expression analysis of key markers of the blood–brain barrier were performed. Results Human brain microvascular endothelial cells exhibit a unique phenotype in response to shear stress compared to static conditions: (1) they do not elongate and align, (2) the rates of proliferation and apoptosis decrease significantly, (3) the mean displacement of individual cells within the monolayer over time is significantly decreased, (4) there is no cytoskeletal reorganization or formation of stress fibers within the cell, and (5) there is no change in expression levels of key blood–brain barrier markers. Conclusions The characteristic response of dhBMECs to shear stress is significantly different from human and animal-derived endothelial cells from other tissues, suggesting that this unique phenotype that may be important in maintenance of the blood–brain barrier. The implications of this work are that: (1) in confluent monolayers of dhBMECs, tight junctions are formed under static conditions, (2) the formation of tight junctions decreases cell motility and prevents any morphological transitions, (3) flow serves to increase the contact area between cells, resulting in very low cell displacement in the monolayer, (4) since tight junctions are already formed under static conditions, increasing the contact area between cells does not cause upregulation in protein and gene expression of BBB markers, and (5) the increase in contact area induced by flow makes barrier function more robust.

In recent years, the rapidly-increasing demand for energy generation from renewable resources has been noticeable. Additional requirements are consequently set on Wind Turbine (WT) systems, primarily reflected in WT size and power rating increases. With the size increase of WT, structural loads/fatigue stress on the wind turbine become larger, simultaneously leading to its accelerated aging and the shortening of its lifetime. The primary goal of this contribution is to establish an approach for structural load reduction while retaining or slightly sacrificing the power production requirements. The approach/control strategy includes knowledge about current fatigue damage and/or damage increments and consists of multi-input multi-output controllers with variable control parameters. By the appropriate selection of the designed Multi-Input Multi-Output (MIMO) controllers, the mitigation of structural loads in accordance with a predefined range of accumulated fatigue damage or damage increments, exactly to the extent required to provide a predefined service lifetime, is obtained. The validation of the aforementioned control strategy is based on the simulation results and the WT model developed by National Renewable Energy Laboratory (NREL). The obtained results prove the efficiency of the proposed control strategy with respect to the reduction of rotor blade bending moments, simultaneously exhibiting no significant impact on the resulting power generation.

Macrophage stimulating protein (MSP) is a serum growth factor that binds to and activates the receptor tyrosine kinase, Recepteur d'Origine Nantais (RON). A non-synonymous coding variant in MSP (689C) has been associated with genetic susceptibility to both Crohn's disease and ulcerative colitis, two major types of inflammatory bowel disease (IBD) characterized by chronic inflammation of the digestive tract. We investigated the consequences of this polymorphism for MSP-RON pathway activity and IBD pathogenesis.RON expression patterns were examined on mouse and human cells and tissues under normal and disease conditions to identify cell types regulated by MSP-RON. Recombinant MSP variants were tested for their ability to bind and stimulate RON and undergo proteolytic activation. MSP concentrations were quantified in the serum of individuals carrying the MSP 689R and 689C alleles.In intestinal tissue, RON was primarily expressed by epithelial cells under normal and disease conditions. The 689C polymorphism had no impact on the ability of MSP to bind to or signal through RON. In a cohort of normal individuals and IBD patients, carriers of the 689C polymorphism had lower concentrations of MSP in their serum.By reducing the quantities of circulating MSP, the 689C polymorphism, or a variant in linkage disequilibrium with this polymorphism, may impact RON ligand availability and thus receptor activity. Given the known functions of RON in regulating wound healing and our analysis of RON expression patterns in human intestinal tissue, these data suggest that decreased RON activity may impact the efficiency of epithelial repair and thus underlie the increased IBD susceptibility associated with the MSP 689C allele.

In sub-Saharan Africa, including Uganda, there is declining soil fertility and limited on-farm use of inorganic fertilizers due to poverty and limited subsidies for inorganic fertilizer use. Thus, integration of soil fertility improving tree species (SFITs) in farming systems remains a plausible option to sustaining soil productivity. However, knowledge of the effects of many of the locally growing farmer perceived soil fertility enhancing tree species on to soil chemical and nutrient contents are thus still lacking, and this has constrained decisions on their adoption and scaling up. The objectives of this paper were to identify farmers' preferred soil fertility improving tree species in agropastoral communities of Kyeizooba subcounty Bushenyi district, and characterize their litter content and assess their effect on selected soil chemical properties. Semistructured questionnaires were administered to 333 randomly selected agropastoral farmers. Litter and soils under canopy soils were sampled from three different environments: Under canopy radius (A), canopy edge (B), open pasture land up to thrice the canopy radius (C). Results revealed Eucalyptus as the most common tree species on livestock farms, followed by Erythrina abyssinica. The highest litter content was recorded for Markhamia lutea (240 g/cm2 under its canopy) followed by Croton macrostachyus (90 g/cm2), and 19 g/cm2 Erythrina abyssinica. Nitrogen was higher (P=.02) in Erythrina abyssinica litter, K and carbon in Croton macrostachyus litter (P=.03). These results give evidence that of soil improvers Erythrina abyssinica, Croton macrostachyus, and Markhamia lutea may positively affect soil fertility. Farmers' indigenous knowledge and or valuation of important tree species can be relied on, and thus, their indigenous knowledge need to be incorporated during identification of tree species for promotion in farming systems.

Collaborative problem solving (CPS) is an important 21st-century skill for academic and career success, and as a result, there is increased interest among businesses and educational institutions in the assessment and development of CPS skills. CPS skills are difficult to measure using traditional forms of assessment, and that difficulty has led to the use of computer environments that allow individuals to interact in complex situations and capture all actions throughout the process. In the current paper, we describe the design of a collaborative online mathematics task and explore student perceptions of the task and the extent to which the task elicits CPS skills. Results revealed areas of difficulty for students and showed that the task mostly elicited skills associated with the social dimension of CPS. We describe modifications to the task to provide better opportunities for students to display a broader range of CPS skills. An upcoming larger scale pilot study will explore whether the redesigned task can elicit the less observed CPS skills.