Tree Approach Research Articles

Occupational stress among farm and ranch operators in the midwestern United States

BackgroundThis study used surveillance data from 2018 and 2020 to test the stability of work-related strain symptoms (high stress, sleep deprivation, exhaustion) with demographic factors, work characteristics, and musculoskeletal symptoms among farm and ranch operators in seven midwestern states of the United States.MethodsCross-sectional surveys were conducted among farm and ranch operators in 2018 (n = 4423) and 2020 (n = 3492). Operators were asked whether, in the past 12 months, they experienced extended work periods that resulted in high stress levels, sleep deprivation, exhaustion/fatigue, or other work-related strain symptoms. Covariates included personal and demographic factors, work characteristics, number of injuries, work-related health conditions, and exposures on the operation. Summary statistics were tabulated for explanatory and outcome variables. The classification (decision) tree approach was used to assess what variables would best separate operators with and without reported strain symptoms, based on a set of explanatory variables. Regularized regression was used to generate effect estimates between the work strain variables and explanatory variables.ResultsHigh stress level, sleep deprivation, and exhaustion were reported more frequently in 2018 than 2020. The classification tree reproduced the 2018 model using 2020 data with approximately 80% accuracy. The mean number of reported MSD symptoms increased slightly from 1.23 in 2018 to 1.41 in 2020. Older age, more time spent in farm work, higher gross farm income (GFI), and MSD symptoms in six body regions (ankles/feet, knees, lower back, neck, shoulders, wrists/hands) were associated with all three work strain symptoms.ConclusionsMusculoskeletal pain and discomfort was a strong predictor for stress, sleep deprivation, and exhaustion among farmers and ranchers. This finding indicates that reducing MSD pain and discomfort is beneficial for both physical and mental health.

Modeling salinized wasteland using remote sensing with the integration of decision tree and multiple validation approaches in Hetao irrigation district of China

The salinized wasteland (SW) is a buffer zone to accumulate salt introduced from irrigation water in salinized irrigation districts. It is crucial to identify the locations and sizes of SW to achieve effective land management and reclamation, as well as agricultural sustainable development. However, the SW is less explored due to the lack of low-cost and effective techniques. In this study, a decision tree-based remote sensing model was proposed to distinguished SW from other land types using the high-resolution Gaofen-1 (GF-1) images in the Hetao Irrigation District of China during 2017–2020. Meanwhile, multiple validation approaches including points (field and random points) and area (small and medium-sized SWs, i.e., SWA, SWB) matching validations were adopted. To establish the model of SW, four spectral indices including Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Normalized Difference Water Index (NDWI), and Salinity Index (SI), as well as a series of multi-period and multi-year remote sensing images were used. The threshold values of the four spectral indices were determined based on the probability density curve. The SW remote sensing model was highly accurate and reliable. The average producer accuracy (PA) and average user accuracy (UA) were respectively 88.0% and 81.6% for field investigation (field points matching validation), and 85.9% and 80.1% for Google Earth image investigation (random points matching validation). Besides, the average overlapping rate and misclassification rate were respectively 95.6% and 2.3% for small-sized typical SWs (area validation), and 92.9% and 4.7% for medium-sized typical SWs during 2017–2020. The model indicated that the SW area in the region was respectively 9969, 9694, 9660, and 9608 ha from 2017 to 2020. The area of small and medium-sized SWs decreased with time, while that of large-sized SWs was the opposite.

Environmental impact analysis and enhancement of factors affecting the photovoltaic (PV) energy utilization in mining industry by sparrow search optimization based gradient boosting decision tree approach

This paper proposes a hybrid technique to recover the efficiency of solar photovoltaic (PV) energy system from the environmental impacts. The proposed technique is the combination of both sparrow search algorithm and gradient boosting decision tree; thus it is named SSA-GBDT method. The purpose of the proposed technique is to improve the efficiency of solar PV energy system and maximization of power removal from PV arrays. The PV module voltage, current and power is measured by SSA and it creates possible database offline. Database with electric parameters is utilized to develop the model online using GBDT. The data set contains some parameters like particle size and dust weight input and maximal power value output variables. Then, the proposed technique is implemented on the MATLAB/Simulink platform and the performance is compared with existing techniques. The performance of PV under normal condition, dust accumulation condition, water drops condition and partial shading conditions are the considered cases. In the cases, photovoltaic reference irradiance and temperature, PV current, voltage and generated power, active and reactive power, grid current and voltage, inverter power is also evaluated. The efficiency comparison of PV power for solution processes like ANN, GBDT, SSA and proposed system are also analyzed.

Development of a Predictive Model for Cytokine Release Syndrome to Inform Risk Stratification and CRS Management Following Immunotherapy

Development of a Predictive Model for Cytokine Release Syndrome to Inform Risk Stratification and CRS Management Following Immunotherapy

Unraveling nonlinear and interaction effects of various determinants on bus gaseous emissions

Urban transit buses equipped with large-displacement engines operate on circular routes several times throughout the day, emitting large amounts of environmentally hazardous exhaust. Hence, understanding the intricate associations between bus emissions and multiple contributors is beneficial for creating sustainable transportation systems, while previous studies focusing on statistical methods fail to unravel them. This paper innovatively leverages the bagged decision tree approach to delineate such complex relationships based on the data collected from CNG-fueled and diesel-powered buses. Relative importance indicates that velocity appears to be the primary factor and is therefore selected as the research objective. Results suggest that the effects of different contributors on bus emissions present nonlinear patterns. More specifically, the influence of speed on CO, CO2, and NOx exhaust generally reveals an increasing-stabilizing tendency while that of HC represents a decreasing-stabilizing mode. Besides, the phenomenon of synergies between determinants is also prevalent, for instance, buses within high-speed and large-slope conditions tend to produce more emissions. These findings can provide nuanced guidance for policy-making and bus route planning issues in consideration of environmental protection and pollution mitigation.

The Generalized STAR Modelling with Minimum Spanning Tree Approach of Weight Matrix for COVID-19 Case in Java Island

The ongoing global Coronavirus 2019 (COVID-19) pandemic poses a major threat. The spread of the COVID-19 virus is likely to occur from one location to another location due to the mobility of people. Many efforts and policies have been made by each country to reduce the spread of the COVID-19 outbreak. The imposition of lockdown and large-scale social restrictions or social distancing has been widely applied to limit the transmission of this virus among the community and provincial levels. Both policies have proven effective in reducing the spread of the COVID-19 virus. To obtain the overview of this case, many researchers were conducted. Here, the Generalized STAR (GSTAR) model was applied to model the increasing number of COVID-19 positive cases per day in six provinces in Java Island. The data was recorded simultaneously in six locations, namely in the Provinces of Banten, Jakarta, West Java, Central Java, Yogyakarta Special Region, and East Java. This paper proposes a new approach in constructing the weight matrix required to build the GSTAR model, namely Minimum Spanning Tree (MST). The weight matrix represents the relationship among observed locations. By using the MST, a topological (undirected graph) network model could be created to show the correlation, centrality, and relationship on the increase of COVID-19 positive cases among the provinces in Java Island. The GSTAR(1;1) with the inverse distance weight matrix using MST presents a good ability to predict the COVID-19 increasing cases of Java island. This is indicated by the final MAPE average score of 19.55.

Growing trees: The acquisition of the left periphery

We suggest here a Growing Trees approach for the description of the acquisition of various syntactic structures in Hebrew, based on the main results reported in Friedmann and Reznick (this volume) and on our own research on a corpus of natural productions. The heart of our account is that stages of acquisition follow the geometry of the syntactic tree, along the lines of the cartographic analysis of the clause, with early stages of acquisition corresponding to small portions of the adult syntactic tree, which keeps growing with the growth of the child. The lower parts of the tree are acquired first, and higher parts are acquired later. We propose three stages of acquisition connected to the development of functional layers of the syntactic tree. In the first stage, the IP is acquired, including the lexical and inflectional layers. This allows for the appearance of A-movement structures, including SV/VS alternations with unaccusative verbs, alongside SV sentences with unergative/transitive verbs. The second stage involves the acquisition of the lower part of the left periphery, up to QP, which allows for the acquisition of subject and object Wh questions, some adjunct questions, yes/no questions, and sentence-initial adverbs. In the third stage, the rich structure of the left periphery is completely acquired, including the higher CP field. This is the stage in which sentential embedding (of finite declarative and interrogative clauses), subject and object relative clauses, why questions, and topicalization appear. A further, different type of stage, which occurs on the already-grown tree and which is independent of structure building, is the acquisition of intervention configurations, allowing for the mastery of structures involving movement of a lexically-restricted object across an intervening lexically-restricted subject. The paper illustrates the fruitful dialogue between the science of syntax acquisition and the cartography of syntactic structures.

Machine Learning Techniques with Item Response Data

This is a two-pronged study. Two aims of this study are both interested in comparison of 1) different R IRT (Item Response Theory) packages and 2) different machine learning techniques. The first part of the present study addresses the performance of a couple of psychometric software available in R. Then, using item parameters from real data sets from the Program of International Student Assessment 2015, three machine learning techniques (classification tree, random forest, naïve Bayes) are assessed in the second part. Between these two parts, connections of IRT and ML are briefly described and reviewed with reference. Findings are 1) two comparisons of two sets of packages for each IRT model are not informative (from the part I) and 2) the classifiers of random forest and naïve Bayes algorithms performed better than the classification tree, which confirms that more information leads to more accurate prediction and ensemble approaches are better than single tree approaches. (from the part II). Finally, implications and caveats of this study are provided.

Paired analysis of tree ring width and carbon isotopes indicates when controls on tropical tree growth change from light to water limitations.

Light and water availability are likely to vary over the lifespan of closed-canopy forest trees, with understory trees experiencing greater limitations to growth by light and canopy trees greater limitation due to drought. As drought and shade have opposing effects on isotope discrimination (Δ13C), paired measurement of ring width and Δ13C can potentially be used to differentiate between water and light limitations on tree growth. We tested this approach for Cedrela trees from three tropical forests in Bolivia and Mexico that differ in rainfall and canopy structure. Using lifetime ring width and Δ13C data for trees of up to and over 200 years old, we assessed how controls on tree growth changed from understory to the canopy. Growth and Δ13C are mostly anti-correlated in the understory, but this anti-correlation disappeared or weakened when trees reached the canopy, especially at the wettest site. This indicates that understory growth variation is controlled by photosynthetic carbon assimilation due to variation in light levels. Once trees reached the canopy, inter-annual variation in growth and Δ13C at one of the dry sites showed positive correlations, indicating that inter-annual variation in growth is driven by variation in water stress affecting stomatal conductance. Paired analysis of ring widths and carbon isotopes provides significant insight in what environmental factors control growth over a tree’s life; strong light limitations for understory trees in closed-canopy moist forests switched to drought stress for (sub)canopy trees in dry forests. We show that combined isotope and ring width measurements can significantly improve our insights in tree functioning and be used to disentangle limitations due to shade from those due to drought.

A Modeling Framework for Decision Support in Periprosthetic Joint Infection Treatment.

In this paper, we present a framework, which aims at facilitating the choice of the best strategy related to the treatment of periprosthetic joint infection (PJI). The framework includes two models: a detailed non-Markovian model based on the decision tree approach, and a general Markov model, which captures the most essential states of a patient under treatment. The application of the framework is demonstrated on the dataset provided by Russian Scientific Research Institute of Traumatology and Orthopedics "R.R. Vreden", which contains records of patients with PJI occurred after total hip arthroplasty. The methods of cost-effectiveness analysis of treatment strategies and forecasting of individual treatment outcomes depending on the selected strategy are discussed.

Price transmission in biofuel-related global agricultural networks

This article investigates the connections among the prices of biofuels, agricultural commodities and other relevant assets in Europe, the US, and Brazil. The analysis includes a comprehensive data set covering price data for 38 traded titles during the period from 2003 to 2020. We used the minimum spanning tree (MST) approach to identify price connections in a complex trading system. Our analysis of mutual price connections reveals the major defining features of world-leading biofuel markets. We provide the characteristics of the main bioethanol and biodiesel markets with respect to government policies and technical and local features of the production and consumption of particular biofuels. Despite a relatively long and dynamically evolving history of biofuels, the biofuel systems in the US, Brazil and Europe do not converge toward the same pattern of relations among fossil fuels, biofuels, agricultural commodities and financial assets.

A genetic programming learning approach to generate dispatching rules for flexible shop scheduling problems

This paper deals with a Genetic Programming (GP) approach for solving flexible shop scheduling problems. The adopted approach aims to generate priority rules in the form of an expression tree for dispatching jobs. Therefore, in a list-scheduling algorithm, the available jobs can be ranked using the tree-based priority rules generated using GP.These priority rules were tested on benchmark problem settings, such as those of Brandimarte and Lawrence, in a static and dynamic environment. The GP approaches were then applied to a special case based on the problem setting of an industrial partner. The goal of these approaches was to minimize the maximum completion time of all jobs, which is known as the makespan.To reach this goal, we considered job assignment and machine sequencing decisions simultaneously in a single-tree representation and compared this single tree with a multi-tree approach, where the terminal sets (job- and machine-related) were strictly separated. This resulted in two parallel GP populations; they were used to first decide which job to choose and then which machine it should be assigned to. Furthermore, for both tree approaches, we implemented an iterative variant that stores recorded information of past schedules to achieve further improvements. Computational experiments revealed a consistent advantage compared to the existing advanced priority rules from the literature with considerably increased performance under the presence of unrelated parallel machines and larger instances in general.

Comparison of Decision Tree and Long Short-Term Memory Approaches for Automated Foot Strike Detection in Lower Extremity Amputee Populations.

Foot strike detection is important when evaluating a person’s gait characteristics. Accelerometer and gyroscope signals from smartphones have been used to train artificial intelligence (AI) models for automated foot strike detection in able-bodied and elderly populations. However, there is limited research on foot strike detection in lower limb amputees, who have a more variable and asymmetric gait. A novel method for automated foot strike detection in lower limb amputees was developed using raw accelerometer and gyroscope signals collected from a smartphone positioned at the posterior pelvis. Raw signals were used to train a decision tree model and long short-term memory (LSTM) model for automated foot strike detection. These models were developed using retrospective data (n = 72) collected with the TOHRC Walk Test app during a 6-min walk test (6MWT). An Android smartphone was placed on a posterior belt for each participant during the 6MWT to collect accelerometer and gyroscope signals at 50 Hz. The best model for foot strike identification was the LSTM with 100 hidden nodes in the LSTM layer, 50 hidden nodes in the dense layer, and a batch size of 64 (99.0% accuracy, 86.4% sensitivity, 99.4% specificity, and 83.7% precision). This research created a novel method for automated foot strike identification in lower extremity amputee populations that is equivalent to manual labelling and accessible for clinical use. Automated foot strike detection is required for stride analysis and to enable other AI applications, such as fall detection.

The U.S.-China trade conflict impacts on the Chinese and U.S. stock markets: A network-based approach

We explore how the U.S.-China trade conflict impacted the Chinese and U.S. stock markets at industry level. Employing a transfer entropy-based technique, the directional non-linear information transmission networks respectively composed of 56 Chinese and 49 U.S. industries are constructed. Then, we identify systemically important industries and transmission paths by implementing the maximum spanning tree approach. Chinese industries exhibit more exposure to the trade tensions when compared to the U.S. counterparts. Results highlight industries of public utilities, industrial equipment manufacturing, and financials playing a dominant role in China’s stock market, while financials, mining and quarrying, retail trade, and manufacturing have central role in the U.S.

EEG Alpha and Beta Band Functional Connectivity and Network Structure Mark Hub Overload in Mild Cognitive Impairment During Memory Maintenance.

Background: While decreased alpha and beta-band functional connectivity (FC) and changes in network topology have been reported in Alzheimer’s disease, it is not yet entirely known whether these differences can mark cognitive decline in the early stages of the disease. Our study aimed to analyze electroencephalography (EEG) FC and network differences in the alpha and beta frequency band during visuospatial memory maintenance between Mild Cognitive Impairment (MCI) patients and healthy elderly with subjective memory complaints.Methods: Functional connectivity and network structure of 17 MCI patients and 20 control participants were studied with 128-channel EEG during a visuospatial memory task with varying memory load. FC between EEG channels was measured by amplitude envelope correlation with leakage correction (AEC-c), while network analysis was performed by applying the Minimum Spanning Tree (MST) approach, which reconstructs the critical backbone of the original network.Results: Memory load (increasing number of to-be-learned items) enhanced the mean AEC-c in the control group in both frequency bands. In contrast to that, after an initial increase, the MCI group showed significantly (p < 0.05) diminished FC in the alpha band in the highest memory load condition, while in the beta band this modulation was absent. Moreover, mean alpha and beta AEC-c correlated significantly with the size of medial temporal lobe structures in the entire sample. The network analysis revealed increased maximum degree, betweenness centrality, and degree divergence, and decreased diameter and eccentricity in the MCI group compared to the control group in both frequency bands independently of the memory load. This suggests a rerouted network in the MCI group with a more centralized topology and a more unequal traffic load distribution.Conclusion: Alpha- and beta-band FC measured by AEC-c correlates with cognitive load-related modulation, with subtle medial temporal lobe atrophy, and with the disruption of hippocampal fiber integrity in the earliest stages of cognitive decline. The more integrated network topology of the MCI group is in line with the “hub overload and failure” framework and might be part of a compensatory mechanism or a consequence of neural disinhibition.

Machine learning-based approach to the risk assessment of potentially preventable outpatient cancer treatment-related emergency care and hospitalizations.

333 Background: Patients undergoing outpatient infusion chemotherapy for cancer are at risk for potentially preventable, unplanned acute care in the form of emergency department (ED) visits and hospital admissions. This can impact outcomes, patient decisions, and costs to the patient and healthcare system. To address this need, the Centers for Medicare &amp; Medicaid Services developed the Chemotherapy Measure (OP-35). Recent randomized controlled data indicate that electronic health record (EHR)-based machine learning (ML) approaches accurately direct supportive care to reduce acute care during radiotherapy. As this may extend to systemic therapy, this study aims to develop and evaluate ML approaches to predict the risk of OP-35 qualifying, potentially preventable acute care within 30 days of infusional systemic therapy. Methods: This study included data from UCSF cancer patients receiving infusional chemotherapy from July 1, 2017, to February 11, 2021, (total 7,068 patients over 84,174 treatments). The data incorporated into the ML included 430 EHR-derived variables, including cancer diagnosis, therapeutic agents, laboratory values, vital signs, medications, and encounter history. Three ML approaches were trained to predict an OP-35 acute care risk following a systemic therapy infusion with least absolute shrinkage selection operator (LASSO), random forest, and gradient boosted trees (GBT; XGBoost) approaches. The models were trained on a subset (75% of patients; before October 12, 2019) of the dataset and validated on a mutually exclusive subset (25% patients; after October 12, 2019) based on the receiver operating characteristic (ROC) curves and calibration plots. Results: There were 1,651 total acute care visits (244 ED visits and 1,407 ED visits converted into hospitalization); 1,310 infusions included a qualifying acute care visit (200 with ED visits only, 0 direct hospital admissions, and 1,110 with both ED visit and hospitalization). Each ML approach demonstrated good performance in the internal validation cohort, with GBT (AUC 0.805) outpacing the random forest (0.750) and LASSO logistic regression (0.755) approaches. Visualization of calibration plots verified concordance between predicted and observed rates of acute care. All three models shared patient age and days elapsed since last treatment as important contributors. Conclusions: EHR-based ML approaches demonstrate high predictive ability for OP-35 qualifying acute care rates on a per-infusion basis, identifying 30-day potentially preventable acute care risk for patients undergoing chemotherapy. Prospective validation of these models is ongoing. Early prediction can facilitate interventional strategies which may reduce acute care, improve health outcomes, and reduce costs.

Efficient Block Propagation in Wireless Blockchain Networks and Its Application in Bitcoin

As the supporting architecture of cryptocurrencies, blockchain is also showing its promising potential to be applied to mobile devices. Being WiFi supported, mobile devices nowadays are able to perform ad-hoc local communications in multi-hop manners, thus can potentially extend cryptocurrency service into areas without stable Internet connection. However, traditional blockchain architecture suffers from the intolerably high block propagation latency and communication cost which will be even trickier if adopted in wireless multi-hop networks (WMNs). This paper aims to reduce this latency and cost by leveraging node weights to distinguish the feedback speed of different nodes and constructing a low-length multicast tree to select a subset of nodes with higher feedback speed to participate in the block verification. Thus, block propagation is depicted by the minimum length multicast tree, intrinsically the Steiner Tree Problem. The primary challenge lies in that the WMNs only allow local communication and the distributed consensus protocol of the blockchain makes a predetermination of receiver nodes impossible. We design our algorithm via a ``toward source'' Steiner tree approach in favor of the distributed environment. Tree construction proceeds by progressively enlarging the searching areas until the accumulated weight of receiver nodes reaches a threshold. Our algorithm provably returns a tree length fairly close to that of the optimal Steiner tree with latency <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(\sqrt{n\log n})$</tex-math></inline-formula> (where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> represents the number of nodes in the network), the best so far. Furthermore, our algorithm is empirically validated to provide instructive insights for efficient block propagation with applications in Bitcoin network.

Understanding the convoluted evolutionary history of the capped-golden langur lineage (Cercopithecidae: Colobinae)†

The phylogenetic position of the capped and golden langur (CG) lineage has been ambiguous owing to the discordance between phylogenies from multiple molecular markers. Previous molecular studies have hypothesised that this discordance likely arises from either a hybridization event that took place between the Indian genus Semnopithecus and the Southeast Asian genus Trachypithecus or from incomplete lineage sorting (ILS). Distinguishing between hybridization and ILS is challenging and these processes can lead to serious difficulties in inferring phylogenies. In this study, we used genetic markers (nine nuclear and eight mitochondrial) in conjunction with coalescent based species tree approach and a test for hybridization using posterior predictive checking to better understand the evolutionary origin of the CG lineage. Both the concatenated nuclear as well as the mitochondrial dataset recovered congruent relationships where CG lineage was sister to Trachypithecus. However, nuclear species tree estimated using different multispecies coalescent methods suggested an opposite result, i.e. CG lineage was sister to Semnopithecus. Hybridization analysis strongly indicates gene flow between Semnopithecus and Trachypithecus that likely gave rise to the hybrid CG lineage. Further, the CG lineage is morphologically intermediate between Semnopithecus and Trachypithecus with respect to skull and body measurements. In light of the above evidences, we argue that the CG lineage needs to be elevated to a new genus of its own. Taxonomic and conservation implications of these results are also discussed.

A Novel Model and Infrastructure for Clinical Outcomes Data Collection and Their Systematic Evaluation for UK Patients Receiving Proton Beam Therapy

AimsTo establish an infrastructure for sustainable, comprehensive data collection and systematic outcomes evaluation for UK patients receiving proton beam therapy (PBT). Materials and methodsA Proton Outcomes Working Group was formed in 2014 to develop a national minimum dataset for PBT patients and to define a clinically integrated informatics solution for data collection. The Christie Proton Beam Therapy Centre formed its Proton Clinical Outcomes Unit in 2018 to collect, curate and analyse outcomes data prospectively for UK-treated patients and retrospectively for UK patients referred abroad for PBT since 2008 via the Proton Overseas Programme (POP). ResultsA single electronic form (eForm) was developed to capture the agreed data, using a data tree approach including conditional logic: data items are requested once, further questions depend on previous answers and are sensitive to tumour site and patient pathway time point. Relevant data automatically populate other forms, saving time, prompting completeness of clinical assessments and ensuring data consistency. Completed eForm data populate the electronic patient record and generate individualised outputs, including consultation letters, treatment summary and surveillance plans, based on organs at risk irradiated, age and sex. All data regarding POP-treated patients are verified and migrated into the system, ensuring that patient data, whether overseas or UK treated, are consistently recorded. The eForm utilises a ‘user friendly’ web portal interface, the Clinical Web Portal, including clickable tables and infographics. Data items are coded to a universally recognised standard comparable with other data systems. Patient-reported outcomes are also integrated, highlighting significant toxicities and prompting a response. Outcomes data can be correlated with dosimetric DICOM data to support radiation dose modelling. ConclusionOutcomes data from both POP-treated and The Christie-treated patients support long-term care, allow evaluation of PBT efficacy and safety, assist future selection of PBT patients and support hypothesis generation for future clinical trials.

Killing vector fields of Bianchi type I spacetimes via Rif tree approach

In this paper, we have adopted a new approach to study the Killing vector fields of locally rotationally symmetric and general Bianchi type I spacetimes. Instead of directly integrating the set of Killing’s equations, an algorithm is developed in Maple which converts these equations to the reduced involutive form (Rif) and consequently it imposes some restrictions on the metric functions in the form of a tree, known as Rif tree. The set of Killing’s equations is then solved for each branch of the Rif tree, giving the explicit form of the Killing vector fields. The structure of Lie algebra is presented for each set of the obtained Killing vector fields and some physical implications of the obtained metrics are discussed.