bluuebunny/crossref_metadata_2025_split huggingface dataset:
Impacts of Land Use Types on Shallow Groundwater Quality Sources in Mathira East Sub-County in Kenya
Authors: Wachira Charles Mwangi , Njambuya Josphine Wambui , Ndiritu George Gatere 
Published: February 2023

Abstract
The shallow groundwater sources of boreholes and wells form major sources of water for domestic and agricultural uses in rural areas of Kenya. Information on their status is inadequate thus hinder their sustainable management. This study investigated the relationship between land-use types, ground water sources and water quality, specifically water physicochemical parameters and feacal coliforms that were then used to calculate the water quality index (WQI). Thirty-six randomly selected groundwater sources were obtained from four stratified land-use types of forest, tea, coffee, and urban, with 18 samples each collected for either boreholes or shallow wells. The PCA grouped groundwater resources into three groups according to land-use types and sources. Spearman rank correction found nutrient levels of PO4 and NO3 were strongly correlated with turbidity, electric conductivity, total hardness, feacal coliforms and WQI with the same trends observed for wells. An overall one-way ANOVA found all water quality parameters significantly differed between land-use types for both boreholes and wells at p < 0.05. However, further post-hoc multi-comparison analyses using the Tukey test of unequal N showed not all water quality parameters changed significantly between land-use types, especially between forests and other land-use types. Comparisons of water sources (boreholes versus wells) using unpaired t-test showed most water quality parameters were significantly higher in wells than boreholes apart from pH, PO4, chloride (in the forest), dissolved oxygen, total alkalinity, chloride (tea), Chloride (coffee) and (dissolved oxygen, PO4, fluoride (urban). The WQIs calculated without FC classified groundwater sources from good water to unsuitable for drinking whereas those with FCs parameters were all classified as completely unsuitable for drinking. Overall, this study found that a significant percentage of the population in the study area relied on shallow groundwater resources that were experiencing various levels of pollution from human settlements, urbanization, and agriculture. Consequently, there is an urgent need for the County and National governments to promote sustainable management of water resources to ensure healthy and safe water is available to its people for domestic and agricultural use.
NewbornTime - improved Newborn Care based on Video and Artificial Intelligence - Study protocol
Authors: Engan Kjersti , Meinich-Bache Øyvind , Brunner Sara , Myklebust Helge , Rong Chunming , García-Torres Jorge , Ersdal Hege L. , Johannessen Anders , Pike Hanne Markhus , Rettedal Siren 
Published: February 2023

Abstract
Background: Approximately 3-8\% of all newborns do not breathe spontaneously at birth, and require time critical resuscitation. Resuscitation guidelines are mostly based on best practice, and more research on newborn resucitation is highly sought for.
Methods: The NewbornTime project will develop artificial intelligence (AI) based solutions for activity recognition during newborn resuscitations based on both visible light spectrum videos and infrared spectrum (thermal) videos. In addition, time-of-birth detection will be developed using thermal videos from the delivery rooms. Deep Neural Network models will be developed, focusing on methods for limited supervision and solutions adapting to on-site environments. A timeline description of the video analysis output enables objective analysis of resuscitation events. The project further aims to use machine learning to find patterns in large amount of such timeline data to better understand how newborn resuscitation treatment is given and how it can be improved.
The automatic video analysis and timeline generation will be developed for on-site usage, allowing for data-driven simulation and clinical debrief for health-care providers, and paving the way for automated real-time feedback. This brings added value to the medical staff, mothers and newborns, and society at large.
Discussion: The project is a interdisciplinary collaboration, combining AI, image processing, blockchain and cloud technology, with medical expertise, which will lead to increased competences and capacities in these various fields.
A Reinforcement Learning-based Approach to Testing GUI of Moblie Applications
Authors: Tao Chuanqi , Gao Yuemeng , Guo Hongjing , Gao Jerry 
Published: February 2023

Abstract
With the popularity of mobile devices, the software market of mobile applications has been booming in recent years. Android applications occupy a vast market share. However, the applications inevitably con- tain defects. Defects may affect the user experience and even cause severe economic losses. This paper proposes ATAC and ATPPO, which apply reinforcement learning to Android GUI testing to mitigate the state explosion problem. The article designs a new reward function and a new state representation. It also constructs two GUI testing models(ATAC and ATPPO) based on A2C and PPO algorithms to save memory space and accelerate training speed. Empirical studies on twenty open-source applications from GitHub demonstrate that: (1) ATAC performs best in 16 of 20 apps in code coverage and defects more exceptions; (2) ATPPO can get higher code coverage in 15 of 20 apps and defects more exceptions; (3)Compared with state-of-art tools Monkey and ARES, ATAC, and ATPPO shows higher code cov- erage and detects more errors. ATAC and ATPPO not only can cover more code coverage but also can effectively detect more exceptions. This paper also introduces Finite-State Machine into the reinforcement learning framework to avoid falling into the local optimal state, which provides high-level guidance for further improving the test efficiency.
Topological and dynamical properties of Torelli groups of partitioned surfaces
Authors: Baik Hyungryul , Shin Hyunshik , Tranchida Philippe 
Published: February 2023

Abstract
Putman introduced a notion of a partitioned surface which is a surface with boundary with decoration restricting how the surface can be embedded into larger surfaces, and defined the Torelli group of the partitioned surfaces. In this paper, we study some topological and dynamical aspects of the Torelli groups of partitioned surfaces. More precisely, first we obtain upper and lower bounds on the cohomological dimension of Torelli groups of partitioned surfaces and show that those two bounds coincide when at most three boundary components are grouped together in the partition of the boundary. Second, we study the asymptotic translation lengths of Torelli groups of partitioned surfaces on the corresponding curve complexes. We show that the minimal asymptotic translation length asymptotically behaves almost like the reciprocal of the Euler characteristic of the surface. This generalizes the previous result of the first and second authors on Torelli groups for closed surfaces.
Genetic algorithm with a Bayesian approach for the detection of multiple points of change of time series of counting exceedances of specific thresholds.
Authors: Sierra Biviana Marcela Suárez , Coen Arrigo , Taimal Carlos Alberto 
Published: February 2023

Abstract
Although the applications of Non-Homogeneous Poisson Processes to model and study the threshold overshoots of interest in different time series of measurements have proven to provide good results, they needed to be complemented with an efficient and automatic diagnostic technique to establish the location of the change-points, which, when taken into account, make the estimated model fit poorly in regards of the information contained in the real model. For this reason, we propose a new method to solve the segmentation uncertainty of the time series of measurements, where the emission distribution of exceedances of a specific threshold is the focus of investigation. One of the great contributions of the present algorithm is that all the days that overflowed are candidates to be a change-point, so all the possible configurations of overflow days are the possible chromosomes, which will unite to have offspring. Under the heuristics of a genetic algorithm, the solution to the problem of finding such change points will be guaranteed to be non-local and the best possible one, reducing wasted machine time evaluating the least likely chromosomes to be a solution to the problem. The analytical evaluation technique will be by means of the Minimum Description Length (\textit{MDL}) as the objective function, which is the joint posterior distribution function of the parameters of each regime and the change points that determines them and which account as well for the influence of the presence of said times.
Thus, one of the practical implications of the present work comes in terms of overcoming the need of modeling the time series of measurements, where the distributions of exceedances of certain thresholds, or where the counting of certain events involving abrupt changes, is the main objective. For this reason, the present proposal will be very useful in phenomena related to climate change, information security, epidemiology, to name a few.
Determinants of the Agroforestry Practices in Mana Districts, Oromyia Region, Ethiopia
Authors: Bekele Birhanu , Yeshiwas Endashwa , kassie Mesert , Zewde Anchinesh 
Published: February 2023

Abstract
Due to little adoption of the agroforestry practices, land degradation has become a serious pressing problem in various parts of the world in general and in study area in particular. Therefore, these studies aim to assess the determents of the agroforestry practices in the study area. Therefore, based on multi-stage sampling techniques 184 randomly selected sample households’ heads was determined. Descriptive and inferential statistics such as T-test, chi-square tests, and binary logit model was used to compare the mean difference between adopters and non-adopters households. The finding the study shows Senegal (L. Britton), home garden, and boundary planting are the most common agroforestry practices in the study area. The mean annual income for adopter farm households' heads was 1148743.00 birr, and for non-adopters, was 138675.00 birr. This implies that agroforestry practices make a significant contribution to the adopter's income. However, lack of farmland (27.7%), pests (16.3%), and low market access (15.2%) are major constraints that determine the adoption of the agroforestry practices in the study area. The results of the binary logistic model specify that age, farm size, and distance negatively affect the adoption of agroforestry practices in the study area. While perception were positively and significantly influence adoption of agroforestry practices in the study area, Therefore, the governmental & non-governmental organizations should have to develop new land policies to remove barriers to land access, tree tenure & an array of exotic tree species should be required to resist pests and drought conditions in study area.
A Collaborative Cache Allocation Strategy for Performance and Link Cost in Mobile Edge Computing
Authors: Zhang Xinyu , Hu Zhigang , Liang Yang , Xiao Hui , Yang Liu , Zheng Meiguang 
Published: February 2023

Abstract
Mobile Edge Computing (MEC) is a new paradigm to address massive content access on mobile networks to support the fast-growing Internet of Things (IoT) services and applications. However, inappropriate cache placement and utilization and requests for cached data at different times are highly variable. Nevertheless, most current optimization approaches lack the adaptive ability to orchestrate dynamic caching environments, and although many studies use online deep learning approaches, many challenges remain. This paper synthesizes the value of cached content and the cost of transmission links to derive a comprehensive utility function that can meet both the performance and link cost requirements of edge computing caching systems by dynamically changing the weight values. To improve the timeliness of the caching policy and the efficiency of deep learning, we propose a collaborative two-stage deep reinforcement learning (CDRL) framework to design the caching mechanism; CDRL cleverly combines double-deep reinforcement learning (DDRL) with deep sarsa. Experimental results show that the proposed approach can effectively improve the performance in terms of cache hit rate, service latency, and link cost.
An Optimal Individual Selection Algorithm Based on "Layer Proximity and Branch Distance Function"
Authors: bingqin qiao , yingjian an 
Published: February 2023

Abstract
Using heuristic method to automatically generate test cases is a research hotspot at present. Although its advantages are obvious, it is slightly insufficient in the selection of optimal individuals. In this paper, aiming at the problems existing in the evaluation and selection of the optimal individual at present, based on the comprehensive analysis of the characteristics of layer proximity and branch distance function, a test case evaluation algorithm combining layer proximity and branch distance function is proposed. The basic idea of this algorithm is to select the individuals with high proximity between the actual execution path and the target path, and then select the individuals with the smallest branch distance in these individuals, so as to obtain the individuals with the best navigation ability. Experiments show that the proposed algorithm can quickly find test cases, especially for the test case generation of multi-layer nested programs.
Penicillin Antibiotic (Ampicillin and Cloxacillin) Degradation Using Non-Thermal Pencil Plasma Jet
Authors: RATHORE VIKAS 
Published: February 2023

Abstract
In the present work an antibiotic drug degradation efficacy of a non-thermal pencil plasma jet in artificial wastewater. Air is used as a plasma-forming gas. The air plasma is produced in a cylindrical co-axial assembly of electrodes powered by a 50 Hz High-voltage power supply. The electrical characterization of plasma is performed and the generated plasma species/radicals are identified using optical emission spectroscopy. The degradation of antibiotics in solution after plasma treatment is analyzed using high performance liquid chromatography with photodiode array detector (HPLC-PDA). In addition, total organic carbon (TOC), and chemical oxygen demand (COD) is also determined.
The HPLC-PDA results reveal that plasma degradation of antibiotics solution followed first-order degradation kinetics with a rate constant of 0.158 min-1 (ampicillin) and 0.366 min-1 (Cloxacillin). Furthermore, the reduction in TOC and COD supported the degradation of antibiotics using plasma jet. It has been observed the plasma 9-minute gives 76.10% and 99.99% degradation in ampicillin and cloxacillin solution respectively. The change in physicochemical properties and various reactive species formed due to which degradation of antibiotics solution take place are measured. Finally, the toxicity of plasma degraded antibiotic solutions are studied on freshwater chlorella algae species. The results reveal that plasma degraded antibiotics solutions are non-toxic toward freshwater algae. The non-thermal plasma jet could be a potential eco-friendly alternative for the degradation of various antibiotic drugs present in wastewater.
Characteristics of Subsequent Contralateral Proximal Femoral Fracture:More Convenient Access Needed to Treat Osteoporosis
Authors: Jiang Yuxuan , Zhu Yangjun , Zhang Binfei , Feng Dongxu 
Published: February 2023

Abstract
Background
Patients with proximal femoral fracture (PFF) have high mortality and many complications. Osteoporosis increases the risk of subsequent fractures, leading to subsequent contralateral PFF. This study was performed to analyze the features of individuals with subsequent PFF following surgical therapy of first PFF and to ascertain whether such patients received an examination or treatment of osteoporosis. The reasons for lack of examination or treatment were also analyzed.
Methods
This retrospective study involved 181 patients with subsequent contralateral PFF who underwent surgical treatment in Xi'an Honghui hospital from September 2012 to October 2021. The patients’ sex, age, hospital day, mechanism of injury, surgical procedure, fracture interval, fracture type, fracture classification, and Singh index of the contralateral hip at the time of the initial and subsequent fractures were recorded. Whether the patients took calcium and vitamin D supplements, used anti-osteoporosis medication, or underwent a dual X-ray absorptiometry (DXA) scan was recorded, as was the start time of each. Patients who had never undergone a DXA scan or received anti-osteoporosis medication took part in a questionnaire.
Results
The 181 patients in this study comprised 60 (33.1%) men and 121 (66.9%) women. Patients with initial PFF and subsequent contralateral PFF had a median age of 80 years (range, 49–96 years) and 82 years (range, 52–96 years), respectively. The median fracture interval was 24 (7–36) months. Contralateral fractures occurred at the highest incidence between 3 months and 1 year (28.7%). The Singh index was not significantly different between the two fractures. In 130 (71.8%) patients, the fracture type was the same. No significant difference was found in the fracture type or fracture stability classification. A total of 144 (79.6%) patients had never received a DXA scan or anti-osteoporosis medication. The main reason for not treating osteoporosis further was concern about the safety of drug interactions (67.4%).
Conclusions
Patients with subsequent contralateral PFF were of advanced age, had a higher proportion of intertrochanteric femoral fractures, had more severe osteoporosis, and had longer hospital stays. The difficulty managing such patients requires multidisciplinary involvement. Most of these patients were not screened or formally treated for osteoporosis. Advanced-age patients with osteoporosis need reasonable treatment and management.
Ancient and recent origins of shared polymorphisms in yeast
Authors: Tellini Nicolò , De Chiara Matteo , Mozzachiodi Simone , Tattini Lorenzo , Vischioni Chiara , Naumova Elena , Warringer Jonas , Bergström Anders , Liti Gianni 
Published: February 2023

Abstract
Shared genetic polymorphisms between populations and species can be ascribed to ancestral variation or to more recent gene flow. Here, we mapped shared polymorphisms in Saccharomyces cerevisiae and its sister species Saccharomyces paradoxus, which diverged 4-6 million years ago. We used a dense map of diagnostic markers (mean distance 15.6 bp) in 1,673 sequenced S. cerevisiae isolates to catalogue 3,852 introgressed blocks (≥ 5 consecutive markers) from S. paradoxus, with the majority being recent and clade-specific. The highly diverged wild Chinese S. cerevisiae lineages were depleted of introgressed blocks, but retained an excess of individual ancestral polymorphisms derived from incomplete lineage sorting, perhaps due to less dramatic population bottlenecks. In the non-Chinese S. cerevisiae lineages, we inferred major hybridisation events and detected cases of overlapping introgressed blocks across distinct clades due to either shared histories or convergent evolution. We experimentally engineered, in otherwise isogenic backgrounds, the introgressed PAD1-FDC1 gene-pair that independently arose in two S. cerevisiae clades and revealed that it potentiates resistance against diverse antifungal drugs and ferulic acid. Overall, our study retraces histories of divergence and secondary contacts across S. cerevisiae and S. paradoxus populations and unveil a functional outcome.
Rainfall–streamflow response times for diverse upland UK micro-basins: quantifying hydrographs to identify the nonlinearity of storm response
Authors: Mindham David , Beven Keith , Chappell Nick 
Published: February 2023

AbstractWhile it is known that antecedent conditions and rainfall profiles contribute to the nonlinearity of streamflow response and that hydrograph shape can be dependent on the nature of rainfall inputs, how antecedent conditions (with similar rainfall inputs) impact hydrograph shape is less known. Here, a data-based mechanistic (DBM) approach is applied to quantify hydrograph shape, in terms of timing and volume, for the purposes of comparing hydrographs across 17 micro-basins at selected localities in upland UK over a 4-year period. The analysis demonstrates the nonlinearity of storm response for small catchments and revealed that with low antecedent conditions and/or small rainfall inputs there was a high variance in hydrograph shape quantifiers and that these variances decrease (at rates micro-basin dependent) as the micro-basins became wetter or as the storms increased in size, potentially converging to a more stable response.
A Novel Continuous-time Hidden Markov Model Based on a Bag of Features Extracted from MR Brain Images for Alzheimer's Stage Progression and Detection
Authors: Shankar Venkatesh Gauri , Sisodia Dilip Singh , Chandrakar Preeti 
Published: February 2023

Background:
Mounting novel solutions for conspicuous neurodegenerative disorders that grow consistently, such as Alzheimer's disease, rely on tracking and identifying disease development, improvement, and progression. Compared to many clinical or survey-based detection methods, early Alzheimer's stage detection can be possible through computer-based MR brain images and discrete stochastic processes.

Aim:
In the case of Alzheimer's stage progression, the existing models illustrate that the learning problem comprises two issues: estimating posterior probabilities of the Alzheimer's stage and computing conditioned statistics of the Alzheimer's end-stage. The proposed model overcomes these issues by restructuring the estimation problem as EM-centered CT- HMM. This paper proposes a novel framework model with two phases; the first phase covers the feature extraction of magnetic resonance imaging based on many computer vision methods known as a collection of bag-of-features (BoF). In the second phase, we used the EM-centered learning method for the continuous-time hidden Markov model (CTHMM), an efficient approach to modeling Alzheimer''s disease progression with time and stages.

Methods:
This paper proposes a novel framework model with two phases; the first phase covers the feature extraction of magnetic resonance imaging based on many computer vision methods known as a collection of bag-of-features (BoF). In the second phase, the EM-centered learning method is used for the continuous-time hidden Markov model (CT-HMM), an efficient approach to modeling Alzheimer's disease progression with time and stages. The proposed CT-HMM is implemented with eight Alzheimer's stages (source: ADNI) to visualize and predict the stage progression of the ADNI MRI dataset.

Results:
The proposed model reported the transition posterior probability as 0.765 (high to low stage progression) and 0.234 (low to high stage progression). The model's accuracy and F1 score are estimated as 97.13 and 96.51, respectively.

Conclusion:
The proposed model’s accuracy and evaluation metrics reported higher results in the work on Alzheimer's stage progression and prediction.