基于注意力机制与自适应特征融合的群养猪身份识别

针对群养猪攻击过程中身体形变和重叠影响猪身份识别精度的问题,开发一种基于注意力机制和自适应特征融合的深度学习方法以提高猪身份识别精度。录制猪栏中8只猪每天8小时共3天的视频,并筛选含攻击的16830帧作为数据集。首先,采用ResNet50提取猪的卷积神经网络(CNN)特征;然后,采用特征金字塔网络(FPN)在ResNet50中选择3层不同尺度的特征,以优化这些特征的定位和语义信息;接着,采用自注意力机制提高这些特征的区分度,并采用自适应空间特征融合(ASFF)以融合不同尺度的特征;最后,利用卷积和Sigmoid函数相结合的检测器对猪身份进行识别。使用该方法后,猪身份识别的均值平均精度(mAP)达到95.59%,速度达到37.6 f/s。结果表明,该方法能够在攻击场景下有效识别猪身份,有助于将攻击识别从群体级细化为个体级。

Risk-Informed Model-Free Safe Control of Linear Parameter-Varying

This paper presents a risk-informed data-driven safe control design approach for a class of stochastic uncertain nonlinear discrete-time systems.The nonlinear system is modeled using linear parameter-varying(LPV)systems.A model-based probabilistic safe controller is first designed to guarantee probabilisticλ-contractivity(i.e.,stability and invariance)of the LPV system with respect to a given polyhedral safe set.To obviate the requirement of knowing the LPV system model and to bypass identifying its open-loop model,its closed-loop data-based representation is provided in terms of state and scheduling data as well as a decision variable.It is shown that the variance of the closedloop system,as well as the probability of safety satisfaction,depends on the decision variable and the noise covariance.A minimum-variance direct data-driven gain-scheduling safe control design approach is presented next by designing the decision variable such that all possible closed-loop system realizations satisfy safety with the highest confidence level.This minimum-variance approach is a control-oriented learning method since it minimizes the variance of the state of the closed-loop system with respect to the safe set,and thus minimizes the risk of safety violation.Unlike the certainty-equivalent approach that results in a risk-neutral control design,the minimum-variance method leads to a risk-averse control design.It is shown that the presented direct risk-averse learning approach requires weaker data richness conditions than existing indirect learning methods based on system identification and can lead to a lower risk of safety violation.Two simulation examples along with an experimental validation on an autonomous vehicle are provided to show the effectiveness of the presented approach.

Endotoxin-induced alterations of adipose tissue function:a pathway to bovine metabolic stress

During the periparturient period, dairy cows exhibit negative energy balance due to limited appetite and increased energy requirements for lactogenesis. The delicate equilibrium between energy availability and expenditure puts cows in a state of metabolic stress characterized by excessive lipolysis in white adipose tissues(AT), increased production of reactive oxygen species, and immune cell dysfunction. Metabolic stress, especially in AT, increases the risk for metabolic and inflammatory diseases. Around parturition, cows are also susceptible to endotoxemia. Bacterial-derived toxins cause endotoxemia by promoting inflammatory processes and immune cell infiltration in different organs and systems while impacting metabolic function by altering lipolysis, mitochondrial activity, and insulin sensitivity. In dairy cows, endotoxins enter the bloodstream after overcoming the defense mechanisms of the epithelial barriers, particularly during common periparturient conditions such as mastitis, metritis, and pneumonia, or after abrupt changes in the gut microbiome. In the bovine AT, endotoxins induce a pro-inflammatory response and stimulate lipolysis in AT, leading to the release of free fatty acids into the bloodstream. When excessive and protracted, endotoxin-induced lipolysis can impair adipocyte's insulin signaling pathways and lipid synthesis. Endotoxin exposure can also induce oxidative stress in AT through the production of reactive oxygen species by inflammatory cells and other cellular components. This review provides insights into endotoxins' impact on AT function, highlighting the gaps in our knowledge of the mechanisms underlying AT dysfunction, its connection with periparturient cows' disease risk, and the need to develop effective interventions to prevent and treat endotoxemia-related inflammatory conditions in dairy cattle.

Abomasal infusion of branched-chain amino acids or branched-chain keto-acids alter lactation performance and liver triglycerides in fresh cows

Background Dairy cows are at high risk of fatty liver disease in early lactation,but current preventative measures are not always effective.Cows with fatty liver have lower circulating branched-chain amino acid(BCAA)concentra-tions whereas cows with high circulating BCAA levels have low liver triglyceride(TG).Our objective was to determine the impact of BCAA and their corresponding ketoacids(branched-chain ketoacids,BCKA)on production performance and liver TG accumulation in Holstein cows in the first 3 weeks postpartum.Methods Thirty-six multiparous Holstein cows were used in a randomized block design experiment.Cows were abomasally infused for the first 21 d postpartum with solutions of 1)saline(CON,n=12);2)BCA(67 g valine,50 g leu-cine,and 34 g isoleucine,n=12);and 3)BCK(77 g 2-ketovaline calcium salt,57 g 2-ketoleucine calcium salt,and 39 g 2-ketoisoleucine calcium salt,n=12).All cows received the same diet.Treatment effects were determined using PROC GLIMMIX in SAS.Results No differences were detected for body weight,body condition score,or dry matter intake averaged over the first 21 d postpartum.Cows receiving BCK had significantly lower liver TG concentrations compared to CON(6.60%vs.4.77%,standard error of the mean(SEM)0.49)during the first 3 weeks of lactation.Infusion of BCA increased milk yield(39.5 vs.35.3 kg/d,SEM 1.8),milk fat yield(2.10 vs.1.69 kg/d,SEM 0.08),and lactose yield(2.11 vs.1.67 kg/d,SEM 0.07)compared with CON.Compared to CON,cows receiving BCA had lower plasma glucose(55.0 vs.59.2 mg/dL,SEM 0.86)but higherβ-hydroxybutyrate(9.17 vs.6.00 mg/dL,SEM 0.80).Conclusions Overall,BCAA supplementation in this study improved milk production,whereas BCKA supplementa-tion reduced TG accumulation in the liver of fresh cows.

Adaptive Sparse Grid Discontinuous Galerkin Method:Review and Software Implementation

This paper reviews the adaptive sparse grid discontinuous Galerkin(aSG-DG)method for computing high dimensional partial differential equations(PDEs)and its software implementation.The C++software package called AdaM-DG,implementing the aSG-DG method,is available on GitHub at https://github.com/JuntaoHuang/adaptive-multiresolution-DG.The package is capable of treating a large class of high dimensional linear and nonlinear PDEs.We review the essential components of the algorithm and the functionality of the software,including the multiwavelets used,assembling of bilinear operators,fast matrix-vector product for data with hierarchical structures.We further demonstrate the performance of the package by reporting the numerical error and the CPU cost for several benchmark tests,including linear transport equations,wave equations,and Hamilton-Jacobi(HJ)equations.

Stability Analysis and Performance Evaluation of Additive Mixed-Precision Runge-Kutta Methods

Additive Runge-Kutta methods designed for preserving highly accurate solutions in mixed-precision computation were previously proposed and analyzed.These specially designed methods use reduced precision for the implicit computations and full precision for the explicit computations.In this work,we analyze the stability properties of these methods and their sensitivity to the low-precision rounding errors,and demonstrate their performance in terms of accuracy and efficiency.We develop codes in FORTRAN and Julia to solve nonlinear systems of ODEs and PDEs using the mixed-precision additive Runge-Kutta(MP-ARK)methods.The convergence,accuracy,and runtime of these methods are explored.We show that for a given level of accuracy,suitably chosen MP-ARK methods may provide significant reductions in runtime.

Energy Stable Nodal DG Methods for Maxwell’s Equations of Mixed-Order Form in Nonlinear Optical Media

In this work,we develop energy stable numerical methods to simulate electromagnetic waves propagating in optical media where the media responses include the linear Lorentz dispersion,the instantaneous nonlinear cubic Kerr response,and the nonlinear delayed Raman molecular vibrational response.Unlike the first-order PDE-ODE governing equations considered previously in Bokil et al.(J Comput Phys 350:420–452,2017)and Lyu et al.(J Sci Comput 89:1–42,2021),a model of mixed-order form is adopted here that consists of the first-order PDE part for Maxwell’s equations coupled with the second-order ODE part(i.e.,the auxiliary differential equations)modeling the linear and nonlinear dispersion in the material.The main contribution is a new numerical strategy to treat the Kerr and Raman nonlinearities to achieve provable energy stability property within a second-order temporal discretization.A nodal discontinuous Galerkin(DG)method is further applied in space for efficiently handling nonlinear terms at the algebraic level,while preserving the energy stability and achieving high-order accuracy.Indeed with d_(E)as the number of the components of the electric field,only a d_(E)×d_(E)nonlinear algebraic system needs to be solved at each interpolation node,and more importantly,all these small nonlinear systems are completely decoupled over one time step,rendering very high parallel efficiency.We evaluate the proposed schemes by comparing them with the methods in Bokil et al.(2017)and Lyu et al.(2021)(implemented in nodal form)regarding the accuracy,computational efficiency,and energy stability,by a parallel scalability study,and also through the simulations of the soliton-like wave propagation in one dimension,as well as the spatial-soliton propagation and two-beam interactions modeled by the two-dimensional transverse electric(TE)mode of the equations.

Investigation of HCAR2 antagonists as a potential strategy to modulate bovine leukocytes

Background Dairy cows experiencing ketosis after calving suffer greater disease incidence and are at greater risk of leaving the herd. In vitro administration of beta-hydroxybutyric acid(BHBA;the primary blood ketone) has inhibitory effects on the function of bovine leukocytes. BHBA is a ligand of HCAR2 and the activation of these receptors promotes an anti-inflammatory response which may be related with immunosuppression observed in transition dairy cattle. The objective of this study was to identify and test antagonists for HCAR2 in bovine immune cells cultured with BHBA.Results We observed expression of HCAR2 at the protein level within lymphocytes, monocytes, and granulocytes. The proportion of cells expressing HCAR2 tended to be greater in mid-lactation compared to early lactation cows;the increase was a result of increased proportion of T and B cells expressing HCAR2. Stimulation of HCAR2 with niacin or BHBA promoted Ca^(2+) mobilization in neutrophils and mononuclear cells. Mononuclear cells treated with BHBA had diminished intracellular Ca^(2+) responses when HCAR2 was knocked down by si RNA silencing, indicating Ca^(2+) mobilization was mediated by HCAR2 signaling. Two candidate antagonists for HCAR2, synthesized from niacin(NA-1 and NA-5), were tested;monocytes and neutrophils pre-treated with NA-1 and NA-5 had reduced Ca^(2+) mobilization after incubation with BHBA. Furthermore, NA-5 but not NA-1 prevented BHBA-associated reductions in cyclic AMP.Conclusions We demonstrated that HCAR2 is present on bovine leukocytes and has greater expression later in lactation. We confirmed that BHBA and niacin derived HCAR2 antagonists alter bovine leukocyte activity. Our results demonstrate that both BHBA and niacin affect bovine leukocyte Ca^(2+) mobilization in a HCAR2-dependent manner.

Changes in structural plasticity of hippocampal neurons in an animal model of multiple sclerosis

Structural plasticity is critical for the functional diversity of neurons in the brain.Experimental autoimmune encephalomyelitis(EAE)is the most commonly used model for multiple sclerosis(MS),successfully mimicking its key pathological features(inflammation,demyelination,axonal loss,and gliosis)and clinical symptoms(motor and non-motordysfunctions).Recentstudieshave demonstrated the importance of synaptic plasticity in EAE pathogenesis.In the present study,we investigated the features of behavioral alteration and hippocampal structural plasticity in EAE-affected mice in the early phase(11 days post-immunization,DPI)and chronic phase(28DPI).EAE-affected mice exhibited hippocampus-related behavioral dysfunction in the open field test during both early and chronic phases.Dendritic complexity was largely affected in the cornu ammonis 1(CA1)and CA3 apical and dentate gyrus(DG)subregions of the hippocampus during the chronic phase,while this effect was only noted in the CA1 apical subregion in the early phase.Moreover,dendritic spine density was reduced in the hippocampal CA1 and CA3 apical/basal and DG subregions in the early phase of EAE,but only reduced in the DG subregion during the chronic phase.Furthermore,mRNA levels of proinflammatory cytokines(Il1β,Tnfα,and Ifnγ)and glial cell markers(Gfap and Cd68)were significantly increased,whereas the expression of activity-regulated cytoskeletonassociated protein(ARC)was reduced during the chronic phase.Similarly,exposure to the aforementioned cytokines in primary cultures of hippocampal neurons reduced dendritic complexity and ARC expression.Primary cultures of hippocampal neurons also showed significantly reduced extracellular signal-regulated kinase(ERK)phosphorylation upon treatment with proinflammatory cytokines.Collectively,these results suggest that autoimmune neuroinflammation alters structural plasticity in the hippocampus,possibly through the ERK-ARC pathway,indicating that this alteration may be associated with hippocampal dysfunctions in EAE.

Explainable Conformer Network for Detection of COVID-19 Pneumonia from Chest CT Scan: From Concepts toward Clinical Explainability

The early implementation of treatment therapies necessitates the swift and precise identification of COVID-19 pneumonia by the analysis of chest CT scans.This study aims to investigate the indispensable need for precise and interpretable diagnostic tools for improving clinical decision-making for COVID-19 diagnosis.This paper proposes a novel deep learning approach,called Conformer Network,for explainable discrimination of viral pneumonia depending on the lung Region of Infections(ROI)within a single modality radiographic CT scan.Firstly,an efficient U-shaped transformer network is integrated for lung image segmentation.Then,a robust transfer learning technique is introduced to design a robust feature extractor based on pre-trained lightweight Big Transfer(BiT-L)and finetuned on medical data to effectively learn the patterns of infection in the input image.Secondly,this work presents a visual explanation method to guarantee clinical explainability for decisions made by Conformer Network.Experimental evaluation of real-world CT data demonstrated that the diagnostic accuracy of ourmodel outperforms cutting-edge studies with statistical significance.The Conformer Network achieves 97.40% of detection accuracy under cross-validation settings.Our model not only achieves high sensitivity and specificity but also affords visualizations of salient features contributing to each classification decision,enhancing the overall transparency and trustworthiness of our model.The findings provide obvious implications for the ability of our model to empower clinical staff by generating transparent intuitions about the features driving diagnostic decisions.

Impact of an oligosaccharide-based polymer on the metabolic profiles and microbial ecology of weanling pigs experimentally infected with a pathogenic E.coli

Background Our previous study has reported that supplementation of oligosaccharide-based polymer enhances gut health and disease resistance of pigs infected with enterotoxigenic E.coli(ETEC)F18 in a manner similar to carbadox.The objective of this study was to investigate the impacts of oligosaccharide-based polymer or antibiotic on the host metabolic profiles and colon microbiota of weaned pigs experimentally infected with ETEC F18.Results Multivariate analysis highlighted the differences in the metabolic profiles of serum and colon digesta which were predominantly found between pigs supplemented with oligosaccharide-based polymer and antibiotic.The relative abundance of metabolic markers of immune responses and nutrient metabolisms,such as amino acids and carbohydrates,were significantly differentiated between the oligosaccharide-based polymer and antibiotic groups(q<0.2 and fold change>2.0).In addition,pigs in antibiotic had a reduced(P<0.05)relative abundance of Lachnospiraceae and Lactobacillaceae,whereas had greater(P<0.05)Clostridiaceae and Streptococcaceae in the colon digesta on d 11 post-inoculation(PI)compared with d 5 PI.Conclusions The impact of oligosaccharide-based polymer on the metabolic and microbial profiles of pigs is not fully understood,and further exploration is needed.However,current research suggest that various mechanisms are involved in the enhanced disease resistance and performance in ETEC-challenged pigs by supplementing this polymer.

MOALG: A Metaheuristic Hybrid of Multi-Objective Ant Lion Optimizer and Genetic Algorithm for Solving Design Problems

This study proposes a hybridization of two efficient algorithm’s Multi-objective Ant Lion Optimizer Algorithm(MOALO)which is a multi-objective enhanced version of the Ant Lion Optimizer Algorithm(ALO)and the Genetic Algorithm(GA).MOALO version has been employed to address those problems containing many objectives and an archive has been employed for retaining the non-dominated solutions.The uniqueness of the hybrid is that the operators like mutation and crossover of GA are employed in the archive to update the solutions and later those solutions go through the process of MOALO.A first-time hybrid of these algorithms is employed to solve multi-objective problems.The hybrid algorithm overcomes the limitation of ALO of getting caught in the local optimum and the requirement of more computational effort to converge GA.To evaluate the hybridized algorithm’s performance,a set of constrained,unconstrained test problems and engineering design problems were employed and compared with five well-known computational algorithms-MOALO,Multi-objective Crystal Structure Algorithm(MOCryStAl),Multi-objective Particle Swarm Optimization(MOPSO),Multi-objective Multiverse Optimization Algorithm(MOMVO),Multi-objective Salp Swarm Algorithm(MSSA).The outcomes of five performance metrics are statistically analyzed and the most efficient Pareto fronts comparison has been obtained.The proposed hybrid surpasses MOALO based on the results of hypervolume(HV),Spread,and Spacing.So primary objective of developing this hybrid approach has been achieved successfully.The proposed approach demonstrates superior performance on the test functions,showcasing robust convergence and comprehensive coverage that surpasses other existing algorithms.

Layered kagome compound Na_(2)Ni_(3)S_(4)with topological flat band

We report structural and electronic properties of Na_(2)Ni_(3)S_(4),a quasi-two-dimensional compound composed of alternating layers of[Ni_(3)S_(4)]^(2-)and Na^(+).The compound features a remarkable Ni-based kagome lattice with a square planar configuration of four surrounding S atoms for each Ni atom.Magnetization and electrical measurements reveal a weak paramagnetic insulator with a gap of about 0.5 eV.Our band structure calculation highlights a set of topological flat bands of the kagome lattice derived from the rotated dxz-orbital with C_(3)+T symmetry in the presence of crystal-field splitting.

Flexible pavement longitudinal joint quality evaluation using non-destructive testing

Longitudinal joint construction quality is critical to the life of flexible pavements.Maintaining deteriorated longitudinal joints has become a challenge for many highway agencies.Improving the joint's quality through better compaction during construction can help achieve flexible pavements with longer service lives and less maintenance.Current quality control(QC)and quality assurance(QA)plans provide limited coverage.Consequently,the risk of missing areas with poor joint compaction is significant.A density profiling system(DPS)is a non-destructive alternative to conventional destructive evaluation methods.It can provide quick and continuous real-time coverage of the compaction during construction in dielectrics.The paper presents several case studies comparing various types of longitudinal joints and demonstrating the use of DPS to evaluate the joint's compaction quality.The paper shows that dielectric measurements can provide valuable insight into the ability of various construction techniques to achieve adequate levels of compaction at the longitudinal joint.The paper proposes a dielectric-based longitudinal joint quality index(LJQI)to evaluate the relative compaction of the joint during construction.It also shows that adopting DPS for assessing the compaction of longitudinal joints can minimize the risk of agencies accepting poorly constructed joints,identify locations of poor quality during construction,and achieve better-performing flexible pavements.

Outcomes and efficacy of magnetic resonance imaging-compatible sacral nerve stimulator for management of fecal incontinence: A multi-institutional study

BACKGROUND Fecal incontinence(FI)is an involuntary passage of fecal matter which can have a significant impact on a patient’s quality of life.Many modalities of treatment exist for FI.Sacral nerve stimulation is a well-established treatment for FI.Given the increased need of magnetic resonance imaging(MRI)for diagnostics,the In-terStim which was previously used in sacral nerve stimulation was limited by MRI incompatibility.Medtronic MRI-compatible InterStim was approved by the United States Food and Drug Administration in August 2020 and has been widely used.AIM To evaluate the efficacy,outcomes and complications of the MRI-compatible InterStim.METHODS Data of patients who underwent MRI-compatible Medtronic InterStim placement at UPMC Williamsport,University of Minnesota,Advocate Lutheran General Hospital,and University of Wisconsin-Madison was pooled and analyzed.Patient demographics,clinical features,surgical techniques,complications,and outcomes were analyzed.Strengthening the Reporting of Observational studies in Epidemiology(STROBE)cross-sectional reporting guidelines were used.RESULTS Seventy-three patients had the InterStim implanted.The mean age was 63.29±12.2 years.Fifty-seven(78.1%)patients were females and forty-two(57.5%)patients had diabetes.In addition to incontinence,overlapping symptoms included diarrhea(23.3%),fecal urgency(58.9%),and urinary incontinence(28.8%).Fifteen(20.5%)patients underwent Peripheral Nerve Evaluation before proceeding to definite implant placement.Thirty-two(43.8%)patients underwent rechargeable InterStim placement.Three(4.1%)patients needed removal of the implant.Migration of the external lead connection was observed in 7(9.6%)patients after the stage I procedure.The explanation for one patient was due to infection.Seven(9.6%)patients had other complications like nerve pain,hematoma,infection,lead fracture,and bleeding.The mean follow-up was 6.62±3.5 mo.Sixty-eight(93.2%)patients reported significant improvement of symptoms on follow-up evaluation.CONCLUSION This study shows promising results with significant symptom improvement,good efficacy and good patient outcomes with low complication rates while using MRI compatible InterStim for FI.Further long-term follow-up and future studies with a larger patient population is recommended.

A Novel Deep Learning-Based Model for Classification of Wheat Gene Expression

Deep learning(DL)plays a critical role in processing and converting data into knowledge and decisions.DL technologies have been applied in a variety of applications,including image,video,and genome sequence analysis.In deep learning the most widely utilized architecture is Convolutional Neural Networks(CNN)are taught discriminatory traits in a supervised environment.In comparison to other classic neural networks,CNN makes use of a limited number of artificial neurons,therefore it is ideal for the recognition and processing of wheat gene sequences.Wheat is an essential crop of cereals for people around the world.Wheat Genotypes identification has an impact on the possible development of many countries in the agricultural sector.In quantitative genetics prediction of genetic values is a central issue.Wheat is an allohexaploid(AABBDD)with three distinct genomes.The sizes of the wheat genome are quite large compared to many other kinds and the availability of a diversity of genetic knowledge and normal structure at breeding lines of wheat,Therefore,genome sequence approaches based on techniques of Artificial Intelligence(AI)are necessary.This paper focuses on using the Wheat genome sequence will assist wheat producers in making better use of their genetic resources and managing genetic variation in their breeding program,as well as propose a novel model based on deep learning for offering a fundamental overview of genomic prediction theory and current constraints.In this paper,the hyperparameters of the network are optimized in the CNN to decrease the requirement for manual search and enhance network performance using a new proposed model built on an optimization algorithm and Convolutional Neural Networks(CNN).

Soil Carbon Pool as Influenced by Soil Microbial Activity—An Overview

Soil is a significant carbon reservoir with the capacity to store carbon twice as much as the atmosphere or plants. Given the significant potential of soil to capture and store atmospheric CO2, it presents a viable solution for mitigating the present and future impacts of climate change. However, due to its high susceptibility to global environmental issues like land degradation, loss of biodiversity, and climate change, monitoring and protecting soil carbon pools is a complex challenge. Intensive agricultural operations have detrimental effects on the soil, including the rapid breakdown of soil organic carbon, which releases excess carbon into the air, causing increased atmospheric CO2 levels and a depletion of the soil carbon reserves. The diversity and abundance of soil microbial communities play a crucial role in controlling essential ecosystem processes, including the decomposition of organic matter and nutrient cycling, including carbon. Heterotrophic soil microorganisms facilitate the soil organic matter turnover to obtain the nutrients and energy required for their growth and maintenance. Therefore, the microbial residues and exudates have up to 80% carbon in the stable soil organic matter fractions. This overview attempts to summarize the information on various carbon pools, soil carbon interaction with microbes, impacts on environmental changes, and strategies to enhance the storage of belowground carbon.

Assessment of Agricultural Soil Quality Indices Using Mechanistic Models

Assessing soil quality is a critical strategy for diagnosing soil status and anticipating concerns in land use systems for agricultural sustainability. In this study, two soil quality assessment indices, the Integrated Quality Index (IQI) and Nemoro Quality Index (NQI), were employed using two indicator selection methods: Total Data Set (TDS) and Minimum Data Set (MDS), focusing on agricultural fields in Golestan province, Iran. A total of 89 soil samples were collected and analyzed for particle size distribution, organic carbon, calcium carbonate equivalent (CCE), electrical conductivity (EC), pH, and plant-essential nutrients, including nitrogen, phosphorus, potassium, zinc, copper, manganese, and iron. Principal component analysis (PCA) was used to extract MDS from TDS, and geostatistical adaptation and correlation analyses were performed to determine the optimal soil quality evaluation index. Our results show that the exponential model better suits the spatial structure of soil quality indicators (IQIMDS: 0.955). Conformity and correlation analyses indicate that the IQI index outperformed the NQI index in estimating soil quality. The superiority of the TDS technique over the MDS technique in terms of accuracy (IQITDSs kappa: 0.155). Linear relationships between different methods showed a higher correlation coefficient (R2 = 0.43) through the application of IQI. This study suggests the use of IQIMDS to provide a reliable measurement that is particularly useful in assessing the quality of agricultural soil.

Clinical peer review in the United States: History, legal development and subsequent abuse

The Joint Commission on Accreditation requires hospitals to conduct peer review to retain accreditation.Despite the intended purpose of improving quality medical care,the peer review process has suffered several setbacks throughout its tenure.In the 1980s,abuse of peer review for personal economic interest led to a highly publicized multimillion-dollar verdict by the United States Supreme Court against the perpetrating physicians and hospital.The verdict led to decreased physician participation for fear of possible litigation.Believing that peer review was critical to quality medical care,Congress subsequently enacted the Health Care Quality Improvement Act(HCQIA)granting comprehensive legal immunity for peer reviewers to increase participation.While serving its intended goal,HCQIA has also granted peer reviewers significant immunity likely emboldening abuses resulting in Sham Peer Reviews.While legal reform of HCQIA is necessary to reduce sham peer reviews,further measures including the need for standardization of the peer review process alongside external organizational monitoring are critical to improving peer review and reducing the prevalence of sham peer reviews.

Analysis on using UPREITs in real estate finance in United States

This paper gave an overview introduction about umbrella partnership real estate investment trust (UPREIT) to the readers who are not related to real estate and accounting major. To show how UPREIT defer capital gain taxes,makes good cash flow and maximize profit in real estate finance,the paper included the theory and structure of UPREIT(real estate investment trust),partnership issues,advantage and disadvantage,and created a proforma to demonstrate how UPREIT works.