Greater Performance Research Articles

Gait, balance, and physical performance as markers of early Alzheimer's disease and related dementia risk.

Declining physical functionality is an indicator of cognitive impairment, distinguishing normal cognition (NC) from dementia. Whether this extends to pre-dementia stages is unclear. Assess physical performance patterns, evaluate relationships with imaging biomarkers, and identify specific measures distinguishing NC, subjective cognitive decline (SCD) and mild cognitive impairment (MCI). Group differences (78 NC, 35 SCD, and 41 MCI) in physical function (global function, balance, gait speed, step length, single leg support) were evaluated with logistic regression while distinguishing between MCI due-to-AD and MCI due-to-vascular etiology. Relationships with imaging biomarkers (cortical atrophy score, white matter hyperintensities volumes) were analyzed with ANCOVA. Participants were 68.6 ± 9.3 years old, had 16.2 ± 3.0 years of education, and 23% were ethnoracial minorities. Physical performance distinguished MCI from NC and SCD. Greater performance on the Mini Physical Performance Test (mini PPT) and balance were associated with lower odds of being SCD versus NC (ORmini PPT = 0.73; 95% CI:0.56-0.97; ORbalance = 0.35, 95%CI:0.16-0.80). AD etiology accounted for most group differences in physical performance versus vascular etiology. Consistent associations between biomarkers, physical performance, and cognition were found. Findings suggest that: 1) changes in mini PPT performance and balance may help detect cognitive impairments, as early as the SCD stage; 2) changes in gait speed, gait cycle parameters, and Timed Up-and-Go may indicate more significant cognitive impairment; 3) neuronal loss is linked to subtle changes in physical functionality as early as SCD. Physical performance may be a valuable tool in early dementia detection in clinical settings and could identify targets for early intervention.

Sports Betting: An Application of Machine Learning to the Game Prediction

The study investigates the use of machine learning to predict the results of football matches, with its main goal being to enhance the effectiveness of sports betting techniques.An assessment of diverse machine learning methods was carried out by analyzing a comprehensive dataset that included European league games spanning 2008 to 2016, such as Random Forest, Gaussian Process Regression, Logistic Regression, K-Nearest Neighbors, AdaBoost, XGBoost, and LightGBM. Our results revealed that the LightGBM and Ada model exhibited great performance, achieving an accuracy of 52.6% and 52.8% in predicting match outcomes.Moreover, we incorporated the concept of double chances into our analysis and a simulation-based betting strategy was used in our model, demonstrating a 3% profit margin. This study demonstrates a hopeful potential of machine learning. It is very useful in sports analysis and betting. At the same time, the study talks about some limitations. It also suggests directions for future research.

A digital twin solution for fault detection in time-critical IIoT applications

ABSTRACT IIoT sensor data plays a pivotal role in monitoring the industrial system’s health and identifying potential faults. However, traditional fault detection approaches often face challenges such as network latency, limited accuracy, and resource-intensive processing. This paper introduces an end-to-end Digital Twin solution that enhances fault detection for IIoT systems. The solution is powered by two key innovations: the integration of a Digital Twin architecture that leverages a collaborative cloud-edge approach for real-time monitoring, and the use of a lightweight two-phased machine-learning ensemble model optimized for resource-constrained environments. The great performance achieved across various fault scenarios demonstrates the effectiveness of the proposed approach. The model provides an average accuracy of 99.71% with a mere 4.8 ms of average estimation delay. These advancements ensure both high accuracy and rapid response times, providing a robust solution for proactive fault detection in dynamic industrial environments.

Toward high-performance rechargeable magnesium batteries with a Cu2Se-CTAB nanoparticle cathode and Mg[B(HFIP)4]2/DME electrolyte.

Developing advanced cathode materials effectively enhances the electrochemical performance of rechargeable magnesium batteries (RMBs). Herein, we designed a CTAB-assisted hydrothermal method to construct Cu2Se nanoparticles as the cathode and Mg[B(HFIP)4]2/DME as the electrolyte shows high specific capacity and great cycling performance in RMBs.

Structural and functional connectivity coupling as an imaging marker for bone metastasis pain in lung cancer patients.

Cancer pain is a common symptom in patients with malignant tumors and associated with poor prognosis and a high risk of death. Structural connectivity (SC) and functional connectivity (FC) couplings have not yet been explored in lung cancer patients with bone metastasis pain. In total, 51 patients with lung cancer without bone metastasis pain (BMP-), 52 patients with lung cancer with bone metastasis pain (BMP+), and 28 healthy controls (HC) were prospectively enrolled in our study. Firstly, SC-FC couplings were measured and analyzed at global, regional, and modular levels. Subsequently, individualized SC-FC coupling networks were constructed based on the Euclidean distance metric. In addition, the convolutional neural network (CNN) model was selected to analyze and classify three groups based on individualized networks. The coupling analysis demonstrated that weaker SC-FC couplings related to lung cancer itself were present at various levels, including global, regional, inter-network, and intra-network couplings. Notably, hyper-couplings related to bone metastasis pain were present in several brain regions, mainly involving the default mode network, frontoparietal network, salience network, and limbic system. Significant positive correlations were observed between regional coupling in the right amygdala and the numeric rating scale scores in BMP+. Moreover, CNN model built on individualized networks exhibited relatively great classification performance. Alterations in SC-FC coupling patterns may play a crucial role in the development and modulation of bone metastasis pain. Understanding these changes could provide valuable insights into the neural mechanisms underlying cancer pain.

DCTCNet: Sequency discrete cosine transform convolution network for visual recognition.

The discrete cosine transform (DCT) has been widely used in computer vision tasks due to its ability of high compression ratio and high-quality visual presentation. However, conventional DCT is usually affected by the size of transform region and results in blocking effect. Therefore, eliminating the blocking effects to efficiently serve for vision tasks is significant and challenging. In this paper, we introduce All Phase Sequency DCT (APSeDCT) into convolutional networks to extract multi-frequency information of deep features. Due to the fact that APSeDCT can be equivalent to convolutional operation, we construct corresponding convolution module called APSeDCT Convolution (APSeDCTConv) that has great transferability similar to vanilla convolution. Then we propose an augmented convolutional operator called MultiConv with APSeDCTConv. By replacing the last three bottleneck blocks of ResNet with MultiConv, our approach not only reduces the computational costs and the number of parameters, but also exhibits great performance in classification, object detection and instance segmentation tasks. Extensive experiments show that APSeDCTConv augmentation leads to consistent performance improvements in image classification on ImageNet across various different models and scales, including ResNet, Res2Net and ResNext, and achieving 0.5%-1.1% and 0.4%-0.7% AP performance improvements for object detection and instance segmentation, respectively, on the COCO benchmark compared to the baseline.

Revealing Crucial Influences of Boron Support on Regulating Geometric and Electronic Structures of 3D Catalyst for Hydrogen Evolution and Oxygen Reduction Reactions.

Building insights into the structure-performance relationship of catalysts has been emphasized recently. However, it remains a challenge due to catalysts' various and complex structures, especially the easily overlooked influence of the support material. Here, we reveal the crucial influences of boron introduction on synthesizing 3D carbon nanotube monoliths with embedded multistate Co metals, i.e., single atoms, clusters, and nanoparticles (Co-BNCNTs), by an interesting chemical blowing-assisted calcination method. The boron introduction can contribute to forming captivating boron-nitrogen pairs, shaping a 3D frame, and regulating electronic structure. The 3D Co-BNCNT monoliths present good catalytic performance for both the hydrogen evolution reaction (HER) at all pH values and the oxygen reduction reaction (ORR). The theoretical calculations indicate that the B incorporation in Co-NCNTs can optimize the free energy of adsorbed hydrogen and facilitate the O2 adsorption and the protonation of the O2* species. Furthermore, the Co-BNCNTs-based zinc-air battery provides great battery performance with a high power density and discharge-charge durability.

Application of physics-informed neural networks (PINNs) solution to coupled thermal and hydraulic processes in silty sands

The accurate modeling of water and heat transport in soils is crucial for both geo-environmental and geothermal engineering. Traditional modeling methods are problematic because they require well-defined boundaries and initial conditions. Recently, physics-informed neural networks (PINNs), which incorporate partial differential equations (PDEs) to solve forward and inverse problems, have attracted increasing attention in machine learning research. In this study, we applied PINNs to tackle hydraulic and thermal transport coupling forward problems in silty sands. A fully connected deep neural network was utilized for training. This neural network model leverages automatic differentiation to apply the governing equations as constraints, based on the mathematical approximations established by the neural network itself. We conducted forward problems and compared the solutions derived from PINNs with those from Finite Element Method (FEM) simulations. The forward problem results demonstrate the PINNs model’s capability in predicting hydraulic transport, heat transport, and thermal–hydraulic coupling in silty sands under various boundary conditions. The PINNs exhibited great performance in simulating the thermal–hydraulic coupling problem. The accuracy of the PINNs solutions shows its potential for simulation in geotechnical engineering.

Antisense oligonucleotide as novel therapies for neurogenetic disorders

Antisense oligonucleotide (ASO) was discovered several decades ago and initially used only as a research tool in the laboratory. In recent years, several ASO therapeutics have been developed for neurological disorders. Some of these therapeutics, including eteplirsen, golodirsen, viltolarsen, nusinersen and inotersen, have been approved by the Food and Drug Administration (FDA) and begun to draw the public's attention as an effective therapeutic approach. These novel therapeutics have shown great performance, while many similar therapeutics are under investigation and in clinical trials. This n-of-1 precision medicine may start a new chapter in the paradigm of therapeutics. Clinicians, clinical geneticists, and genetic counselors may know about this novel therapy, but very few may understand the background in details. During genetic counseling, they have the responsibility to convey the effectiveness, side effects and cost of such therapies to patients and their families. As these target therapies will require precise genetic diagnosis before treatment, healthcare professionals and genetic counselors play a vital role in relating the patients to the corresponding ASO drugs. This review has elaborated the mechanism of ASO therapies, including basic rationales, modifications, side effects and delivery routes. It also systemically summarized the FDA-approved ASO therapeutics and their applications for various neurological disorders, and discussed the limitations and challenges the real-world market may face and issues genetic counselor should take into consideration in the near future.

Enhanced Electrochemical Performance of Dual-Ion Batteries with T-Nb2O5/Nitrogen-Doped Three-Dimensional Porous Carbon Composites.

Niobium pentoxide (T-Nb2O5) is a promising anode material for dual-ion batteries due to its high lithium capacity and fast ion storage and release mechanism. However, T-Nb2O5 suffers from the disadvantages of poor electrical conductivity and fast cycling capacity decay. Herein, a nitrogen-doped three-dimensional porous carbon (RMF) was prepared for loading niobium pentoxide to construct a composite system with excellent electrochemical performance. The obtained T-Nb2O5/RMF composites have a well-developed pore structure and a high specific surface area of 1568.5 m2 g-1, which could effectively increase the contact area between the material and electrolyte, improving the electrode reaction and lithium-ion transfer diffusion. Nitrogen doping increased surface polarity, creating more active sites and accelerating the electrode reaction rate. The introduction of T-Nb2O5 imparted high power density and excellent cycling stability to the battery. The composites exhibited good electrochemical performance when used as dual-ion battery anode, with a stable cycle life of 207.2 mA h g-1 at 1 A g-1 current density after 650 cycles and great rate performance of 181.5 mA h g-1 at 5A g-1 was also obtained. This work provides the possibility for applying T-Nb2O5/RMF as an anode for a high-performance dual-ion battery.

Bio particle swarm optimization and reinforcement learning algorithm for path planning of automated guided vehicles in dynamic industrial environments

Automated guided vehicles play a crucial role in transportation and industrial environments. This paper presents a proposed Bio Particle Swarm Optimization (BPSO) algorithm for global path planning. The BPSO algorithm modifies the equation to update the particles’ velocity using the randomly generated angles, which enhances the algorithm’s searchability and avoids premature convergence. It is compared with Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Transit Search (TS) algorithms by benchmark functions. It has great performance in unimodal optimization problems, and it gains the best fitness value with fewer iterations and average runtime than other algorithms. The Q-learning method is implemented for local path planning to avoid moving obstacles and combines with the proposed BPSO for the safe operations of automated guided vehicles. The presented BPSO-RL algorithm combines the advantages of the swarm intelligence algorithm and the Q-learning method, which can generate the globally optimal path with fast computational speed and support in dealing with dynamic scenarios. It is validated through computational experiments with moving obstacles and compared with the PSO algorithm for AGV path planning.

Heat-resistant boron Carbonitride with nitrogen-vacancies and oxygen-doping for efficient tetracycline removal: Adsorption characteristics, mechanism and regeneration

The increasing environmental risks associated with tetracycline (TC) have been of widespread interest in the development of adsorbents for the efficient treatment of TC. This work synthesized a high-performance boron carbonitride (BCNvO) with nitrogen-vacancies (Nv) and oxygen-doping (Od) via a hydrogen peroxide mediated strategy. The adsorption capacity of BCNvO-1 for TC (202.85 mg/g) is 1.79 times higher than that of BCN (113.08 mg/g). The adsorption process is well described by the pseudo-second-order kinetic and controlled by surface and intra-pore diffusion. Langmuir can well characterize isothermal adsorption with the fitted maximum adsorption up to 440.53 mg/g. Thermodynamics indicates a spontaneous and endothermic progress in BCNvO-1 adsorption. The abundant pore structures promote pore filling of TC by BCNvO-1. Nv and Od enhance BCNvO-1 proton capture, and the captured protons strengthen hydrogen bonding and electrostatic interactions during adsorption. The functional groups, aromatic regions, and BN rings on BCNvO-1 are involved in hydrogen bonding and π-π stacking to TC. BCNvO-1 has high adsorption performance, great environmental appropriateness and thermal regeneration performance, suggesting good prospects for its potential application in environmental wastewater treatment.

The impact of anions on electrooxidation of perfluoroalkyl acids by porous Magnéli phase titanium suboxide anodes.

Previous studies have indicated the great performance of electrooxidation (EO) to mineralize per- and polyfluoroalkyl substances (PFASs) in water, but different anions presented in wastewater may affect the implementation of EO treatment in field applications. This study invetigated EO treatment of perfluorooctane sulfonate (PFOS) and perfluorooctanoic acid (PFOA), two representative perfluoroalkyl acids (PFAAs), using porous Magnéli phase titanium suboxide anodes in electrolyte solutions with different anions present, including NO3-, SO42-, CO32- and PO43-. The experiment results indicate that CO32- enhanced PFAS degradation, while NO3- suppressed the degradation reactions with its concentration higher than 10 mM. SO42- and PO43- exhibited less impact. Further studies with electrochemical characterizations and radical quenching experiments illustrate the mechanisms of how the anions may impact EO performance.

Reproducibility of automatic adipose tissue segmentation using proton density fat fraction images between 1.5 and 3.0 T magnetic resonance.

Deep learning (DL)-based adipose tissue segmentation methods have shown great performance and efficacy for adipose tissue distribution analysis using magnetic resonance (MR) images, an important indicator of metabolic health and disease. The aim of this study was to evaluate the reproducibility of whole-body adipose tissue distribution analysis using proton density fat fraction (PDFF) images at different MR strengths. A total of 24 volunteers were imaged using both 1.5 and 3.0 T clinical MR imaging (MRI) scanners at two sites. Whole-body PDFF images were acquired covering from neck to knee, and grouped into three subparts: thorax, abdomen, and thigh. The PDFF images were then segmented automatically into subcutaneous adipose tissue (SAT) and internal adipose tissue (IAT) using a U-Net DL model. The volumes of whole body (WH), total adipose tissue (TAT), SAT, and IAT for total body and each subpart were measured, and the volume ratio of TAT/WH, SAT/WH, IAT/WH, SAT/TAT, and IAT/SAT were also calculated. Additionally, the reproducibility of PDFF values of SAT and IAT for total body and subparts were evaluated. The intraclass correlation coefficient (ICC) and Pearson correlation coefficient of these volumes and volume ratios in whole-body between the two scanners were very close to one. The paired t-test and Bland-Altman plots for all comparisons showed no significant differences (P>0.05) when comparing the results from the 1.5 T scanner minus those from the 3.0 T scanner. The mean bias for WH, TAT, SAT, and IAT was -6.89 cm3 (P=0.95), -67.21 cm3 (P=0.40), 19.31 cm3 (P=0.74), and -18.84 cm3 (P=0.69), respectively. Good reproducibility performances were also found in each subpart, except for the indices of IAT volume, TAT/WH ratio, and SAT/TAT ratio in the thorax due to different susceptibility effects across MR strengths. The results also demonstrated good reproducibility between PDFF values of the two scanners with the mean bias for WH, thorax, abdomen, and thigh being -0.19% (P=0.219), -0.30% (P=0.118), 0.086% (P=0.494), and 0.24% (P=0.186) for SAT, respectively, as well as 0.35% (P=0.136), 0.46% (P=0.150), 0.58% (P=0.255), and 0.40% (P=0.169) for IAT, respectively. Good reproducibility of whole-body adipose tissue distribution analysis using the DL method between 1.5 and 3.0 T MR images was demonstrated, which may facilitate the whole-body adipose tissue distribution analysis using the quantitative MR-PDFF images.

Acquisition of musical skills and abilities in older adults—results of 12 months of music training

BackgroundOlder adults can acquire new skills across different domains. Practicing a musical instrument has been identified as a promising activity for improving cognition, promoting well-being, and inducing brain plasticity in older individuals. However, the mechanisms of these changes are still poorly understood. This study aims to assess musical skill acquisition in musically naïve older adults over one year of practice, focusing on individual factors influencing this process and the relations between musical skills.MethodsOne hundred fifty-six healthy older adults (age = 69.5 years ± 3.2) from Hannover and Geneva with no prior musical training participated in weekly piano practice (PP) or ‘music culture’ (MC) sessions over a one-year period. Baseline assessments included the Cognitive Reserve Index questionnaire (CRIq) and Cognitive Telephone Screening Instrument (CogTel). Musical abilities were measured using piano performance ratings (PP group), music quizzes (MC group), and aptitude tests such as the Beat Alignment Test (BAT), Melodic Discrimination Test (MDT) and Midi Scale Analysis (MSA) at baseline and six-, twelve and 18-month timepoints. The interrelationship between musical abilities was investigated through correlational analyses, and changes impacted through individual characteristics were modeled using Bayesian statistics.ResultsThe PP group demonstrated moderate improvements in piano articulation and dynamics, while the MC group achieved higher scores in the music quiz. Modest improvements in MDT and MSA were observed in both groups, with the PP group showing greater progress is MSA. Higher global cognitive functioning and musical sophistication was associated with greater performance in MDT for both groups. We did not identify any links between individual characteristics, like age, CogTel, CRIq, and musical sophistication, and improvement in musical aptitude tests. Changes in different musical aptitude test scores were not correlated, and neither the development of piano skills nor the music quiz correlated with initial performances on the musical aptitude tests.ConclusionMusically naïve older adults can acquire diverse musical abilities, which progress independently, suggesting a broad spectrum of musical abilities rather than a single general musical aptitude. Future research should also explore genetic and psychosocial factors influencing musical development.Trial RegistrationThe Ethikkomission of the Leibniz Universität Hannover approved the protocol on 14.08.17 (no. 3604–2017), the neuroimaging part and blood sampling was approved by the Hannover Medical School on 07.03.18. The full protocol was approved by the Commission cantonale d’éthique de la recherche de Genève (no. 2016–02224) on 27.02.18 and registered at clinicaltrials.gov on 17.09.18 (NCT03674931, no. 81185).

CIRCULARLY SHAPED METAMATERIAL FRACTAL RECONFIGURABLE ANTENNA FOR 5G NETWORKS

This research is an attempt to highlight that the presented antenna design is proposed for modern wireless communication systems, including 5G networks. The proposed antenna design is structured to realize a reconfigurable antenna terminal to suit smart wireless systems. Therefore, the proposed antenna is structured from twelve-unit cells of metamaterial (MTM) inclusions. For this, effectively, the proposed antenna profile is reduced to 0.21λ0, where λo is the free space wavelength at 2.45GHz, which occupies 40 × 30 mm2 equivalently. This is accomplished by using T-resonator inductors to conduct 3rd iteration Hilbert-shaped MTM inclusions. The proposed antenna is printed on a substrate to cover the frequency bands from 2.6GHz to 4.4GHz. To optimize the antenna performances, a numerical-parametric analysis based on CST MWS commercial software package formulations is invoked for this study. The suggested antenna performance is numerically evaluated for validation using the HFSS software suite. The antenna has great performance while being sufficiently small to be used with integrated electronics.

Pencak Silat as a Comprehensive Method of Mental, Physical, and Spiritual Growth: A Systematic Review

Objectives. The study aimed to better understand physical performance by reviewing the literature on the role that Pencak Silat plays in developing elite athletes. The traditional Indonesian martial art Pencak Silat provides a comprehensive method encompassing mental, physical, and spiritual growth. Materials and methods. The literature regarding Pencak Silat impact on the development of elite athletes was analysed using a PRISMA filter. This study used a systematic evaluation of pertinent literature to determine Pencak Silat impact on mental endurance, motor ability, and physical fitness. The preparation of Pencak Silat athletes for competition through physical conditioning was addressed in articles published between 2019 and 2024. Electronic searches were conducted through Web of Science, PubMed, and Scopus. Having reviewed the literature, all the works that discuss the development of elite athletes in Pencak Silat were compiled. Results. This study’s thorough literature review revealed that Pencak Silat contributes to enhancing the resilience, agility, flexibility, and strength of athletes’ bodies. Furthermore, the Pencak Silat concept imparts the courage, discipline, and strategic thinking abilities that are necessary to become exceptional athletes. The results of this study also demonstrate Pencak Silat capacity to achieve a balance between mental and physical attributes, which is crucial for overcoming obstacles in both domestic and international competition. It is anticipated that the study’s findings will contribute to a better understanding of Pencak Silat as an all-encompassing and excellent instrument for athlete development. Conclusions. Pencak Silat is a method of self-development that helps athletes become exceptional in all spheres of their lives — physically, psychologically, and spiritually. It is not just a martial art. Pencak Silat, which emphasizes endurance, mental toughness, and strategy, is an effective instrument for attaining great performance in sports.

Electrode Selection and Catalyst Evaluation in Hydrogen Production from Alkaline Water Electrolysis: A Review

Generating hydrogen, through alkaline water electrolysis shows promise as an energy source. This review delves into the significance of choosing the electrodes and evaluating catalysts to enhance the efficiency and performance of hydrogen production. It summarizes the activation energy and losses linked to reactions in alkaline electrolysis emphasizing the necessity for electrode materials and catalysts. The review also touches upon challenges such as electricity consumption and platinum group metal based electro catalysts proposing various electrode materials and catalysts with superior activity and selectivity for hydrogen production. Additionally, it discusses electrolysis cell designs that facilitate separating by-products from hydrogen gas. The study reveals that with low over potentials of 70, 318, and 361 mV at 10, 500, and 1000 mA cm−2, respectively, NiOx/NF exhibits strong alkaline hydrogen evolution activity, resulting in great performance in alkaline HER. Moreover, it outlines advancements in alkaline water electrolysis technology focusing on enhanced efficiency and reduced operating costs associated with electricity consumption. Overall this review underscores the role of selecting electrodes and evaluating catalysts in optimizing hydrogen production from alkaline water electrolysis.

University Management System: A Modern Approach to Academic Administration

The **University Board Framework (UMS): A Cutting-Edge Way to Manage Academic Affairs** is purposed to streamline the complex and diverse processes within academic institutions. Since college expands and adapts with computerized progressions, well-timed administration frameworks have developed, enabling the institution to take up managerial challenges and increase independent direction and the benefits for clients in their activities and services. The students, staff, and pupils, therefore, expect great performance. This research analyses and implementation of an UMS zeroing in on coordination functions like student enrollment, managing courses, tracking participation, assessments, and results processing together within an integrated platform. Utilizing cutting-edge innovations, for example, distributed computing, information bases, and easy to use interfaces, the framework is supposed to streamline asset portion, mechanize routine undertakings, and guarantee information security. The review underlines that the framework has some key parts, including continuous updates, dashboard customization, and smooth specialized apparatuses for any partners. It likewise ponders difficulties during execution, fundamentally information movement and client acknowledgment, while proposing adaptable ways of beating these issues. Beginning testing demonstrates the way that the framework can upgrade functional productivity, decrease manual blunders, and give complete examination to scholastic and managerial independent direction. In this manner, UMS gets rid of any impediment between customary administration rehearses and cutting-edge mechanical arrangements, furnishing an effective framework for developing scholarly greatness and institutional advancement. This investigation focuses on the revolutionary energy of innovation in schooling and calls for the wide-scale usage of College The board Frameworks in all establishments across the globe.

Surface Hydroxyl Groups Functionalized Porous CeO2 for Enhanced Selective Adsorption of As(III).

The adsorption technique has been considered as one of the promising methods to remove arsenic ions in aqueous systems. However, the adsorbents are usually poorly selective and have low capacity. Herein, a kind of porous CeO2 containing surface hydroxyl group which synthesized facilely possesses the great performance of selective adsorption of As(III) with 98.57% removal against 14 other coexisting metal ions. The results showed that the processes of As(III) and As(V) uptake were heterogeneous monolayer adsorptions and included external and intraparticle diffusions. The adsorption capacities at pH 2.5 reached 111.24 and 56.89 mg/g for As(III) and As(V), respectively. In addition, it also showed that As(III) could be oxidized to As(V) in the adsorption process. Density functional theory calculations revealed that the OH group in H3AsO30 and the As atom in H3AsO40 have affinity with lattice oxygen (O2-) in CeO2, while the O atom in H2AsO4- preferred the Ce atom in CeO2. This study provides a novel porous CeO2 containing hydroxyl groups for selective and efficient removal of arsenic and elucidates the mechanism of As(III) and As(V) adsorptions.