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Chronic low back pain is a leading cause of disability worldwide. A clinical model for its cause is lacking. Defining a cause based clinical model and a framework of understanding back pain in terms of peripheral structural and central functional pain is essential for optimal management. We describe the results of the largest published audit of 41 chronic low back pain patients, receiving outpatient sublingual ketamine therapy for defined central functional pain along with conventional peripheral structural pain management. Our clinical model assigns Movement Dysfunction as the primary cause for low back pain symptoms and restores it with Movement Therapy focused rehabilitation which is also defined. Patients were derived from a tertiary single neurosurgical specialist practice in Brisbane Australia over a three year period. Severe pain and disability measurements more than halved and only 13% of patients ceased ketamine prematurely due to predominantly non-sinister side effects common to all pharmaceutical therapies. All other surveyed metrics of utility were highly favourable in this challenging cohort of chronic back pain patients biased to poor outcomes. Outpatient ketamine maintains high efficacy and safety used in conjunction with a unique clinical model that describes chronic low back pain. This paper builds on our previous publications that describe the disease of movement dysfunction as an integral factor to the development of a cause based clinical model for the condition of chronic low back pain symptoms. Our clinical application of this model, applying the necessary dual approach of controlling symptoms arising from peripheral structural pain and central functional pain in conjunction with elimination of root causation has shown favourable outcomes in patients with high levels of pain and disability based on their tertiary referral origin and high Oswestry Disability Scores. Removing chronic low back pain from its position as one of the world's leading causes of pain and disability is more likely if the rehabilitation industry can replicate and test treatment algorithms based around established clinical models of disease which is the important subject of this paper. [ABSTRACT FROM AUTHOR]

The rapid spread of Internet of Things technologies has enabled a continuous monitoring of indoor environmental quality in office environments by integrating monitoring devices equipped with low-cost sensors and cloud platforms for data storage and visualization. Critical aspects in the development of such monitoring systems are effective data acquisition, processing, and visualization strategies, which significantly influence the performance of the system both at monitoring device and at cloud platform level. This paper proposes novel strategies to address the challenges in the design of a complete monitoring system for indoor environmental quality. By adopting the proposed solution, one can reduce the data rate transfer between the monitoring devices and the server without loss of information, as well as achieve efficient data storage and aggregation on the server side to minimize retrieval times. Finally, enhanced flexibility in the dashboard for data visualization is obtained, thus enabling graph modifications without extensive coding efforts. The functionality of the developed system was assessed, with the collected data in good agreement with those from other instruments used as references. [ABSTRACT FROM AUTHOR]

The Woka-Cuona rift zone on the southeastern side of the Qinghai-Tibet Plateau is characterized by complex geological background conditions, comprising three independent or semi-grabens that traverse from south to north across the Himalayan and Gangdise terranes. Conducting research on the distribution patterns and genesis mechanisms of geothermal resources within the Woka-Cuona rift zone has certain guiding significance for understanding the genesis mechanisms of the geothermal system in the southern Tibetan rift and its exploitation. This paper utilized methods such as data collection, ground investigations, and geochemical analyses to analyze the distribution characteristics and evolutionary processes of geothermal waters in the Cuona rift area based on the geological background conditions of the study area. The research findings demonstrate a significant correlation between the occurrence of geothermal waters in the Cuona rift zone and geological structures, with most geothermal waters primarily distributed near intersections of graben boundary faults and east–west-trending faults. Different regions exhibit variations in the intensity of geothermal activity and geochemical characteristics, with the genesis of geothermal waters associated with deep magmatic activity, characterized by Na+ and K+ as the primary cations and Cl− as the primary anions. Geothermal waters mainly originate from atmospheric precipitation and snowmelt water from surrounding mountainous areas, with recharge elevations ranging from 4500 to 6200 m and an average elevation of 5400 m. [ABSTRACT FROM AUTHOR]

As a highly efficient and flexible data collection device, Unmanned Aerial Vehicles (UAVs) have gained widespread application because of the continuous proliferation of Internet of Things (IoT). Addressing the high demands for timeliness in practical communication scenarios, this paper investigates multi-UAV collaborative path planning, focusing on the minimization of weighted average Age of Information (AoI) for IoT devices. To address this challenge, the multi-agent twin delayed deep deterministic policy gradient with dual experience pools and particle swarm optimization (DP-MATD3) algorithm is presented. The objective is to train multiple UAVs to autonomously search for optimal paths, minimizing the AoI. Firstly, considering the relatively slow learning speed and susceptibility to local minima of neural network algorithms, an improved particle swarm optimization (PSO) algorithm is utilized for parameter optimization of the multi-agent twin delayed deep deterministic policy gradient (MATD3) neural network. Secondly, with the introduction of the dual experience pools mechanism, the efficiency of network training is significantly improved. Experimental results show DP-MATD3 outperforms MATD3 in average weighted AoI. The weighted average AoI is reduced by 33.3% and 27.5% for UAV flight speeds of v = 5 m/s and v = 10 m/s, respectively. [ABSTRACT FROM AUTHOR]

Background: Person-centred healthcare focuses on placing the beliefs and values of service users at the centre of decision-making and creating the context for practitioners to do this effectively. Measuring the outcomes arising from person-centred practices is complex and challenging and often adopts multiple perspectives and approaches. Few measurement frameworks are grounded in an explicit person-centred theoretical framework. Aims: In the study reported in this paper, the aim was to develop a valid and reliable instrument to measure the experience of person-centred care by service users (patients)–The Person-centred Practice Inventory-Care (PCPI-C). Methods: Based on the 'person-centred processes' construct of an established Person-centred Practice Framework (PCPF), a service user instrument was developed to complement existing instruments informed by the same theoretical framework–the PCPF. An exploratory sequential mixed methods design was used to construct and test the instrument, working with international partners and service users in Scotland, Northern Ireland, Australia and Austria. A three-phase approach was adopted to the development and testing of the PCPI-C: Phase 1 –Item Selection: following an iterative process a list of 20 items were agreed upon by the research team for use in phase 2 of the project; Phase 2 –Instrument Development and Refinement: Development of the PCPI-C was undertaken through two stages. Stage 1 involved three sequential rounds of data collection using focus groups in Scotland, Australia and Northern Ireland; Stage 2 involved distributing the instrument to members of a global community of practice for person-centred practice for review and feedback, as well as refinement and translation through one: one interviews in Austria. Phase 3: Testing Structural Validity of the PCPI-C: A sample of 452 participants participated in this phase of the study. Service users participating in existing cancer research in the UK, Malta, Poland and Portugal, as well as care homes research in Austria completed the draft PCPI-C. Data were collected over a 14month period (January 2021-March 2022). Descriptive and measures of dispersion statistics were generated for all items to help inform subsequent analysis. Confirmatory factor analysis was conducted using maximum likelihood robust extraction testing of the 5-factor model of the PCPI-C. Results: The testing of the PCPI-C resulted in a final 18 item instrument. The results demonstrate that the PCPI-C is a psychometrically sound instrument, supporting a five-factor model that examines the service user's perspective of what constitutes person-centred care. Conclusion and implications: This new instrument is generic in nature and so can be used to evaluate how person-centredness is perceived by service users in different healthcare contexts and at different levels of an organisation. Thus, it brings a service user perspective to an organisation-wide evaluation framework. [ABSTRACT FROM AUTHOR]

Linked open data on collection agents contribute to increased discoverability, accessibility and transparency of natural history collections. Despite major efforts to digitise and open up museum and university object collections, related information is often stored in internal resources. This paper describes a project conducted at the Museum für Naturkunde Berlin (MfN) contributing to its collection disclosure and development initiatives. Information on historical collectors and other collection agents was transferred from the internal MfN collector wiki to Wikidata. For a total of 600 collection agents, existing Wikidata items were enriched or new items created. Special emphasis was put on linking these people to the Museum, to document their affiliation with the MfN, its collection and its archive. Within the project, an open participatory approach was taken. Several Wikidata edit-a-thons were organised to test this collaborative and innovative format for possible future application by the Museum. By opening up institutional silos and openly sharing data on agents connected to museum holdings, these data become more widely accessible and reusable, for example, as a resource for transdisciplinary provenance research. [ABSTRACT FROM AUTHOR]

Terrorism research has gained much traction since the 9/11 attacks, but some sub genres of terrorism, such as right-wing terrorism, have remained under-studied areas. Unsurprisingly data sources to study these phenomena are scarce and frequently face unique data collection obstacles. This paper explores five major, social-scientific terrorism databases in regards to data on right-wing terrorist events. The paper also provides an in-depth examination of the utilization of criminal legal proceedings to research right-wing terrorist acts. Lastly, legal case databases are introduced and discussed to show the lack of available court information and case proceedings in regards to right-wing terrorism. [ABSTRACT FROM AUTHOR]

This paper analyzes the exceeding of selected presidential powers regarding appointments in the Czech Republic. Included in the analysis were the presidential appointment powers set out in the Constitution, including the appointment of professors and rectors. All controversial cases of appointments from 1993 to 2023 involving Václav Havel, Václav Klaus, and Miloš Zeman were included in the analysis. The data collection presented 58 controversial cases, of which the author identified 19 exceeding cases. The results of this thesis provide insights into how presidential appointment powers are interpreted and how theoretical interpretation clashes with the practice of individual presidents. [ABSTRACT FROM AUTHOR]

During the sowing operation, the consistency and stability of the row dispenses of a seeder are important indicators for evaluating sowing performance. A multi-channel real-time weighing device was designed to study and analyze the consistency of row dispenses in seeder under identical conditions, as well as the stability of dispenses from a seed dispenser. This device utilized two Arduino boards as controllers for data acquisition, processing, and transmission. Based on the Serial Peripheral Interface (SPI), data exchange between multiple Arduinos was achieved in a master-slave configuration, allowing for data acquisition from 14 pressure sensors in a one-master, one-slave setup. Pressure data was collected using pressure sensors, HX711 converter chips, and Arduino. Lab VIEW was utilized as the upper computer to read data from the Arduino host serial port and provide real-time display and storage. This paper presents the structural and working principles of the device. Experimental tests on the weighing unit were conducted using a test bench to evaluate measurement errors. The absolute error mean ranged from 0.143 g to 0.262 g. Additionally, the impact of the seed impact force on the error was simulated using EDEM. The device was used to evaluate a six-row wheat seeder under the experimental conditions of the groove wheel having a length of 2.5 cm and a groove wheel speed of 25 r/min. The maximum coefficient of variation for seeding stability (Y1) was 2.38%, the minimum was 0.83%, and the mean was 1.21%. The coefficient of variation for consistency of seeding in rows (Y2) was 1.86%. [ABSTRACT FROM AUTHOR]

Accurate surveying of vegetated areas presents significant challenges due to obstructions that obscure visibility and compromise the precision of measurements. This paper introduces a methodology employing the DJI Zenmuse L2 Light Detection and Ranging (LiDAR) sensor, which is mounted on a Matrice 350 RTK drone. The DJI Zenmuse L2 sensor excels at capturing detailed terrain data under heavy foliage, capable of collecting 1.2 million points per second and offering five returns, thus enhancing the sensor's ability to detect multiple surface responses from a single laser pulse. In a case study conducted near a creek heavily obscured by tree coverage, traditional aerial imaging techniques are found insufficient for capturing critical topographic features, such as the creek banks. Employing LiDAR, the study aims to map these obscured features effectively. The collected data is processed using DJI Terra software, which supports the accurate projection and analysis of the LiDAR data. To validate the accuracy of the data collected from the LiDAR sensor, traditional survey methods are deployed to ground truth the data and provide an accuracy assessment. Ground control points (GCPs) are established using a GNSS receiver to provide geodetic coordinates, which then assist in setting up a total station. This total station measures vertical and horizontal angles, as well as the slope distance from the instrument to positions underneath the tree coverage on the ground. These measurements serve as checkpoints to validate the accuracy of the LiDAR data, thus ensuring the reliability of the survey. This paper discusses the potential of integrating LiDAR data with traditional surveying data, which is expected to enhance the ability of surveyors to map environmental features efficiently and accurately in complex and vegetated terrains. Through detailed procedural descriptions and expected outcomes, the study aims to provide valuable insights into the strategic application of geospatial technologies to overcome common surveying challenges. [ABSTRACT FROM AUTHOR]

Anomalous bright spots, called the string of bead-like response, are typical seismic migration profile features in carbonate fractured reservoirs in the Tarim Basin, and they are indicators of high-quality oil and gas reservoirs. Correctly recognizing the correspondence between fractures and the SBLR can contribute to the efficient drilling of target carbonate fractured reservoirs. Physical models can describe fractured reservoirs more directly and efficiently than real situations and have obvious advantages in accurately and quantitatively designing parameters such as dipping angles and the number of layers of fractured reservoirs. Under such a background, according to the real tectonic characteristics of the Tarim Basin, among the main hydrocarbon reservoirs, fractured reservoirs with various parameters were designed and a physical model was constructed according to the real stratigraphic parameters. After seismic data acquisition and processing, the response characteristics of the string of bead-like response were extracted and summarized from seismic migration profiles for all fractured reservoirs, which provided targeted analyses and discussions on the fracture dipping angle, number of fracture layers, overlying stratigraphic influences, and planar attributes of the fractured reservoirs. In general, the larger the fracture dip, the more difficult it is to identify, while the slope of reflection strength and maximum absolute amplitude attributes can be important markers for fractured reservoir identification. The physical modeling study of fractured reservoirs in this paper can provide a basis for the analysis and prediction of carbonate fractured reservoirs in the Tarim Basin. [ABSTRACT FROM AUTHOR]

The main goal of the TRacking Aerosol Convection interactions ExpeRiment (TRACER) project was to further understand the role that regional circulations and aerosol loading play in the convective cloud life cycle across the greater Houston, Texas, area. To accomplish this goal, the United States Department of Energy and research partners collaborated to deploy atmospheric observing systems across the region. Cloud and precipitation radars, radiosondes, and air quality sensors captured atmospheric and cloud characteristics. A dense lower-atmospheric dataset was developed using ground-based remote sensors, a tethersonde, and uncrewed aerial systems (UASs). TRACER-UAS is a subproject that deployed two UAS platforms to gather high-resolution observations in the lower atmosphere between 1 June and 30 September 2022. The University of Oklahoma CopterSonde and the University of Colorado Boulder RAAVEN (Robust Autonomous Aerial Vehicle – Endurant Nimble) were flown at two coastal locations between the Gulf of Mexico and Houston. The University of Colorado Boulder RAAVEN gathered measurements of atmospheric thermodynamic state, winds and turbulence, and aerosol size distribution. Meanwhile, the University of Oklahoma CopterSonde system operated on a regular basis to resolve the vertical structure of the thermodynamic and kinematic state. Together, a complementary dataset of over 200 flight hours across 61 d was generated, and data from each platform proved to be in strong agreement. In this paper, the platforms and respective data collection and processing are described. The dataset described herein provides information on boundary layer evolution, the sea breeze circulation, conditions prior to and nearby deep convection, and the vertical structure and evolution of aerosols. The quality-controlled TRACER-UAS observations from the CopterSonde and RAAVEN can be found at 10.5439/1969004 (Lappin, 2023) and 10.5439/1985470 (de Boer, 2023), respectively. [ABSTRACT FROM AUTHOR]

In mobile eye-tracking research, the automatic annotation of fixation points is an important yet difficult task, especially in varied and dynamic environments such as outdoor urban landscapes. This complexity is increased by the constant movement and dynamic nature of both the observer and their environment in urban spaces. This paper presents a novel approach that integrates the capabilities of two foundation models, YOLOv8 and Mask2Former, as a pipeline to automatically annotate fixation points without requiring additional training or fine-tuning. Our pipeline leverages YOLO's extensive training on the MS COCO dataset for object detection and Mask2Former's training on the Cityscapes dataset for semantic segmentation. This integration not only streamlines the annotation process but also improves accuracy and consistency, ensuring reliable annotations, even in complex scenes with multiple objects side by side or at different depths. Validation through two experiments showcases its efficiency, achieving 89.05% accuracy in a controlled data collection and 81.50% accuracy in a real-world outdoor wayfinding scenario. With an average runtime per frame of 1.61 ± 0.35 s, our approach stands as a robust solution for automatic fixation annotation. [ABSTRACT FROM AUTHOR]

Automated test generation tools enable test automation and further alleviate the low efficiency caused by writing hand-crafted test cases. However, existing automated tools are not mature enough to be widely used by software testing groups. This paper conducts an empirical study on the state-of-the-art automated tools for Java, i.e., EvoSuite, Randoop, JDoop, JTeXpert, T3, and Tardis. We design a test workflow to facilitate the process, which can automatically run tools for test generation, collect data, and evaluate various metrics. Furthermore, we conduct empirical analysis on these six tools and their related techniques from different aspects, i.e., code coverage, mutation score, test suite size, readability, and real fault detection ability. We discuss about the benefits and drawbacks of hybrid techniques based on experimental results. Besides, we introduce our experience in setting up and executing these tools, and summarize their usability and user-friendliness. Finally, we give some insights into automated tools in terms of test suite readability improvement, meaningful assertion generation, test suite reduction for random testing tools, and symbolic execution integration. [ABSTRACT FROM AUTHOR]

Background and Objectives: Children with autism spectrum disorder (ASD) continue to experience significant delays in diagnosis and interventions. One of the main factors contributing to this delay is a shortage of developmental-behavioral specialists. Diagnostic evaluation of ASD by primary care pediatricians (PCPs) has been shown to be reliable and to decrease the interval from first concern to diagnosis. In this paper, we present the results of a primary care ASD diagnosis program in which the PCP serves as the primary diagnostician and leverages the infrastructure of the primary care medical home to support the child and family during the pre- and post-diagnostic periods, along with data on parental satisfaction with this model. Methods: Retrospective data from a cohort of patients evaluated through this program were analyzed to determine the mean age at diagnosis and interval from referral for evaluation to diagnosis. We used survey methodology to obtain data from parents regarding their satisfaction with the process. Results: Data from 8 of 20 children evaluated from April 2021 through May 2022 showed a median age of diagnosis of 34.5 months compared to the national average of 49 months. Mean interval from referral for evaluation to diagnosis was 3.5 months. Parental survey responses indicated high satisfaction. Conclusions: This model was successful in shortening the interval from referral to diagnosis resulting in significant decrease of age at diagnosis compared with the national average. Widespread implementation could improve access to timely diagnostic services and improve outcomes for children with ASD. [ABSTRACT FROM AUTHOR]

Objective: Energy usage in has been increased due to the rising demand of cloud infrastructure. The government policy has been focused on building the green IT data center. The energy data need to be collected in order to monitor the energy usage. However, in an old typical data center, the building has been built with no support of such data collection. In this research, we aim to design the energy data collection system for our existing data center, a case study of data center in Thailand at the university. Based on the collected data, an energy usage monitoring system and prediction can be developed. Methods: In the case study of Kasetsart University data center, the building and electric layouts were predetermined. The building layout and existing IT hardware were investigated. We designed the meter types and the number of meters to be installed for the building the energy data collection system. The corresponding database system was also designed for data logging, data visualization and analysis purpose. Results: As a result, 25 installed meters along with the add-on network system were installed for logging data. A data usage example was demonstrated by building the data visualization and analysis. The presented 1 year dataset collected showed the changes of energy usages which can be used to compare with real activities happening in the campus. This encourages the integration of other related environment data such as outside temperature which may affect the electric billing cost. The dataset can be used for prediction of the electric usage; thus, the policy for reducing the electric billing cost could be established. In this paper, as a data note, we focus on the methodology of data collection required for data center. [ABSTRACT FROM AUTHOR]

Rotary machines commonly use rolling element bearings to support rotation of the shafts. Most machine performance imperfections are related to bearing defects. Thus, reliable bearing condition monitoring systems are critically needed in industries to provide early warning of bearing fault so as to prevent machine performance degradation and reduce maintenance costs. The objective of this paper is to develop a smart monitoring system for real-time bearing fault detection and diagnostics. Firstly, a smart sensor-based data acquisition (DAQ) system is developed for wireless vibration signal collection. Secondly, a modified variational mode decomposition (MVMD) technique is proposed for nonstationary signal analysis and bearing fault detection. The proposed MVMD technique has several processing steps: (1) the signal is decomposed into a series of intrinsic mode functions (IMFs); (2) a correlation kurtosis method is suggested to choose the most representative IMFs and construct the analytical signal; (3) envelope spectrum analysis is performed to identify the representative features and to predict bearing fault. The effectiveness of the developed smart sensor DAQ system and the proposed MVMD technique is examined by systematic experimental tests. [ABSTRACT FROM AUTHOR]

With the emergence of wireless rechargeable sensor networks (WRSNs), the possibility of wirelessly recharging nodes using mobile charging vehicles (MCVs) has become a reality. However, existing approaches overlook the effective integration of node energy replenishment and mobile data collection processes. In this paper, we propose a joint energy replenishment and data collection scheme (D-JERDG) for WRSNs based on deep reinforcement learning. By capitalizing on the high mobility of unmanned aerial vehicles (UAVs), D-JERDG enables continuous visits to the cluster head nodes in each cluster, facilitating data collection and range-based charging. First, D-JERDG utilizes the K-means algorithm to partition the network into multiple clusters, and a cluster head selection algorithm is proposed based on an improved dynamic routing protocol, which elects cluster head nodes based on the remaining energy and geographical location of the cluster member nodes. Afterward, the simulated annealing (SA) algorithm determines the shortest flight path. Subsequently, the DRL model multiobjective deep deterministic policy gradient (MODDPG) is employed to control and optimize the UAV instantaneous heading and speed, effectively planning UAV hover points. By redesigning the reward function, joint optimization of multiple objectives such as node death rate, UAV throughput, and average flight energy consumption is achieved. Extensive simulation results show that the proposed D-JERDG achieves joint optimization of multiple objectives and exhibits significant advantages over the baseline in terms of throughput, time utilization, and charging cost, among other indicators. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) are becoming increasingly valuable as a new type of mobile communication device and autonomous decision-making device in many application areas, including the Internet of Things (IoT). UAVs have advantages over other stationary devices in terms of high flexibility. However, a UAV, as a mobile device, still faces some challenges in optimizing its trajectory for data collection. Firstly, the high complexity of the movement action and state space of the UAV's 3D trajectory is not negligible. Secondly, in unknown urban environments, a UAV must avoid obstacles accurately in order to ensure a safe flight. Furthermore, without a priori wireless channel characterization and ground device locations, a UAV must reliably and safely complete the data collection from the ground devices under the threat of unknown interference. All of these require the proposing of intelligent and automatic onboard trajectory optimization techniques. This paper transforms the trajectory optimization problem into a Markov decision process (MDP), and deep reinforcement learning (DRL) is applied to the data collection scenario. Specifically, the double deep Q-network (DDQN) algorithm is designed to address intelligent UAV trajectory planning that enables energy-efficient and safe data collection. Compared with the traditional algorithm, the DDQN algorithm is much better than the traditional Q-Learning algorithm, and the training time of the network is shorter than that of the deep Q-network (DQN) algorithm. [ABSTRACT FROM AUTHOR]

The growing availability of smart meter data has facilitated the development of energy-saving services like demand response, personalized energy feedback, and non-intrusive-load-monitoring applications, all of which heavily rely on advanced machine learning algorithms trained on energy consumption datasets. To ensure the accuracy and reliability of these services, real-world smart meter data collection is crucial. The Plegma dataset described in this paper addresses this need bfy providing whole- house aggregate loads and appliance-level consumption measurements at 10-second intervals from 13 different households over a period of one year. It also includes environmental data such as humidity and temperature, building characteristics, demographic information, and user practice routines to enable quantitative as well as qualitative analysis. Plegma is the first high-frequency electricity measurements dataset in Greece, capturing the consumption behavior of people in the Mediterranean area who use devices not commonly included in other datasets, such as AC and electric-water boilers. The dataset comprises 218 million readings from 88 installed meters and sensors. The collected data are available in CSV format. [ABSTRACT FROM AUTHOR]

While combinatorial genetic data collection from biological systems in which quantitative phenotypes are controlled by active and inactive alleles of multiple genes (multi-gene systems) is becoming common, a standard analysis method for such data has not been established. The currently common approaches have three major drawbacks. First, although it is a long tradition in genetics, modeling the effect of an inactive allele (a null mutant allele) contrasted against that of the active allele (the wild-type allele) is not suitable for mechanistic understanding of multi-gene systems. Second, a commonly-used additive model (ANOVA with interaction) mathematically fails in estimation of interactions among more than two genes when the phenotypic response is not linear. Third, interpretation of higher-order interactions defined by an additive model is not intuitive. I derived an averaging model based on algebraic principles to solve all these problems within the framework of a general linear model. In the averaging model: the effect of the active allele is contrasted against the effect of the inactive allele for easier mechanistic interpretations; there is mathematical stability in estimation of higher-order interactions even when the phenotypic response is not linear; and interpretations of higher-order interactions are intuitive and consistent—interactions are defined as the mean effects of the last active genes added to the system. Thus, the key outcomes of this study are development of the averaging model, which is suitable for analysis of multi-gene systems, and a new, intuitive, and mathematically and interpretationally consistent definition of a genetic interaction, which is central to the averaging model. [ABSTRACT FROM AUTHOR]

This paper explores the development of a Quadcopter-Rover System specifically designed for environmental survey applications. The system combines the capabilities of a quadcopter and a rover to provide a comprehensive and versatile solution for data collection and analysis. The paper presents a detailed overview of the system's modelling, design, and manufacturing of the two main components: the quadcopter and the differential wheel robot (the rover). The quadcopter’s main task is to carry the rover to/from the ground destination and collect aerial data while the rover`s main task is ground exploration and data collection. The paper discusses the development of a robust and efficient control algorithm that enables autonomous and coordinated operation between the quadcopter and the rover. Experimental results demonstrate the system's effectiveness in conducting environmental surveys, showcasing its ability to accurately navigate challenging terrains, and collect valuable data for environmental analysis. The Quadcopter-Rover System offers significant potential in applications such as ecological monitoring, disaster management, and precision agriculture, where comprehensive and efficient data collection is crucial for informed decision-making. [ABSTRACT FROM AUTHOR]

This paper aims to empower cardiovascular (CV) researchers by promoting diversity, equity, and inclusion (DE&I) principles throughout the research cycle. It defines DE&I and introduces practical strategies for implementation in recruitment, retention, and team dynamics within CV research. Evidence-based approaches supporting underrepresented populations' participation are outlined for each research phase. Emphasizing the significance of inclusive research environments, the paper offers guidance and resources. We invite CV researchers to actively embrace DE&I principles, enhancing research relevance and addressing longstanding CV health disparities. [ABSTRACT FROM AUTHOR]

The traditional monitoring method of water resources environment mainly relies on manual sampling and laboratory analysis, which is limited in time and space and can not realize the comprehensive monitoring of water resources environment. With the continuous development of computer technology and network communication technology, real-time data monitoring method of water resources environment based on computer remote data acquisition and image analysis has gradually become a new solution. This paper aims to propose a real-time data monitoring method of water resources environment based on computer remote data acquisition and image analysis, in order to achieve accurate and efficient monitoring of water resources environment, timely acquisition and analysis of key data of water resources environment, and provide scientific basis for relevant decisions. In this paper, the key parameters of water quality, water quantity and water ecology are collected by using computer remote data acquisition and image analysis. Using computer image analysis technology, the collected image data are processed and analyzed, and the related characteristics and indicators of water resources environment are extracted. The results show that the real-time data monitoring method of water resources environment based on computer remote data acquisition and image analysis can monitor the data changes of water resources environment accurately and efficiently. Through the analysis and processing of the real-time data, the problems in the water resources environment can be found and warned in time, and the corresponding measures can be taken to regulate and manage. [ABSTRACT FROM AUTHOR]

Statistical analysis is an important part of the research process. Researchers are advised to include a statistician from the moment that the study is being planned. The statistical plan informs the research process, including sample size requirements and the most robust data collection. Once the data are collected, descriptive and inferential statistical analyses are performed. The results of this analysis determine whether the findings are significant, which leads to an interpretation of the findings. The importance of the statistical plan and analysis for the researcher is self-evident. However, it is also important for the reader of published papers to have some knowledge of statistical analysis. This allows critical review of all aspects of the published manuscript. The intent of this paper is to review some basic statistical concepts and thus allow the reader to become a better consumer of the literature. [ABSTRACT FROM AUTHOR]

Due to the high cost of data acquisition in many specific fields, such as intelligent agriculture, the available data is insufficient for the typical deep learning paradigm to show its superior performance. As an important complement to deep learning, few-shot learning focuses on pattern recognition tasks under the constraint of limited data, which can be used to solve practical problems in many application fields with data scarcity. This survey summarizes the research status, main models and representative achievements of few-shot learning from four aspects: model fine-tuning, meta-learning, metric learning and data enhancement, and especially introduces the few-shot learning-driven typical applications in intelligent agriculture. Finally, the current challenges of few-shot learning and its development trends in intelligent agriculture are prospected. [ABSTRACT FROM AUTHOR]

This research is motivated by the need for the guarantee company to improve the management of the availability of information technology services to all company stakeholders in the guarantee company data center migration project. The guarantee company is currently collaborating with data center colocation service providers to support information technology infrastructure needs as a location for placing hyper-converged infrastructure servers. One of the objectives of this study is to provide recommendations to company management regarding improvements related to operational efficiency, increased scalability and capacity, reliability, increased availability, increased security factors, compliance with regulations, and the ability to increase data recovery in the event of a disaster by evaluating and maintained for enterprise data center migration plans. This research focuses on how the benefits of data center migration are currently at the highest level, which will be explained in this paper, where the author will provide a systematic and measurable visualization regarding facts and relationships in carrying out the stages of activity, starting with data collection, analysis, and making suggestions in improving colocation data services in the area of the country of Indonesia. [ABSTRACT FROM AUTHOR]

With the advancement of machine-learning and deep-learning technologies, the estimation of the state of charge (SOC) of lithium-ion batteries is gradually shifting from traditional methodologies to a new generation of digital and AI-driven data-centric approaches. This paper provides a comprehensive review of the three main steps involved in various machine-learning-based SOC estimation methods. It delves into the aspects of data collection and preparation, model selection and training, as well as model evaluation and optimization, offering a thorough analysis, synthesis, and summary. The aim is to lower the research barrier for professionals in the field and contribute to the advancement of intelligent SOC estimation in the battery domain. [ABSTRACT FROM AUTHOR]

Context: The landscape provides not only a living space for all life forms, including humans, but also a spatial base and set of resources for the implementation of individual human activities. Inappropriate implementation of human activities, disrespecting the properties of the landscape's natural resources, causes the degradation of natural resources and, consequently, the human living. Objectives: The aim of this paper is to develop new methodological procedures and algorithms for effective assessment of natural capital based on the geosystem approach. Methods: Each territorial unit (geosystem) represents a unique combination of natural assets that create a certain potential for the development of individual activities and eco-stabilization functions. In this study, we developed a new approach and algorithms to assess the natural capital of landscapes for sustainable use. This involves selecting indicators and their functional interpretation, as well as collecting available spatial data and statistics for GIS analysis, synthesis, and modeling. Results: The methodological procedure consists of the determination of indicators for natural capital assessment, the determination of their functional values and weighting coefficients, the determination of the suitability of the geosystem for the implementation of individual activities based on the value of natural capital, and the determination of restrictions and limiting factors. The set of data on landscape assets can be categorized into abiotic, land cover and biotic, and socio-economic indicators, which can either support human activities or limit them. Options for sustainable use of natural capital were split into two groups of potential activities: (I) natural capital for landscape planning activities and (II) specific activities or functions (e.g., natural capital for energy use, recreation, regulation services). The modeling of eco-stabilizing natural capital in Trnava LTSER pointed to low spatial ecological stability, mainly in the central part of the district. Discussion: Discussion pointed to strength, novelty and opportunities of implementing methodological approach to natural capital assessment. Conclusions: As an output of this methodological approach, a comprehensive digital spatial database of landscape-ecological data for the assessment of natural capital and the suitability of its use for socio-economic activities has been created in Slovakia. The database represents a set of consistent spatial information on natural capital assets and other indicators, including land cover and socio-ecological indicators. The methodological approach can be applicable to any territory on the basis of a modification. [ABSTRACT FROM AUTHOR]

Objective: Consumer-centred care is fundamental to high-quality health care, with allied health professionals playing a pivotal role in hospital settings. Allied health typically operates within standard weekday working-hours. Consumer preferences for receiving allied health services are largely unexplored but could inform whether weekend and/or out-of-hours services are required. This study aims to understand consumer preferences for hospital-based inpatient and outpatient allied health services. Methods: Using a cross-sectional survey and convenience sampling approach, consumers of a public health service in Melbourne, Australia were surveyed about preferences for allied health service delivery. Electronic health record reviews compared the accuracy of self-reported service delivery times. Descriptive statistics, concordance and predictive values were calculated. Responses to free-text survey items were analysed using content analysis. Results: Of 120 participants (79% response rate), most (69%) received allied health services, however, almost half of inpatient responders (44%) were unsure of the specific allied health professional involved. Audit results found moderate–high concordance overall (range, 77–96%) between self-reported and audit-identified allied health services by profession. Most inpatient responders had no strong day of week preference, equally selecting weekdays and weekend days, with most preferring services between 8 am and 4 pm. Outpatient responders (81%) preferred a weekday appointment between 8 am and 12 pm or before 8 am (29%) to complete scheduled activities early in the day. Conclusion: While provision of allied health services during standard working-hours was preferred by most consumers, some inpatient and outpatient consumers are receptive to receiving weekend and out-of-hours services, respectively. Decisions about offering these services should consider operational capacity and research evidence. What is known about the topic? Research on consumer acceptance of out-of-hours or weekend allied health services is lacking, with an assumption of consumer willingness and demand for these service models. What does this paper add? Hospital inpatients are generally indifferent to the day of allied health services, preferring weekday or weekend day equally, typically between 8 am and 4 pm, while outpatients favour weekday mornings. Although consumers reported that their needs were met by allied health services, there was a lack of awareness about the specific services they received. What are the implications for practitioners? Results highlight the importance of not only aligning service delivery with consumer preferences for timing, but also enhancing awareness and understanding of the available allied health services for consumers. [ABSTRACT FROM AUTHOR]

Background: Parenting-related leave policies have gained increasing endorsement across Organisation for Economic Co-operation and Development (OECD) countries in recent decades. Previous reviews have focused on the short-term impacts and found predominantly positive effects on children. Although there is a growing interest in the long-term impact during adolescence and young adulthood, a comprehensive assessment of this aspect is currently lacking. Methods: We systematically reviewed studies from three electronic databases (Scopus, Web of Science and PubMed), which used quasi-experimental design and examined policies legislating the introduction or expansion of parenting-related leave policies in North America or Europe. We looked at studies focused on well-being beyond the age of 12 and analyzed the findings across different domains of well-being: health, education and labour market outcomes. Results: The quasi-experimental evidence is rather limited. The introduction of leave policies or gender-specific quotas produces substantial benefits in the long run. Further, maternal socioeconomic and educational background appears to play a substantial moderating role between leave and adolescents' well-being. Adolescents with mothers who have higher levels of education have demonstrated a more pronounced advantage from the extended time spent together, thereby accentuating pre-existing disparities. Conclusions: Though the expansion of already long leaves might not generate significant outcomes, the introduction of leave policies or gender-specific quotas produces substantial long-term benefits. This evidence entails considerable policy implications for countries that lack a national leave policy or offer only short durations of paid leave, such as the USA. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to investigate business-to-business sales executives' navigation of challenges and changes in planning during two separate periods (prevaccine and postvaccine) of time, which were impacted by a disruptive event (the COVID-19 pandemic). Design/methodology/approach: This study uses a two-phase qualitative data collection approach. Thirteen executives, primarily from the Business-to-Business (B2B) manufacturing industry, were interviewed in phase one (2–3 months before the first COVID-19 vaccine). The second period of data collection was collected 4–5 months after vaccines became available. Findings: The prevaccine business environment focused on short-term challenges, while the vaccine created exponential changes to long-term sales practices, suggesting the need to focus on critical inflection points that occur after the initial disruptive event. Research limitations/implications: This exploratory study is a step toward developing a deeper understanding of managing disruptive events within a business-to-business sales environment by stressing the importance of both the actual disruptive event and the inflection points that follow the event. Practical implications: New business models are constantly developing and evolving. However, this study suggests the biggest changes could occur after an inflection point from the disruption. Thus, firms need to consider different planning strategies before and after certain inflection points following a disruptive event. First, firms should adapt from their predisruption strategy to focus on short-term challenges during the initial phases of a disruption, likely halting most of the long-term planning. Second, inflection points create the need to move beyond short-term challenges and changes to focus on long-term changes. Third, long-term strategies and planning postinflection point will be different, and likely more complex, than long-term strategies and planning predisruption. Originality/value: Most studies look at a disruptive event through a single data collection period. This longitudinal study compares prevaccine and postvaccine thought processes to explore the impact of an inflection point. [ABSTRACT FROM AUTHOR]

This paper delves into the intricate semantic nuances and sociopragmatic functions inherent in the Marathi address term "māuli" in the context of a colossal religious gathering. Through morphosyntactic analysis, it uncovers the term's grammatical gender and its generic usage to encompass feminine and motherly attributes, embracing inclusiveness and generosity. Drawing from primary data collected during the Aṣāḍhi pilgrimage of 2023 through semi-structured interviews and participant observations, supplemented by secondary sources such as albums and songs, the study quantitatively and qualitatively examines the frequency and contextual use of "māuli". Findings reveal its multifaceted sociopragmatic functions, including identity establishment, solidarity marking, politeness expression, and religiosity demonstration. However, its efficacy is constrained by temporal and spatial limitations, potentially hindering broader societal benefits. This exploration sheds light on the complex interplay between linguistic expressions, social dynamics, and religious practices within the Marathi cultural milieu. [ABSTRACT FROM AUTHOR]

Grade inflation in Philippine higher education is a growing concern, with instructors increasingly assigning higher grades to students. This paper investigates instructor-driven causes behind grade inflation and its impacts. Autoethnography was used as a method. The study locale was public state universities and colleges in Taguig City and Muntinlupa City, Philippines; data collection involved immersion, a personal diary, and informal discussions with faculty and students. Thematic analysis, guided by research inquiries, unveiled the factors driving this trend. Results suggest compassionate grading, influenced by a desire to support students facing challenges, fosters inflated success perceptions. Laziness, apathy, and sycophancy also contribute to lax grading, hindering genuine academic growth. Meanwhile, the impacts extend beyond academia, affecting graduates' skill acquisition and potentially dissatisfying employers. Recommendations advocate for enhanced accountability among instructors through rigorous evaluation systems and continuous professional development to safeguard academic standards. Combating grade inflation necessitates collaborative efforts to uphold education quality and integrity, benefiting students, institutions, and society. [ABSTRACT FROM AUTHOR]

The Mantingan Mosque, the second oldest mosque after the Great Mosque of Demak is located in Mantingan Village, Tahunan District, Jepara Regency. It was built in 1481 Saka or 1559 AD by Sultan Hadlirin. This mosque is rich in white stone carvings that reflect the influence of Hindu, Buddhist, Chinese and Islamic culture. This paper investigates the implications and pragmatic power of the carved ornaments on the interior walls of the Mantingan Mosque. The study used observation and interview techniques as data collection methods. Data was collected through interactions with mosque administrators, visitors, traders and the community around the mosque. Contextual analysis was used to process the data. Findings show that the carved ornaments in the Mantingan Mosque did not merely have a role as aesthetic elements, but also had deep pragmatic power in social and religious contexts. These ornaments show how Hindu, Buddhist, Chinese and Islamic cultural elements did not only combine visually but also played a pragmatic role in supporting the functions of the mosque as a center of the community activities. It shows that the use of ornaments in the Mantingan Mosque was a representation of cultural integration and the sustainability of the socio-religious function of the mosque. [ABSTRACT FROM AUTHOR]

The theory and application of the Taylor Dispersion technique for measuring diffusion coefficients in binary systems is reviewed. The theory discussed in this paper includes both the ideal Taylor–Aris model and the estimation of corrections required to account for small deviations from this ideal associated with a practical apparatus. Based on the theoretical treatment, recommendations are given for the design of practical instruments together with suggestions for calibration, data acquisition and reduction, and the rigorous estimation of uncertainties. The analysis indicates that relative uncertainties on the order of 1% are achievable in practice. [ABSTRACT FROM AUTHOR]

The phenomenon called "return to the city" has intensified in Rzeszów. After a clear decline in the number of inhabitants at the beginning of the present century, for several years there has been a steady increase in population, which is not related to incorporating more towns into the city. The paper presents the analysis of data collected during the study carried out in the city. Residents of the city declared satisfaction with their place of residence. The main reasons for satisfaction were the cleanliness of the city, aesthetics, work, noticeable city development, developed infrastructure, and a high level of security and peace. [ABSTRACT FROM AUTHOR]

Purpose: Human trafficking occurs when a person uses force, fraud, or coercion to obtain an act or service from another person. While trafficking is often depicted as a crime perpetrated by strangers, recent literature has found family members can also be traffickers. Only four peer reviewed studies have specifically examined familial trafficking. Method: The current study builds upon this sparse literature by reviewing medical charts of patients in a large hospital system who were identified as survivors of familial trafficking between October 2017 and September 2021. Descriptive statistics are reported for all 39 identified patients. Results: Majority of patients were female (92.3%) and under 36 years old (56.4%; M = 34.2, SD = 11.6). Despite most patients now being adults, all except one were minors at the time the trafficking began. Parents were the most common perpetrator (66.7%) followed by aunt/uncle (12.8%). Nine cases were chosen, given level of detail in patient charts, to be highlighted in a case series to further describe the experiences of these survivors. Cases examined included labor trafficking, parents as perpetrators, intersection of substance use, and males as victims. Force was the most common means of control used and often manifested as physical or sexual abuse that preceded the exploitation. Another reoccurring theme was substance use being a factor when parents were perpetrators. Conclusion: The findings from this paper can help inform screening techniques by encouraging providers to inquire about familial relationships. Findings may also be helpful for providers working with this population and future research endeavors. [ABSTRACT FROM AUTHOR]

The digital transformation of critical infrastructures, such as energy or water distribution systems, is essential for their smart management. Faster issue identification and smoother services enable better adaptation to consumers' evolving demands. However, these large-scale infrastructures are often outdated. Their digital transformation is crucial to enable them to support societies. This process must be carefully planned, providing guidance that ensures that the data they rely on are dependable and that the system remains fully operational during the transition. This paper presents a formal model that supports reliable data acquisition in legacy critical infrastructures, facilitating their evolution towards a data-driven smart system. Our model provides the foundation for a flexible transformation process while generating dependable data for system management. We demonstrate the model's applicability in a use case within the water distribution domain and discuss its benefits. [ABSTRACT FROM AUTHOR]

This paper presents the initial vicarious radiometric calibration results for Landsat 9 OLI using a combination of ground-based techniques and test sites located in Nevada, California, and South Dakota, USA. The field data collection methods include the traditional reflectance-based approach and the automated Radiometric Calibration Test Site (RadCaTS). The results for top-of-atmosphere spectral radiance show an average ratio (OLI/ground measurements) of 1.03, 1.01, 1.00, 1.02, 1.02, 1.01, 0.98, and 1.01 for Landsat 9 OLI bands 1–8, which is within the design specification of ±5% for spectral radiance. The results for top-of-atmosphere reflectance show an average ratio (OLI/ground measurements) of 0.99, 0.99, 1.00, 1.02, 1.01, 1.02, 1.00, and 1.00 for Landsat 9 OLI bands 1–8, which is within the design specification of ±3% for top-of-atmosphere reflectance. [ABSTRACT FROM AUTHOR]

Understanding political and social effects of violence in local populations through public opinion surveys has become increasingly common internationally. Yet while researchers are attuned to possible challenges induced during survey implementation, this work focuses almost uniformly on respondents. This paper considers survey enumerators as critical actors for data collection in violent research settings. We present survey results from 245 enumerators in Cote d ˆ ’Ivoire to show that their personal feelings of insecurity and exposure to violence while conducting surveys may condition challenges faced and compromises made to gathering data. We shed light on how academic research in violent political settings poses unique security concerns for enumerators, with ramifications for data integrity. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge of managing and maintaining road pavements effectively throughout their lifecycle. Emphasizing the need for a cohesive data collection and handling approach, the paper highlights the vulnerability caused by the absence of a structured information system, resulting in reworks, information loss, and misinterpretation of collected data. A unified data handling and collection structure, introducing the concept of a pavement Digital Twin through a standardized data structure is presented. The Digital Twin aims to integrate information regarding pavement failures, with the future aim of predicting deterioration and facilitating informed decision-making in management and maintenance interventions. Complexities in pavement failures, categorized into surface and subsurface modes, prompt the necessity for a reliable classification and representation system. The proposed methodology introduces a grid of cells for surface failures and three-dimensional voxels for subsurface failures, providing a structured approach for data integration throughout the pavement's lifecycle. An approach for the semantic representation of pavement distresses is also presented. The proposed methodology stresses the importance of scalability and flexibility in data storage, forming the basis for a comprehensive Digital Twin of road pavements. Finally, through the use of multiple pavement failure datasets, the methodology is shown to have a high potential in providing a structure for comprehensive management of road data. [ABSTRACT FROM AUTHOR]

Objective The objective of this paper was to describe peri-procedural events and complications of infants requiring laser photocoagulation for retinopathy of prematurity (ROP) in a level IV neonatal intensive care unit. Study Design A retrospective chart review was performed of neonates requiring ROP exams from January 2017 to August 2020. Baseline maternal and neonatal characteristics, ROP exam findings, and associated treatment were analyzed. Group characteristics were compared based on the need for laser photocoagulation. Subgroup analysis of the laser group including respiratory outcomes, cardiorespiratory index (CRI) scores, and pain scores was also performed. Results Neonatal and maternal characteristics in the laser (n = 27) and non-laser (n = 172) groups were assessed. Of the 81.5% (22/27) that required re-intubation for laser, 36% (8/22) had >1 intubation and 18% (4/22) had >1 extubation attempt. The average duration of intubation following laser was 2.46 ± 7.13 days, with 40% (9/22) needing peri-extubation steroids and 18% (4/22) racemic epinephrine to facilitate extubation. Mean total respiratory support time post-laser was 8.65 ± 15.23 days. Mean neonatal pain, agitation, and sedation scores after laser were zero immediately after the procedure, 0.09 ± 0.33 at 12 hours, 0.11 ± 0.47 at 24 hours, and 0.11 ± 0.51 at 48 hours. The mean CRI scores were 1 ± 0 immediately after the procedure, 1.17 ± 0.4 at 12 hours, 1.41 ± 0.20 at 24 hours, and 1 ± 0 at 48 hours. Conclusion Nearly all infants undergoing laser photocoagulation for ROP in our cohort required intubation and continued respiratory support. Despite stability during the procedure, complications from intubation were common. Key Points Routine intubation for laser is associated with complications. Need for post-procedural respiratory support is common. Avoiding intubation may mitigate these neonatal complications. [ABSTRACT FROM AUTHOR]

Background Before the advent of antenatal steroids, early non-invasive respiratory support (NIV), and intratracheal surfactant, antenatal terbutaline was also used to improve lung compliance and reduce the incidence of respiratory distress syndrome (RDS). Objectives The objective of this paper was to study the association between antenatal terbutaline and endotracheal intubation (ET) within the first 24 hours of life, RDS, bronchopulmonary dysplasia (BPD), and intraventricular hemorrhage (IVH) in infants with the gestational age (GA) of <32 weeks, and to study the association between antenatal terbutaline, and ET or NIV within the first 24 hours of life, and RDS in infants with the GA of 32 to 36 weeks. Method This was a retrospective medical record review of preterm infants delivered at a single tertiary care center from October 2016 to December 2020. Multivariable logistic regression was used to explore the association between antenatal terbutaline and neonatal respiratory support. Result 1,794 infants were included, 234 (13.0%) had the GA of <32 weeks and 1,560 (86.9%) had the GA of 32 to 36 weeks. Antenatal terbutaline, corticosteroid, or both agents were administered in 561 (31.3%), 1,461 (81.4%), and 555 (30.9%), respectively. Antenatal terbutaline was significantly associated with a reduction in ET (adjusted odds ratio [aOR] = 0.40, 95% confident interval [CI] 0.19–0.82, p = 0.012) in infants with the GA of <32 weeks, but not in infants with the GA of 32–36 weeks. Antenatal terbutaline was not associated with RDS or BPD but was significantly associated with a reduction in grade III-IV IVH (aOR 0.11, CI 0.01–0.98; p = 0.048), in infants with the GA of <32 weeks. Conclusion In a state-of-the-art neonatal care setting, antenatal terbutaline was associated with a reduction in ET during the first 24 hours in infants with the GA of <32 weeks. The use of antenatal terbutaline to improve acute neonatal respiratory outcomes merits reconsideration. Key Points The neonatal respiratory benefits of antenatal terbutaline in the era of antenatal corticosteroids were uncertain. Terbutaline is associated with a reduction in endotracheal intubation in a modern care setting. The role of terbutaline, and potentially other betamimetics, to improve neonatal respiratory outcomes merits reconsideration. [ABSTRACT FROM AUTHOR]

The energy consumption from the building sector accounts for more than 21% of the total in China, where the energy-use intensity of public buildings could reach 3 times that of residential buildings. Energy efficiency improvement (EEI) is considered to be one of the most rapid and cost-effective means for the building sector to achieve carbon neutrality. The Chinese government has carried out a series of policy tool exploration to promote the energy efficiency improvement efforts by public building owners since over a decade ago, but encountered various problems and obstacles. This paper investigated and compared the current status challenges, and problems in the implementation of EEI policy tools in China and developed countries through literature review, field investigations and interviews with experts and stakeholders, and finally also put forward some possible coping strategies for Chinese government. Results indicated that energy grading and labeling, energy benchmarking, carbon-emission trading and energy-consumption quota management were the main 4 EEI policy tools designed based on measured energy consumption data, where the first 3 ones had been widely used over the developed countries but the last one was unique to China. Difficulty in large-scale collection of building energy-consumption data, lack of scientific methods on building energy-use level evaluation, limited data application and feedback, undesirable association between stakeholders, etc., were the main obstacles for achieving the long-term effectiveness of the EEI policy tools. Cooperating with energy supply enterprises to collect data, establishing an effective energy grading system and a closed loop of policy implementation, exploring a new model of building carbon-emission trading and a differential pricing mechanism, combined with innovative financial supports, might be the solutions. The main contribution of this study is that a comprehensive investigation and analysis on the current development status, obstacles, and possible solutions of the public building EEI policy tools in China was carried out, which is expected to provide a reference for the government of China and even the world. [ABSTRACT FROM AUTHOR]