Активная цифровая рейка: концепция динамической адаптации измерительной шкалы для прецизионного нивелирования

Maternal denning plays a vital role in the development and survival of highly altricial polar bear cubs by providing protection from external conditions. The denning period remains challenging to study and monitor because polar bear dens are often remote and difficult to access. Denning is typically inferred from satellite telemetry data, yet the accuracy of these measures in capturing important denning behaviors that are relevant to management and monitoring is unclear. We installed cameras at 13 den sites in Svalbard, Norway, over a 6‐year period, 9 of which yielded observations of behavior and phenology of polar bears at den emergence, and we compared these observations with denning behavior inferred from telemetry data (location, temperature, and activity levels) from satellite collars worn by denning bears. We next developed Bayesian generalized linear models to accurately predict denning behaviors (i.e., observations from cameras) from collar sensor data. From the camera data, mean date of observed den breakout was 9 March (SD = ±6.5 days, n = 7) and departure from the den site occurred 12.1 days later (±10.1 days, n = 7). Estimates of den breakout date based on joint analysis of collar temperature and activity data indicated breakout occurred on average 0.7 days later (±11.4 days, n = 7) and estimates based on collar temperature thresholds alone indicated breakout occurred 4.0 days later (±6.6 days, n = 7) compared to the camera data. Location data from collars suggested departure occurred on average 3.2 days later (±7.0 days, n = 7) than camera observations. We found that the probability a bear had broken out of the den could be accurately predicted from changes in collar temperature, activity, and ordinal date (e.g., a 1 SD decrease in collar temperature increased the probability of breakout by 18.5 percentage points). Post‐den emergence behavior was influenced by external environmental temperature, time of day, and the amount of time since den breakout; bears were more likely to emerge and stay outside longer given warmer temperatures and increasing time since den breakout. Our study highlights the importance of the post‐emergence period for cub acclimatization and development and provides new monitoring tools to study polar bear denning behavior, which is increasingly vulnerable to disruption in a rapidly changing Arctic.

Over the last decade in South Africa, public procurement has evolved significantly and attracted much scholarly research interest. There are challenges that exist in public procurement from provincial to national levels, these challenges are a combination of various areas of concern in the public sector. The researcher has unpacked these challenges into clear study constructs. The key procurement challenges resonate with the quality of PEOPLE, PROCESS and TECHNOLOGY amassed by the individual higher education institutions. In this study three higher education institutions were selected. The procurement policies and principles that entrench the higher education institutions (HEIs) in terms of delivering high levels of service and maintaining financially viable institution. Universities as institutions of higher learning have a mandate in ensuring that efficient and effective public procurement occurs at all levels. Against this backdrop, this study explored and identified the public procurement challenges at higher education institutions in KwaZulu-Natal (KZN). The procurement system in these institutions exposed a gap on qualified and capable talent, in accord processes and the dearth of adoption emerging technology to achieve efficiency and effectiveness of procurement systems. The findings of study revealed the challenges, inefficiencies and technological barriers in the procurement systems. To model a financially and operationally feasible, 4 IR technological driven procurement system, a conceptual model was created with an agile and resilient procurement system's potential in consideration, with a focus on building adaptability and demand responsiveness. The main aim of this study is to identify the public procurement challenges of the selected HEIs and to detail the public procurement system by focusing on the main constructs of the study: people, process and technology. This study made use of an exploratory and descriptive qualitative research approach. The research setting will be the campuses of the selected HEIs. The gathering of primary data via semi-structured in-depth interviews were collected. A sample size of thirty procurement employees and management of the three HEIs were interviewed. Thematic analysis was used as the data analysis technique. The qualitative data was analysed and processed by NVivo.20 software.

Changes in lighting accompany modern urbanization trends and can lead to various pathologies based on circadian disturbances. In this study, we assessed the changes in the circadian rhythm of core body temperature (Tcore) and locomotor activity of Wistar-Kyoto rats (WKY) and spontaneously hypertensive rats (SHR) following exposure to different lighting conditions: extended light phase of the day (16 h-8 h, 20 h-4 h, 24 h-0 h), light pollution, monochromatic light, and bright light therapy. The telemetry data was collected after experimental lighting conditions during periods with standard lighting (12 h of light and 12 h of darkness) and was processed using linear and cosinor analysis. The daily rhythms of rats' parameters persisted in accordance with the standard lighting regime. Tcore changes were observed in both groups compared to the initial period: in WKY, a decrease in Tcore during the darkness and an increase during the light; in SHR, the opposite trend, with Tcore increased during the darkness and decreased during the light phase of the day. A relationship between Tcore and activity was observed with weak correlation. WKY exhibited more pronounced signs of adaptive variation and desynchronization compared to SHR, which could be associated with a wider range of functional capabilities of the organism without cardiovascular pathology.

This paper proposes an estimation method to identify the electrical model parameters of photovoltaic (PV) modules and makes a comparison with other methods already popular in the technical literature. Based on the full single-diode model, the mathematical description of the I–V characteristic of modules is generally represented by a coupled nonlinear equation with five unknown parameters, which is difficult to solve by an analytical approach. The aim of the proposed method is to find the five unknown parameters that guarantee the minimum absolute error between the P–V curves generated by the electrical model and the P–V curves provided by the manufacturers' datasheets for different external conditions such as temperature and irradiance. The first advantage of the proposed method is that the parameters are estimated using the P–V curves instead of I–V curves, since most of the applications that use the electrical model want to accurately estimate the extracted power. The second advantage is that the value ranges of each unknown parameter respect their physical meaning. In order to prove the effectiveness of the proposition, a comparison among methods is carried out using both types of P–V and I–V curves: those obtained by manufacturers' datasheets and those extracted experimentally in the laboratory.

Although antiretroviral therapy (ART) is available to treat HIV+ persons and prevent transmission, ineffective delivery of care may delay ART use, impede viral suppression (VS), and contribute to racial/ethnic disparities along the continuum of care. This study tested the effects of a bi-directional laboratory health information exchange (LHIE) intervention on each of these outcomes. We used a quasi-experimental, interrupted time-series design to examine whether the LHIE intervention improved ART use and VS, and reduced racial/ethnic disparities in these outcomes among HIV+ patients (N = 1181) in a comprehensive HIV/AIDS clinic in Southern California. Main outcome measures were ART pharmacy fill and HIV viral load laboratory data extracted from the medical records over 3 years. Race/ethnicity and an indicator for the intervention (after vs. before) were the main predictors. The analysis involved 3-stage, multivariable logistic regression with generalized estimating equations. Overall, the intervention predicted greater odds of ART use (odds ratio [OR] = 2.50; 95% confidence interval: 2.29 to 2.73; P < 0.001) and VS (OR = 1.12; 95% confidence interval: 1.04 to 1.21; P < 0.05) in the final models that included sociodemographic, behavioral, and clinical covariates. Before the intervention, there were significant black/white disparities in ART use OR = 0.75 (0.58-0.98; P = 0.04) and VS OR = 0.75 (0.61-0.92; P = 0.001). After the intervention, the black/white disparities decreased after adjusting for sociodemographics and the number of HIV care visits, and Latinos had greater odds than whites of ART use and VS, adjusting for covariates. The intervention improved overall ART treatment and VS, and reduced black/white disparities. LHIE interventions may hold promise if implemented among similar patients.

Reducing electricity consumption and integrating renewable power generation sources represent two of the main drivers of the so-called Smart Grid. Machine-to-Machine (M2M) communications will play a key role on making such a Smart Grid a reality, since they will enable the required bidirectional real-time bulk information exchange. However, communications for the Smart Grid present specific requirements from both technical and economic perspectives, so it is crucial to evaluate how existing communication architectures and technologies meet them before undertaking the important investments needed to deploy this kind of infrastructure on a large scale. The main goal of this paper is to evaluate, from different perspectives, the core M2M communications infrastructure of a platform designed to reduce electricity consumption and integrate renewable generation at residential level. Such a communications infrastructure is fully based on widely deployed wireless communications technologies such as IEEE 802.11 and General Packet Radio Service (GPRS). Notably, the paper assesses the operational costs of using different security solutions in the GPRS segment and the performance of the selected communications technologies based on different metrics (goodput, in the case of IEEE 802.11, and transmission time, in the case of GPRS).

Enabling bidirectional real time interaction between biological and technical systems: Structural basics of a control oriented modeling of biology-technology-interfaces

Humans extract regularities from the environment to form expectations that guide perception and optimize behavior. Although the prefrontal cortex (PFC) is central to this process, the relative contributions of orbitofrontal (OFC) and lateral PFC (LPFC) remain unclear. Here, we show that the brain tracks sound regularities in an auditory deviance detection task to predict when a target deviant will occur. Intracranial EEG in epilepsy patients reveals prefrontal engagement, with earlier expectancy-related modulation in OFC and later modulation in LPFC. Connectivity analyses indicate bidirectional and asymmetrical expectancy-related information exchange between the two areas, with a first lead by OFC, consistent with its role in initiating predictive encoding. Converging causal evidence shows that OFC lesions abolish sensitivity to expectancy, whereas LPFC lesions yield only modest effects not significantly different from controls. Together, these results provide electrophysiological and causal evidence for distinct, temporally organized contributions of prefrontal subregions to predictive processing.

Two-way relay network (TWRN) with orthogonal frequency division multiplexing (OFDM) is an emerging paradigm, for high spectral efficiency, increased capacity in 5G wireless communications. The TWRN allows bidirectional information exchange between two users with the assistance of intermediate relay nodes by efficiently using the available spectral resources. By removing back- propagating self-interference each source node obtains the information of other source node. The accuracy of the self-interference cancellation heavily depends on the precision of the joint estimation of impairments i.e. tracking the individual oscillators and perfect channel state information (CSI). In this paper, we propose two expectation maximization (EM) based algorithms, expectation conditional maximization (ECM) and space alternating generalized expectation maximization (SAGE) which iteratively estimate multiple carrier frequency offsets (MCFOs) and frequency-selective channel coefficients in amplify-and-forward (AF) multi-relay cooperative TWR communication systems over a quasi-static channel. The Cramer-Rao lower bounds (CRLBs) for the joint estimation of multiple impairments are derived. The mean square error (MSE) performances of the proposed estimators achieve corresponding CRLBs. The performance is also evaluated by means of system bit error rate (BER). SAGE based algorithm greatly reduces the complexity compared to the ECM based algorithm.

An interface is an exchange of information between two computer programs that typically run on different computer systems.1 Interfaces are useful when information generated by one computer program (e.g., a patient registration system) can be used to replace manual data entry on another program (e.g

: A common characteristic of most artificial recognition systems in use today is that they are bottom up approaches, meaning that high-level modules do not influence processing of low-level modules. However, there are some problems and ambiguities at the level of sensory processing, and preprocessing of the signal, that cannot be resolved without taking into account cognitive level expectations. The major goal of our research within this project was to construct a functioning recognition system, based upon fundamental principles of human perception and cognition that exhibits the following properties: The ability to utilize contextual information during the recognition process. The ability to selectively focus attention on important regions of the input data. The ability to adapt preprocessing parameters to changes in the operating environment. The ability to engage in bi-directional information exchange with the user. The system that we constructed implements salient aspects of human perception and cognition such as saccadic eye movements, selective attention and cognitive level feedback during the recognition process, therefore having the potential to reduce the cognitive workload of the soldier when faced with the analysis of complex sensory environments. In this report we provide a brief description of the models and algorithms that we developed within the scope of this project and we present the performance of those models when tested on real-world datasets. We conclude the report with the summary of the most important results.

Breaking barriers in deployment of renewable energy

This research is a deep learning-driven assistive device specifically engineered to support visually impaired individuals in identifying Philippine New Generation Series (NGS) peso coins. The primary goal of this study is to facilitate independent and accurate coin recognition through an accessible and user-friendly auditory feedback system. The system leverages computer vision and artificial intelligence to distinguish between various denominations based on their visual features. To train deep learning models, a custom dataset consisting of 1,419-coin images was manually collected, ensuring diversity in coin orientation, lighting conditions, and background environments to reflect real-world usage. Three deep learning architectures-Convolutional Neural Network (CNN), ResNet50, and VGG16-were selected for evaluation, given their proven efficacy in image classification tasks. These models were trained and validated on the prepared dataset, and their performance was compared in terms of accuracy. Among the three, the CNN model outperformed the others, attaining an accuracy of 99.89%, demonstrating its superior capability in recognizing coin types with high precision and minimal error. The finalized CNN model was then integrated into a functional hardware prototype which combines the trained model with a camera module and a microcontroller-based processing unit. Upon capturing an image of the coin, the system classifies its denomination and provides real-time auditory feedback, thereby enabling blind or visually impaired users to accurately identify coins without external assistance.

Every company should think about producing new goods, the pAdvances in communication technology have led to changes in business practices. Shared businesses are a new way of presenting products through a platform in the virtual environment. In this business, people's assets are shared on a peer-to-peer basis. In addition to generating revenue, this business has created employment, increased productivity, reduce energy consumption and environmental pollution in the world. This study tries to investigate the barriers to the development of shared businesses in Tehran. The research method in this study is qualitative and the data collection tool is a semi-structured interview. The informed statistical population includes 30 university lecturers, elites, graduates, entrepreneurs and manufacturers. The subjects of the recorded interviews were extracted by the lecturers of the Business Management Department and analyzed using the network themes method in MAXQDA software. Findings show that 64 barriers have been identified as the basic themes and after reviewing the opinions of experts, these barriers have been classified into 6 main themes. These barriers are: trust barriers, technology barriers, knowledge and information barriers, economic barriers, political barriers and managerial barriers.

Pakistan's solar mission: Do solar finance and subsidy remove the barriers to solar installations?