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Global policies increasingly focus on the importance of maintaining or improving the integrity of ecosystems, but defining, assessing, and monitoring integrity in marine protected areas (MPAs) remains a challenge. In this paper, we conceptualized ecological integrity along dimensions of heterogeneity and stability containing seven components: physical structure, diversity, function, persistence, resistance, resilience, and natural variability. Through a structured literature search, we identified indicators and metrics used for quantifying ecosystem status components in the marine environment, then reviewed MPA management plans worldwide for inclusion of these components. We evaluated 202 papers applying 83 ecological indicators built from 72 metrics. Ecosystem components were most comprehensively addressed by metrics of taxa presence, organisms count, and area occupied by benthic organisms, and community structure, biomass, and percent cover indicators. Of the 557 MPA management plans we reviewed globally, 93% used at least one ecosystem status term or its synonym in an ecologically relevant context, but 39% did not address any components of stability. In particular, resistance was mentioned in only 1% of management plans, but in some cases it may be inferred from indicators and metrics used to track the best addressed component in management plans, diversity. Plans for MPAs with both an ecological/biological purpose and a research and education purpose contained ecosystem status terms more frequently than other plans, suggesting that engagement with the scientific community may have improved the application of these terms. An improved understanding of how to operationalize and measure ecological integrity can help MPA monitoring and management. [ABSTRACT FROM AUTHOR]

Violence can be committed anywhere, even in crowded places. It is hence necessary to monitor human activities for public safety. Surveillance cameras can monitor surrounding activities but require human assistance to continuously monitor every incident. Automatic violence detection is needed for early warning and fast response. However, such automation is still challenging because of low video resolution and blind spots. This paper uses ResNet50v2 and the gated recurrent unit (GRU) algorithm to detect violence in the Movies, Hockey, and Crowd video datasets. Spatial features were extracted from each frame sequence of the video using a pretrained model from ResNet50V2, which was then classified using the optimal trained model on the GRU architecture. The experimental results were then compared with wavelet feature extraction methods and classification models, such as the convolutional neural network and long short‐term memory. The results show that the proposed combination of ResNet50V2 and GRU is robust and delivers the best performance in terms of accuracy, recall, precision, and F1‐score. The use of ResNet50V2 for feature extraction can improve model performance. [ABSTRACT FROM AUTHOR]

The performance of face recognition (FR) has reached a plateau for public benchmark datasets, such as labeled faces in the wild (LFW), celebrities in frontal‐profile in the wild (CFP‐FP), and the first manually collected, in‐the‐wild age database (AgeDB), owing to the rapid advances in convolutional neural networks (CNNs). However, the effects of faces under various fine‐grained conditions on FR models have not been investigated, owing to the absence of relevant datasets. This paper analyzes their effects under different conditions and loss functions using K‐FACE, a recently introduced FR dataset with fine‐grained conditions. We propose a novel loss function called MixFace, which combines classification and metric losses. The superiority of MixFace in terms of effectiveness and robustness was experimentally demonstrated using various benchmark datasets. [ABSTRACT FROM AUTHOR]

This paper presents methods for reducing the peak‐to‐average power ratio (PAPR) of the orthogonal time frequency space (OTFS) signal. These methods mainly consist of two operations: symbol interferometry (SI) and either μ‐law or A‐law companding. SI spreads the data of one OTFS symbol onto all symbols and is implemented using a simple inverse fast Fourier transform operation on each OTFS symbol. During the second operation, the PAPR of the OTFS signal is significantly reduced. For our performance analysis, the complementary cumulative distribution function, probability density function, and bit error rate are illustrated through simulations performed in MATLAB. The performance is also analyzed using a solid‐state power amplifier at the transmitter and compared with OTFS, μ‐law‐based OTFS, and SI OTFS systems. The results indicate that the proposed OTFS system achieves a low PAPR. [ABSTRACT FROM AUTHOR]

With an explosive increase of data produced annually, researchers have been attempting to develop solutions for systems that can effectively handle large amounts of data. Single‐operating‐system (OS) non‐uniform memory access (NUMA) abstraction technology is an important technology that ensures the compatibility of single‐node programming interfaces across multiple nodes owing to its higher cost efficiency compared with scale‐up systems. However, existing technologies have not been successful in optimizing user performance. In this paper, we introduce a single‐OS NUMA abstraction technology that ensures full compatibility with the existing OS while improving the performance at both hypervisor and guest levels. Benchmark results show that the proposed technique can improve performance by up to 4.74 × on average in terms of execution time compared with the existing state‐of‐the‐art open‐source technology. [ABSTRACT FROM AUTHOR]

In this paper, we mathematically investigate a downlink non‐orthogonal multiple access (NOMA) system for short‐packet communications (SPC) in which the near users are used as full‐duplex (FD) relays to forward intended signals from the source to a far user. In addition, partial relay selection is employed to enhance the performance of the FD relays under the impact of imperfect interference cancellation. At the far user, selection combining (SC) or maximal ratio combining (MRC) is employed to combine the signals received from the source and the selected FD relay. The analytical expressions for the average block error rate (BLER) of two users over flat Rayleigh fading channels are derived. Furthermore, closed‐form asymptotic expressions of the average BLERs at the near and far users in high signal‐to‐noise ratio (SNR) regimes are obtained. The numerical results show that the analytical BLERs of the near user and far user closely match the simulation results. [ABSTRACT FROM AUTHOR]

This paper demonstrates improved throughput and energy efficiency of wireless communications by exploiting nonorthogonal multiple access (NOMA), multiple input–multiple output (MIMO), and radio frequency energy harvesting (EH) technologies. To assess the performance of NOMA MIMO communications with EH (MMe), we consider the nonlinear characteristics of EH devices and propose explicit expressions for throughput and outage probability. Based on our results, the system performance is significantly mitigated by EH nonlinearity and is considerably improved by increasing the number of antennas. Additionally, by appropriately adjusting the system parameters, our NOMA MMe innovation can avert complete outages while optimizing system performance. Moreover, the results demonstrate the superiority of the NOMA MMe over its orthogonal multiple access MMe counterparts. [ABSTRACT FROM AUTHOR]

A novel inductance simulation circuit employing only two dual‐output voltage‐differencing buffered amplifiers (DO‐VDBAs) and a single capacitance (grounded) is proposed in this paper. The reported configuration is a purely resistor‐less realization that provides electronically controllable realized inductance through biasing quantities of DO‐VDBAs and does not rely on any constraints related to matched values of parameters. This structure exhibits excellent behavior under the influence of tracking errors in DO‐VDBAs and does not exhibit instability at high frequencies. The simple and compact metal‐oxide semiconductor (MOS) implementation of the DO‐VDBAs (eight MOS per DO‐VDBA) and adoption of grounded capacitance make the proposed circuit suitable for on‐chip realization from the perspective of chip area consumption. The function of the pure grounded inductance is validated through high pass/bandpass filtering applications. To test the proposed design, simulations were performed in the PSPICE environment. Experimental validation was also conducted using the integrated circuit CA3080 and operational amplifier LF‐356. [ABSTRACT FROM AUTHOR]

Ongoing assessment of patients with Alzheimer's disease (AD) in postapproval studies is important for mapping disease progression and evaluating real‐world treatment effectiveness and safety. However, interpreting outcomes in the real world is challenging owing to variation in data collected across centers and specialties and greater heterogeneity of patients compared with trial participants. Here, we share considerations for observational postapproval studies designed to collect harmonized longitudinal data from individuals with mild cognitive impairment or mild dementia stage of disease who receive therapies targeting the underlying pathological processes of AD in routine practice. This paper considers key study design parameters, including proposed aims and objectives, study populations, approaches to data collection, and measures of cognition, functional abilities, neuropsychiatric status, quality of life, health economics, safety, and drug utilization. Postapproval studies that capture these considerations will be important to provide standardized data on AD treatment effectiveness and safety in real‐world settings. [ABSTRACT FROM AUTHOR]

In this paper, we describe a neural network‐based application that recommends multiple items using dialog context input and simultaneously outputs a response sentence. Further, we describe a multi‐item recommendation by specifying it as a set of clothing recommendations. For this, a multimodal fusion approach that can process both cloth‐related text and images is required. We also examine achieving the requirements of downstream models using a pretrained language model. Moreover, we propose a gate‐based multimodal fusion and multiprompt learning based on a pretrained language model. Specifically, we propose an automatic evaluation technique to solve the one‐to‐many mapping problem of multi‐item recommendations. A fashion‐domain multimodal dataset based on Koreans is constructed and tested. Various experimental environment settings are verified using an automatic evaluation method. The results show that our proposed method can be used to obtain confidence scores for multi‐item recommendation results, which is different from traditional accuracy evaluation. [ABSTRACT FROM AUTHOR]

This paper presents the performance evaluation of an amplify‐and‐forward (AF) relay‐assisted multiuser multiple input–multiple output (MU–MIMO) downlink transmission system for correlated fading channels. The overall system performance was improved by incorporating a double‐quadrature spatial modulation (DQSM) scheme. The bit error rate (BER) performance and detection complexity of the AF–MU–MIMO–DQSM system were analyzed and compared with those of a conventional AF–MU–MIMO system under the same conditions and parameters. The results showed that the correlated fading channel severely affected the performance of systems with higher spectral efficiency (SE). Considering an SE of 12 bpcu/user, the AF–MU–MIMO–DQSM system yielded a gain of up to 3 dB in BER performance compared with that of its conventional counterpart for the analyzed cases. In terms of detection complexity, the AF–MU–MIMO–DQSM system showed a reduction of up to 56 % compared with that of the conventional system for the optimal maximum likelihood detection criterion. [ABSTRACT FROM AUTHOR]

To treat the novel COronaVIrus Disease (COVID), comparatively fewer medicines have been approved. Due to the global pandemic status of COVID, several medicines are being developed to treat patients. The modern COVID medicines development process has various challenges, including predicting and detecting hazardous COVID medicine responses. Moreover, correctly predicting harmful COVID medicine reactions is essential for health safety. Significant developments in computational models in medicine development can make it possible to identify adverse COVID medicine reactions. Since the beginning of the COVID pandemic, there has been significant demand for developing COVID medicines. Therefore, this paper presents the transfer‐learning methodology and a multilabel convolutional neural network for COVID (MLCNN‐COV) medicines development model to identify negative responses of COVID medicines. For analysis, a framework is proposed with five multilabel transfer‐learning models, namely, MobileNetv2, ResNet50, VGG19, DenseNet201, and Inceptionv3, and an MLCNN‐COV model is designed with an image augmentation (IA) technique and validated through experiments on the image of three‐dimensional chemical conformer of 17 number of COVID medicines. The RGB color channel is utilized to represent the feature of the image, and image features are extracted by employing the Convolution2D and MaxPooling2D layer. The findings of the current MLCNN‐COV are promising, and it can identify individual adverse reactions of medicines, with the accuracy ranging from 88.24% to 100%, which outperformed the transfer‐learning model's performance. It shows that three‐dimensional conformers adequately identify negative COVID medicine responses. [ABSTRACT FROM AUTHOR]

This paper presents a novel circular fractal ring monopole antenna for ultra‐wideband (UWB) hardware with dual band‐notched properties. The proposed antenna consists of four crescent‐shaped nested rings, a tapered feeding line at the front of the dielectric material, and a semicircular ground plane on the backside. In this design, the nested rings are used both as a radiation element and a band rejection element. The proposed antenna has a bandwidth of 9.03 GHz, which works efficiently in the range of 2.63 GHz–11.66 GHz with the dual notched bands of Worldwide Interoperability for Microwave Access (WiMAX) at 3.15 GHz–3.66 GHz and wireless local area network (WLAN) at 4.9 GHz–5.9 GHz, respectively. The antenna has a compact size of 20 mm × 30 mm × 1 mm (0.177 × 0.265 × 0.0084 λ0) and is implemented using a flame‐retardant type 4 (FR4) material. It has a maximum gain of approximately 4 dB in its operating range, and experimental results support the simulation predictions with high accuracy. The findings of this study imply that the designed antenna can be utilized in UWB applications. [ABSTRACT FROM AUTHOR]

This paper proposes a surveillance model for plant pests that can optimally allocate resources among survey tools with varying properties. While some survey tools are highly specific for the detection of a single pest species, others are more generalized. There is considerable variation in the cost and sensitivity of these tools, but there are no guidelines or frameworks for identifying which tools are most cost‐effective when used in surveillance programs that target the detection of newly invaded populations. To address this gap, we applied our model to design a trapping surveillance program in New Zealand for bark‐ and wood‐boring insects, some of the most serious forest pests worldwide. Our findings show that exclusively utilizing generalized traps (GTs) proves to be highly cost‐effective across a wide range of scenarios, particularly when they are capable of capturing all pest species. Implementing surveillance programs that only employ specialized traps (ST) is cost‐effective only when these traps can detect highly damaging pests. However, even in such cases, they significantly lag in cost‐effectiveness compared to GT‐only programs due to their restricted coverage. When both GTs and STs are used in an integrated surveillance program, the total expected cost (TEC) generally diminishes when compared to programs relying on a single type of trap. However, this relative reduction in TEC is only marginally larger than that achieved with GT‐only programs, as long as highly damaging species can be detected by GTs. The proportion of STs among the optimal required traps fluctuates based on several factors, including the relative pricing of GTs and STs, pest arrival rates, potential damage, and, more prominently, the coverage capacity of GTs. Our analysis suggests that deploying GTs extensively across landscapes appears to be more cost‐effective in areas with either very high or very low levels of relative risk density, potential damage, and arrival rate. Finally, STs are less likely to be required when the pests that are detected by those tools have a higher likelihood of successful eradication because delaying detection becomes less costly for these species. [ABSTRACT FROM AUTHOR]

Gastrointestinal helminth parasites undergo part of their life cycle outside their host, such that developmental stages interact with the soil and dung fauna. These interactions are capable of affecting parasite transmission on pastures yet are generally ignored in current models, empirical studies and practical management. Dominant methods of parasite control, which rely on anthelmintic medications for livestock, are becoming increasingly ineffective due to the emergence of drug‐resistant parasite populations. Furthermore, consumer and regulatory pressure on decreased chemical use in agriculture and the consequential disruption of biological processes in the dung through nontarget effects exacerbates issues with anthelmintic reliance. This presents a need for the application and enhancement of nature‐based solutions and biocontrol methods. However, successfully harnessing these options relies on advanced understanding of the ecological system and interacting effects among biotic factors and with immature parasite stages. Here, we develop a framework linking three key groups of dung and soil fauna—fungi, earthworms, and dung beetles—with each other and developmental stages of helminths parasitic in farmed cattle, sheep, and goats in temperate grazing systems. We populate this framework from existing published studies and highlight the interplay between faunal groups and documented ecological outcomes. Of 1756 papers addressing abiotic drivers of populations of these organisms and helminth parasites, only 112 considered interactions between taxa and 36 presented data on interactions between more than two taxonomic groups. Results suggest that fungi reduce parasite abundance and earthworms may enhance fungal communities, while competition between dung taxa may reduce their individual effect on parasite transmission. Dung beetles were found to impact fungal populations and parasite transmission variably, possibly tied to the prevailing climate within a specific ecological context. By exploring combinations of biotic factors, we consider how interactions between species may be fundamental to the ecological consequences of biocontrol strategies and nontarget impacts of anthelmintics on dung and soil fauna and how pasture management alterations to promote invertebrates might help limit parasite transmission. With further development and parameterization the framework could be applied quantitatively to guide, prioritize, and interpret hypothesis‐driven experiments and integrate biotic factors into established models of parasite transmission dynamics. [ABSTRACT FROM AUTHOR]

This paper implements a simultaneous solar and thermal energy harvesting system, as a hybrid energy harvesting (HEH) system, to convert ambient light into electrical energy through photovoltaic (PV) cells and heat absorbed in the body of PV cells. Indeed, a solar panel equipped with serially connected thermoelectric generators not only converts the incoming light into electricity but also takes advantage of heat emanating from the light. In a conventional HEH system, the diode block is used to provide the path for the input source with the highest value. In this scheme, at each time, only one source can be handled to generate its output, while other sources are blocked. To handle this challenge of combining resources in HEH systems, this paper proposes a method for collecting all incoming energies and conveying its summation to the load via the current mirror cells in an approach similar to the maximum power point tracking. This technique is implemented using off‐the‐shelf components. The measurement results show that the proposed method is a realistic approach for supplying electrical energy to wireless sensor nodes and low‐power electronics. [ABSTRACT FROM AUTHOR]

Many invasive and some native tree species in North America exhibit strong allelopathic effects that may contribute to their local dominance. Pyrogenic carbon (PyC; including soot, charcoal, and black carbon) is produced by the incomplete combustion of organic matter and is widespread in forest soils. Many forms of PyC have sorptive properties that can reduce the bioavailability of allelochemicals. We investigated the potential for PyC produced by controlled pyrolysis of biomass ("biochar" [BC]) to reduce the allelopathic effects of black walnut (Juglans nigra) and Norway maple (Acer platanoides), a common native tree species and a widespread invasive species in North America, respectively. Seedling growth of two native tree species (Acer saccharinum [silver maple] and Betula papyrifera [paper birch]) in response to leaf‐litter‐incubated soils was examined; litter incubation treatments included leaves of black walnut, Norway maple, and a nonallelopathic species (Tilia americana [American basswood]) in a factorial design with varying dosages; responses to the known primary allelochemical of black walnut (juglone) were also examined. Juglone and leaf litter of both allelopathic species strongly suppressed seedling growth. BC treatments substantially mitigated these effects, consistent with the sorption of allelochemicals; in contrast no positive effects of BC were observed in leaf litter treatments involving controls or additions of nonallelopathic leaf litter. Treatments of leaf litter and juglone with BC increased the total biomass of silver maple by ~35% and in some cases more than doubled the biomass of paper birch. We conclude that BCs have the capacity to largely counteract allelopathic effects in temperate forest systems, suggesting the effects of natural PyC in determining forest community structure, and also the applied use of BC as a soil amendment to mitigate allelopathic effects of invasive tree species. [ABSTRACT FROM AUTHOR]

Wetlands in arid or semiarid zones are vital for maintaining biodiversity but face growing threats. Flooding regime variability is a key driver of ecological dynamism in these systems, dictating primary productivity on a large spatial scale. The functional composition or diversity of wetland-dependent bird species has been found to be sensitive to fluctuations in hydrological regimes and can thus be indicative of cascading ecosystem responses associated with climate change. In this paper, we investigate whether large-scale changes in inundation and fire--a significant additional biodiversity determinant in (semi-)arid landscapes--are reliable predictors of functional group responses of wetland-dependent birds along a perennial channel of the Okavango Delta, Botswana. We fit generalized additive models (GAMs) to 6 years of bird survey data collected along ~190-km-long annual transects and use remotely sensed landscape-level inundation estimates, as well as spatiotemporal distance to fire, to predict the responsiveness of seven trait-based functional group abundances. During the surveys, a total of 89 different wetland-dependent bird species were recorded, including 76 residents, across all years, with below-surface feeding waders consistently the most abundant functional group. Including estimated spatiotemporal variability in flooding and fire, as well as their interactions, improved model fit for all seven functional groups, explaining between 46.8% and 68.3% of variability in functional group abundances. Covariates representing longer-term variability in inundation generally performed better than shorter-term ones. For example, variability in inundation over the 5 months preceding a survey best predicted the responses of all functional groups, which also all exhibited responsiveness to the interaction between flooding and fire. We were able to interpret the responses of individual functional groups, based on the resource exploitation assumption. Overall, our results suggest that perennial waters in dryland wetlands offer functional refugia to wetland-dependent birds and highlight the indicative power of large-scale traitbased bird monitoring. Our findings demonstrate the potential utility of such a monitoring regime for dryland wetland ecosystems vulnerable to industrial-scale anthropogenic pressure and associated climate change. [ABSTRACT FROM AUTHOR]

Despite its high prevalence among dementias, Lewy body dementia (LBD) remains poorly understood with a limited, albeit growing, evidence base. The public‐health burden that LBD imposes is worsened by overlapping pathologies, which contribute to misdiagnosis, and lack of treatments. For this report, we gathered and analyzed public‐domain information on advocacy, funding, research outputs, and the therapeutic pipeline to identify gaps in each of these key elements. To further understand the current gaps, we also conducted interviews with leading experts in regulatory/governmental agencies, LBD advocacy, academic research, and biopharmaceutical research, as well as with funding sources. We identified wide gaps across the entire landscape, the most critical being in research. Many of the experts participated in a workshop to discuss the prioritization of research areas with a view to accelerating therapeutic development and improving patient care. This white paper outlines the opportunities for bridging the major LBD gaps and creates the framework for collaboration in that endeavor. Highlights: A group representing academia, government, industry, and consulting expertise was convened to discuss current progress in Dementia with Lewy Body care and research.Consideration of expert opinion,natural language processing of the literature as well as publicly available data bases, and Delphi inspired discussion led to a proposed consensus document of priorities for the field. [ABSTRACT FROM AUTHOR]

The article discusses recent advancements in artificial intelligence (AI) and their impact on speech and language comprehension. It highlights the emergence of speech and language technologies as major topics in AI research, enabling computers to interpret human language in both written and spoken forms. The article also provides summaries of 12 papers included in a special issue on speech and language technologies, covering topics such as question answering, coreference resolution, essay grading, named entity recognition, morphological analysis, code generation, Alzheimer's disease recognition, backchannel prediction, spoken-to-written text conversion, multimodal speech recognition, and language tutoring systems. The authors express their gratitude to the contributors and emphasize the importance of these studies for the future design and implementation of AI systems. [Extracted from the article]

This paper presents the development of language tutoring systems for non‐native speakers by leveraging advanced end‐to‐end automatic speech recognition (ASR) and proficiency evaluation. Given the frequent errors in non‐native speech, high‐performance spontaneous speech recognition must be applied. Our systems accurately evaluate pronunciation and speaking fluency and provide feedback on errors by relying on precise transcriptions. End‐to‐end ASR is implemented and enhanced by using diverse non‐native speaker speech data for model training. For performance enhancement, we combine semisupervised and transfer learning techniques using labeled and unlabeled speech data. Automatic proficiency evaluation is performed by a model trained to maximize the statistical correlation between the fluency score manually determined by a human expert and a calculated fluency score. We developed an English tutoring system for Korean elementary students called EBS AI PengTalk and a Korean tutoring system for foreigners called KSI Korean AI Tutor. Both systems were deployed by South Korean government agencies. [ABSTRACT FROM AUTHOR]

This article discusses the management of invasive alien species (IAS) in China. It highlights the negative impacts of biological invasions on ecosystems, the environment, and the economy. The article presents 24 papers that cover a wide range of topics related to IAS management, including risk analysis, mechanisms of invasion success, performance of successful IAS, and impact and control of invasive species. The studies use various methods such as field surveys, experiments, ecological modeling, and molecular techniques to assess invasion risks, investigate mechanisms, and develop control strategies. The article emphasizes the importance of interdisciplinary research and effective management strategies to address the increasing threat of IAS in China. [Extracted from the article]

The paper [[8]] "Dynamic Power and Bandwidth Allocation for DVB-based LEO Satellite Systems" proposes an efficient power and bandwidth allocation method employing two linear machine learning algorithms with inputs of channel conditions and TD. The fifth-generation (5G) technology has recently been consolidating its function as an infrastructure for new services, such as ultrareliable low-latency communications (URLLC) and massive machine-type communications, by further expanding the high-speed data service of the mobile communication system. In their paper titled [[1]] "Reliability-guaranteed Multi-path Allocation Algorithm in Mobile Network", Lee and Ko investigated the method of selecting higher reliable network paths for further enhancing URLLC services. [Extracted from the article]

Taxi‐demand forecasting and hotspot prediction can be critical in reducing response times and designing a cost effective online taxi‐booking model. Taxi demand in a region can be predicted by considering the past demand accumulated in that region over a span of time. However, other covariates—like neighborhood influence, sociodemographic parameters, and point‐of‐interest data—may also influence the spatiotemporal variation of demand. To study the effects of these covariates, in this paper, we propose three models that consider different covariates in order to select a set of independent variables. These models predict taxi demand in spatial units for a given temporal resolution using linear and ensemble regression. We eventually combine the characteristics (covariates) of each of these models to propose a robust forecasting framework which we call the combined covariates model (CCM). Experimental results show that the CCM performs better than the other models proposed in this paper. [ABSTRACT FROM AUTHOR]

In the process of autonomous vehicle motion planning and to create comfort for vehicle occupants, factors that must be considered are the vehicle's safety features and the road's slipperiness and smoothness. In this paper, we build a mathematical model based on the combination of a genetic algorithm and a neural network to offer lane‐change solutions of autonomous vehicles, focusing on human vehicle control skills. Traditional moving planning methods often use vehicle kinematic and dynamic constraints when creating lane‐change trajectories for autonomous vehicles. When comparing this generated trajectory with a man‐generated moving trajectory, however, there is in fact a significant difference. Therefore, to draw the optimal factors from the actual driver's lane‐change operations, the solution in this paper builds the training data set for the moving planning process with lane change operation by humans with optimal elements. The simulation results are performed in a MATLAB simulation environment to demonstrate that the proposed solution operates effectively with optimal points such as operator maneuvers and improved comfort for passengers as well as creating a smooth and slippery lane‐change trajectory. [ABSTRACT FROM AUTHOR]

Stroke is the leading cause of permanent disability in adults, and it can cause permanent brain damage. According to the World Health Organization, 795 000 Americans experience a new or recurrent stroke each year. Early detection of medical disorders, for example, strokes, can minimize the disabling effects. Thus, in this paper, we consider various risk factors that contribute to the occurrence of stoke and machine learning algorithms, for example, the decision tree, random forest, and naive Bayes algorithms, on patient characteristics survey data to achieve high prediction accuracy. We also consider the semisupervised self‐training technique to predict the risk of stroke. We then consider the near‐miss undersampling technique, which can select only instances in larger classes with the smaller class instances. Experimental results demonstrate that the proposed method obtains an accuracy of approximately 98.83% at low cost, which is significantly higher and more reliable compared with the compared techniques. [ABSTRACT FROM AUTHOR]

Renewable energy is promoted massively to overcome problems that fossil fuel power plants generate. One popular renewable energy type that offers easy installation is a photovoltaic (PV) system. However, the energy harvested through a PV system is not optimal because influenced by exposure to solar irradiance in the PV module, which is constantly changing caused by weather. The maximum power point tracking (MPPT) technique was developed to maximize the energy potential harvested from the PV system. This paper presents the MPPT technique, which is operated on a new high‐gain voltage DC/DC converter that has never been tested before for the MPPT technique in PV systems. Fuzzy logic (FL) was used to operate the MPPT technique on the converter. Conventional and adaptive perturb and observe (P&O) techniques based on variables step size were also used to operate the MPPT. The performance generated by the FL algorithm outperformed conventional and variable step‐size P&O. It is evident that the oscillation caused by the FL algorithm is more petite than variables step‐size and conventional P&O. Furthermore, FL's tracking speed algorithm for tracking MPP is twice as fast as conventional P&O. [ABSTRACT FROM AUTHOR]

This paper presents a transition‐limited pulse‐amplitude modulation (TLPAM) signaling method to enable a high data rate and robust wireline communications. TLPAM signaling addresses the impact of high intersymbol interference (ISI) ratios in conventional M‐ary PAM signaling methods by limiting the maximum voltage transition level between adjacent symbols. The implementation of a TLPAM signaling encoder is realized by setting back the most significant bits (MSBs) in the queue. The correlation between TLPAM's maximum transition level, effective data rate, and eye width/height is analyzed with various channel loss parameters, followed by characterization and measurement results with a realistic channel setup. The analysis and experimental results reveal the effectiveness of the proposed TLPAM signaling scheme for achieving a high data rate with minimal interference. [ABSTRACT FROM AUTHOR]

The document titled "Reviewers acknowledgement" is a list of individuals who have reviewed articles submitted for publication in Volume 13 of FEBS Open Bio. The editors express their gratitude to these reviewers for their time and expertise in carefully scrutinizing the experimental design and results of the papers. The list includes a diverse range of names from various cultural backgrounds, reflecting the global nature of the research community. [Extracted from the article]

This paper surveys recent multiagent reinforcement learning and neural Myerson auction deep learning efforts to improve mobility control and resource management in autonomous ground and aerial vehicles. The multiagent reinforcement learning communication network (CommNet) was introduced to enable multiple agents to perform actions in a distributed manner to achieve shared goals by training all agents' states and actions in a single neural network. Additionally, the Myerson auction method guarantees trustworthiness among multiple agents to optimize rewards in highly dynamic systems. Our findings suggest that the integration of MARL CommNet and Myerson techniques is very much needed for improved efficiency and trustworthiness. [ABSTRACT FROM AUTHOR]

Near‐term iterative forecasting is a powerful tool for ecological decision support and has the potential to transform our understanding of ecological predictability. However, to this point, there has been no cross‐ecosystem analysis of near‐term ecological forecasts, making it difficult to synthesize diverse research efforts and prioritize future developments for this emerging field. In this study, we analyzed 178 near‐term (≤10‐yr forecast horizon) ecological forecasting papers to understand the development and current state of near‐term ecological forecasting literature and to compare forecast accuracy across scales and variables. Our results indicated that near‐term ecological forecasting is widespread and growing: forecasts have been produced for sites on all seven continents and the rate of forecast publication is increasing over time. As forecast production has accelerated, some best practices have been proposed and application of these best practices is increasing. In particular, data publication, forecast archiving, and workflow automation have all increased significantly over time. However, adoption of proposed best practices remains low overall: for example, despite the fact that uncertainty is often cited as an essential component of an ecological forecast, only 45% of papers included uncertainty in their forecast outputs. As the use of these proposed best practices increases, near‐term ecological forecasting has the potential to make significant contributions to our understanding of forecastability across scales and variables. In this study, we found that forecastability (defined here as realized forecast accuracy) decreased in predictable patterns over 1–7 d forecast horizons. Variables that were closely related (i.e., chlorophyll and phytoplankton) displayed very similar trends in forecastability, while more distantly related variables (i.e., pollen and evapotranspiration) exhibited significantly different patterns. Increasing use of proposed best practices in ecological forecasting will allow us to examine the forecastability of additional variables and timescales in the future, providing a robust analysis of the fundamental predictability of ecological variables. [ABSTRACT FROM AUTHOR]

This paper presents a study on the use of encoder‐derived features in decoder‐side depth estimation. The scheme of multiview video encoding does not require the transmission of depth maps (which carry the geometry of a three‐dimensional scene) as only a set of input views and their parameters are compressed and packed into the bitstream, with a set of features that could make it easier to estimate geometry in the decoder. The paper proposes novel recursive block splitting for the feature extraction process and evaluates different scenarios of feature‐driven decoder‐side depth estimation, performed by assessing their influence on the bitrate of metadata, quality of the reconstructed video, and time of depth estimation. As efficient encoding of multiview sequences became one of the main scopes of the video encoding community, the experimental results are based on the "geometry absent" profile from the incoming MPEG Immersive video standard. The results show that the quality of synthesized views using the proposed recursive block splitting outperforms that of the state‐of‐the‐art approach. [ABSTRACT FROM AUTHOR]

Based on its simplicity and user‐friendly characteristics, OpenMP has become the standard model for programming on shared‐memory architectures. Checkpointing‐aided parallel execution (CAPE) is an approach that utilizes the discontinuous incremental checkpointing technique (DICKPT) to translate and execute OpenMP programs on distributed‐memory architectures automatically. Currently, CAPE implements the OpenMP execution model by utilizing the DICKPT to distribute parallel jobs and their data to slave machines, and then collects the results after executing these distributed jobs. Although this model has been proven to be effective in terms of performance and compatibility with OpenMP on distributed‐memory systems, it cannot fully exploit the capabilities of multicore processors. This paper presents a novel execution model for CAPE that utilizes two levels of parallelism. In the proposed model, we add another level of parallelism in the form of multithreaded processes on slave machines with the goal of better exploiting their multicore CPUs. Initial experimental results presented near the end of this paper demonstrate that this model provides significantly enhanced CAPE performance. [ABSTRACT FROM AUTHOR]

This paper proposes four‐ring slot resonator‐based MIMO antennas of 75 × 150 mm2 without and with CSRR structures in the sub‐6‐GHz range. These orthogonal‐fed antennas have shown diverse characteristics with dual polarization. L‐shaped parasitic structures have increased the isolation (i.e., >40 dB) in the single‐element antenna over the band of 3.4 GHz–3.8 GHz. A set of three CSRR structures in the MIMO antenna reduced the coupling between antenna ports placed in an inline arrangement and enhanced the isolation from 12 dB to 20 dB and the diversity characteristics. The S‐parameters of both MIMO antennas are measured and used to evaluate MIMO parameters like ECC, TARC, MEG, and channel capacity loss. The simulation results show the variations in the gain and directivity on exciting linear and dual polarizations. The diversity performance of the reported MIMO antennas is suitable for 5G applications. [ABSTRACT FROM AUTHOR]

This paper presents a flexible finite‐difference technique for analyzing the nonuniform guiding structures. Because the voltage and current variations along the nonuniform structure differ for each segment, this work considers the adaptable discretization steps. This technique increases the accuracy of the final response. Moreover, by applying the singular value decomposition and discarding the nonprincipal singular values, an optimal lower rank approximation of the discretization matrix is obtained. The computational cost of the introduced method is significantly reduced using the optimal discretization matrix. Also, the proposed method can be extended to the nonuniform waveguides. The technique is verified by analyzing several practical transmission lines and waveguides with nonuniform profiles. [ABSTRACT FROM AUTHOR]

In vehicular edge computing (VEC) networks, the rapid expansion of intelligent transportation and the corresponding enormous numbers of tasks bring stringent requirements on timely task offloading. However, many tasks typically appear within a short period rather than arriving simultaneously, which makes it difficult to realize effective and efficient resource scheduling. In addition, some key information about tasks could be learned due to the regular data collection and uploading processes of sensors, which may contribute to developing effective offloading strategies. Thus, in this paper, we propose a model that considers the deterministic demand of multiple tasks. It is possible to generate effective resource reservations or early preparation decisions in offloading strategies if some feature information of the deterministic demand can be obtained in advance. We formulate our scenario as a 0‐1 programming problem to minimize the average delay of tasks and transform it into a convex form. Finally, we proposed an efficient optimal offloading algorithm that uses the interior point method. Simulation results demonstrate that the proposed algorithm has great advantages in optimizing offloading utility. [ABSTRACT FROM AUTHOR]

This paper describes a novel end‐to‐end deep generative model‐based speaker recognition system using prosodic features. The usefulness of variational autoencoders (VAE) in learning the speaker‐specific prosody representations for the speaker recognition task is examined herein for the first time. The speech signal is first automatically segmented into syllable‐like units using vowel onset points (VOP) and energy valleys. Prosodic features, such as the dynamics of duration, energy, and fundamental frequency (F0), are then extracted at the syllable level and used to train/adapt a speaker‐dependent VAE from a universal VAE. The initial comparative studies on VAEs and traditional autoencoders (AE) suggest that the former can efficiently learn speaker representations. Investigations on the impact of gender information in speaker recognition also point out that gender‐dependent impostor banks lead to higher accuracies. Finally, the evaluation on the NIST SRE 2010 dataset demonstrates the usefulness of the proposed approach for speaker recognition. [ABSTRACT FROM AUTHOR]

Under limited transmission conditions, many factors affect the efficiency of video transmission. During the flight of an unmanned aerial vehicle (UAV), frequent network switching often occurs, and the channel transmission condition changes rapidly, resulting in low‐video transmission efficiency. This paper presents an efficient video coding flow for UAVs working in the 5G nonstandalone network and proposes two bit controllers, including time and spatial bit controllers, in the flow. When the environment fluctuates significantly, the time bit controller adjusts the depth of the recursive codec to reduce the error propagation caused by excessive network inference. The spatial bit controller combines the spatial bit mask with the channel quality multiplier to adjust the bit allocation in space to allocate resources better and improve the efficiency of information carrying. In the spatial bit controller, a flexible mini graph is proposed to compute the channel quality multiplier. In this study, two bit controllers with end‐to‐end codec were combined, thereby constructing an efficient video coding flow. Many experiments have been performed in various environments. Concerning the multi‐scale structural similarity index and peak signal‐to‐noise ratio, the performance of the coding flow is close to that of H.265 in the low bits per pixel area. With an increase in bits per pixel, the saturation bottleneck of the coding flow is at the same level as that of H.264. [ABSTRACT FROM AUTHOR]

As a leading cause of forest health degradation, non‐native invasive plant species are a key focus for many forest management and conservation efforts. These efforts come at a high price for resource‐limited agencies and organizations making cost‐effectiveness an important objective of invasion response plans. In this paper, we present an approach to guide the prioritization of locations for invasion management using species distribution models that account for the non‐equilibrium of invasive species distributions and use readily available land use data as the primary explanatory variables. This approach takes advantage of the relatively high spatial resolution, as well as the broad, continuous geographic coverage, of land use data to provide results at a landscape scale relevant to practitioners responsible for invasive species management. In our example from northern Virginia, we simultaneously modeled a suite of invasive plant species to identify common indicators of invasion. We found that the proportions of surrounding non‐forested land use types (grasses, crops, and development) were the most common and strongest indicators of invasion risk. These outcomes can guide managers of large protected areas to focus on major divides between forest and non‐forest land over linear disturbances. We also found useful species‐specific traits that can inform specific management actions. Additionally, we demonstrate through a case study how organizations that manage multiple smaller properties can take advantage of the projected distribution maps when considering acquiring or administering properties. [ABSTRACT FROM AUTHOR]

Browning of surface waters, also known as brownification, is a process of decreasing water transparency, particularly in boreal lakes surrounded by intensively managed forests and wetlands. In this paper, we review the ecological consequences and ecosystem‐based management (EBM) of browning through a systematic review approach and adopt an interdisciplinary approach to formulating new governance of this complex phenomenon. To understand the effects of browning on the recreational value of freshwaters, we present primary survey data on public perceptions of recreational fishing tourists on water quality in Finland. We identify a need to develop EBM beyond the EU's Water Framework Directive (WFD) to fully account for the extensive implications of browning. We also highlight the need for a better understanding of the within‐lake microbial processes to estimate the browning‐associated changes in the greenhouse gas balance of lakes. Tourist perceptions of the quality of waterbodies in Finland were largely in agreement with the general proportion of waterbodies classified in a good or excellent ecological status class, but these perceptions may be detached from biological quality assessment criteria. Consequently, we suggest that the EBM of inland waters should improve the utilization of information on not only biogeochemical processes but also users' perspectives on aquatic ecosystems beyond the EU WFD. [ABSTRACT FROM AUTHOR]

This paper proposed a novel method for constructing quasi‐cyclic low‐density parity‐check (QC‐LDPC) codes of medium to high code rates that can be applied in cloud data storage systems, requiring better error correction capabilities. The novelty of this method lies in the construction of sparse base matrices, using a girth greater than 4 that can then be expanded with a lift factor to produce high code rate QC‐LDPC codes. Investigations revealed that the proposed large‐sized QC‐LDPC codes with high code rates displayed low encoding complexities and provided a low bit error rate (BER) of 10−10 at 3.5 dB Eb/N0 than conventional LDPC codes, which showed a BER of 10−7 at 3 dB Eb/N0. Subsequently, implementation of the proposed QC‐LDPC code in a software‐defined radio, using the NI USRP 2920 hardware platform, was conducted. As a result, a BER of 10−6 at 4.2 dB Eb/N0 was achieved. Then, the performance of the proposed codes based on their encoding–decoding speeds and storage overhead was investigated when applied to a cloud data storage (GCP). Our results revealed that the proposed codes required much less time for encoding and decoding (of data files having a 10 MB size) and produced less storage overhead than the conventional LDPC and Reed–Solomon codes. [ABSTRACT FROM AUTHOR]

The large amount of secondary effects in complementary metal–oxide–semiconductor technology limits its application in the ultra‐nanoscale region. Circuit designers explore a new technology for the ultra‐nanoscale region, which is the quantum‐dot cellular automata (QCA). Low‐energy dissipation, high speed, and area efficiency are the key features of the QCA technology. This research proposes a novel, low‐complexity, QCA‐based one‐bit digital comparator circuit for the ultra‐nanoscale region. The performance of the proposed comparator circuit is presented in detail in this paper and compared with that of existing designs. The proposed QCA structure for the comparator circuit only consists of 19 QCA cells with two clock phases. QCA Designer‐E and QCA Pro tools are applied to estimate the total energy dissipation. The proposed comparator saves 24.00% QCA cells, 25.00% cell area, 37.50% layout cost, and 78.11% energy dissipation compared with the best reported similar design. [ABSTRACT FROM AUTHOR]

Researchers have recently shown an increased interest in estimating the direction‐of‐arrival (DOA) of wideband noncircular sources, but existing studies have been restricted to subspace‐based methods. An off‐grid sparse recovery‐based algorithm is proposed in this paper to improve the accuracy of existing algorithms in low signal‐to‐noise ratio situations. The covariance and pseudo covariance matrices can be jointly represented subject to block sparsity constraints by taking advantage of the joint sparsity between signal components and bias. Furthermore, the estimation problem is transformed into a single measurement vector problem utilizing the focused operation, resulting in a significant reduction in computational complexity. The proposed algorithm's error threshold and the Cramer–Rao bound for wideband noncircular DOA estimation are deduced in detail. The proposed algorithm's effectiveness and feasibility are demonstrated by simulation results. [ABSTRACT FROM AUTHOR]

Challenges in cognitive radio and tactical communications include recognizing anonymously received signals and estimating parameters in a blind or semiblind manner. In this paper, we examine this issue for orthogonal frequency division multiplexing (OFDM) signaling. There are several parameters in OFDM signaling, and the blind receiver must extract and consider the synchronization issue. We assume that the blind receiver is aware of modulation type, OFDM, and not aware of chip duration and the length of cyclic prefix. First, we present new criteria based on kurtosis to estimate these parameters and compare their performance at different levels of additive white Gaussian noise with methods based on correlation, kurtosis, maximum likelihood, and matched filter. Then, we perform synchronization and estimate the start time based on these criteria and several new criteria in two steps: fine and coarse synchronization. Finally, in a more practical setup, we present the idea of jointly estimating the mentioned parameters and the signal start time as coarse synchronization. We compare different criteria and show that one of the proposed criteria has the highest efficiency. [ABSTRACT FROM AUTHOR]

This paper proposes a method that can reduce the complexity of a system matrix by analyzing the characteristics of a pseudoinverse matrix to receive a binomial frequency division multiplexing (BFDM) signal and decode it using the least squares (LS) method. The system matrix of BFDM can be expressed as a band matrix, and as this matrix contains many zeros, its amount of calculation when generating a transmission signal is quite small. The LS solution can be obtained by multiplying the received signal by the pseudoinverse matrix of the system matrix. The singular value decomposition of the system matrix indicates that the pseudoinverse matrix is a band matrix. The signal‐to‐interference ratio is obtained from their eigenvalues. Meanwhile, entries that do not contribute to signal generation are erased to enhance calculation efficiency. We decode the received signal using the pseudoinverse matrix and the removed pseudoinverse matrix to obtain the bit error rate performance and to analyze the difference. [ABSTRACT FROM AUTHOR]

Hardware security primitives, also known as physical unclonable functions (PUFs), perform innovative roles to extract the randomness unique to specific hardware. This paper proposes a novel hardware security primitive using a commercial off‐the‐shelf flash memory chip that is an intrinsic part of most commercial Internet of Things (IoT) devices. First, we define a hardware security source model to describe a hardware‐based fixed random bit generator for use in security applications, such as cryptographic key generation. Then, we propose a hardware security primitive with flash memory by exploiting the variability of tunneling electrons in the floating gate. In accordance with the requirements for robustness against the environment, timing variations, and random errors, we developed an adaptive extraction algorithm for the flash PUF. Experimental results show that the proposed flash PUF successfully generates a fixed random response, where the uniqueness is 49.1%, steadiness is 3.8%, uniformity is 50.2%, and min‐entropy per bit is 0.87. Thus, our approach can be applied to security applications with reliability and satisfy high‐entropy requirements, such as cryptographic key generation for IoT devices. [ABSTRACT FROM AUTHOR]

This paper investigates the effect of hardware impairments (HIs) and imperfect channel state information (ICSI) on a SWIPT‐assisted adaptive nonorthogonal multiple access (NOMA)/orthogonal multiple access (OMA) system over independent and nonidentical Rayleigh fading channels. In the NOMA mode, the energy‐constrained near users act as a relay to improve the performance for the far users. The OMA transmission mode is adopted to avoid a complete outage when NOMA is infeasible. The best user selection scheme is considered to maximize the energy harvested and avoid error propagation. To characterize the performance of the proposed systems, closed‐form and asymptotic expressions of the outage probability for both near and far users are studied. Moreover, exact and approximate expressions of the ergodic rate for near and far users are investigated. Simulation results are provided to verify our theoretical analysis and confirm the superiority of the proposed NOMA/OMA scheme in comparison with the conventional NOMA and OMA protocol with/without HIs and ICSI. [ABSTRACT FROM AUTHOR]

In this issue, Kresovich and colleagues have published a hallmark paper in Molecular, Environmental, Genetic and Analytic Epidemiology. By applying artificial intelligence to the Sister Study they created a new methylation‐based breast cancer risk score (mBCRS) based on blood DNA methylation. Using a prospective design and after accounting for age and questionnaire‐based breast cancer risk factors, the Odds PER Adjusted standard deviation (OPERA) for mBCRS and polygenic risk score (PRS) was 1.58 (95% CI: 1.38, 1.81) and 1.58 (95% CI: 1.36, 1.83), respectively, and the corresponding area under the receiver operating curve was 0.63 for both. Therefore, mBCRS could be as powerful as the current best PRS in differentiating women of the same age in terms of their breast cancer risk. These risk scores are among the strongest known breast cancer risk‐stratifiers, shaded only by new mammogram risk scores based on measures other than conventional mammographic density, such as Cirrocumulus and Cirrus, which when combined have an OPERA as high as 2.3. The combination of PRS and mBCRS with the other measured risk factors gave an OPERA of 2.2. OPERA has many advantages over changes in areas under the receiver operator curve because the latter depend on the order in which risk factors are considered. Although more replication is needed using prospective data to protect against reverse causation, there are many novel molecular and analytic aspects to this paper which uncovers a potential mechanism for how genetic and environmental factors combine to cause breast cancer. [ABSTRACT FROM AUTHOR]

Plant species invasion represents one of the major drivers of biodiversity change globally, yet there is confusion about the nature of nonindigenous species (NIS) impact. This confusion stems from differing notions of what constitutes invasive species impact and the scales at which it should be assessed. At local scales, the mechanisms of the impact on local competitors can be classified into four scenarios: (1) minimal impact from NIS inhabiting unique niches; (2) neutral impact spread across the community and proportional to NIS abundance; (3) targeted impact on a small number of competitors with overlapping niches; and (4) pervasive impact that is disproportionate to NIS abundance and caused by modifications that filter out other species. I developed a statistical test to distinguish these four mechanism scenarios based on plant community rank‐abundance curves and then created a scale‐independent standardized impact score. Using an example long‐term dataset with high native plant diversity and an abundance gradient of the invasive vine, Vincetoxicum rossicum, I show that the impact resulted in either targeted or pervasive extirpations. Regardless of whether the NIS impact is neutral, targeted, or pervasive, the net outcome will be the homogenization of ecosystems and reduced biodiversity at larger scales, perhaps reducing ecosystem resilience. The framework and statistical evaluation of impact presented in this paper provide researchers and managers with an objective approach to quantifying NIS impact and prioritizing species for further management actions. [ABSTRACT FROM AUTHOR]

The use of gamified learning interventions is expanding in postsecondary education as a means to improve students' motivation and learning outcomes. Virtual laboratory simulations have been used in science education to supplement students' learning, as well as to increase engagement with course material. Due to COVID‐19, many instructors sought to replace or supplement hands‐on 'wet‐lab' work in an online environment. In this paper, we explored how the use of head‐mounted display technology in two laboratory simulations impacts learner motivation and learning outcomes. We used a mixed‐methods approach to analyze the experience of 39 undergraduate participants, examining test scores pre‐ and postsimulation, qualitative feedback, and quantitative experience ratings. The head‐mounted display technology was described as easy to use, with eye strain identified as a common occurrence. Participants had increased test scores following the laboratory simulations, with no significant difference between simulation groups. Very positive self‐reported measures of motivation and learner engagement were documented. Ninety‐one percent of participants agreed that virtual reality laboratory simulation would be a good supplement to regular teaching modalities. Overall, our results suggest that immersive virtual reality laboratory simulations experienced through head‐mounted display technology can be used to enhance learning outcomes and increase learner motivation. [ABSTRACT FROM AUTHOR]