Resource Selection Research Articles

Travel specific resource selection by female Kodiak brown bears during the sockeye salmon spawning season

BackgroundAccess to salmon resources is vital to coastal brown bear (Ursus arctos) populations. Deciphering patterns of travel allowing coastal brown bears to exploit salmon resources dispersed across the landscape is critical to understanding their behavioral ecology, maintaining landscape connectivity for the species, and developing conservation strategies.MethodsWe modeled travel behavior of 51 radio-collared female Kodiak brown bears (U. a. middendorffi) from 2008 to 2015 during the sockeye salmon (Oncorhynchus nerka) stream spawning season to identify landscape patterns associated with travel pathways. To accomplish this, we first identified behavioral states of marked individuals, and then developed a resource selection function (RSF) to evaluate environmental covariates that were predictors of selection during travel behavior.ResultsLandcover edges, elderberry-salmonberry stands, lowland tundra, elevation, terrain position, and stream length influenced selection for travel corridors. The RSF validated well and was comparable to corridors identified by pathways used by bears while travelling.ConclusionsModels identified spatial predictions of the relative probability of selection while bears were travelling during the salmon spawning season and identified areas that contained potential movement corridors important for bears inhabiting Kodiak Island. Our results characterized factors influencing travel, identified important movement corridors, and provided managers with information to make informed resource management decisions.

Phenological variation in biotic interactions shapes population dynamics and distribution in a range-shifting insect herbivore.

Phenological responses to climate change vary across trophic levels. However, how trophic phenological synchrony determines species' distributions through its effects on population dynamics has rarely been addressed. Here, we show that phenological variation underlies population and geographical range dynamics in a range-shifting herbivore, and demonstrate its interplay with changing trophic interactions. Using a novel modelling approach, we identify drivers of variation in phenology and population growth (productivity) for populations of the brown argus butterfly (Aricia agestis) feeding on ancestral and novel host plants in the UK. We demonstrate host plant-specific links between phenology and productivity, highlighting their role in the consumer's range expansion. Critically, later butterfly phenology is associated with higher productivity in the annual second brood, especially on novel annual hosts where later activity improves synchrony with germinating plants. In turn, later phenology and higher second brood productivity are associated with more rapid range expansion, particularly in regions where only the novel hosts occur. Therefore, phenological asynchrony imposes limits on local population growth, influencing consumer resource selection, evolutionary responses and emergent range dynamics. How existing and future trophic phenological synchrony determine population dynamics will be critical for the ecological and evolutionary outcomes of climate change.

Deep reinforcement learning for autonomous SideLink radio resource management in platoon-based C-V2X networks: An overview

Dynamic and autonomous SideLink (SL) Radio Resource Management (RRM) is essential for platoon-based cellular vehicular networks. However, this task is challenging due to several factors. These include the limited spectrum below 6 GHz, stringent vehicle-to-everything (V2X) communications requirements, uncertain and dynamic environments, limited vehicle sensing capabilities, and inherent distributed operation. These limitations often lead to resource collisions, data packet loss, and increased latency. Current standardized approaches in Long-Term Evolution-V2X (LTE-V2X) and New Radio-V2X (NR-V2X) rely on random resource selection, limiting their efficiency. Moreover, RRM is inherently a complex combinatorial optimization problem. It may involve conflicting objectives and constraints, making traditional approaches inadequate. Platoon-based communication necessitates careful resource allocation to support a diverse mix of communication types. These include safety-critical control messaging within platoons, less time-sensitive traffic management information between platoons, and even infotainment services like media streaming. Optimizing resource sharing inter- and intra- platoons is crucial to avoid excessive interference and ensure overall network performance. Deep Reinforcement Learning (DRL), combining Deep Learning (DL) and Reinforcement Learning (RL), has recently been investigated for network resource management. It offers a potential solution for these challenges. A DRL agent, represented by deep neural networks, interacts with the environment and learns optimal decision-making through trial and error. This paper overviews proposed DRL-based methods for autonomous SL RRM in single and multi-agent platoon-based C-V2X networks. It considers both intra- and inter-platoon communications with their specific requirements. We discuss the components of Markov Decision Processes (MDP) used to model the sequential decision-making of RRM. We then detail the DRL algorithms, training paradigms, and insights on the achieved results. Finally, we highlight challenges in existing works and suggest strategies for addressing them.

In Vitro Inducted Tetraploid Elsholtzia splendens Nakai ex F. Maek. Alters Polyphenol Species and Synthesis

Elsholtzia splendens Nakai ex F. Maek. has been employed in traditional Chinese medicine for millennia. Nevertheless, the small size and the paucity of research on its pharmacological effects have restricted its extensive utilisation in clinical medicine. Polyploid breeding represents an effective method for the rapid enhancement of plant biomass and metabolites. In this study, the most effective in vitro method for inducing tetraploid formation was identified as axillary buds treated in a solution of colchicine at a concentration of 1% for 24 h. Meanwhile, a comparison between tetraploids and diploids yielded two significant findings: (1) The presence of 6-zonocolpate and 8-zonocolpate pollen grains can be used as distinguishing characteristics for diploid and tetraploid, respectively. (2) Genome duplication resulted in alterations to the polyphenol species and synthesis pathway in E. splendens. The accumulation of wogonin, oroxylin A, baicalin, chrysin, acacetin and related derivatives was markedly greater in tetraploid plants, whereas apigenin, naringenin, scutellarein and related derivatives were found to accumulate to a greater extent in diploid plants. It is noteworthy that wogonin and oroxylin A were uniquely detected in tetraploids, indicating that the generated tetraploids may harbor novel pharmacological value. The findings not only provided new insights into the metabolic mechanism of polyploidisation but also established a foundation for the selection and breeding of novel genetic resources of E. splendens.

Evaluating habitat selection of François’ langur in the karst mountains of China: Implications for conservation strategies

The François’ langur is an endangered primate species endemic to Asia, found in the karst mountain regions of China and Vietnam. These langurs are distributed across isolated locations, where habitat loss and fragmentation pose significant threats to their survival. The Mayanghe National Nature Reserve (MNNR) is home to the largest population of wild François' langurs in the world, estimated at 500–600 individuals. To understand the habitat selection of François’ langurs in the MNNR, a survey was conducted from September 2017 to August 2019. Using species presence data and environmental variables, we analyzed habitat selection through a resource selection function (RSF). Significant habitat differences were found between distribution and control sites. The best model included slope, distance to river, distance to road, vegetation type, and tree height. The distribution of François’ langur is primarily influenced by its reliance on specific vegetation types, sensitivity to human disturbance, and geographical factors. This study clarifies the ecological requirements of wild François’ langurs by analyzing their habitat selection and highlights the importance of understanding these factors to inform conservation efforts.

ВИКОРИСТАННЯ ЕЛЕКТРОННИХ РЕСУРСІВ НА РІЗНИХ ЕТАПАХ УРОКУ ІНФОРМАТИЧНОЇ ОСВІТНЬОЇ ГАЛУЗІ В ПОЧАТКОВИХ КЛАСАХ

The article analyses the peculiarities of conducting a competence-based lesson «I Explore the World» in the field of computer science. It is explained why the main type of computer science lesson in primary school is a combined lesson. The main stages of a combined lesson in the field of computer science and its structural components are characterized, described and presented graphically. The timing of each lesson block is scheduled, taking into account the recommendations of the Ministry of Education and Science and the requirements of the Sanitary Regulations. For each component, a selection of key learning activities is offered that can be used during this part of the lesson. The main didactic criteria that should be taken into account when choosing or developing electronic resources to support the teaching of computer science in primary school are systematized and described. The importance of the organizational component of the computer science lesson in primary school as necessary to ensure the safety of students in the computer classroom is determined. An electronic resource for teaching safety rules has been selected, which will help to better memorize the educational material. The necessity of using health-saving technologies is substantiated, their place in the overall structure of the lesson is determined. Specific tools and a selection of electronic resources for accompanying movements and gymnastics are described. The author explains the need to divide the study of a new topic into two stages: the research stage and the practical stage. The methodology for conducting the research stage with the use of time constraints is described, and free electronic resources for explaining the material of the new topic are selected. Free electronic resources, software environments and integrated educational and developmental software packages for primary school students, which are advisable to use at the stage of formation and improvement of practical skills, are considered. Electronic resources have been selected for use at the final stage of the lesson, which help to consolidate the learning material, develop critical thinking and provide feedback between participants in the educational process.

Spatial Ecology of a Resident Avian Predator During the Non-Breeding Period in Managed Habitats of Southeastern Europe.

Describing home range and resource selection is crucial for understanding ecological needs and creating conservation programs. Still, our knowledge of spatial and behavioural ecology for most species remains limited. Here, we used satellite transmitters to investigate core and home range sizes, habitat selection, and roost characteristics in seven tawny owl males in Western Serbia during the autumn-winter period 2023. Using minimum convex polygon (MCP) and autocorrelated kernel density estimation (AKDE), we found clear variability in core area and home range sizes. Also, adult and heavier males have smaller core area and home ranges than juvenile and lightweight individuals. The Bhattacharyya coefficient showed minor home range overlap in tagged males. The final model for evaluation of habitat selection suggests that the likelihood of owl occurrence was positively correlated with the share of anthropogenic infrastructure and negatively associated with the increase in the proportion of cultivated land within the home range. However, scores of model performance metrics showed moderate predictive accuracy, implying that other unmeasured variables may dictate species presence. Our study illustrates the ecological plasticity and ability of the tawny owl to adapt to a human-modified environment while providing new information about the spatial ecology of this widespread predator in Europe.

Risk avoidance during dispersal: Temporal and behavioral shifts in selection by non-resident eastern wolves (Canis c.f. lycaon)

Dispersal is important for connectivity and persistence of small, disjunct wildlife populations. However, in human-altered landscapes dispersing animals are often more likely than residents to use anthropogenic features that increase mortality risk. As such, quantifying spatial and temporal responses of dispersing animals to anthropogenic features will improve our understanding of the potential for small, isolated populations to expand in human-altered landscapes. The distribution of eastern wolves (Canis c.f. lycaon; a threatened species in Canada) is limited to portions of eastern Canada within and adjacent to the population core in Algonquin Park. Recently, it was found that non-resident eastern wolves dispersing outside of Algonquin Park survive poorly, primarily due to high rates of human-caused mortality. We deployed Global Positioning System collars on 26 non-resident eastern wolves. We evaluated resource selection of dispersing individuals during different movement states to better understand their behavioral responses to anthropogenic landscape features during this critical, but risky period of their life history. Eastern wolves avoided areas with high building density during the day. However, during directed movements outside the winter period, eastern wolves selected areas closer to unpaved roads and areas with higher building densities at night. During winter, eastern wolves selected areas with high building densities at night while displaying tortuous movements. We demonstrate that harvested species may increase and relax avoidance of anthropogenic features at both daily and seasonal timescales. Failing to account for temporal and state-specific variation in resource selection patterns of dispersing animals may underestimate their ability to move through human-altered landscapes.

Interplay of physical and social drivers of movement in male African savanna elephants

Abstract Despite extensive research into the behavioral ecology of free-ranging animal groups, questions remain about how group members integrate information about their physical and social surroundings. This is because a) tracking of multiple group members is limited to a few easily manageable species; and b) the tools to simultaneously quantify physical and social influences on an individual’s movement remain challenging, especially across large geographic scales. A relevant example of a widely ranging species with complex social structure and of conservation concern is the African savanna elephant. We evaluate highly synchronized GPS tracks from five male elephants in Etosha National Park in Namibia by incorporating their dynamic social landscape into an established resource selection model. The fitted model predicts movement patterns based simultaneously on the physical landscape (e.g., repeated visitation of waterholes) and the social landscape (e.g., avoidance of a dominant male). Combining the fitted models for multiple focal individuals produces landscape-dependent social networks that vary over space (e.g., with distance from a waterhole) and time (e.g., as the seasons change). The networks, especially around waterholes, are consistent with dominance patterns determined from previous behavioral studies. Models that combine physical landscape and social effects, based on remote tracking, can augment traditional methods for determining social structure from intensive behavioral observations. More broadly, these models will be essential to effective, in-situ conservation and management of wide-ranging social species in the face of anthropogenic disruptions to their physical surroundings and social connections.

Under cover: the nuanced influence of functional properties of cover on resource selection by pygmy rabbits (Brachylagus idahoensis)

Abstract Animals at risk of predation select habitat that enhances security from predators. Two properties of cover related to security are concealment (i.e., habitat structure that blocks an individual from detection by others) and visibility (i.e., visual information accessible relative to habitat structure). Although these properties are often negatively correlated, they are not always inverse; animals in habitat with heterogeneous structure may be able to select for both. We investigated habitat use by pygmy rabbits (Brachylagus idahoensis) at 2 scales (patch and microsite) to evaluate the influence of both structural properties of cover and visual properties (concealment and visibility) on habitat use by prey. We contrasted vegetation structure at paired used and unused patches. At each patch, we measured concealment and viewshed (i.e., visibility) in 3 orientations (i.e., aerial, terrestrial, and overall) and structural density using lidar. We also measured heights of the 3 tallest shrubs. Additionally, within used patches, we assessed the density of fecal pellets as an index of intensity of use and also measured distance to nearest burrow. At the patch scale, rabbits selected for structural properties of cover (dense vegetation and tall shrubs), but not visual properties of cover. Pygmy rabbits more intensively used microsites associated with high terrestrial concealment and in proximity to burrows. Our results suggest that pygmy rabbits may perceive greater threat from terrestrial as opposed to aerial predators at both scales, and they also indicate a nuanced relationship between properties of cover and habitat use.

Coordinación, cooperación y colaboración interorganizacional para la transformación digital empresarial

The ability of an entrepreneur to create is limited by the lack of resources. Among the dominant resources in emerging countries, the creation and adoption of new technologies stands out. It is understood that the power to compete and lead in the markets resides in the rarity and distinction in the selection of resources. Many companies choose to increase the complexity of their products and promote the sophistication of services, however; They neglect to strengthen the business model by diversifying their values with the support of external agents. The research objective of this article is to understand the disposition of enterprises to collaborate, cooperate, and coordinate with external agents in order to adopt technology programs and digital competencies. The methodology is based on closed questionaries for twenty-one managers. The results indicate that there is a greater willingness to collaborate and cooperate than to coordinate, which translates into a greater openness of businesses to work together in an operational rather than strategic context. In depth, the interest in collaboration for the development of digital competencies and cooperation in the transfer of personnel focused on technology exceeds, in comparison, the interest in jointly planning investments in digital transformation programs or making joint hires of information technology specialists. Additionally, companies have high expectations for collaborative work in generating new digital platforms and launching new products, but they lack initiative in creating patents, licenses, or new ventures. Realidad Empresarial No. 18, 2024: 65-75.

Of goats and heat, the differential impact of summer temperature on habitat selection and activity patterns in mountain goats of different ecotypes.

Climate change disproportionately affects northern and alpine environments, with faster rates of warming than the global average. Because alpine and northern species are particularly well adapted to cool temperatures, most species must modify their behavior when temperatures exceed a critical threshold. Evaluating how temperature increases affect species inhabiting northern and alpine environments is therefore essential to understand the effects of projected climate change on these ecosystems. We analyzed the influence of temperature on the activity patterns and habitat selection of four populations of a cold-adapted, mountain specialist, the mountain goat (Oreamnos americanus). We collected GPS location and activity sensor data during 2010-2019 from 223 mountain goats from two distinct ecotypes: coastal and continental. Using a resource selection modeling approach, we determined that mountain goats of both ecotypes decreased selection for alpine meadows when temperatures increased. Reduced selection for open, forage rich habitat was associated with increased selection for habitat dominated by snow/ice patches in coastal areas, and by forests in continental sites. Mountain goats in continental environments selected higher elevation habitats only when temperature increased, whereas goats in coastal environments selected higher elevation habitat at all temperatures. Mountain goats of both ecotypes reduced the proportion of time spent active when temperatures increased during the middle of the day. Our study reveals that mountain goats use diverse tactics to mitigate thermal stress, and that these tactics vary between ecotypes, highlighting the need for considering adaptation to specific environments within a species when assessing climate change impacts on populations.

Resource selection by a megaomnivore in a marine foraging habitat

AbstractHabitat‐based approaches to animal conservation are bolstered by an understanding of resource selection, that is, use of resources (i.e., habitat features) relative to their availability in the environment. Quantifying resource selection is especially valuable when data characterizing animal space use are limited, as is often the case with mobile and/or cryptic species. Documenting associations with habitat features can better inform management in space in time, while also revealing key insight into movement ecology and behavior. Here, we evaluate resource selection by a megaomnivore whose highly mobile nature within marine habitats has resulted in an incomplete understanding of drivers of space use. We used satellite telemetry to track 29 green turtles (Chelonia mydas) from an eastern Pacific foraging aggregation in San Diego Bay, California, USA during 2013–2023. Tracking produced 5023 Fastloc‐GPS points which we used to model selection for local environmental resources relative to their availability. We employed logistic models to evaluate associations with seagrass, bathymetry, and water temperatures, implementing a framework that additionally allowed us to explore the roles of season, diel period, and turtle body size. Our methods demonstrate an approach for down‐weighting observations according to assumed telemetry error and autocorrelation. Results from fine‐scale resource selection models provide evidence that green turtles in San Diego Bay select for eelgrass meadows (Zostera marina), particularly during the warmest months of the year, but the strength of this selection changes from day to night. We additionally found day–night shifts in depth and temperature selection that changed with turtle body size and season. We discuss these findings in the context of diel patterns in resting and foraging behavior in addition to seasonal changes in thermally sensitive metabolic rates. Our study documents resource associations and provides quantitative information for the management of sea turtle foraging populations and their habitats. We offer key insight into habitat use by green turtles in the eastern Pacific at a pivotal time when multiple indicators point to population growth and expansion within the region.

Seasonal fine‐scale resource selection of elk in the central Appalachian Mountains

Resource selection by animals varies depending on spatial and temporal context, thus it is important to account for these factors when conducting studies that examine this behavior relative to the availability and distribution of resources. Many resource selection studies combine fine‐scale animal location data obtained using global positioning system (GPS) technology and coarse‐scale, remotely sensed, spatial layers. Fewer studies combine fine‐scale animal location data with vegetation data collected in the field, which can identify important fine‐scale drivers of resource selection. Understanding fine‐scale resource selection of ungulates inhabiting regions dominated by closed‐canopy forests may provide important information regarding management actions that improve habitat quality. From April 2021 to August 2022, we conducted vegetation surveys at locations (n = 1294) used by 39 GPS‐collared elk Cervus canadensis and at paired random locations (n = 2509) available to elk. We used conditional logistic regressions to compare seasonal (spring, summer, fall, winter) vegetation characteristics between used and available locations. We also conducted a species indicator analysis to compare plant species found at used and available locations during peak foraging periods (dawn/dusk). Regardless of season, the probability of use by elk increased as tree basal area (m2/ha) decreased, and the presence of herbaceous plants increased. Clovers (Trifolium spp.) were indicators of locations used by elk during primary foraging periods regardless of season. Other plants that were indicators of locations used by elk during at least one season included purple‐crown vetch Securigera varia, bird's foot trefoil Lotus corniculatus, and black medick Medicago lupulina. This research highlights the importance of managed openings, ecotones, and other communities characterized by reduced tree cover and increased herbaceous plants to meet the foraging needs of elk in landscapes dominated by closed‐canopy forests. Creating and maintaining managed herbaceous openings and restoring open‐forest communities will likely facilitate elk population growth and expansion.

A Novel Spectrum Allocation and Resource Selection in Cognitive Radio using Multi-Antenna Spectrum Sharing Model

A Novel Spectrum Allocation and Resource Selection in Cognitive Radio using Multi-Antenna Spectrum Sharing Model

Automatic plant phenotyping analysis of Melon (Cucumis melo L.) germplasm resources using deep learning methods and computer vision

Cucumis melo L., commonly known as melon, is a crucial horticultural crop. The selection and breeding of superior melon germplasm resources play a pivotal role in enhancing its marketability. However, current methods for melon appearance phenotypic analysis rely primarily on expert judgment and intricate manual measurements, which are not only inefficient but also costly. Therefore, to expedite the breeding process of melon, we analyzed the images of 117 melon varieties from two annual years utilizing artificial intelligence (AI) technology. By integrating the semantic segmentation model Dual Attention Network (DANet), the object detection model RTMDet, the keypoint detection model RTMPose, and the Mobile-Friendly Segment Anything Model (MobileSAM), a deep learning algorithm framework was constructed, capable of efficiently and accurately segmenting melon fruit and pedicel. On this basis, a series of feature extraction algorithms were designed, successfully obtaining 11 phenotypic traits of melon. Linear fitting verification results of selected traits demonstrated a high correlation between the algorithm-predicted values and manually measured true values, thereby validating the feasibility and accuracy of the algorithm. Moreover, cluster analysis using all traits revealed a high consistency between the classification results and genotypes. Finally, a user-friendly software was developed to achieve rapid and automatic acquisition of melon phenotypes, providing an efficient and robust tool for melon breeding, as well as facilitating in-depth research into the correlation between melon genotypes and phenotypes.

The Impact of OER’s Continuous Improvement Cycles on Students’ Performance: A Longitudinal Analysis of the RISE Framework

Open educational resources (OER) have been praised for revolutionizing education. However, practitioners and instructors battle keeping OER updated and measuring their impact on students’ performance. Few studies have analyzed the improvement of OER over time in relation to achievement. This longitudinal study uses learning analytics through the open-source Resource Inspection, Selection, and Enhancement (RISE) analysis framework to assess the impact of continuous improvement cycles on students’ outcomes. Panel data (i.e., performance and use) from 190 learning objectives of OER of an introductory sociology course were analyzed using a hierarchical linear model. Results show that more visits to an OER do not improve student achievement, but continuous improvement cycles of targeted OER do. Iterative implementation of the RISE analysis for resource improvement in combination with practitioners’ expertise is key for students’ learning. Given that the RISE classification accounted for 65% of the growth of students’ performance, suggesting a moderate to large effect, we speculate that the RISE analysis could be generalized to other contexts and result in greater student gain. Institutions and practitioners can improve the OER’s impact by introducing learning analytics as a decision-making tool for instructional designers. Yet, user-friendly implementation of learning analytics in a “click-and-go” application is necessary for generalizability and escalation of continuous improvement cycles of OER and tangible improvement of learning outcomes. Finally, in this article, we identify the need for efficient applications of learning analytics that focus more on “learning” and less on analytics.

Efficient approximate Bayesian inference for quantifying uncertainty in multiscale animal movement models

It is becoming increasingly important for wildlife managers and conservation ecologists to understand which resources are selected or avoided by an animal and how to best predict future spatial distributions of animal populations in the long term. However, inferring the patterns of space use by animals is a challenging multiscale inference problem, and formal uncertainty quantification of parameter estimates is an essential component of models that provide useful predictions across scales. In this study, we develop an approximate Bayesian inference framework for step selection models of animal movement which quantifies the uncertainty in estimates of resource selection and avoidance parameters within the Bayesian paradigm. The framework allows joint inference of movement and resource selection parameters of animals and is multiscale in that parameters inferred from fine scale movement steps scale to produce predictions of long-term patterns of space use. Our analysis focuses on simulated movement data in which we test the performance of our framework by altering movement parameters in the data-generating process. In our simulations, individuals respond to two environmental covariates and we employ all combinations of positive and negative selection coefficients corresponding to attraction to an environmental feature and avoidance of an environmental feature, respectively. In all scenarios, we recover the movement parameters used for the simulation of synthetic movement data using variational inference, an approximate Bayesian method, allowing us to formally quantify the uncertainty associated with each parameter for varying data set sizes. Our framework successfully recovered all combinations of movement parameters of the simulated data and accurately captured their posterior distributions given the available data suggesting that the framework is reliable and suitable for inferring how animals select resources and move on a landscape.Notably, our analysis shows that even for reasonably large data sets (circa 10,000 observations) there can still be considerable uncertainty associated with resource selection parameters which can in turn lead to inaccurate predictions of long term space use if not properly incorporated into the modelling approach. To further illustrate the utility of our approach, we also present a case study of its application to an example data set consisting of GPS locations of a fisher (Martes pennanti). Our approach will be of interest to ecologists looking to address conservation questions such as when and where animals are likely to spend most of their time. Furthermore, the approach could be used to predict new suitable areas for conservation based on how GPS collared animals use or avoid resources while including uncertainty around the predictions, thereby helping to make informed management decisions.

Advancing Organizational Effectiveness Through Strategic Workforce Planning and Technology Integration

Abstract: This comprehensive examination delves into the pivotal roles of strategic workforce planning and technological advancements in bolstering organizational effectiveness. Spanning various critical dimensions such as human resource demand forecasting, strategic recruitment and selection, employee training and development, diversity and inclusivity, and the integration of technology in HR practices, the paper articulates a multi-faceted approach to optimizing workforce capabilities. By leveraging predictive analytics, organizations can forecast future employment needs, while AI-enhanced recruitment processes streamline candidate selection, ensuring alignment with organizational goals. The deployment of advanced training methodologies, coupled with a focus on creating a diverse and inclusive work environment, not only enriches organizational culture but also drives innovation and improves overall performance. The paper further explores the strategic use of HR analytics, AI in recruitment, and digital platforms in employee development and engagement, highlighting their significance in adapting to the evolving work landscape. Through an in-depth analysis, it becomes evident that the integration of strategic HR planning and cutting-edge technologies is not merely an option but a necessity for organizations aiming to thrive in the competitive global market.

Exploring Intellectual Freedom and Collection Development Policies in School Libraries: Perspectives from US and Kenya.

Information is one of the essentials for decision making and intellectual development, thus it is difficult for democratic societies to exist without access to diverse information (Knox, 2011). Within school libraries, access to information resources should be enhanced through the selection, evaluation, and acquisition of diverse resources in different formats to cater to the different needs of students, support the curriculum and promote their intellectual freedom. The fight for intellectual freedom in schools is not a recent development and the number of challenges and book bans is escalating and anticipated to continue rising in the years ahead (ALA,2022). While issues regarding intellectual freedom and censorship are global affairs, much is known about the US, but this study also focuses on Kenya, two countries with varying approaches on different issues thus addressing a significant research gap on intellectual freedom beyond the US. Employing a mixed methods approach, the study involves semi structured interviews among school librarians (10 from the US and 10 from Kenya) and an analysis of the school’s collection development policies. A qualitative thematic analysis of the data is being carried out to identify school librarians’ perspectives on censorship and advocacy for intellectual freedom through policies. The study aims at creating an awareness of the social reality regarding intellectual freedom between the two contexts. School librarians, administrators and policy makers will be able to restructure existing policies to enhance intellectual freedom for students.