Exploratory Phase Research Articles

Barriers and facilitators to implementing evidence-based integrated HIV and behavioral health care: perspectives from seven federal ending the HIV epidemic jurisdictions

ABSTRACT The federal Ending the HIV Epidemic (EHE) initiative was created to reduce new US HIV infections, largely through pre-exposure prophylaxis and HIV treatments that reduce HIV transmissibility to zero. Behavioral health disorders (mental health and substance use) remain significant barriers to achieving EHE goals. Addressing behavioral health (BH) disorders within HIV primary care settings has been promoted as a critical EHE strategy. Implementation of efficacious HIV-BH care integration and its impact on HIV-related health outcomes is not well documented. In a federally-funded, exploratory phase implementation science study, we used the Collective Impact Framework to engage partners in seven EHE jurisdictions about the feasibility, acceptability, and sustainability of implementing HIV-BH integration interventions within local HIV settings. Partners concluded that full integration will remain the exception unless health systems invest in collaborative practice, professional training, appropriate health technology, and inter-system communication. Partners supported smaller incremental improvements including transdiagnostic approaches to reinforce each team member’s sense of value in the shared endeavor. This early phase implementation science study identified research and implementation gaps that are critical to fill to end the HIV epidemic. Both the Collective Impact Framework and implementation science show promise for guiding future implementation of evidence-based HIV-BH intervention integration.

Scoping Review: Digitalisation in the organisational culture of safety and security agencies

Digitalisation has a significant impact on most aspects of society. State agencies with safety and security tasks have to develop effective strategies to manage the opportunities and risks associated with technological innova-tions and increased networking. Emerging digital technologies have the potential to affect work practices and thus the organisational culture of such agencies. To examine the extent to which scientific research has addressed the impact of digitalisation on organisational culture so far, a systematic scoping review was conducted. Preliminary results show that the overall research topic is still in an exploratory phase and studies are primarily from the global north. The analyses show that the majority of studies focuses on artefacts around information data leading to respective work practices around data work.

Direct stellarator coil design using global optimization: application to a comprehensive exploration of quasi-axisymmetric devices

Many stellarator coil design problems are plagued by multiple minima, where the locally optimal coil sets can sometimes vary substantially in performance. As a result, solving a coil design problem a single time with a local optimization algorithm is usually insufficient and better optima likely do exist. To address this problem, we propose a global optimization algorithm for the design of stellarator coils and outline how to apply box constraints to the physical positions of the coils. The algorithm has a global exploration phase that searches for interesting regions of design space and is followed by three local optimization algorithms that search in these interesting regions (a ‘global-to-local’ approach). The first local algorithm (phase I), following the globalization phase, is based on near-axis expansions and finds stellarator coils that optimize for quasisymmetry in the neighbourhood of a magnetic axis. The second local algorithm (phase II) takes these coil sets and optimizes them for nested flux surfaces and quasisymmetry on a toroidal volume. The final local algorithm (phase III) polishes these configurations for an accurate approximation of quasisymmetry. Using our global algorithm, we study the trade-off between coil length, aspect ratio, rotational transform and quality of quasi-axisymmetry. The database of stellarators, which comprises approximately 200 000 coil sets, is available online and is called QUASR, for ‘quasi-symmetric stellarator repository’.

Effects of immersing Japanese quail eggs in various doses of riboflavin on reproductive, growth performance traits, blood indices and economics

This investigation aimed to evaluate the impact of immersion (IM) riboflavin treatment on the hatchability, production efficiency, and carcass characteristics of Japanese quail eggs. A total of 260 eggs of Japanese quail birds were used for hatching and were randomly divided into 4 treatments with 5 replicates (13 eggs/replicate) in a fully randomized design. Hatching eggs were immersed in riboflavin for 2 min before incubation. The experiment treatments were designed as follows: G1 control group with no treatment, G2 treated with 3 g/L vit. B2 (IM), G3 treated with 4 g/L vit. B2 (IM) and G4 were treated with 5 g/L vit. B2 (IM). After hatching, 128 Japanese quail chicks, aged 7 d, were randomly grouped into 4 treatment groups, with 32 birds in each group. When quails were given vitamin B2 via immersion, they demonstrated significant enhancements in live body weight, body weight gain, feed consumption, and feed conversion ratio at different stages compared to the control group. Compared to control and other groups, the carcass parameters of Japanese quails given a 4 g/L immersion solution showed a significant improvement (P < 0.05). Hatchability and fertility (%) were considerably raised by Vit.B2 treatments of 3, 4, and 5g; the group immersed in 5 g/L had the highest percentages compared to the other groups. Furthermore, treated chickens with all concentrations of vitamin B2 had significantly higher blood indices than the controls. During the exploratory phase (1-6 wk) of age, the highest returns were reported in G4 treated with 5g/L vit. B2 (IM). Treating Japanese quail eggs with different dosages of vitamin B2 by immersion may be recommended to improve their productive and reproductive performance, blood indices, carcass traits, and economic efficiency.

Unveiling school community perceptions of nutrition-friendly school initiatives in preschool settings in rural Sindh province, Pakistan: an exploratory study.

In 1995, the World Health Organization launched its Global School Health Initiative to expand the Health Promoting School (HPS). The objective of this study was to explore the perception of the school community in implementing nutrition-friendly school initiatives in preschool settings. This paper delineates the exploratory phase of a mixed-method study, which broadly aims to assess the adoption of the Nutrition Friendly School Initiative (NFSI) through a preschool preparedness intervention package in rural Sindh province, Pakistan. The study sites include three campuses of the Aga Khan School (Mirpur Sakro, Junior Campus Vur, and a community-based school in Sujawal). Participants were selected purposively from these campuses and constituted a committee named 'school community,' which was responsible for implementing all activities outlined in the intervention package. Data was gathered through in-depth interviews and consultative meeting with the school community. Thematic analysis was employed for data analysis. The analysis established five major themes that represent the participants' perception of school-based nutrition interventions in preschool settings. These five themes include (i) Challenges in health and nutrition for pre-school age children, (ii) Clarity in roles and responsibilities for school-based nutrition intervention, (iii) Advancing school-based nutrition activities and interventions, (iv) Recognizing resources requirements, (v) Opportunities and challenges for the way forward in school-based nutrition. Findings also suggest sustainability and scalability measures that include the aligning School Nutrition Policy with the school handbook, ways to engage with parents, a nutrition theme corner, the inclusion of a parenting component in the nutrition manual, and capacity building of the school community. Qualitative findings have guided the refinement of the intervention package, proposing additional measures for sustainability and scalability across AKES, P. The school community is hopeful that the implementation of the refined intervention package will enhance preschool preparedness toward achieving nutrition-friendly school checklist items. This study holds strong potential for replication in a public school setting and presents an opportunity to launch a school accreditation program to certify schools as Nutrition-friendly.

Intelligent Learning-Based Methods for Determining the Ideal Team Size in Agile Practices.

One of the significant challenges in scaling agile software development is organizing software development teams to ensure effective communication among members while equipping them with the capabilities to deliver business value independently. A formal approach to address this challenge involves modeling it as an optimization problem: given a professional staff, how can they be organized to optimize the number of communication channels, considering both intra-team and inter-team channels? In this article, we propose applying a set of bio-inspired algorithms to solve this problem. We introduce an enhancement that incorporates ensemble learning into the resolution process to achieve nearly optimal results. Ensemble learning integrates multiple machine-learning strategies with diverse characteristics to boost optimizer performance. Furthermore, the studied metaheuristics offer an excellent opportunity to explore their linear convergence, contingent on the exploration and exploitation phases. The results produce more precise definitions for team sizes, aligning with industry standards. Our approach demonstrates superior performance compared to the traditional versions of these algorithms.

Trajectories of the Creative Process: A Multiple-Drafts Analysis of Graphic Design

ABSTRACT Most experimental analyses of creativity look at one-shot production tasks. However, real-world creativity occurs over long periods of time and involves extensive exploration and revision. We carried out a week-long study in which graphic design students created an advertisement for a fictional business, submitting daily drafts of their evolving ad. By analyzing the successive changes to these ads across the 7 daily drafts, we sought to characterize the trajectory of the creative process for each designer. In doing so, we elaborated on the core concept of the Geneplore model (i.e. generation + exploration) by proposing a 2 × 2 predictive scheme of creative trajectories in which a) the generative phase specifies either a global plan of the final product or merely a kernel idea for it, and b) the exploratory phase proceeds in either a linear or nonlinear manner, thereby resulting in four basic trajectories. We analyzed the relative frequency of these trajectory-types in our 37 graphic design students using a novel “change analysis” method. In addition, we examined how the novelty, quality, and stylistic features of the final ad related to the trajectory-type of the creator. The results revealed that there are multiple routes toward achieving a comparable level of novelty in a creative product.

Multi-Channel Cooperative Spectrum Sensing and Scheduling for Cognitive IoT Networks

This paper presents a novel multi-channel cooperative spectrum sensing and scheduling (MC$_2$S$_3$) framework for spectrum and energy harvesting cognitive Internet of Things (IoT) networks. We address the challenge of maximizing network throughput by formulating a combinatorial problem that jointly optimizes the sensing scheduling of primary channels (PCs), the assignment of IoT devices for sensing scheduled PCs, and the clustering and allocation of IoT nodes to efficiently use discovered idle PCs; subject to spectrum utilization and collision avoidance constraints. Recognizing the inherent complexity of the underlying NP-hard mixed-integer non-linear programming (MINLP) problem, we propose a decomposition strategy that decouples the master problem into PC exploration and exploitation sub-problems. In the exploration phase, we derive closed-form solutions for optimal sensing durations and detection thresholds that satisfies spectrum utilization and collision avoidance constraints, which are then used to develop a priority metric to rank PCs. The proposed PC ranking informs a sequential PC scheduling and IoT sensing assignment approach that exploits a linear bottleneck assignment (LBA) strategy, proceeding until further scheduling does not enhance network utility. For the exploitation phase, we leverage a non-orthogonal multiple access (NOMA) strategy to multiplex multiple IoT nodes on a single PC, employing an iterative linear sum assignment (LSA) method for efficient allocation to maximize utilization of idle PCs. Numerical results validate the efficacy of our proposed methodologies, reaching an accuracy of approximately 99% in the order of milliseconds, significantly outperforming time complexity of brute-force benchmarks.

Improved Multi-Strategy Sand Cat Swarm Optimization for Solving Global Optimization.

The sand cat swarm optimization algorithm (SCSO) is a novel metaheuristic algorithm that has been proposed in recent years. The algorithm optimizes the search ability of individuals by mimicking the hunting behavior of sand cat groups in nature, thereby achieving robust optimization performance. It is characterized by few control parameters and simple operation. However, due to the lack of population diversity, SCSO is less efficient in solving complex problems and is prone to fall into local optimization. To address these shortcomings and refine the algorithm's efficacy, an improved multi-strategy sand cat optimization algorithm (IMSCSO) is proposed in this paper. In IMSCSO, a roulette fitness-distance balancing strategy is used to select codes to replace random agents in the exploration phase and enhance the convergence performance of the algorithm. To bolster population diversity, a novel population perturbation strategy is introduced, aiming to facilitate the algorithm's escape from local optima. Finally, a best-worst perturbation strategy is developed. The approach not only maintains diversity throughout the optimization process but also enhances the algorithm's exploitation capabilities. To evaluate the performance of the proposed IMSCSO, we conducted experiments in the CEC 2017 test suite and compared IMSCSO with seven other algorithms. The results show that the IMSCSO proposed in this paper has better optimization performance.

Co-creation of a toolkit to assist risk communication and clinical decision-making in severe preeclampsia: SPOT-Impact study design

ABSTRACT Globally, the incidence of hypertensive disorders of pregnancy, especially preeclampsia, remains high, particularly in low- and middle-income countries. The burden of adverse maternal and perinatal outcomes is particularly high for women who develop a hypertensive disorder remote from term (<34 weeks). In parallel, many women have a suboptimal experience of care. To improve the quality of care in terms of provision and experience, there is a need to support the communication of risks and making of treatment decision in ways that promote respectful maternity care. Our study objective is to co-create a tool(kit) to support clinical decision-making, communication of risks and shared decision-making in preeclampsia with relevant stakeholders, incorporating respectful maternity care, justice, and equity principles. This qualitative study detailing the exploratory phase of co-creation takes place over 17 months (Nov 2021-March 2024) in the Greater Accra and Eastern Regions of Ghana. Informed by ethnographic observations of care interactions, in-depth interviews and focus group and group discussions, the tool(kit) will be developed with survivors and women with hypertensive disorders of pregnancy and their families, health professionals, policy makers, and researchers. The tool(kit) will consist of three components: quantitative predicted risk (based on external validated risk models or absolute risk of adverse outcomes), risk communication, and shared decision-making support. We expect to co-create a user-friendly tool(kit) to improve the quality of care for women with preeclampsia remote from term which will contribute to better maternal and perinatal health outcomes as well as better maternity care experience for women in Ghana.

The (Surprising) Sample Optimality of Greedy Procedures for Large-Scale Ranking and Selection

Ranking and selection (R&amp;S) aims to select the best alternative with the largest mean performance from a finite set of alternatives. Recently, considerable attention has turned toward the large-scale R&amp;S problem which involves a large number of alternatives. Ideal large-scale R&amp;S procedures should be sample optimal; that is, the total sample size required to deliver an asymptotically nonzero probability of correct selection (PCS) grows at the minimal order (linear order) in the number of alternatives, k. Surprisingly, we discover that the naïve greedy procedure, which keeps sampling the alternative with the largest running average, performs strikingly well and appears sample optimal. To understand this discovery, we develop a new boundary-crossing perspective and prove that the greedy procedure is sample optimal for the scenarios where the best mean maintains at least a positive constant away from all other means as k increases. We further show that the derived PCS lower bound is asymptotically tight for the slippage configuration of means with a common variance. For other scenarios, we consider the probability of good selection and find that the result depends on the growth behavior of the number of good alternatives: if it remains bounded as k increases, the sample optimality still holds; otherwise, the result may change. Moreover, we propose the explore-first greedy procedures by adding an exploration phase to the greedy procedure. The procedures are proven to be sample optimal and consistent under the same assumptions. Last, we numerically investigate the performance of our greedy procedures in solving large-scale R&amp;S problems. This paper was accepted by Baris Ata, stochastic models and simulation. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72091211, 72071146, 72161160340]. Supplemental Material: The e-companion and data files are available at https://doi.org/10.1287/mnsc.2023.00694 .

Abstract PO1-12-09: Doublet or triplet antiemetic prophylaxis for trastuzumab deruxtecan-induced nausea and vomiting: An open-label, randomized, multicenter, exploratory phase 2 study

Abstract Background: Trastuzumab deruxtecan (T-DXd) is classified as an anticancer agent that poses a moderate emetic risk according to international guidelines for antiemetic therapy, which recommend emesis prophylaxis using a two-drug combination therapy comprising 5-hydroxytryptamine-3 receptor antagonist (5-HT3RA) and dexamethasone (DEX). The incidence rates of nausea and vomiting in patients in DESTINY BREAST01 and DESTINY BREAST03 were 77.7%, 45.7%, 72.8%, and 44%, respectively. However, no effective antiemetic therapy was observed. The high nausea and vomiting incidence associated with T-DXd is problematic. Hence, this exploratory phase 2 study aimed to assess the efficacy and safety of treatment with 5-HT3RA and DEX, with or without a neurokinin-1 receptor antagonist (NK1RA), in preventing T-DXd-induced nausea and vomiting. Methods: We conducted an open-label, randomized exploratory phase 2 study at 14 centers in Japan. Patients with breast cancer scheduled to receive T-DXd were enrolled in this study. Patients were randomly assigned to receive granisetron and dexamethasone (arm GD) or granisetron, dexamethasone, and aprepitant (fosaprepitant; arm GDA). Patients in both treatment arms were administered granisetron (1 mg i.v. 30 min before T-DXd on day 1). Those in the GD arm were administered DEX (6.6 mg i.v. 30 min before T-DXd on day 1 and 8 mg p.o. on days 2–3). The patients in the GDA arm were administered DEX (9.9 mg i.v. 30 min before T-DXd on day 1) and NK1-RA (125 mg of aprepitant p.o. 60 min before chemotherapy on day 1 and 80 mg p.o. on days 2 and 3, or 150 mg fosaprepitant i.v. 60 min before T-DXd on day 1). The primary endpoint was complete response (CR; no emesis or rescue therapy) during the overall phase (0–120 h after the start of T-DXd). The primary endpoint was estimated using the CR rate and 95% confidence intervals (CIs) following the Pearson–Clopper method. The Cochran–Mantel–Haenszel test was used to compare the exploratory results between the two treatment arms with age strata and previous experience with chemotherapy-induced nausea and vomiting. Logistic regression analyses were performed to determine the risk factors associated with the non-achievement of CR, non-achievement of complete control (CC), and non-achievement of total control (TC) in the overall and long-overall phases. Results: Between September 2020 and March 2023, 40 patients were enrolled and randomly assigned to either the GD (n = 19) or GDA (n = 21) arm. In the GDA group, one patient who did not complete the rescue medication listed in the diary was excluded from the efficacy analysis, including rescue medication use. The CR rates during the overall phase were 36.8% and 70.0% in the GD and GDA groups, respectively (odds ratio [OR], 0.1334; 95% confidence interval [CI], 0.0232–0.7672; P = 0.0190), with a difference of 33.2%. The CR rates during the long-overall phase were 31.6% in the GD arm and 70.0% in the GDA arm (OR, 0.1073; 95% CI, 0.0185–0.6239; P = 0.0087), a difference of 38.4%. NK1RA use was associated with a significant decrease in the non-achievement of CR, CC, and TC in the overall and long-overall phases. Only patients in the GDA arm (33.3%) reported being “very satisfied” with the treatment. No grade 3 or 4 toxicities related to antiemetic therapy were observed. Conclusions: Breast cancer patients receiving T-DXd require triple therapy, including mandatory NK1RA administration, for antiemetic prophylaxis. Further investigation is needed to explore the development of more appropriate combination therapies for T-DXd-induced nausea and vomiting. Citation Format: Manabu Futamura, Hirotoshi Iihara, Hiroko Bando, Yoshihiro Kawaguchi, Yutaka Mizuno, Akari Murakami, Masaaki Kawai, Tomokazu Iyoda, Kazshige Ishida, Kazuhiro Ishihara, Takumi Nakada, Makoto Takeuchi, Mika Kitahora, Yoshihisa Tokumaru, Mototsugu Shimokawa, Nobuhisa Matsuhashi. Doublet or triplet antiemetic prophylaxis for trastuzumab deruxtecan-induced nausea and vomiting: An open-label, randomized, multicenter, exploratory phase 2 study [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-12-09.

Solution of engineering design and truss topology problems with improved forensic-based investigation algorithm based on dynamic oppositional based learning

The forensic-based investigation (FBI) is a metaheuristic algorithm inspired by the criminal investigation process. The collaborative efforts of the investigation and pursuit teams demonstrate the FBI’s involvement during the exploitation and exploration phases. When choosing the promising population, the FBI algorithm’s population selection technique focuses on the same region. This research aims to propose a dynamic population selection method for the original FBI and thereby enhance its convergence performance. To achieve this objective, the FBI may employ dynamic oppositional learning (DOL), a dynamic version of the oppositional learning methodology, to dynamically navigate to local minima in various locations. Therefore, the proposed advanced method is named DOLFBI. The performance of DOLFBI on the CEC2019 and CEC2022 benchmark functions is evaluated by comparing it with several other popular metaheuristics in the literature. As a result, DOLFBI yielded the lowest fitness value in 18 of 22 benchmark problems. Furthermore, DOLFBI has shown promising results in solving real-world engineering problems. It can be argued that DOLFBI exhibits the best convergence performance in cantilever beam design, speed reducer, and tension/compression problems. DOLFBI is often utilized in truss engineering difficulties to determine the minimal weight. Its success is comparable to other competitive MAs in the literature. The Wilcoxon signed-rank and Friedman rank tests further confirmed the study’s stability. Convergence and trajectory analyses validate the superior convergence concept of the proposed method. When the proposed study is compared to essential and enhanced MAs, the results show that DOLFBI has a competitive framework for addressing complex optimization problems due to its robust convergence ability compared to other optimization techniques. As a result, DOLFBI is expected to achieve significant success in various optimization challenges, feature selection, and other complex engineering or real-world problems.

Abstract PO5-18-04: Effect of Digoxin on clusters of circulating tumor cells in patients with metastatic breast cancer (DICCT)

Abstract Background: The presence of circulating tumor cell (CTC) clusters is associated with tumor progression and with a bad prognosis in various cancer types1,2. In breast cancer, a pre-clinical model demonstrated that Na/K-ATPase inhibitors such as digoxin could dissolve CTC clusters and reduce metastasis3. DICCT is a single-arm, therapeutic exploratory phase I study aimed to examine whether digoxin can disrupt CTC clusters in metastatic breast cancer patients (NCT03928210). Material and methods: Metastatic breast cancer patients in whom clusters of CTCs have been detected were eligible. Patients with concomitant heart disease were excluded. After progressive disease in any line of therapy, a window of opportunity before initiation of subsequent systemic therapy was used to administer digoxin treatment for seven (7) days. For CTC detection, blood samples were processed with the FDA-cleared Parsortix system (ANGLE plc, UK). Captured CTCs were stained for EPCAM, HER2, EGFR (positive CTC selection) and CD45 (exclusion marker). To gain insight into the cluster-dissolution ability of digoxin, the primary endpoint was size of CTC clusters (i.e. number of cells within each cluster) under digoxin therapy. The cumulative effect of digoxin treatment on the primary endpoint was assessed by a linear regression model. Results: Nine (9) patients with CTC clusters detected at baseline were enrolled. A target level of digoxin above 0.7 ng/ml could be confirmed at day 7 for all patients. For quantitative and qualitative analysis of CTC clusters, 5.5 to 27 ml of blood were analyzed at baseline, at day 0 pre-treatment, and post 2 hours, 3 days and 7 days of digoxin treatment. The average cluster size ranged between 2 and 11.8 cells (median 2.9) considering all samples from all patients. Digoxin treatment reduced cluster size in 8 patients (88.9%) to different extents, and the proportion of CTC cluster over single CTCs was also reduced in 6 patients (66.7%). The effect of digoxin was evident in both homotypic (cancer cells only) and heterotypic (aggregates of CTCs and white blood cells) clusters. The treatment was well tolerated, and no adverse events related to the study treatment occurred. All patients completed the treatment according to the protocol. Conclusion: DICCT provides the first-in-human proof-of-principle that digoxin induces a partial dissolution of CTC clusters in patients with metastatic breast cancer at drug levels that are safe and well tolerated. Conceptualization of follow-up trials including Na/K-ATPase inhibitors and patient outcome endpoints are underway.

Fluid geochemistry of the Cerro Galán geothermal system (Southern Puna, Argentina): Implications for the geothermal potential of one of the youngest giant calderas in the Andes

The exploration of novel geothermal systems, particularly those promising for electrical power generation, plays a fundamental role in incorporating new renewable sources into the energy matrix. Geothermal systems associated with volcanic calderas are considered ideal targets for exploration. This study focuses on the geochemical features of fluids from the Cerro Galán hydrothermal system, which is hosted within a major resurgent caldera with >3.5 Myr of magmatic evolution situated on the Southern Puna (Central Volcanic Zone of the Andes, NW Argentina). The main aim is constructing the first geochemical conceptual model and provide information on the geothermal potential of this interesting resource. The main hydrothermal reservoir consists of a NaCl aquifer with estimated temperatures up to 187 °C at depth. This reservoir is likely hosted within the fractured pre-caldera basement rocks, mainly including Miocene-Pliocene volcanic rocks and Proterozoic-Cambrian igneous and metamorphic rocks. The confinement of the deep reservoir is attributed to the deposits of the Toconquis Group and Cueva Negra Ignimbrite, along with the basal section of the Cerro Galán Ignimbrite, which exhibit low permeability due to hydrothermal alteration. The presence of a phreatic explosion crater near one of the hot spring-rich areas is likely indicating past over-pressurization of the hydrothermal aquifer, resulting from efficient sealing. Furthermore, the absence of anomalous soil CO2 flux values on the top of the reservoir, except where the thermal spring discharges are located, can be explained by an effective cap-rock layer. Deep circulation of meteoric water, enriched with atmospheric gases, receives inputs of magmatic fluids (∼11% of primordial helium), leading to the development of the hydrothermal NaCl aquifer. However, this deep fluid contribution might be underestimated due to significant crustal assimilation (up to 50%) involved in the magma genesis of the Cerro Galán Volcanic Complex, a process which modifies the He isotopic signature of the magmatic endmember. The hot springs, characterized by high flow rate (up to 459 m3/h) are positioned at the intersection between the caldera margins and the NNE-SSW oriented tectonic structures, suggesting favorable permeability conditions. The preliminary geothermal gradient for the Cerro Galán area is estimated at around 98–101 °C/km. Such a high gradient can be attributed to the considerable heat flux generated by the transcrustal plumbing system of the Cerro Galán caldera, which includes the shallow crystal mush reservoir (<4 km depth). The preliminary geothermal potential of this giant caldera was performed using the volumetric method along with Monte Carlo simulations. The results indicate a probable power production capacity of 2.09 MWe and 10.85 MWe at 90 and 50% confidence level, respectively. The results presented in this work constitute a foundational knowledge base to promote a more advanced exploration phase for the geothermal resource. Additionally to the local energy demand, lithium and other metal mining operations, which are operating independently from the National Interconnected System, could potentially be interested in power generation through binary cycles.

An Improved Harris Hawks Optimization Algorithm for Solving the Permutation Flow Shop Scheduling Problem

In this paper, an improved Harris Hawks optimization algorithm is proposed to solve the permutation flow shop scheduling problem with the objective of minimizing the completion time. Logistic chaotic mapping and inverse learning strategy are used to generate a high-quality initial population. A golden sine algorithm is introduced to improve the position update method. A nonlinear escape energy factor and adaptive t-distribution strategy are introduced to solve the problem of imbalance between the exploration and exploitation phases of the HHO algorithm. The effectiveness of the improved Harris Hawks optimization algorithm is verified by testing it on the Reeves benchmark test set and comparing it with other algorithms.

Organizational Climate Scale for Public Service: Development and Validation

This research presents four studies that developed and validated the Organizational Climate Perception Scale for Public Service (OCPS-PS). The first qualitative study consulted the literature and conducted a focus group to develop the initial version of the scale. The second study involved expert evaluation and pre-testing, aiming at the semantic and face validation of the items. This study resulted in 80 items forming the thirteen dimensions of organizational climate. The third study obtained the first quantitative sample for the exploratory validation phase of the scale. The final study, using a new sample, conducted confirmatory tests for the validation of the scale. A methodology for applying the scale was developed, allowing all interested parties to use the OCPS-PS for the assessment of the organizational climate in public service. The results of the four conducted studies indicate the adequacy of the OCPS-PS according to the proposed criteria of validity and reliability. Finally, the OCPS-OS was built to be applied in different public organizations and at different government levels.

Secretary bird optimization algorithm: a new metaheuristic for solving global optimization problems

This study introduces a novel population-based metaheuristic algorithm called secretary bird optimization algorithm (SBOA), inspired by the survival behavior of secretary birds in their natural environment. Survival for secretary birds involves continuous hunting for prey and evading pursuit from predators. This information is crucial for proposing a new metaheuristic algorithm that utilizes the survival abilities of secretary birds to address real-world optimization problems. The algorithm's exploration phase simulates secretary birds hunting snakes, while the exploitation phase models their escape from predators. During this phase, secretary birds observe the environment and choose the most suitable way to reach a secure refuge. These two phases are iteratively repeated, subject to termination criteria, to find the optimal solution to the optimization problem. To validate the performance of SBOA, experiments were conducted to assess convergence speed, convergence behavior, and other relevant aspects. Furthermore, we compared SBOA with 15 advanced algorithms using the CEC-2017 and CEC-2022 benchmark suites. All test results consistently demonstrated the outstanding performance of SBOA in terms of solution quality, convergence speed, and stability. Lastly, SBOA was employed to tackle 12 constrained engineering design problems and perform three-dimensional path planning for Unmanned Aerial Vehicles. The results demonstrate that, compared to contrasted optimizers, the proposed SBOA can find better solutions at a faster pace, showcasing its significant potential in addressing real-world optimization problems.

User experience and interface assessment for metaverse platforms on nurses and nursing students: A cross-sectional study

BackgroundThe metaverse, a rapidly evolving virtual environment, offers new opportunities for healthcare education. The effectiveness of the metaverse as a learning tool depends on user readiness and platform characteristics. AimThis study aimed to examine the current use of metaverse platforms among nurses and nursing students in South Korea and explore the relationship between user characteristics and their metaverse experience, focusing on presence, usability, and user experience. DesignThis was a cross-sectional descriptive study. SettingsRegistered nurses and nursing students from various healthcare settings and educational institutions in South Korea participated in this study. ParticipantsThis study included 428 participants, comprising 188 nurses and 240 nursing students. MethodsBetween September and November 2022, participants provided voluntary informed consent. The participants engaged with one of the following two metaverse platforms: ZEPETO (mobile-based) or Gather (screen-based). After four structured exploration phases, the participants completed online questionnaires. These surveys assessed general characteristics, metaverse self-efficacy, sense of presence, usability, and user experience. ResultsMost participants had previous experience with the metaverse and rated their metaverse self-efficacy at 3.60. Nurses scored higher than nursing students in terms of presence, usability, and user experience in the metaverse. Higher work self-efficacy in nurses and academic self-efficacy in nursing students were associated with more positive experiences in the metaverse. Nurses consistently rated higher across all subdomains of presence, usability, and user experience than nursing students. The type of metaverse platform also significantly influenced user experience. ConclusionsA significant proportion of Korean nurses and nursing students are familiar with the metaverse, reflecting a global trend towards virtual environments in education and healthcare. Although the metaverse holds promise for healthcare education, its effectiveness depends on user readiness, platform characteristics, and the development of a reliable, structured, and user-friendly educational programme.

IHHO: an improved Harris Hawks optimization algorithm for solving engineering problems

Harris Hawks optimization (HHO) algorithm was a powerful metaheuristic algorithm for solving complex problems. However, HHO could easily fall within the local minimum. In this paper, we proposed an improved Harris Hawks optimization (IHHO) algorithm for solving different engineering tasks. The proposed algorithm focused on random location-based habitats during the exploration phase and on strategies 1, 3, and 4 during the exploitation phase. The proposed modified Harris hawks in the wild would change their perch strategy and chasing pattern according to updates in both the exploration and exploitation phases. To avoid being stuck in a local solution, random values were generated using logarithms and exponentials to explore new regions more quickly and locations. To evaluate the performance of the proposed algorithm, IHHO was compared to other five recent algorithms [grey wolf optimization, BAT algorithm, teaching–learning-based optimization, moth-flame optimization, and whale optimization algorithm] as well as three other modifications of HHO (BHHO, LogHHO, and MHHO). These optimizers had been applied to different benchmarks, namely standard benchmarks, CEC2017, CEC2019, CEC2020, and other 52 standard benchmark functions. Moreover, six classical real-world engineering problems were tested against the IHHO to prove the efficiency of the proposed algorithm. The numerical results showed the superiority of the proposed algorithm IHHO against other algorithms, which was proved visually using different convergence curves. Friedman's mean rank statistical test was also inducted to calculate the rank of IHHO against other algorithms. The results of the Friedman test indicated that the proposed algorithm was ranked first as compared to the other algorithms as well as three other modifications of HHO.