Effective Means Research Articles

Enhancing educational experience through establishing a VR database in craniosynostosis: report from a single institute and systematic literature review.

Craniosynostosis is a type of skull deformity caused by premature ossification of cranial sutures in children. Given its variability and anatomical complexity, three-dimensional visualization is crucial for effective teaching and understanding. We developed a VR database with 3D models to depict these deformities and evaluated its impact on teaching efficiency, motivation, and memorability. We included all craniosynostosis cases with preoperative CT imaging treated at our institution from 2012 to 2022. Preoperative CT scans were imported into SpectoVR using a transfer function to visualize bony structures. Measurements, sub-segmentation, and anatomical teaching were performed in a fully immersive 3D VR experience using a headset. Teaching sessions were conducted in group settings where students and medical personnel explored and discussed the 3D models together, guided by a host. Participants' experiences were evaluated with a questionnaire assessing understanding, memorization, and motivation on a scale from 1 (poor) to 5 (outstanding). The questionnaire showed high satisfaction scores (mean 4.49 ± 0.25). Participants (n = 17) found the VR models comprehensible and navigable (mean 4.47 ± 0.62), with intuitive operation (mean 4.35 ± 0.79). Understanding pathology (mean 4.29 ± 0.77) and surgical procedures (mean 4.63 ± 0.5) was very satisfactory. The models improved anatomical visualization (mean 4.71 ± 0.47) and teaching effectiveness (mean 4.76 ± 0.56), with participants reporting enhanced comprehension and memorization, leading to an efficient learning process. Establishing a 3D VR database for teaching craniosynostosis shows advantages in understanding and memorization and increases motivation for the study process, thereby allowing for more efficient learning. Future applications in patient consent and teaching in other medical areas should be explored.

Knowledge, attitude, and perception of University students toward COVID-19 Pandemic

Background: An increasing rate of COVID-19 cases throughout the world has increased the need to investigate the knowledge, attitude, and perception (KAP) of university students toward the COVID-19 pandemic to prevent its rapid spread as public cooperation and awareness regarding prevention is the only way to deal with this pandemic. Methods: A retrospective cross-sectional study was conducted in Pakistan in April 2020. Data was collected by Google statistics and percent reliability was calculated through SPSS. Results: The number of respondents n=678, out of which 443(65.2%) were female and 235(34.85%) were male. The perception of students regarding this disease is shown in a table in which 33.2% of students consider it very dangerous. 54.2% thought it moderately dangerous. 62.38% of students consider muscle pain and 73.59% of students consider throat pain as a clinical presentation of COVID-19. 80.7% of students thought that exposure to a suspected case of COVID-19 serves as a means of acquiring COVID-19 infection. The most effective measure of prevention comes out to be hand washing and covering face during coughing and sneezing. The main cause of the spread is a lack of public awareness and cooperation. Social media campaigns are an effective means of educating students about quarantine and controlling its rapid spread. Conclusion: Researchers concluded that It’s the need of the hour to increase awareness related to protective hygienic measures, rapid spread, and providing reliable approaches through social media campaigns to manage this pandemic. We believe that students should be involved in educating their surrounding society.

Impact of Third-Generation Antiseizure Medications on People with Epilepsy in a Low-Income Population: The Brivaracetam Experience in a Real-World Study.

Third-generation antiseizure medications, such as brivaracetam, are recognized for their superior safety, tolerability, and pharmacokinetic profiles. However, their potential benefits are often limited in low-income populations because of challenges related to availability and affordability. We aimed to evaluate the effectiveness and safety of brivaracetam for treating epilepsy in a low-income population, within a real-world setting. This retrospective cohort study included individuals with epilepsy from a low-income population in Bogotá, Colombia, who were treated with brivaracetam between January 2020 and July 2023. Effectiveness (mean seizure reduction and ≥ 50% seizure reduction) and safety (retention rate and adverse events) were evaluated. A total of 106 individuals were included, with a median age of 33 years (interquartile range: 24-44). Most had focal epilepsy with a median disease duration of 25.4 years (standard deviation: 13.6). The baseline seizure frequency was 4 seizures per month (interquartile range: 2-15) and individuals had previously received a mean of 4.4 (standard deviation: 1.8) antiseizure medications. The mean percentage seizure reduction at 3, 6, and 12 months was 55.3%, 66.9%, and 63.8%, respectively. Additionally, 60%, 63.8%, and 65.9% of individuals achieved a ≥ 50% seizure reduction at 3, 6, and 12 months, respectively. Retention rate at 3 months was 89% (n = 95) and 18.7% (n = 20) reported adverse effects. In a real-world setting, brivaracetam has been shown to be safe and effective for the treatment of epilepsy in individuals from a low-income population. This study suggests that people with epilepsy living in this context can significantly benefit from the use of third-generation antiseizure medications.

INFLUENCE OF SAVINGS AND CREDIT COOPERATIVES SERVICES ON THE SOCIO-ECONOMIC DEVELOPMENT OF THE BENEFICIARIES A CASE OFZIGAMA CREDIT AND SAVING SOCIETY

Background:Savings and Credit Cooperatives (SACCOs) play a pivotal role in socio-economic development, particularly in developing countries. They provide financial services to their members, fostering economic growth and reducing poverty. However, the extent of their impact on the socio-economic development of their beneficiaries remains under-explored. This research aims to investigate the influence of SACCOs, with a specific focus on the Zigama Credit and Saving Society, on the socio-economic development of their beneficiaries from 2018 to 2023.The researchwas guided by three specific objectives: To assess the impact of Saving Services provided by Zigama Credit and Saving Society on the financial well-being of its beneficiaries. To examine the influence of Credit Services offered by Zigama Credit and Saving Society on the socio-economic development of its beneficiaries. And to find out the effectiveness of Training programs conducted by Zigama Credit and Saving Society in enhancing the financial literacy and skills of its beneficiaries. This research considered three theories in its theoretical framework in particular and these include Cooperative theory, Financial Inclusion Theory and Social capital theory. Methods and Materials:For this study, a descriptive research designs were employed, and data were collected by using both quantitative and qualitative methods, using interview guides and questionnaires.A sample size of 398 individuals was selected from the broader staff population of 77,010members, beneficiaries and staff of Zigama CSS, following Taro Yamanes formula for sample size determination.The collected data were analysed through statistical analysis, encompassing descriptive measures such as the mean and standard deviation to provide an overview of the data. Additionally, inferential statistical techniques were be used, focusing on the application of the Pearson correlation coefficient (r) and multiple linear regression analysis. Results: The research findings revealed that Beneficiaries perceive ZigamaCSSs saving services positively, with high satisfaction regarding interest rates (Mean=4.44, Std=0.78), accessibility (Mean=4.47, Std=0.75), and incentives for saving (Mean=4.49, Std=0.73). The societys credit services are highly regarded, with strong agreement on loan accessibility (Mean=4.38, Std=0.73), reasonable interest rates (Mean=4.35, Std=0.79), flexible loan repayment terms (Mean=4.41, Std=0.71), transparent credit approval process (Mean=4.37, Std=0.75), and positive impact on socio-economic status (Mean=4.37, Std=0.77). Training programs offered by Zigama CSS are effective, as indicated by high satisfaction with curriculum content (Mean=4.38, Std=0.73), delivery effectiveness (Mean=4.51, Std=0.69), active participation (Mean=4.22, Std=0.82), improved financial decision-making skills (Mean=4.56, Std=0.66), and strong recommendation to others (Mean=4.63, Std=0.62). There is a very strong positive correlation between SACCO services and the socio-economic development of beneficiaries (r = 0.937, p < .001), indicating that nearly 94% of the variance in beneficiaries socio-economic development can be explained by SACCO services. In conclusion, Zigama CSSs services significantly contribute to the socio-economic development of its beneficiaries, highlighting the importance of SACCO services in enhancing financial inclusion and well-being. Conclusion: Furthermore, the research made recommendations including the fact that SACCOs should prioritize customer satisfaction, expand financial education programs, ensure a supportive regulatory environment, and increase awareness about SACCO benefits. And also suggested areas that Further research could improve including comparative studies between SACCOs, longitudinal studies on SACCO impacts, qualitative studies on SACCO effectiveness, analyses of COVID-19 impacts, policy analyses, gender analyses, studies on technology adoption, and studies on SACCO impacts in rural communities.

Combining ability and genetic distance analysis of mid altitude sub-humid agroecology adapted maize inbred lines for high grain yield

Understanding the combining ability effects, heterosis, and genetic relationships between parental lines would be helpful in the maize breeding program to develop high-yielding and genetically stable maize varieties for various contrasting environments. Hence, the objective of this study was to estimate combining ability effects, heterosis, genetic distance (GD) and associations for grain yield and other agronomic traits. Forty-five F1 diallel crosses, four commercial checks, and ten inbred lines were evaluated at Bako and Jima Agricultural Research Centers, Ethiopia. Significant differences were observed among environments, genotypes and genotype by environment interaction for grain yield and almost all studied agronomic traits. Combining ability analysis showed both additive and non-additive gene effects significantly controlled grain yield and all other studied traits. The preponderance of general combining ability (GCA) effects indicates the importance of additive gene action inheriting most agronomic traits. Lines L2, L7, and L9 were the best combiners for grain yield, whereas lines L3 and L4 had desirable GCA values to improve days to flowering and plant height. Among the top ten crosses, L6 × L9, L6 × L7 and L4 × L9 were good specific combiners and had 18.8 %, 17.2 % and 16.2 % grain yield advantage over the best check, BH546. These hybrids also had high mid and better-parent heterosis compared with other crosses. The associations of GD with mean of F1 and SCA effects were positive and highly significant for grain yield and some other traits. In contrast, correlations of GD with mid and better parent heterosis were non-significant for grain yield and most other traits. The results of this study are particularly useful for breeders who envisage combining conventional and molecular methods.

The Philosophy of Contract between Theory and Practice

This study provides a means of understanding Iraqi contract law's effectiveness. The foundation of the contract law mostly dealt with via the verbal interaction (deals and offers) between two different groups of individual (vendors and consumers) with tangible items or their actions. This study will consider the current contracts law used in the state of Iraq by employing models and theories of theory and practice contract law. Their confidence in successful agreement comes from authentications and legitimacy approaches. Additionally, it addresses issues pertaining to contract law in this jurisdiction and will be recognized by the efficacy of contract law in Iraqi nation determine whether Contract Law is useful to the Iraqi nation" is the stated study goal.

Verb-centered means of expressing self-presentation in the English-language job-hunting discourse

This article presents a semantic, syntactic, and contextual analysis of the language means of self-presentation, conceptualized as the dominant speech strategy in the job-hunting discourse. The scope of our research is limited to the written variety of texts expressed via the genres of CVs/resumes and cover letters. The material was taken from English-language job-hunting resources: Balance, Career Addict, Career Blog, Indeed, Resume.io, StandOut CV, and Zety. It is stipulated that self-presentation can be viewed as a system of interrelated speech tactics: self-nomination, self-description, presentation of professional achievements, description of professional responsibilities, and identification with an ideal candidate type. Each tactic has its unique semantic profile. All self-presentation tactics roughly fall into two major categories—nominative and verb-centered. The latter category, which incorporates the tactics of presentation of professional achievements and description of professional responsibilities, was the primary focus of this research. A careful semantic and syntactic analysis revealed that active verbs with two-complement (in some cases, three-complement) valency are the core of the aforementioned tactics. This leads us to believe that the actant situation almost always includes the agent and theme, sometimes the benefactive. As a result of semantic analysis, lexical units were classified according to their sentimental modality—positive, negative, or neutral. Our research documented the prevalence of positive evaluation semantics in self-presentation units. A syntactic analysis was employed to identify the formal aspects of sentences containing self-presentation. The analysis showed that most sentence structures fall into three basic types of syntax reduction: a) subject omission ([I] perform regular check-ups); b) omission of the subject and the link verb ([I am] professionally trained); c) the omission of the subject and the verb of possession ([I have] numerous awards). The authors conclude that the self-presentation blueprint of verb-centered constructions identified in this article persists across all verb-centered tactics proving to be an effective means of constructing written job-hunting discourse.

Efficacy of X-Ray Phytosanitary Irradiation on the Infectivity and Reproduction of Angiostrongylus cantonensis in Experimentally Infected Rats.

Angiostrongylus cantonensis is a globally distributed nematode and the leading cause of eosinophilic meningitis in humans. As a global hotspot for this disease, Hawaii's agricultural exports may be contributing to the spread of A. cantonensis. Phytosanitary irradiation doses of 150 or 400 Gy provide quarantine security against multiple insect pests. We evaluated the invitro and invivo effects of phytosanitary irradiation on infectious, third-stage, A. cantonensis larvae. In vitro experiments directly exposed larvae to irradiation doses ranging from 200 to 1,000 Gy. Results showed low mortality and no dose response across all treatments 27 days post-irradiation. In vivo studies isolated larvae from wild-caught Parmarion martensi after exposure to x-ray irradiation at doses of 0, 150, and 400 Gy and infected them into laboratory rats. Fourteen rats were assigned to each treatment and infected with 50 larvae from their assigned irradiation dose. Results at 3 and 6 weeks post-infection demonstrated a significant negative dose response in regard to the number of larvae that migrated to the brain and adults found in the pulmonary artery. No irradiated larvae that grew into adults were able to produce eggs. These findings indicate that x-ray irradiation does not result in the direct mortality of A. cantonensis larvae; however, it does affect the infectivity and reproduction of A. cantonensis within its definitive host, the rat. Phytosanitary irradiation at doses ≥150 Gy appears to be an effective means of preventing the establishment of viable populations of A. cantonensis, thus reducing the potential for global spread due to agricultural exports from Hawaii.

A Reinforcement Learning-Based Incentive Mechanism for Task Allocation Under Spatiotemporal Crowdsensing

With the development of the Industrial Internet of Things (IoT), the work of large-scale data collection makes spatiotemporal crowdsensing (SC) play an important role. Mobile devices equipped with sensors could act as workers to collect and process data for uploading. In the task allocation process, a fully static allocation fails to meet the needs of realistic conditions, while a completely dynamic allocation fails to achieve the desired results. Therefore, we assume a task-scheduled execution scenario that combines the above two conditions. In the pre-allocation process, an original time location constraints (ORTA) allocation algorithm is first proposed. Then it is optimized (OPTA) to fully utilize the remaining time of the workers and increase the matched number. In addition, the design of the incentive mechanism is an effective means to improve the task completion rate of the platform. To efficiently utilize the limited platform budget in the long run, a Q-learning-based algorithm is proposed to identify target inspire tasks and subsequently increase their reward to attract workers’ participation. Finally, comparison experiments are conducted on real datasets to verify the effectiveness of our algorithm. Furthermore, the experiments on a Raspberry Pi local terminal are conducted under a satellite-based environment.

Generative Listener EEG for Speech Emotion Recognition Using Generative Adversarial Networks with Compressed Sensing.

Currently, emotional features in speech emotion recognition are typically extracted from the speeches, However, recognition accuracy can be influenced by factors such as semantics, language, and cross-speech datasets. Achieving consistent emotional judgment with human listeners is a key challenge for AI to address. Electroencephalography (EEG) signals prove to be an effective means of capturing authentic and meaningful emotional information in humans. This positions EEG as a promising tool for detecting emotional cues conveyed in speech. In this study, we proposed a novel approach named CS-GAN that generates listener EEGs in response to a speaker's speech, specifically aimed at enhancing cross-subject emotion recognition. We utilized generative adversarial networks (GANs) to establish a mapping relationship between speech and EEGs to generate stimulus-induced EEGs. Furthermore, we integrated compressive sensing theory (CS) into the GAN-based EEG generation method, thereby enhancing the fidelity and diversity of the generated EEGs. The generated EEGs were then processed using a CNN-LSTM model to identify the emotional categories conveyed in the speech. By averaging these EEGs, we obtained the event-related potentials (ERPs) to improve the cross-subject capability of the method. The experimental results demonstrate that the generated EEGs by this method outperform real listener EEGs by 9.31% in cross-subject emotion recognition tasks. Furthermore, the ERPs show an improvement of 43.59%, providing evidence for the effectiveness of this method in cross-subject emotion recognition.

A Review of Robust Machine Scheduling

Robust optimization (RO) has been recognized as an effective means to deal with unanticipated events in highly uncertain and risky environments. This paper systematically reviews two types of emerging RO machine scheduling approaches—robust machine scheduling (R-MS) and distributionally R-MS (DR-MS) methods—which usually offer tractable formulations and analytical results for machine scheduling problems under uncertainty. First, after highlighting the advantages of RO methods over the stochastic approach in terms of tractability and robustness, we use the bibliometric method to analyze the literature related to R-MS/DR-MS problems and classify them from the following aspects: (1) uncertain factors, (2) uncertainty descriptions, (3) robustness criteria, (4) machine environments and (5) solution methods. Second, we discuss the uncertainty descriptions, and the robust feasibility and robust optimality criteria. We further provide a state-of-the-art review of R-MS/DR-MS models in different machine environments and discuss the performance of the R-MS/DR-MS models. Third, we review and discuss the existing exact, approximation, online, and heuristic solution methods for solving R-MS/DR-MS models. Finally, we present future research opportunities in two promising areas: green machine scheduling problems and machine learning-enabled algorithms. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Machine scheduling plays an essential role in industrial and service systems, such as manufacturing, power generation, transportation and medical systems. However, in practice, scheduling systems usually operate in highly uncertain environments due to noisy measurements, prediction errors, and implementation deviations. To ensure robust feasibility and robust optimality, robust machine scheduling (R-MS) and distributionally R-MS (DR-MS) approaches have been recently proposed to hedge against the uncertainties related to processing time, release time, due date, machine breakdown, etc. This paper provides a comprehensive review of the R-MS/DR-MS models and algorithms in different machine environments from the aspects of uncertainty descriptions, robustness criteria and solution methods. This paper further highlights the challenges of R-MS problems and provides promising and valuable research opportunities in terms of problem formulations and algorithm designs.

Summarization of Video using Audio

Abstract: Online education has emerged as a highly effective means of delivering quality education to students. Its popularity has increased due to the high-quality visual and graphical content, delivered by subject matter experts, and the convenience of learning anytime and anywhere. However, students may face time constraints that prevent them from fully engaging with the course content. To address this issue, video transcript summarizers have gained popularity. These tools extract the most important topics from a video, allowing students to understand the essence of the class without having to watch the entire video. Our system focuses on developing a module using txtai.pipeline with Python to summarize online class videos. We use Whisper, a general-purpose speech recognition model, to train our model on a large dataset of diverse audio. The model takes the URL of a video as input and uses two algorithms to summarize the content: TF-IDF and Gensim. The summarization process is subjective, and we have incorporated two prominent methods: cosine similarity and ROUGE score. The former does not require a human-generated summary for reference, while the latter does. Our results show that the efficiency obtained using cosine similarity is greater than 90% in both TF-IDF and Gensim cases. The efficiency obtained in the case of ROUGE score is between 40-50%.

Parametric design optimization and thermodynamic analysis of plate fin heat exchanger for helium liquefaction system

The plate-fin heat exchanger is one of the most important components of helium liquefaction system as it recovers cold energy from helium gas in the cold box. Because of larger heat transfer surface area and compact geometrical configuration, plate-fin heat exchangers are widely used. The design of heat exchanger using helium as a cryogenic fluid needs high thermodynamic performance with minimum pressure drop which particularly requires the optimization of dimensional array and its geometrical configurations. In this regard, a parametric investigation was performed to compute the effective geometrical parameters and its effect on thermodynamic (effectiveness and log-mean temperature difference) performance has been discussed in detail. The present study aims to design optimization and thermodynamic performance evaluation of plate-fin type heat exchangers used in helium liquefaction systems. The developed helium liquefaction model has been simulated for the most critical liquefier with and without liquid nitrogen precooling, mixed mode, and refrigeration mode. Each component of the liquefaction model has been technically and thermodynamically analyzed and, in particular, four plate-fin heat exchangers have been considered and designed accurately using Aspen EDR®. Initially, the sensitivity analysis was carried out to characterize the most significant geometrical parameters (fin thickness, height, number and arrangement of layers, frequency, and fin type) and their effect on thermodynamic performance and pressure drop. After that, the artificial intelligence model was used to determine the optimal range of such geometrical variables in which the heat exchanger has maximum effectiveness and log mean temperature difference. Finally, the operating conditions obtained from cycle modeling and optimized geometrical datasets have been applied in commercial simulation software Aspen EDR® for plate-fin heat exchangers design. Finally, a comparative thermodynamic analysis has been carried out to determine the performance of all the designed heat exchangers operating at four different modes (boundary conditions).

Dietary shift can enhance the environmental benefits of crop redistribution

Crop redistribution is widely recognized as an effective means of reducing the environmental costs of agricultural production without technological improvements. However, changes in food supply driven by dietary shift are rarely taken into account in crop redistribution. Based on inventory analysis of water consumption, land demand and carbon emission in crops production, this study constructed a linear optimization model to explore crop redistribution strategy and the saving potential of environmental costs in context of dietary shift. The results showed that crops production in 2030 would increase water consumption by 2.84%, land demand by 1.19%, and carbon emission by 1.31% compared with 2020 if the diet remained unchanged. Crop redistribution considering dietary shift provided an effective approach to achieve significant environmental benefits, including reductions in water consumption of 3.33%–14.91%, arable land use of 1.13%–13.90%, and carbon emission of 6.17%–20.26% compared to the baseline scenario, among which, dietary shift contributed 2.64 ± 6.12%, 0.75 ± 7.06%, and 1.32 ± 7.19%, respectively. The findings highlighted that appropriate dietary shift could enhance the environmental benefits of crop redistribution. Insights into the dietary shift and concomitant environmental benefits debate on crop redistribution have important policy implications for improving the sustainability of agricultural production.

Multi-Level Cross-Modal Alignment for Image Clustering

Recently, the cross-modal pretraining model has been employed to produce meaningful pseudo-labels to supervise the training of an image clustering model. However, numerous erroneous alignments in a cross-modal pretraining model could produce poor-quality pseudo labels and degrade clustering performance. To solve the aforementioned issue, we propose a novel Multi-level Cross-modal Alignment method to improve the alignments in a cross-modal pretraining model for downstream tasks, by building a smaller but better semantic space and aligning the images and texts in three levels, i.e., instance-level, prototype-level, and semantic-level. Theoretical results show that our proposed method converges, and suggests effective means to reduce the expected clustering risk of our method. Experimental results on five benchmark datasets clearly show the superiority of our new method.

Abstract 4761: Understanding three-drug combinations: optimizing scheduling while avoiding toxicity

Abstract The promise of combination therapies in managing tumor resistance is constrained by the therapeutic window (the difference between efficacious and toxic doses). We have sought to understand the impact of dose scheduling on the emergence of resistance, using a mathematical model-based approach linking pharmacokinetics (PK) to a parsimonious evolutionary model of tumor growth incorporating the emergence of drug resistance. Here, we analyze the potential of a three-drug combination to minimize toxicity while preventing the emergence of resistance. We simulated eight tumor subpopulations representing resistance to one, two or three hypothetical drugs, using an exponential growth model with growth penalties based on drug concentrations. We used the model to simulate the effects of pairs of antagonistic, synergistic, and additive drugs on the growth of sensitive and resistant populations in a tumor. Final tumor volume and resistant population were used to compare the efficacy of various drug schedules. We assumed an overlapping neutropenia-like toxicity as the dose-limiting toxicity for the three drug combination, which we have previously shown to be proportional to the peak moving average drug concentration over 18 days. Final tumor volume and peak toxicity were used to compare the effectiveness of various dosing strategies and interaction combinations. Synchronous dosing results in the greatest drug interaction effect between any two drugs. When all interactions between the three drugs are either antagonistic or synergistic, the greatest effect is observed when all drugs are dosed simultaneously. The drugs are most toxic when all three are dosed simultaneously or nearly simultaneously. Our work shows that time sensitivity—that is, the magnitude of the changes in efficacy associated with small changes in schedule—varies with phase offset. Scheduling is most time sensitive when all drugs are dosed simultaneously or nearly simultaneously; dosing of two drugs simultaneously is less time sensitive. Three-drug combinations are an effective means of preventing complete resistance. While simultaneous dosing of two or more drugs confers the greatest efficacy, such simultaneous dosing results in greater peak toxicity because of higher simultaneous drug concentrations. A potential compromise is the simultaneous dosing of two of the three drugs, which results in an intermediate toxicity. Dosing of two drugs simultaneously is also less time sensitive, advantageous in a practical sense as it is more robust to patient dosing error. While the approach used here is based on hypothetical drug properties, it provides a framework that is readily applicable to real-world drug combinations, with the potential for practical insights to guide the framing of the Target Product Profile for three-drug combinations. Citation Format: Madison Stoddard, Lin Yuan, Debra Van Egeren, Andrew Chen, Dean Bottino, Arijit Chakravarty. Understanding three-drug combinations: optimizing scheduling while avoiding toxicity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4761.

Anomaly Detection for the MIL-STD-1553B Multiplex Data Bus Using an LSTM Autoencoder

Due to the modernization of commercial and military aircraft, real-time systems and their connectivity to ground based networks, including the Internet, that were thought to be “air-gapped”, are becoming more susceptible to cyber-attack. Most real-time systems that communicate using the Military Standard 1553B Multiplex data bus (MIL-STD-1553B) protocol do not have the ability to detect cyber-attacks. These systems were originally developed with safety and redundancy in mind, not security. These two factors introduce attack vectors to MIL-STD-1553B communication buses and expose associated avionics systems to exploitation. Recent approaches to anomaly detection for the MIL-STD-1553B data bus have leveraged statistical analysis, Markov Chain modelling, remote terminal fingerprinting and signature-based detection. However, their comparative effectiveness is unknown. Regarding the statistical analysis technique, the lack of accuracy and precision in detecting the start and stop time of anomalous events are not ideal for conducting investigations due to the sheer volume of messages still required to be manually analysed. Deep learning techniques offer an effective means of anomaly detection and applying these techniques to the MIL-STD-1553B data bus could provide more accurate and precise detection times when anomalies or attacks are present, when compared to known statistical analysis, leading to more efficient forensic investigations of anomalous events.

DECISION-MAKING PROCESS BY USING THE FORMALIZED METHODS IN THE CONDITIONS OF RISKS AND UNCERTAINTIES

In this article is discussed management of solving problems and realization of algorithm in MS-Excel in the conditions of risk and uncertainty. Problems of getting settlement in the condition of risks and uncertainty can be defined as a game of two players who gets a settlement (or solve problems) and the other is “nature.” The first player takes some steps, selects alternatives; The second player- “nature” is indifferent towards the first player’s actions but can be one of the players among several situations. The first player must choose alternatives in such a way that increases his reimburse which is generally two changeable function of uncertainty, getting decision bases on the fact, that is unknown probability of different scenery. In the condition of risk getting solution bases on fact, that every situation of events, development can be a giver probability of its realization. This allows you to weigh each meaning of effectiveness and choose a situation for realization with the lowest level. In modern condition it is possible to emphasize that management problem is central as economic as well as from the management point of view. However, trainers of pivotal questions are focused on management phenomenon. It should be remarked that this subject is developed and has accumulated various empirical materials on the bases of which, can be issued for each level of the leaders the recommendations for optimization of management. Some of them work effectively, practically in any situations, but others mainly in definite socio-cultural spheres. In this article based on analyses of motivational and psychological theories there are highlighted the most important principles in management. Also there is noted that the executives should be more communicative. As for management decisions, it may be personality’s cognitive features such are: proffesionalism, characteristics of the situations, systematic thinking, methods of making decisions and also methods of the control and corrections. Keywords : management, solving, uncertainty, risk, realization.

Resistivity size effect in epitaxial face-centered cubic Co(001) layers

Metastable face-centered cubic (fcc) Co layers are deposited by reactive magnetron sputtering in 5 mTorr N2 at 400 °C followed by vacuum annealing at 500 °C. The resulting phase-pure Co(001)/MgO(001) layers contain negligible nitrogen and exhibit a surface roughness &amp;lt;0.8 nm and a cube-on-cube epitaxial relationship with the substrate with Co[100]ǁMgO[100]. The measured resistivity vs thickness d = 10–1000 nm indicates a bulk resistivity ρo = 6.4 ± 0.3 μΩ cm for fcc Co at room temperature and ρo = 1.3 ± 0.1 μΩ cm at 77 K, and an effective electron phonon mean free path λ = 27 ± 2 nm and 79 ± 6 nm at 295 and 77 K, respectively. The resulting ρo × λ benchmark quantity is 3–5 times larger than that predicted from first principles, suggesting a breakdown of the Fuchs–Sondheimer model at small dimensions. The overall results indicate that fcc Co exhibits no intrinsic conductance benefit over stable hcp Co nor conventional Cu for narrow interconnects. The developed method for growth of epitaxial fcc Co(001) layers provides opportunities to study this metastable material for potential spintronic applications.

Numerical investigation into the thermo-hydro-mechanical behavior in unsaturated soils considering hypergravity effect

Unsaturated soils are commonly found in coastal regions and marine sediments, where understanding the thermo-hydro-mechanical (THM) behavior, especially regarding phase transition, is crucial for related practical engineering applications. Centrifugal model offers an effective means to reflect the prototype with smaller scale and shorter duration under hypergravity conditions. Present studies on similarity based on governing equation analysis of the THM coupled responses are mainly focused on saturated rather than unsaturated soils, nor involved with different boundary conditions. This paper theoretically derives the similarity of THM behavior of unsaturated soils between the centrifugal model and prototype, and numerically assesses the similarity considering Dirichlet / Neumann condition based on the open-source FEM code OpenGeoSys. In addition, numerical results shows that phase transition between liquid water and water vapor will occur under temperature variation, and pore pressure difference rather than initial pore pressure causes the difference of THM coupled responses. Significant pore pressure variation will be amplified with increase of gas diffusion coefficient, and large air entry pressure corresponds to high liquid saturation and obvious phase transition.