Alternative Direction Research Articles

Revealing Inclined Twin Related Defects in III–V Nanowires Grown in Popular Alternative Crystallographic Directions

Revealing Inclined Twin Related Defects in III–V Nanowires Grown in Popular Alternative Crystallographic Directions

A Digital-based Model Proposal for Optimum Building Orientation in Architecture in Ecological Context Nur Sumeyye

In order to make optimum use of the heat energy coming from the sun and the cooling effect of the wind, it is very important that the buildings are positioned on the land in positions that can provide the highest level of user comfort with minimum energy consumption. Determining the layout of the building is a critical design parameter that affects energy consumption. However, when there are vistas such as sea, forest, etc. that are desired to be seen from the first degree interior volumes around the building, this factor becomes decisive in the building orientations. As a result of this, climatic comfort factors such as natural ventilation provided by wind and heat energy from the sun are ignored. While the orientation of the interior spaces towards the landscape is preferred by the users, the climate element and the comfort it provides should be the priority in the architectural design. In this study, an orientation optimization model has been proposed for building orientation based on utilizing vista and climatic data in the context of passive design. With the parametric and numerical-based approach developed in the study, the way to use the vista factor, which has a multivariate and complex structure, as an “input data” has been investigated. In order to test and verify the developed optimization model, “The Canal Istanbul” artificial waterway project area, which is a very up-to-date subject, was chosen as the plot research area. The Canal feasibility studies have been prepared by the authorities. The data obtained from these reports were used quantitatively in the model. In this context, the technical and ecological data of the canal route were investigated from the reports prepared for the Canal Istanbul route. Thus, as a result of the data accessed from various sources, the climate type and ecological characteristics of the region have been moved to a quantitative dimension as numerical data. An optimization model has been developed for optimum building orientation in terms of regional climate, taking into account the zoning decisions and basic planning criteria determined in the focus of these data. The developed model primarily places the building in the desired point position over the 1000 and 5000 zoning plans of the region, with the vista as priority. Afterwards, the model perceives the vista spline that enters the field of view of the building. Then, staying within the boundaries of this spline, it turns the structure in alternative directions and analyzes it according to each direction. At this stage, the model presents a mathematical dataset by comparing the alternative orientations of the building with the “optimal direction”, “prevailing wind direction” and “north direction” angles. Finally, these data are optimized within the model and suggestions are made to the designer to make a choice, based on the results obtained.

On the Detection of Population Heterogeneity in Causation Between Two Variables: Finite Mixture Modeling of Data Collected from Twin Pairs.

Causal inference is inherently complex and relies on key assumptions that can be difficult to validate. One strong assumption is population homogeneity, which assumes that the causal direction remains consistent across individuals. However, there may be variation in causal directions across subpopulations, leading to potential heterogeneity. In psychiatry, for example, the co-occurrence of disorders such as depression and substance use disorder can arise from multiple sources, including shared genetic or environmental factors (common causes) or direct causal pathways between the disorders. A patient diagnosed with two disorders might have one recognized as primary and the other as secondary, suggesting the existence of different types of comorbidity. For example, in some individuals, depression might lead to substance use, while in others, substance use could lead to depression. We account for potential heterogeneity in causal direction by integrating the Direction of Causation (DoC) model for twin data with finite mixture modeling, which allows for the calculation of individual-level likelihoods for alternate causal directions. Through simulations, we demonstrate the effectiveness of using the Direction of Causation Twin Mixture (mixDoC) model to detect and model heterogeneity due to varying causal directions.

Individual, Host-Vector Interactions, and Environmental Risk Factors for Plasmodium knowlesi Malaria Among At-Risk Communities in Peninsular Malaysia: A Case-Control Study.

Background: Highlighting the individual, host-vector interactions, and environmental risk factors for knowlesi malaria were consequential toward more focused and effective prevention and control strategies. This study aims to identify the individual, host-vector interactions, and environmental risk factors for Plasmodium knowlesi malaria among at-risk communities in Peninsular Malaysia. Materials and Methods: A case-control study was conducted involving laboratory-confirmed cases of P. knowlesi malaria, while a locality-matched individual with no history of fever and tested negative for malaria was taken as control. Univariate and multiple logistic regression were applied to evaluate the potential risk factors among respondents using IBM SPSS Statistics for Windows, Version 26.0. Results: Results showed higher cases among males as compared to females (76.1% vs. 23.9%). Multiple logistic regression analysis showed being male is 3.51 higher risk (p < 0.001) to become a case. Respondents whose place of work or study is near the forest edge have 44.0% lower risk (p = 0.030), while those living in the Orang Asli village were 56.0% lower risk as compared to the organized village to become a case (p = 0.035). Conclusion: These findings demonstrated that gender emerges as an independent individual risk factor while residing near a forest edge, in an Orang Asli village, or occupying workers' longhouses situated in hilly areas lowered the environmental risk among respondents. These findings attested that alternative directions must be considered in addressing the known risk factors associated with this type of malaria and the design of prevention and control programs should be tailored to the unique characteristics of each population.

Hybrid plug-and-play CT image restoration using nonconvex low-rank group sparsity and deep denoiser priors

Objective. Low-dose computed tomography (LDCT) is an imaging technique that can effectively help patients reduce radiation dose, which has attracted increasing interest from researchers in the field of medical imaging. Nevertheless, LDCT imaging is often affected by a large amount of noise, making it difficult to clearly display subtle abnormalities or lesions. Therefore, this paper proposes a multiple complementary priors CT image reconstruction method by simultaneously considering both the internal prior and external image information of CT images, thereby enhancing the reconstruction quality of CT images.Approach. Specifically, we propose a CT image reconstruction method based on weighted nonconvex low-rank regularized group sparse and deep image priors under hybrid plug-and-play framework by utilizing the weighted nonconvex low rankness and group sparsity of dictionary domain coefficients of each group of similar patches, and a convolutional neural network denoiser. To make the proposed reconstruction problem easier to tackle, we utilize the alternate direction method of multipliers for optimization.Main results. To verify the performance of the proposed method, we conduct detailed simulation experiments on the images of the abdominal, pelvic, and thoracic at projection views of 45, 65, and 85, and at noise levels of1×105and1×106, respectively. A large number of qualitative and quantitative experimental results indicate that the proposed method has achieved better results in texture preservation and noise suppression compared to several existing iterative reconstruction methods.Significance. The proposed method fully considers the internal nonlocal low rankness and sparsity, as well as the external local information of CT images, providing a more effective solution for CT image reconstruction. Consequently, this method enables doctors to diagnose and treat diseases more accurately by reconstructing high-quality CT images.

Looking beyond “the tool itself”: Towards a political systems understanding of e-participation

In this paper, we argue that e-participation research is at a crossroads as its theories and empirical scope are increasingly detached from the contemporary relationship between information technology and political participation. We illustrate this challenge through two developments: (1) the dissolving boundaries between online and offline spheres of political participation and (2) the growing dissociation between ICTs and democracy. In light of these developments, we present a potential path forward for the field, inspired by the so-called "systemic turn" in research on deliberative democracy and democratic innovations. We argue for a perspective that emphasises the relationship between e-participation and the political system in which it is enacted. This allows us, in our conclusion, In our conclusion, this allows us to present alternative potential directions for future research within the field.

Trends of abstraction research in mathematics education: A bibliometric analysis

Abstraction is fundamental in mathematics learning because students can discover the studied concepts through abstraction. Bibliometric analyses of abstraction research in mathematics education have yet to be published. A bibliometric analysis is conducted to explore trends in abstraction research. The mathematics education researchers will gain insights from studying the development of abstraction research over the last fifteen years. The primary objective of this study is to evaluate the primary journals published, the most productive authors, universities, and countries and to identify current trends in abstraction research. Data were collected from the Scopus database and analysed using VOSviewer and R software. A thorough review was conducted on 271 articles published between 2008 and 2022. The collected data was analysed and presented using R studio and VOSviewer software. The publication of abstraction research has increased every year. Abstraction studies related to geometry, computational thinking, and preschool are trend and abstraction studies related to gesture, preschool child, arithmetic, physiology, mathematical concepts, geometry, language, and cognition. Abstraction research is exciting because it will still trend until 2022. This study offers valuable insights to researchers interested in mathematics education for exploring alternative research directions to the primary research trends. Based on these results, recommendations for further research are given so that they can explore various options for research trends.

Anarchic Unfreedom: Critical International Theory Versus Neoliberalism and the New Right

In recent years, a number of voices have raised concerns about the role of critical theory in the contemporary political crisis. Critical theory has been accused of providing intellectual resources for the global rise of the new right, whether in Trump’s America or in Putin’s Russia. While acknowledging the ostensibly critical, even emancipatory appearance of the new right, my paper insists on the need for critical theory. Indeed, a desire for liberation is expressed not only by the new right, but also by the ruling neoliberal discourse. Both neoliberalism and the new right make emancipatory use of a key modern political distinction: the distinction between hierarchy and anarchy, or inside and outside. Both promise liberation from hierarchies and want to achieve freedom through anarchic competition, either on globalized markets or in international relations. However, upon close examination, the promised freedom is an anarchic unfreedom. It submits all competitors to a powerful competitive logic, justifies hierarchic submission and harbors an enormous potential for violence. Critical International Relations theory is uniquely suited to diagnose this problem and to point us into an alternative direction.

Comparative numerical study of the energy performance of two different configurations of a hybrid photovoltaic/thermal air collector

Solar energy is a renewable alternative to fossil fuels. When solar energy is converted into electricity by photovoltaic (PV) solar modules, the heat generated increases the temperature of the PV solar cells and, as a result, reduces their electrical efficiency. Several techniques are used to cool PV solar cells in order to reduce their operating temperature. One technique is the use of hybrid photovoltaic/thermal (PVT) solar collectors, which simultaneously produce electricity and useful heat. In the airflow channel, the heat and mass transfer equations and those derived from the energy balance on the solid layers of the collector were discretized using the finite difference method with an implicit alternating directions (ADI) scheme. All the algebraic equations obtained after discretization were solved using the Thomas algorithm. The aim is to make a numerical comparison of the energy performance of the Verre-PV-Tedlar and Verre-PV-Verre flat-plate air PVT hybrid solar collectors in the climatic conditions of Lomé, Togo. Under the same operating conditions, the average electrical efficiencies of the Verre-PV-Verre and Verre-PV-Tedlar flat-plate air PVT hybrid collectors are 15.34% and 13.85% respectively. There is a 1.49% improvement in electrical efficiency for a Verre-PV-Verre air-source PVT hybrid collector compared with a Verre-PV-Tedlar PVT hybrid collector. The Verre-PV-Verre flat plate PVT air hybrid collector has an average daily electrical energy gain of 0.564 kWh and a thermal energy gain of 0.839 kWh, while the Verre-PV-Tedlar flat plate PVT air hybrid collector has an average daily electrical energy gain of 0.548 kWh and a thermal energy gain of 1.403 kWh.

Deciphering the multi-electron redox chemistry of metal-sulfide electrode toward advanced aqueous Cu ion storage

While neutral aqueous metal batteries, featuring cost-effectiveness and non-flammability, hold significant potential for large-scale energy storage, their practical application is hampered by the limited specific capacity of cathode materials (less than 500 mAh g−1). Herein, capacity-oriented CoS2 and rate-optimized Co9S8 cathodes are developed based on the aqueous copper ion system. The charge-storage mechanism is systematically investigated through a series of ex-situ tests and density functional theory calculations, focusing on the reversible transitions of Co9S8→Cu7S4→Cu9S5/Cu1.8S and CoS2→Cu7S4→Cu2S, which are associated with the redox reactions of Cu2+/Cu+‖Co2+/Co and Cu2+/Cu+‖S22−/S2−, respectively. The electrochemical results show that CoS2 can exhibit a superior capacity of 619 mAh g−1 at 1 A g−1 after 400 cycles, while Co9S8 maintains an outstanding rate performance of 497 mAh g−1 at 10 A g−1 (the retention rate is 95% compared to 521 mAh g−1 at 1 A g−1). As a proof of concept, an advanced CoS2//Zn hybrid aqueous battery demonstrates a working voltage of 1.20 V and a specific energy of 663 Wh kgcathode−1. This work provides an alternative direction for developing sulfide cathodes in energetic aqueous metal batteries.

The inverse problem of time-fractional order and volatility for European option under time-fractional Black-Scholes model

Abstract In this paper, we discuss the inverse problem of time-fractional order and volatility for European option pricing under the time-fractional Black-Scholes model. This inverse problem is an optimization problem, which is proposed by the Tikhonov regularization method, and the alternate direction multiplier method (ADMM) is used to identify the time-fractional order and volatility of the pricing model. Numerical simulation and empirical analysis are given to demonstrate the effectiveness of the proposed method.

Transitive reasoning: A high-performance computing model for significant pattern discovery in cognitive IoT sensor network

Current research on the Internet of Things (IoT) has given rise to a new field of study called cognitive IoT (CIoT), which aims to incorporate cognition into the designs of IoT systems. Consequently, the CIoT inherits specific attributes and challenges from IoT. The CIoT applications generate vast, diverse, constantly changing, and time-dependent data due to the billions of devices involved. The efficient operation of these CIoT systems requires the extraction of valuable insights from vast data sources in a computationally efficient manner. Therefore, this study proposes transitive reasoning to glean significant concepts and patterns from a 21.25-year environmental dataset. To reduce the effects of missing entries, the proposed methodology includes a grouping of data using probabilistic clustering and applying total variance regularization in the alternate direction method of multipliers (ADMM) to regularize the sensory data. As a result, noisy entries will be less conspicuous. Afterward, it calculates the transitional plausibility value for each cluster using the transited value and then turns it into binary data to create concept lattices. In addition, each concept that is formed is assigned a weight, and the concept with the largest transitive strength value is chosen, followed by calculating the mean value. Therefore, this pattern is seen as significant. Experimental results on 21.25-year environmental data show an accuracy of over 99.5%, outperforming competing methods, as shown by cross-validation using multiple metrics.

&lt;b&gt;The Potential Effectiveness of an Ancient Chinese Herbal Formula Yupingfengsan for the Prevention of COVID-19: A Systematic Review&lt;/b&gt;

The COVID-19 has been spreading all over the world, and human health is at stake. Conventional medicine played an important role in the treatment and syndrome relief, while there is limited research evidence to support any prevention or antiviral treatment for COVID-19. Yupingfengsan is a classic formula with the effect of replenishing qi, consolidating exterior, and arresting sweating. Yupingfengsan has been widely used to treat various diseases in China and Southeast Asia for hundreds of years. Accumulating evidence suggests that YPFS has both immune-regulatory and anti-virus effects clinically, but leaving the mechanism elusive. In this paper, we reviewed the recent progress of Yupingfengsan in immune-regulatory and anti-virus activities, including the effect on mucosal immunity, biological antagonism, macrophages, adaptive immunity, and natural killer cells. From this systematic review, we speculate that YPFS can play an effective role in the prevention of SARS-CoV-2, but further experimental studies are expected to investigate the specific mechanisms. This review suggests an alternative direction for the prevention of COVID-19. Received: 12 July 2024 | Revised: 18 September 2024 | Accepted: 21 October 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Xin Yang: Methodology, Formal analysis, Investigation, Resources, Writing - original draft, Visualization. Rong Tian: Conceptualization, Methodology, Formal analysis, Investigation, Writing - original draft, Writing - review &amp; editing, Supervision, Funding acquisition. Catherine Shi: Conceptualization, Methodology, Formal analysis, Investigation, Writing - original draft, Writing - review &amp; editing. Alan Wang: Conceptualization, Methodology, Formal analysis, Investigation, Resources, Writing - original draft, Writing - review &amp; editing, Supervision.

Bayesian feedback in the framework of ecological sciences

In ecological studies, it is not uncommon to encounter scenarios where the same phenomenon (e.g., species occurrence, species abundance) is observed using two different types of samplers. For example, species data can be collected from scientific sampling with a completely random sample pattern, but also from opportunistic sampling (e.g., whale watching from commercial fishing vessels or bird watching from citizen science), where observers tend to look for particular species in areas where they expect to find them.Species Distribution Models (SDMs) are widely used tools for analysing this type of ecological data. In particular, two models are available for the aforementioned data: a geostatistical model (GM) for data collected where the sampling design is not directly related to the observations, and a preferential model (PM) for data obtained from opportunistic sampling.The integration of information from disparate sources can be addressed through the use of expert elicitation and integrated models. This paper focuses on a sequential Bayesian procedure for linking two models by updating prior distributions. The Bayesian paradigm is implemented together with the integrated nested Laplace approximation (INLA) methodology, which is an effective approach for making inference and predictions in spatial models with high performance and low computational cost. This sequential approach has been evaluated through the simulation of various scenarios and the subsequent comparison of the results from sharing information between models using a variety of criteria. The procedure has also been exemplified on a real dataset.The primary findings indicate that, in general, it is preferable to transfer information from the independent (with a completely random sampling) model to the preferential model rather than in the alternative direction. However, this depends on several factors, including the spatial range and the spatial arrangement of the sampling locations.

Using Bifluxon Tunneling to Protect the Fluxonium Qubit

Encoding quantum information in quantum states with disjoint wave-function support and noise-insensitive energies is the key behind the idea of qubit protection. While fully protected qubits are expected to offer exponential protection against both energy relaxation and pure dephasing, simpler circuits may grant partial protection with currently achievable parameters. Here, we study a fluxonium circuit in which the wave functions are engineered to minimize their overlap while benefiting from a first-order-insensitive flux sweet spot. Taking advantage of a large superinductance (L∼1 μH), our circuit incorporates a resonant tunneling mechanism at zero external flux that couples states with the same fluxon parity, thus enabling bifluxon tunneling. The states |0⟩ and |1⟩ are encoded in wave functions with parities 0 and 1, respectively, ensuring a minimal form of protection against relaxation. Two-tone spectroscopy reveals the energy-level structure of the circuit and the presence of 4π quantum-phase slips between different potential wells corresponding to m=±1 fluxons, which can be precisely described by a simple fluxonium Hamiltonian or by an effective bifluxon Hamiltonian. Despite suboptimal fabrication, the measured relaxation (T1=177±3 μs) and dephasing (T2E=75±5 μs) times not only demonstrate the relevance of our approach but also open an alternative direction toward quantum computing using partially protected fluxonium qubits. Published by the American Physical Society 2024

Coupled filtration–consolidation model for vacuum-preloaded soft ground

The widespread utilisation of vacuum-assisted prefabricated vertical drains for managing clayey soft ground has led to the development of numerous consolidation models. However, these models have limitations when describing the filtration behaviour of soil under conditions of high water content, without the formation of a particle network. To address this issue effectively, in this work, based on the compressional rheology theory, a two-dimensional axisymmetric model incorporating the compressive yield stress Py(ϕ) and a hindered setting factor r(ϕ) was developed to couple the filtration and consolidation of soil under vacuum preloading. A novel approach for determining the unified ϕ–Py–r relationships was introduced. The equation governing such fluid/solid and solid/solid interactions was solved using the alternative direction implicit method, and the numerical solutions were validated against the one-dimensional filtration cases, three-dimensional laboratory model tests and large-scale field trials. Further parametric analysis suggests that the radius of the representative unit and r(ϕ) exclusively affect the dewatering rate of the clayey slurry, while the gel point and Py(ϕ) influence both the dewatering rate and the final deformation.

Unleashing Leaders' Innovative Work Behavior in Energy Company: The Role of Business Acumen and Innovative Self‐Efficacy

AbstractInnovation is crucial for the sustainability of a company, hence innovative work behavior among its members, including leaders, is key. It is posited that an individual's comprehension of the various business dimensions of their job can lead them to exhibit innovative work behavior. Previous research indicates that innovative self‐efficacy is a strong predictor of such behavior. This study aims to examine the role of innovative self‐efficacy in mediating the effect of business acumen on innovative work behavior. Participants in this study were leaders at PT A, the company in the energy sector that is also tasked with overseeing the energy transition in Indonesia (n = 244). The result shows that innovative self‐efficacy partially mediates the effect of business acumen on innovative work behavior. These findings can provide alternative directions for developing more precise innovation‐oriented leadership preparation programs.

Adaptive content-aware spatial regularized correlation filter tracking algorithm

In order to solve the annoying boundary effects in correlation filter (CF) trackers induced by cyclic shift when sampling training patches, and improve the tracking performance, an adaptive content aware spatially regularized correlation filter (ACSRCF) is proposed. Firstly, real negative samples are generated from the background area around the target object, so as to alleviate the filter degradation by the fake negative samples induced from the circularly shifted object patches. Secondly, the locality sensitive histogram (LSH) based foreground feature is extracted and incorporated with the spatial regularization weight which is updated adaptively according to the varied object-oriented appearances. Thereafter, the CF model is optimized using the alternative direction method of multipliers (ADMM) in which the model is decomposed into two sub-problems and the LSH-based features are used in iteration for obtaining the optimal solutions. Finally, the proposed method is evaluated on 5 public benchmarks. The experimental results show that the accuracy and success rate scores of our method on OTB 50 dataset are 90.3%and 66.1%, respectively, exceeding the other CF trackers .The data on the OTB100 dataset is 92.2%and 69.2%, and the accuracy first ranks among all the trackers, while the success rate is ahead of other CF trackers.

Joint LPI waveform and passive beamforming design for FDA-MIMO-DFRC systems

Dual-functional Radar-Communication (DFRC) systems have been recognized as one of the most promising technologies in the field of wireless communications. Nevertheless, the low probability of intercept (LPI) performance in the DFRC systems cannot be overlooked. Based on the frequency diverse array multiple-input–multiple output (FDA-MIMO) radar, a DFRC system is proposed to enhance target detection in clutter environment and achieve desirable LPI against an underlying hostile interceptor. The issue can be cast into an optimization problem that maximizes the radar signal-to-interference-plus-noise ratio (SINR) while satisfying the communication quality-of-service (QoS) requirement of each user under one of three metrics, the required LPI performance and the constant-modulus waveform constraint. To solve this challenging problem, we reformulate it into an equivalent but more tractable form by resorting to an auxiliary variable. Subsequently, we employ the alternative direction method of multipliers (ADMM) and majorization-minimization (MM) algorithms to solve the resultant problem. Simulation results validate that the proposed LPI FDA-MIMO-DFRC scheme exhibits superior sensing performance over the conventional scheme with MIMO.

Technological Change and Techno-Social Systems: Re-Examining Sustainable Development and Digitalisation in Africa

This article argues that understanding currently dominant technological change models in low-and-middle income countries is important to addressing challenges to sustainable development with enhanced knowledge and effective policies. Combining such understanding with using systems thinking, as a theoretical framework, helps in illuminating techno-social systems and their overlaps with economic and human development systems, therefore highlighting possible leverage points for interventions to usher technological change towards sustainable development objectives. The proposed conceptual synthesis between technological change models and systems thinking is then critically applied to case studies related to digitalisation in Africa, where challenges to sustainability are amplified by continuous pressure for technological advancement, making local capabilities a central issue. The case studies examine how continental digitalisation indicators are ahead of industrialisation and human development indicators, with similar issues in digitalising agri-food ecosystems. We show that, while Africa is currently increasing in digitalisation, correlations between digitalisation and sustainable development are not as direct, or necessarily positive, as initially assumed. Similar trends are seen in digitalisation and agri-food ecosystems, where farm-raised data is monetised off-farm, thus removing opportunities for farmers to realise return on their knowledge investment. Examining the cases, using the proposed synthesis approach, reveals that digitalisation can contribute to development indicators when coupled with enhancing employment in productive sectors and that the prevailing order of ‘technology-push, demand-pull’ models suggests more investment in technological improvement. The article contributes to theory by illuminating overlaps between two theoretical/conceptual areas and to praxis by informing alternative policy directions.