Miracle-dz/UniFAR-FTUs huggingface dataset:
Critical Factors for Women’s Representation in Top Management Leadership Positions in Local Government

This study explores the critical factors behind women’s representation in upper-echelon II managerial positions in local government organizations in Indonesia, which contemporary studies rarely studied. This research interviewed 12 informants at the East Nusa Tenggara (ENT) Provincial Government in Indonesia using a qualitative approach and purposive sampling techniques. This article found eight factors that are critical in influencing women's representation in echelon II positions in local government organizations from three primary dimensions. First, a network of political proxies and representation of ethnicity and electoral areas are factors from the socio-political dimension. Second, the three factors related to the intra-organizational dimension are patriarchal culture, gender stereotypes, and masculine leadership. Lastly, academic respect, self-confidence, and domestic responsibility are three factors from the individual dimension. The practical implication of this study is that for women to be elected to echelon II positions, they need to expand their network or political connections with political party leaders affiliated with elected political officials or the family actors of elected political officials. Additionally, women must adopt masculine leadership models to be accepted by their male colleagues and adapt to a patriarchal organizational environment. Finally, women need to increase their confidence in a leadership capacity.
Lithography light source fault detection

High productivity is a key requirement for today's advanced lithography exposure tools. Achieving targets for wafers per day output requires consistently high throughput and availability. One of the keys to high availability is minimizing unscheduled downtime of the litho cell, including the scanner, track and light source. From the earliest eximer laser light sources, Cymer has collected extensive performance data during operation of the source, and this data has been used to identify the root causes of downtime and failures on the system. Recently, new techniques have been developed for more extensive analysis of this data to characterize the onset of typical end-of-life behavior of components within the light source and allow greater predictive capability for identifying both the type of upcoming service that will be required and when it will be required. The new techniques described in this paper are based on two core elements of Cymer's light source data management architecture. The first is enhanced performance logging features added to newer-generation light source software that captures detailed performance data; and the second is Cymer OnLine (COL) which facilitates collection and transmission of light source data. Extensive analysis of the performance data collected using this architecture has demonstrated that many light source issues exhibit recognizable patterns in their symptoms. These patterns are amenable to automated identification using a Cymer-developed model-based fault detection system, thereby alleviating the need for detailed manual review of all light source performance information. Automated recognition of these patterns also augments our ability to predict the performance trending of light sources. Such automated analysis provides several efficiency improvements for light source troubleshooting by providing more content-rich standardized summaries of light source performance, along with reduced time-to-identification for previously classified faults. Automation provides the ability to generate metrics based on a single light source, or multiple light sources. However, perhaps the most significant advantage is that these recognized patterns are often correlated to known root cause, where known corrective actions can be implemented, and this can therefore minimize the time that the light source needs to be offline for maintenance. In this paper, we will show examples of how this new tool and methodology, through an increased level of automation in analysis, is able to reduce fault identification time, reduce time for root cause determination for previously experienced issues, and enhance our light source performance predictability.
Accuracy of aortic annulus measurements by three-dimensional transesophageal echocardiography and predictive value for thoracic aorta aneurysm in patients with aortic regurgitation

Background Accurate assessment of the aortic annulus (AA) dimension and judgment of thoracic aorta aneurysm is crucial for patients with aortic regurgitation (AR) before surgery. The aim of this study was to evaluate the accuracy and reproducibility of three-dimensional transesophageal echocardiography (3D-TEE) methods for AA measurement and explore the predictive value of the AA dimensions obtained by 3D-TEE for high-risk thoracic aorta aneurysms using the gold standard of multi-slice computed tomography (MSCT). Methods 3D-TEE was performed on 111 patients with pure moderate-to-severe AR, and MSCT examination was conducted simultaneously. AA dimensions were obtained using reconstruction software for these two imaging techniques. Thoracic aortic diameters at standard anatomic landmarks were also measured by MSCT. All patients were divided into two groups depending on the presence of high-risk thoracic aorta aneurysms. Results Compared to MSCT, 3D-TEE overestimated all AA parameters. However, no statistically significant differences were found in the average bias between methods (minimum diameter: 26.07±3.57 vs. 25.88±3.68 mm, P=0.52; maximum diameter: 32.30±2.68 vs. 31.78±4.06 mm, P=0.11; area: 669.76±155.19 vs. 660.05±168.28 mm2, P=0.44; perimeter: 93.52±10.42 vs. 92.26±11.71 mm, P=0.12). 3D-TEE demonstrated good correlations with MSCT measurement for AA minimum diameter, maximum diameter, area, and perimeter (r=0.63, 0.64, 0.74, 0.69, P<0.05 for all). According to the multivariate logistic regression analysis, the AA minimum diameter obtained by 3D-TEE was the best predictor for the presence of high-risk thoracic aorta aneurysms. The sensitivity and specificity for prediction were 84.6% and 63.9%, respectively, for an AA minimum diameter ≥25.74 mm (AUC: 0.759, 95% CI: 0.668–0.850). Conclusions AA measurements obtained by the 3D-TEE method are feasible and reliable for patients with pure AR. The AA minimum diameter measured by 3D-TEE can effectively predict the presence of high-risk thoracic aorta aneurysms.
Automatic Identification of Requirements from Specification in Special Machinery Engineering: A Human-in-the-Loop Classification Approach

This paper proposes a solution based on artificial intelligence (AI) techniques to reduce manual effort in early project stages of special machinery engineering. The assistance system utilizes machine learning to classify text fragments from customer specification documents as relevant requirement or not. By automating this classification process, the system aims to avoid manual reading and extraction of requirements for further processing. A human-in-the-loop approach is used to ensure that requirements are not lost in the process. Decision support is provided by a human, when the system is uncertain. This allows for continuous improvements. The evaluation results demonstrate the potential in improving the efficiency and effectiveness of special machinery engineering. Additionally, the approach facilitates a successful transition to automated processes, leveraging artificial intelligence and ensures that the final classification is reliable while minimizing the risk of missing crucial requirements
A Heuristic Algorithm for the Temporal Knapsack Problem

We introduce a temporal knapsack problem originated in cloud computing. We have a server and a set of virtual machines (VMs). The server is divided by two identical NUMA nodes with their own CPU cores and RAM. For each VM, we know its CPU and RAM capacity, profit, and time interval when it is active. Some VMs we call small. We can put them in any NUMA node. Other VMs we call large. Such a VM occupies two nodes of the server and requires half of its own CPU and RAM on each node. We need to find a subset of VMs and their location on the server to maximize the total profit under the server's CPU and RAM capacity constraints during the whole-time interval. To tackle this NP-hard problem, we design an adaptive large neighborhood search method based on the destroy and repair approach. At the destroy stage, some VMs are removed from the current solution. At the repair stage, a new solution is created based on the tabu search meta-heuristic with flip and swap neighborhoods and penalties. To obtain an initial solution, we apply a fast greedy algorithm. Computational experiments are conducted for the semi-synthetic test instances with up to 500 VMs. Computational results are discussed and compared with the near-optimal solutions of commercial software Gurobi.
On the exterior acoustic radiation modes of structures

The use of a modal‐style approach for the analysis of the exterior radiation characteristics of structures has recently received increasing attention. This approach generally seeks to find a set of orthogonal functions, or acoustic modes, that diagonalizes a radiation operator. These acoustic modes are found through an eigenfunction or singular value decomposition of the radiation operator. The eigenvalue or singular value associated with a given acoustic mode is directly proportional to the radiation efficiency of that acoustic mode. The acoustic mode represents a particular velocity pattern on the surface of the radiator. As with the analogous problem of finding structural natural frequencies and mode shapes, the accuracy of the acoustic modal representation depends on the number of degrees of freedom in the radiation operator. The radiation efficiency of the most efficient acoustic mode has a finite upper bound, and converges fastest with increasing degrees of freedom. Each additional degree of freedom.
Diversity Begets Stability in an Evolving Network

Complex evolving systems such as the biosphere, ecosystems and societies exhibit sudden collapses, for reasons that are only partially understood. Here we study this phenomenon using a mathematical model of a system that evolves under Darwinian selection and exhibits the spontaneous growth, stasis and collapse of its structure. We find that the typical lifetime of the system increases sharply with the diversity of its components or species. We also find that the prime reason for crashes is a naturally occurring internal fragility of the system. This fragility is captured in the network organizational character and is related to a reduced multiplicity of pathways between its components. This work suggests new parameters for understanding the robustness of evolving molecular networks, ecosystems, societies, and markets.
Interval-parameter robust quadratic programming for water quality management under uncertainty

Effective planning of water quality management is important for facilitating sustainable socio-economic development in watershed systems. An interval-parameter robust quadratic programming (IRQP) method is developed by incorporating techniques of robust programming and interval quadratic programming within a general optimization framework. The IRQP improves upon existing quadratic programming methods, and can tackle uncertainties presented as interval numbers and fuzzy sets as well as their combinations. Moreover, it can deal with nonlinearities in the objective function such that economies-of-scale effects can be reflected. The developed method is applied to a case study of a water quality management under uncertainty. A number of decision alternatives are generated based on the interval solutions as well as the projected applicable conditions. They represent multiple decision options with various environmental and economic considerations. Willingness to accept a low economic revenue will guarantee satisfying the water quality requirements. A strong desire to acquire a high benefit will run the risk of violating environmental criteria.
The Nutrition Transition and the Double Burden of Malnutrition in Sub-Saharan African Countries: How Do These Countries Compare with the Recommended LANCET COMMISSION Global Diet?

Background: Over the last two decades, many sub-Saharan African (SSA) countries have undergone dietary and nutrition transitions fuelled by rapid urbanisation, economic development, and globalisation. The aim of the current study was to examine outcomes of the nutrition transition and the epidemiologic transition in SSA countries in terms of food intake, health, and socioeconomic and development factors. Methods: Food balance sheet data—specifically, per capita energy intake per day and per capita gram intake per day—from the CountrySTAT framework of the Food and Agricultural Organization (FAO) were analysed for major food commodities. Additionally, selected health and development indicators supplied by UNICEF, the WHO and the World Bank were analysed. Results: Four dietary patterns emerged. The diet of the southern African/island cluster (South Africa, Mauritius, Eswatini, Namibia, Cabo Verde, and the outlier Seychelles) resembles a Westernised diet, with median values high on sugar/sweeteners, alcohol, meat, animal fats, eggs, and dairy. On the other hand, the diet of countries in the other three clusters appears to be more traditional, with countries in the desert/semi-arid cluster consuming more cereals and pulses/tree nuts, countries in the tropical coastal cluster consuming more fish and vegetable oils, and countries in the equatorial cluster consuming more starchy roots and fruit and vegetables. The resulting median values of health indicators also indicate a higher prevalence of non-communicable diseases in the southern African/island cluster, whereas stunting and anaemia are higher in the other three clusters. Conclusions: SSA countries are in different stages of the nutrition transition. By superimposing clusters generated using macronutrient intake values on a map of the climatic regions in Africa, one can clearly see the importance of climate on the availability of food and food intake patterns. Climate change presents a great challenge to healthy eating, as the link between climate regions and diets is illustrated.
EFFECTS OF EXPANDED POLYSTYRENE BEADS ON THE STRENGTH PROPERTIES OF BRICK AGGREGATE CONCRETE

Bangladesh's construction industry has grown significantly over the past two decades. The construction industry is progressing at a faster speed, which has raised the raw demand for building materials. Finding an alternative to natural aggregate in concrete casting as a partial replacement is a smart concept to protect natural resources. Our environment is severely harmed by the production of bricks. On the other hand, polystyrene, a common plastic packaging material, degrades to harm the environment and produces useless plastic waste. To conserve natural resources, it can be a good idea to use plastic trash as a partial replacement for natural aggregate in concrete casting. The addition of EPS to concrete offers a variety of benefits. These include reduced self-weight in structures, lighter weights during construction, and higher thermal resistance. In this experiment on the characteristics of concrete structure, expanded polystyrene beads have been used in place of coarse aggregate in this experiment. In various combinations (1:1:2), (1:1.5:3), and (1:2:4), EPS beads replace 0%, 10%, 20%, and 30% of the volume of the course aggregate portion. The compressive strength, unit weight and splitting tensile strength of these mixes are all measured for the complete testing procedure. The optimal levels of compressive strength, splitting tensile strength, and unit weight of 12.65 MPa, 1.89 MPa, and 46.28 kg/m3 respectively, and the normal concrete's compressive strength, splitting tensile strength, and unit weight of about 19.16 MPa, 2.05 MPa, and 12.24 kg/m3 are successfully developed in Grade M25 EPS-based concrete with a replacement percentage of 10%. The results indicate that EPS beads have the potential to produce lightweight concrete with a moderate strength grade that might be used in a wide range of applications.
Temperature dependence of the exchange-bias field of ferromagnetic layers coupled with antiferromagnetic layers

The temperature dependence of the exchange-bias field of a ferromagnetic layer coupled with an antiferromagnetic layer is studied. The focus is on the phenomenon that the exchange-bias field of some ferromagnet-antiferromagnet (F-AF) coupled systems does not show a monotonic dependence on temperature. Instead, the exchange-bias field peaks at some temperature for some ferromagnet-antiferromagnet coupled systems. This phenomenon is successfully explained by a model proposed recently. It is predicted that it is more likely to observe a peak of the exchange bias field of a F-AF system versus temperature if the N\'eel temperature is higher than the Curie temperature and/or the AF layer is relatively thin. This prediction has been well supported by experimental data.
Initial Growth of Sesame under Nutrient Omission

Sesame is an oilseed, nutritionally important in the world, for its seeds are rich sources of protein and calcium. Fertilizer management is fundamental for the development of this crop and consequently to increase its productivity. In this context, this research aimed to evaluate the initial development of sesame under nutrient omission in the Cerrado of Mato Grosso, Brazil. The experiment was conducted under controlled conditions in a greenhouse, at the Federal University of Rondonópolis. The soil used was Oxisol and the crop used was black sesame. The design was in randomized blocks, with five treatments (absence of nitrogen, absence of phosphorus, absence of potassium, complete fertilization and control (absence of nutrients)) and four repetitions. The variables analyzed were pH, chlorophyll index, plant height, number of leaves, stem diameter, leaf area, aboveground dry mass, root volume, and root dry mass. The pH variable showed a statistical difference 45 days after emergence. The chlorophyll index showed a difference between 30 and 45 days after emergence. The stem diameter, leaf area, plant height, number of leaves, aboveground dry mass, root volume, and dry mass showed a difference among treatments. The absence of nitrogen, phosphorus, and potassium significantly affects the initial development of sesame.
Grassland ecosystem type drives AM fungal diversity and functional guild distribution in North American grasslands

Nutrient exchange forms the basis of the ancient symbiotic relationship that occurs between most land plants and arbuscular mycorrhizal (AM) fungi. Plants provide carbon (C) to AM fungi and fungi provide the plant with nutrients such as nitrogen (N) and phosphorous (P). Nutrient addition can alter this symbiotic coupling in key ways, such as reducing AM fungal root colonization and changing the AM fungal community composition. However, environmental parameters that differentiate ecosystems and drive plant distribution patterns (e.g., pH, moisture), are also known to impact AM fungal communities. Identifying the relative contribution of environmental factors impacting AM fungal distribution patterns is important for predicting biogeochemical cycling patterns and plant‐microbe relationships across ecosystems. To evaluate the relative impacts of local environmental conditions and long‐term nutrient addition on AM fungal abundance and composition across grasslands, we studied experimental plots amended for 10 years with N, P, or N and P fertilizer in different grassland ecosystem types, including tallgrass prairie, montane, shortgrass prairie, and desert grasslands. Contrary to our hypothesis, we found ecosystem type, not nutrient treatment, was the main driver of AM fungal root colonization, diversity, and community composition, even when accounting for site‐specific nutrient limitations. We identified several important environmental drivers of grassland ecosystem AM fungal distribution patterns, including aridity, mean annual temperature, root moisture, and soil pH. This work provides empirical evidence for niche partitioning strategies of AM fungal functional guilds and emphasizes the importance of long‐term, large scale research projects to provide ecologically relevant context to nutrient addition studies.
An empirical model for bulk modulus and cohesive energy of rocksalt‐, zincblende‐ and chalcopyrite‐structured solids

In this paper we present empirical models for ground‐state properties such as bulk modulus and cohesive energy of rocksalt‐, zincblende‐ and chalcopyrite‐structured solids. The bulk moduli and cohesive energy of these solids exhibit a linear relationship when plotted on a log–log scale versus the nearest‐neighbor distance d (Å), but fall on different straight lines according to the product of valence electrons of the solids. We have applied the proposed relations to alkali halides, alkaline‐earth chalcogenides, binary and ternary tetrahedral semiconductors and found a better agreement with experimental data as compared to the values evaluated by earlier researchers. (© 2009 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Backward consistency concept and round-off error propagation dynamics in recursive least-squares algorithms

A new approach for the analysis of the propagation of roundoff errors in recursive algorithms is presented. This approach is based on the concept of backward consistency. In general, this concept leads to a decomposition of the state space of the algorithm, and in fact, to a manifold. This manifold is the set of state values that are backward consistent. Perturbations within the manifold can be interpreted as resulting from perturbations on the input data. Hence, the error propagation on the manifold corresponds exactly (without averaging or even linearization) to the propagation of the effect of a perturbation of the input data at some point in time on the state of the algorithm at future times. We apply these ideas to the Kalman filter and its various derivatives. In particular, we consider the conventional Kalman filter, some minor variations of it, and its square-root forms. Next we consider the Chandrasekhar equations, which apply to time-invariant state-space models. Recursive least-squares (RLS) parameter estimation is a special case of Kalman filtering, and hence the previous results also apply to the RLS algorithms. We also consider in detail two groups of fast RLS algorithms: the fast transversal filter algorithms and the fast lattice/fast QR RLS algorithms.
Transcutaneous Functional Electrical Stimulation Controlled by a System of Sensors for the Lower Limbs: A Systematic Review

In the field of transcutaneous functional electrical stimulation (FES), open-loop and closed-loop control strategies have been developed to restore functions of the lower limbs: walking, standing up, maintaining posture, and cycling. These strategies require sensors that provide feedback information on muscle activity or biomechanics of movement. Since muscle response induced by transcutaneous FES is nonlinear, time-varying, and dependent on muscle fatigue evolution, the choice of sensor type and control strategy becomes critical. The main objective of this review is to provide state-of-the-art, emerging, current, and previous solutions in terms of control strategies. Focus is given on transcutaneous FES systems for the lower limbs. Using Compendex and Inspec databases, a total of 135 review and conference articles were included in this review. Recent studies mainly use inertial sensors, although the use of electromyograms for lower limbs has become more frequent. Currently, several researchers are opting for nonlinear controllers to overcome the nonlinear and time-varying effects of FES. More development is needed in the field of systems using inertial sensors for nonlinear control. Further studies are needed to validate nonlinear control systems in patients with neuromuscular disorders.