Alternative Solution Research Articles

Linear gyrokinetic theory of two-dimensional ion-temperature-gradient mode in tokamaks

The investigation of the two-dimensional (2D) linear ion-temperature-gradient (ITG) mode in toroidal plasma is carried out in ballooning mode representation. A novel approach to solve the radial envelope structure is proposed by constructing radial differential eigenequation. The important lemma (∂ω/∂θk)|θk=0=0 is proved and employed to construct a numerically solvable eigenequation. We have developed a numerical solver which gives the 2D solution consistent with the results obtained by the gyrokinetic global simulation code. Our newly proposed method presents an alternative solution to the 2D ITG eigenvalue problem.

Penerapan Sistem Pendukung Keputusan Pada Pengambilan Keputusan Penilaian Kinerja Karyawan dengan Menerapkan Metode MOOSRA

The quality of the company will increase as employee performance increases. There are many ways that companies or organizations can improve employee performance. One way is to provide awards or rewards for employees who excel and for employees who are not able to provide their best performance, they will be given as a consequence. Rewards will be given by assessing employee performance. So far, Kofipindo has only relied on leaders' decisions to assess employee performance. This makes employees less motivated to show their best performance. Decision support systems are alternative solutions or alternative means and steps to resolve problems so that problems can be resolved effectively and efficiently. The Moosra method is used to calculate the weight of the criteria and determine the level of rejection as the main criterion. Where the decision support system is used as a model or reference in the decision-making process, especially in the employee performance assessment process. The Moosra method examines the suitability of separating a set of alternative value data with certain criteria described in the decision matrix. The aim of the research is to assist in the employee performance assessment process, making it easier for companies to provide rewards for the performance results of selected employees. The results obtained in the research process were Alternative (A10) with the name Roland Zebua as the employee with the best performance assessment.

Monolithic PMN-39PT nanograting-assisted second harmonic generation enhancement.

Second harmonic generation plays a vital role in frequency conversion which mutually promotes the laser technology and allows the wavebands extension of new coherent source. The monolithic crystals are supposed to be a superior choice for harmonic generation due to long interaction distance, however, the phase-mismatch brought a sharp reduction in the conversion efficiency. Although birefringent phase-matching and quasi-phase-matching techniques are commonly utilized to fill the phase gap in monolithic crystals, these techniques are limited by the natural refractive index of crystal and the domain engineering, respectively. In recent years, subwavelength structures evolve as a flexible scheme to realize phase matching by engineering the geometry features of crystals. Here, structured nanogratings are designed and fabricated on a monolithic PMN-39PT (Pb(Mg1/3Nb2/3)O3-0.39PbTiO3) substrate, a novel ferroelectric crystal with promising optical prospect, for enhancing second harmonic generation, where birefringent or quasi phase-matching is hard to achieve. The nanograting-assisted second harmonic generation enhancement is observed which is not limited by the availability of thin crystalline films. Meanwhile, a boost in the second harmonic signal synchronously promotes the cascading third harmonic generation. This method may provide an alternative solution for enhanced harmonic generation on monolithic substrates and develop potential nonlinear optical materials for frequency conversion.

The potential of N-fixing bacteria consortium inoculation and NPK application in increasing soil N-total, plant N uptake and tomato (Lycopersicum esculentum Mill.) yields in Andisol

Nitrogen is an essential nutrient for growing tomato plants (Lycopersicum esculentum Mill.) on Andisol soil order. However, many farmers tend to use an excessive amount of inorganic fertilizers, which can have adverse effects on both the environment and tomato yield. To combat this problem, an alternative solution is to replace the inorganic fertilizer through inoculation of Azotobacter as an N-fixing bacteria. An experiment was conducted to examine the combined effects of the N-fixing consortium with NPK on total soil N, N uptake, Azotobacter spp. population, growth and the tomato plants yield. The experiment was performed on Andisols, located roughly 1250 m above sea level in West Java Province. The experimental design used was a Randomized Block Design, in three replications, consisting of A (Control), B (N-fixing inoculation), C (N-fixing + ¼ NPK), D (N-fixing + ½ NPK), E (N-fixing + ¾ NPK), F (N-fixing + 1 NPK), and G (1 NPK). The experiment revealed that the Lembang Andisol had an acidic pH, moderate available N, and high P retention. Applying Azotobacter sp. and Azotobacter vinelandii (N-fixing consortium) combined with one dose of NPK increased plant height and the total soil N content. N-fixing inoculation combined with 1/4 NPK, ½ NP, and ¾ NPK increased plant N uptake. The inoculation of N-fixing + ¼ NPK increased fruit weight, the number of fruits, and the yield per hectare of tomatoes. Inoculating Azotobacter sp. and Azotobacter vinelandii in mixed culture can reduce NPK usage by 75%, resulting in higher tomato yield.

The Importance of Eating Healthy and Nutritionally Balanced Food for Elementary School Children


 Health and well-being are important aspects of human life. One of the key factors that affect health is diet. The food we consume directly affects our physical and mental state. This activity aims to increase elementary school children's knowledge of the importance of eating healthy and nutritionally balanced foods. Method As an alternative solution to the problems that have been identified, counselling activities are carried out on the importance of eating healthy and nutritionally balanced foods in elementary school children. The method to increase this knowledge is counselling on the importance of eating healthy and nutritionally balanced foods. The techniques used in this activity include advising on the importance of eating healthy, nutritionally balanced foods and distributing leaflets. Based on the evaluation results, there has been a significant increase in knowledge about eating healthy and nutritionally balanced foods. Education and awareness about a healthy and nutritionally balanced diet are very important. The public needs to be adequately informed about the nutritional value of food and ways to adopt a healthy diet. Thus, it is expected to improve the quality of life, reduce disease risk, and create a healthier and more productive society overall.

Influence of Applied Loads on Free Vibrations of Functionally Graded Material Plate–Shell Panels

An analysis of the influence of applied loads on free vibrations of plate and shell panels made of functionally graded materials is analyzed in the present work. Formulations for the static analysis considering geometrically nonlinear behavior, as well as linear buckling and free vibrations analyses are considered. A calculation of the through-thickness stress distribution is also performed. The finite element model is based on a higher order shear deformation theory using a non-conforming flat triangular plate/shell element with three nodes, and eight degrees of freedom per node is used in the numerical implementation. The results obtained with this numerical model are presented, discussed, and compared with alternative solutions published by other authors in some benchmark applications.

Electrokinetic remediation: Past experiences and future roadmap for sustainable remediation of metal-contaminated soils

Soil pollution due to heavy metals has become a serious environmental concern in the past decade, adversely affecting soil conditions and global food security. Due to this, considerable attention has been given to developing suitable remediation technologies, particularly for heavy metals. Among them, Electrokinetic Remediation (EKR) is a promising developing technique due to its great efficiency for fine-grained soils. The use of an electric field to remove contaminants from soil is effective on a wide range of matrices irrespective of the heterogeneity. In this context, the present study provides a detailed discussion on the design and operational considerations of EKR, mainly, power, electrode, and electrolyte characteristics. The major limitations of EKR, innovative modifications of EKR operations to overcome the limitations, and EKR-integrated technologies have been addressed. The optimization of regulating parameters ensures maximum efficiency towards removal of contaminants at minimum cost of treatment, thus, highlighting the engineering aspect of electrokinetics at field scale. The sustainability concerns arising from EKR and its possible alternative solutions have been discussed, to provide useful information and prospects to researchers in the field.

Ghana’s education and labour unions join civil society organisations to take on the IMF

ABSTRACT This article will explain how a series of educator strikes in 2022 in Ghana led to increased awareness of and calls for tax justice and debt relief from a growing movement of public sector workers and civil society organisations. We chart how the issues and demands of teacher organisations and other public sector workers shifted and increased over time, bringing in a new public imaginary to critique IMF driven austerity policies, build and link to global campaigns around debt relief, and signal new possibilities for generating more stable domestic public sector tax revenues. We examine the evolving strategies of growing educator strikes and show how unions are joining together with other civil society and advocacy groups to challenge fiscal austerity and government policies that affect not only their schools and classrooms but their lives and livelihoods. This case describing what Ghanaian teacher unions are doing to organise members to learn about and challenge austerity policies highlights the importance of public sector workers’ knowledge, understanding and engaging governmental macro-economic policies and offering alternative solutions.

JWST observations of 13CO_2 ice: Tracing the chemical environment and thermal history of ices in protostellarenvelopes

The structure and composition of simple ices can be severely modified during stellar evolution by protostellar heating. Key to understanding the involved processes are thermal and chemical tracers that can be used to diagnose the history and environment of the ice. The 15.2 mu m bending mode of CO_2 in particular has proven to be a valuable tracer of ice heating events but suffers from grain shape and size effects. A viable alternative tracer is the weaker CO_2 isotopologue band at 4.39 mu m, which has now become accessible at high S/N with the James Webb Space Telescope (JWST). In this study we present JWST NIRSpec observations of CO_2 ice in five deeply embedded Class 0 sources that span a wide range in masses and luminosities (0.2 - $L_ ) taken as part of the Investigating Protostellar Accretion Across the Mass Spectrum (IPA) program. The band profiles vary significantly depending on the source, with the most luminous sources showing a distinct narrow peak at 4.38 mu m. We first applied a phenomenological approach with which we demonstrate that a minimum of three to four Gaussian profiles are needed to fit the absorption feature of CO_2 . We then combined these findings with laboratory data and show that a 15.2 mu m CO_2 bending-mode-inspired five-component decomposition can be applied to the isotopologue band, with each component representative of CO_2 ice in a specific molecular environment. The final solution consists of cold mixtures of CO_2 with CH_3OH H_2O and CO as well as segregated heated pure CO_2 ice at 80 K. Our results are in agreement with previous studies of the CO_2 ice band, further confirming that CO_2 is a useful alternative tracer of protostellar heating and ice composition. We also propose an alternative solution consisting only of heated mixtures of CO_2 CH_3OH and CO_2 H_2O ices and warm pure CO_2 ice at 80 K (i.e., no cold CO_2 ices) for decomposing the ice profiles of HOPS 370 and IRAS 20126, the two most luminous sources in our sample that show strong evidence of ice heating resulting in ice segregation.

DERNA Enables Pareto Optimal RNA Design.

The design of an RNA sequence that encodes an input target protein sequence is a crucial aspect of messenger RNA (mRNA) vaccine development. There are an exponential number of possible RNA sequences for a single target protein due to codon degeneracy. These potential RNA sequences can assume various secondary structure conformations, each with distinct minimum free energy (MFE), impacting thermodynamic stability and mRNA half-life. Furthermore, the presence of species-specific codon usage bias, quantified by the codon adaptation index (CAI), plays a vital role in translation efficiency. While earlier studies focused on optimizing either MFE or CAI, recent research has underscored the advantages of simultaneously optimizing both objectives. However, optimizing one objective comes at the expense of the other. In this work, we present the Pareto Optimal RNA Design problem, aiming to identify the set of Pareto optimal solutions for which no alternative solutions exist that exhibit better MFE and CAI values. Our algorithm DEsign RNA (DERNA) uses the weighted sum method to enumerate the Pareto front by optimizing convex combinations of both objectives. We use dynamic programming to solve each convex combination in time and space. Compared with a CDSfold, previous approach that only optimizes MFE, we show on a benchmark data set that DERNA obtains solutions with identical MFE but superior CAI. Moreover, we show that DERNA matches the performance in terms of solution quality of LinearDesign, a recent approach that similarly seeks to balance MFE and CAI. We conclude by demonstrating our method's potential for mRNA vaccine design for the SARS-CoV-2 spike protein.

A GCC Artificial Food Additives Management based Mobile Application Development

Artificial food additives pose significant health risks to Gulf Cooperation Council (GCC) citizens despite regional authorities' extensive medical, legislative, and technological efforts. Literature highlights the detrimental impacts of these additives, including malnutrition, digestive disorders, respiratory problems, skin issues, hives, nausea, diarrhea, shortness of breath, allergic reactions, high blood pressure, and tumors. The research project at hand aims at becoming the first official and comprehensive mobile application of its own in the GCC region that manages the calculation and demonstration of an up-to-date health and legal knowledge base of the impacts of artificial additives, enhances the awareness, automatically recognizes the artificial additives, and provides alternative solutions, for both android and IOS mobile platforms. This research project introduces "Weqaya," a pioneering mobile application designed to manage, educate, and raise awareness about the effects of artificial additives. Weqaya provides real-time health and legal information, identifies additives, and suggests alternative solutions for Android and iOS platforms. The project employs an Agile-based SDLC model to explore, develop, and evaluate the food additive phenomena in Weqaya. The application's usability evaluation scores a promising 95.21%, indicating its potential utility for GCC health ministries, dietitians, academics, researchers, and food producers in enhancing knowledge and promoting non-artificial food options.

Analysis of Physical Tourism Tickets: Can Security Printing be Used?

Selling physical tickets at tourist destinations is often a problem in the world of tourism. The number of brokers and fake tickets is always the main problem. Physical tickets are very easy to counterfeit and very easy to distribute to irresponsible brokers. As a result, many tourists are deceived and often have to pay twice or even more just because they bought fake tickets. This research analyzes the problems caused by physical tickets and analyzes alternative security printing solutions that can be applied to ticketing systems using descriptive qualitative research methods. The results of this research are that ticketing using security printing will require greater costs and still pose a risk of ticket counterfeiting. Security printing might have benefits if it were given more value, such as embossing for braille letters aimed at blind tourists. However, for greater benefits, digital ticketing will have a smaller risk of counterfeiting and have greater profits.

Integrated convolutional neural networks for joint super-resolution and classification of radar images

Deep learning techniques have been widely used for two-dimensional (2D) and three-dimensional (3D) computer vision problems, such as object detection, super-resolution (SR) and classification to name a few. Radar images suffer from poor resolution as compared to optical images, hence developing a high-accuracy model to solve computer vision problems, such as a classifier, is a challenge. This is because of the lack of high-frequency details in the input images which makes it difficult for the classifier model to generate accurate predictions. Ways of addressing this challenge include training the learning model with a large dataset or using a more complicated model, such as deeper layer architecture. However, employing such approaches might result in the overfitting of the model, where the model might not generalize well on previously unseen data. Also, generating a large dataset for training the model is a challenging task, especially in the case of radar images. An alternate solution for achieving high accuracy in radar classification problems is provided in this paper wherein a CNN-enabled super-resolution (SR) model is integrated with the classifier model. The SR model is trained to generate high-resolution (HR) millimeter-wave (mmW) images from any input low-resolution (LR) mmW images. These resolved images from the SR model will be used by the classifier model to classify the input images into appropriate classes, consisting of threat and non-threat objects. The training data for the dual CNN layers are generated using a numerical model of a near-field coded-aperture computational imaging (CI) system. This trained dual CNN model is tested with simulated data generated from the CI numerical model wherein a high classification accuracy of 95% and a fast inference time of 0.193 s are obtained, making it suitable for real-time automated threat classification applications. For fair comparison, the developed CNN model is also validated with experimentally generated reconstruction data, in which case, a classification accuracy of 94% is obtained.

Electric-field-assisted in situ separation of mandelic acid in an enzymatic milli-reactor improves the control of reaction conversion and enhances the enantiomeric ratio

Enantiopure or enantiomerically enriched chemicals are often used as active pharmaceutical ingredients (APIs). However, API synthesis and purification on a preparative scale is usually costly, time-consuming, and difficult to scale up in conventional batch reactors. To develop an alternative solution, we applied kinetic enantioselective synthesis with simultaneous product separation. More specifically, we constructed a continuously operated packed-bed milli-reactor (PBR) with an orthogonally applied electric field for in situ separation. We tested our concept in the enzymatic production of mandelic acid (MA) enantiomers from racemic methyl mandelate (rMM) by immobilised Aspergillus niger lipase. The electric field selectively extracted the product (mandelate anions) whilst leaving almost 100 % of the electroneutral substrate in the reaction mixture. Such selective product separation significantly enhanced MA formation by shifting the reaction toward product synthesis. The applied electric current efficiently controlled the overall rMM conversion, reaching more than 90 % compared to 30% without current. We identified the electric current providing the highest (R)-(-)-MA to (S)-(+)-MA enantiomeric ratio equals to 5.20. This ratio was above 3.86 achieved in an ordinary PBR, albeit subject to further optimization. Our PBR with in situ product separation provided superior process control and significantly enhanced enantiomerically enriched chemical synthesis.

Privacy-proof Live Surgery Streaming: Development and Validation of a low-cost, Real-time Robotic Surgery Anonymization Algorithm.

Develop a pioneer surgical anonymization algorithm for reliable and accurate real-time removal of out-of-body images, validated across various robotic platforms. The use of surgical video data has become common practice in enhancing research and training. Video sharing requires complete anonymization, which, in the case of endoscopic surgery, entails the removal of all nonsurgical video frames where the endoscope can record the patient or operating room staff. To date, no openly available algorithmic solution for surgical anonymization offers reliable real-time anonymization for video streaming, which is also robotic-platform- and procedure-independent. A dataset of 63 surgical videos of 6 procedures performed on four robotic systems was annotated for out-of-body sequences. The resulting 496.828 images were used to develop a deep learning algorithm that automatically detected out-of-body frames. Our solution was subsequently benchmarked against existing anonymization methods. In addition, we offer a post-processing step to enhance the performance and test a low-cost setup for real-time anonymization during live surgery streaming. Framewise anonymization yielded an ROC AUC-score of 99.46% on unseen procedures, increasing to 99.89% after post-processing. Our Robotic Anonymization Network (ROBAN) outperforms previous state-of-the-art algorithms, even on unseen procedural types, despite the fact that alternative solutions are explicitly trained using these procedures. Our deep learning model ROBAN offers reliable, accurate, and safe real-time anonymization during complex and lengthy surgical procedures regardless of the robotic platform. The model can be used in real-time for surgical live streaming and is openly available.

Seismic collapse assessment of single-column bridge piers using the applied element method

Several single-column-bent bridges have collapsed in the past under earthquakes. Different factors associated with poor structural design and/or construction including inadequate and/or prematurely terminated longitudinal reinforcement (LR), insufficient concrete confinement, or insufficient transverse reinforcement (TR) were identified and reported. Different alternative designs or retrofitting solutions were also proposed and examined. One of the well-known samples of this bridge type was the elevated Hanshin Expressway Bridge (HEB), which collapsed in the 1995 Kobe earthquake. In that incident, 18 circular single-column piers (SCPs), monolithically connected to the deck, failed and collapsed. Here, the seismic progressive collapse behaviour of SCPs of the HEB was investigated using non-linear dynamic analyses of three-dimensional numerical models created by way of the applied element method (AEM). The results obtained agreed well with field observations after the earthquake. Two alternative design solutions for improving the seismic response of the original model were also considered and the efficiency of the solutions was assessed. Results showed that the AEM is capable of modelling the progressive collapse procedure for concrete bridges with sufficient accuracy. The results proved greater effects of the anchorage length of the LR as well as the space between the TR in the collapse behaviour of the pier.

Extensive hypothesis testing for estimation of crash frequency models

Estimating crash data count models poses a significant challenge which requires extensive knowledge, experience, and meticulous hypothesis testing to capture underlying trends. Simultaneous consideration of multiple modelling aspects is required including, among others, functional forms, likely contributing factors, and unobserved heterogeneity. However, model development, frequently affected by time and knowledge, can easily overlook crucial modelling aspects such as identification of likely contributing factors, necessary transformations, and distributional assumptions. To facilitate model development and an estimation that can extract as many insights as possible, an optimization framework is proposed to generate and simultaneously test a diverse array of hypothesis. The framework comprises a mathematical programming formulation and three alternative solution algorithms. The objective function involves minimizing the Bayesian Information Criterion (BIC) to avoid overfitting. The solution algorithms include metaheuristics to deal with an NP-hard problem and search through a complex and nonconvex space. The metaheuristics also enable to handle unique datasets through varying search strategies. The effectiveness of the proposed framework was ascertained using three distinct datasets, and published models used as benchmarks. The results highlighted the ability of the proposed framework to estimate crash data count models, surpassing benchmark models in terms of insights and goodness-of-fit. The framework provides several advantages, such as robust hypothesis testing, uncovering unique specifications and vital insights in the data, and leveraging existing knowledge to enhance search efficiency. The framework also exposes the vulnerability of traditional analyst efforts to fall into local optima, bias, and limitations in creating more efficient models. In a compelling example using crash data from Washington, the proposed framework unveiled insights overlooked by a benchmark published model, identifying speed, interchanges, and grade breaks as likely crash contributors, and revealing the potential danger of excessively wide shoulders. Conversely, the benchmark model identified fewer contributing factors and missed a crucial non-linear relationship between crash safety and shoulder widths. While wider shoulders are typically associated with improved safety, the proposed models suggest a safety threshold beyond which further widening could decrease safety. The introduction of random parameters in the analysis revealed a more nuanced relationship with crash frequency, thereby underlining the limitations of models incapable of capturing heterogeneity.

Relationship of Knee Abduction Moment to Trunk and Lower Extremity Segment Acceleration during Sport-Specific Movements.

The knee abduction moment (KAM) has been identified as a significant predictor of anterior cruciate ligament (ACL) injury risk; however, the cost and time demands associated with collecting three-dimensional (3D) kinetic data have prompted the need for alternative solutions. Wearable inertial measurement units (IMUs) have been explored as a potential solution for quantitative on-field assessment of injury risk. Most previous work has focused on angular velocity data, which are highly susceptible to bias and noise relative to acceleration data. The purpose of this pilot study was to assess the relationship between KAM and body segment acceleration during sport-specific movements. Three functional tasks were selected to analyze peak KAM using optical motion capture and force plates as well as peak triaxial segment accelerations using IMUs. Moderate correlations with peak KAM were observed for peak shank acceleration during single-leg hop; peak trunk, thigh, and shank accelerations during a deceleration task; and peak trunk, pelvis, and shank accelerations during a 45° cut. These findings provide preliminary support for the use of wearable IMUs to identify peak KAM during athletic tasks.

Assessing the Performance of Jatropha Curcas and Moringa oleifera Conditioners Towards Improving Fecal Sludge Treatment for Agricultural Purpose

Moringa oleifera (MO) and Jatropha Curcas (JC) conditioners have been proposed as alternative solutions to replace chemical conditioners for fecal sludge (FS) treatment. However, their effectiveness in treating FS for safe agricultural use has not been thoroughly evaluated. The aim of this study was to assess the suitability of FS treated with Moringa oleifera and Jatropha Curcas conditioners for agricultural use. Physicochemical parameters Indexes, salinity and sodium hazard levels (Wilcox Diagram analysis), and removal efficiency of nutrients and pathogens were evaluated. Qualitative experiments were conducted using dewatering treatment chambers containing untreated control FS samples, FS treated with Moringa oleifera and Jatropha Curcas conditioners at the University of Dar es Salaam Water Laboratory.

Methyl benzoate-related compounds as potential repellents and fumigants for the control of cigarette beetle, Lasioderma serricorne (F.)

The cigarette beetle, Lasioderma serricorne (F.), presents a significant management challenge as a stored product pest. The beetle's increasing resistance to conventional fumigants necessitates the search for alternative solutions to mitigate losses in agro-based industries. Here, we constructed a small molecule compounds library containing twenty-eight methyl benzoate-related compounds (MBCs) and evaluated their repellent and toxic effects against cigarette beetles by Y-tube olfactometer behavioral assays, free-choice box assays and fumigation assays. We found that six MBCs (methyl 3-methoxybenzoate, methyl benzoate, methyl 3-formylbenzoate, methyl 2-aminopyridine-3-carboxylate, methyl 2-aminobenzoate and methyl 4-amino-2-methoxybenzoate) act as repellents for cigarette beetles. Notably, methyl benzoate (MeBA) was effective across a broader range of concentrations (200–4000 ppm) compared to other MBCs. Four compounds (methyl 3-formylbenzoate, methyl 2-aminopyridine-3-carboxylate, methyl 2-aminobenzoate and methyl 4-amino-2-methoxybenzoate) required higher minimum concentrations (500–4000 ppm) than MeBA for repellence, while methyl 3-methoxybenzoate was effective at a concentration (10 ppm) lower than that for MeBA. Mixtures of MeBA with methyl 3-methoxybenzoate and methyl 3-formylbenzoate enhanced the repellent effect against cigarette beetles when compared to MeBA alone. Additionally, exposure to varying concentrations of MeBA, methyl 3-methoxybenzoate, methyl 3-formylbenzoate, and methyl 2-aminobenzoate resulted in 100% mortality of cigarette beetles. Our findings indicate that specific MBCs offer the potential for their use as repellents and fumigants for this problematic stored product pest.