Daily Problem Research Articles

Entrepreneurship Development Based on Financial Management for the Community Kota Bambu Selatan - Palmerah, West Jakarta

The waste problem in Indonesia is no longer strange, recently the waste problem, which is becoming increasingly mounting day by day, has become a topic of conversation that has attracted the attention of every group. The problem of waste has become a daily problem for Indonesian people. Various types of waste have colored our every point of view. Waste is a serious matter that must be dealt with immediately. You can imagine the waste thrown away by households and industry. And like it or not, we have to admit that the Indonesian people still don't understand much about waste. Various methods have been taken by the government and some communities to reduce the volume of waste in Indonesia. Therefore, as an alternative way, I tried to use unused drink packaging cardboard into a unique wall decoration with high selling value. Carrying out community service activities regarding used goods waste is very challenging for our implementing team. Our activities include socializing the use of domestic waste, apart from being able to process it into craft innovations, to support the Urban MSME program RT 11/RW 06 Kota Bambu Selatan Subdistrict, Palmerah, West Jakarta and socializing the use of domestic waste to create waste from used goods. Later, residents, especially the Karang Taruna RT 11/RW 06, Kota Bambu Selatan Village, Palmerah, West Jakarta, can start creating creativity from used goods

Risk-aware microgrid operation and participation in the day-ahead electricity market

This work examines the daily bidding problem of a grid-connected microgrid with locally deployed resources for electricity generation, storage and its own electricity demand. Trading electricity in energy markets may offer economic incentives but exposes the microgrid to financial risk caused by market commitments. Hence, a multi-objective, two-stage stochastic mixed integer linear programming (MILP) model is formulated, extending prior work of a risk-neutral microgrid bidding approach. The multi-objective model minimises both expected total cost of day-ahead microgrid operations and financial risk from bidding measured by conditional value-at-risk (CVaR). Bidding curves derived as first stage decisions are always feasible under present market rules – including a limitation on the number of break points per submitted curve – while being near optimal for the microgrid’s day-ahead recourse schedule. The proposed optimisation model is embedded in a variant of the ɛ-constrained method to generate bidding curve candidates with different trade-offs between the two conflicting objectives. Moreover, scenario reduction is used to compromise accuracy of the uncertainty set for better computational performance. Particularly, the marginal relative probability distance between initial and reduced scenario set is suggested to make a decision on the extent of scenario reduction. The proposed solution procedure is tested in a computational study to demonstrate its applicability to generate optimal microgrid bidding curve candidates with different emphasis between total cost and CVaR in reasonable computational time.

Exploring the impact of the first rule of Functional Analytic Psychotherapy on fear of intimacy, vulnerability, and responsiveness: An analog process analysis

Functional Analytic Psychotherapy (FAP) is an efficacious but complex treatment. Despite a solid empirical foundation, research on the treatment is limited. While five process-focused rules guide the treatment, developers made an early statement that they believed only the first rule of FAP, watching for the occurrence of daily life problems occurring in the therapy session, was needed to guide treatment effectively. The current study was designed to assess this claim through the use of a FAP analog procedure in hopes of increasing the accessibility of the treatment and thus facilitating implementation efforts. In this study, 81 undergraduate participants engaged in a closeness-generating class in which they were randomized into two groups. In the experimental condition, research assistants had access to a self-report measure regularly used to inform clinicians about potential daily life problems that may occur in the therapy room. No such information was available to the research assistants in the control condition. Findings indicated that while fear of intimacy, a core construct in FAP, decreased on average following the procedure, the experimental condition had no impact on this change. Further, when research assistants had access to the daily life problem information, participants found them less effective at responding to disclosures. These findings suggest the importance of a nuanced case conceptualization, the limitations of the first rule of FAP, and the strengths and limitations of the use of an analog paradigm to study FAP.

Comparative Study of Cantilever Biosensor for Detection of Tuberculosis

Abstract: Now everywhere in the world sensors are used to solve daily human problem. In the medical field sensors are used to diagnose various diseases. In this article for detection of tuberculosis, micro cantilever biosensor has been design and simulate. Rectangular shape cantilever biosensors with silicon, poly-silicon and aluminium material were designed and analysed the displacement. The displacements of this biosensor are compared with the other available models. The MEMS based micro cantilever biosensor were design in COMSOL Multiphysics. 10N to 100N load force are applied on the surface of cantilever for deliberate the interaction of antigen antibody. Stress generated on the surface of cantilever when antigen antibody binds together and it get deflects. This deflection is measured for 10N to 100N load force which is compare with other modules. The displacement generated by cantilever was 1.71 x 1028 µm for 100N force which is higher than all the other models, thus the proposed cantilever model is the best model for detection of tuberculosis

Cosine and cotangent similarity measures for intuitionistic fuzzy hypersoft sets with application in MADM problem

Intuitionistic fuzzy hypersoft sets (IFHSSs) are a novel model that is projected to address the limitations of Intuitionistic fuzzy soft sets (IFSSs) regarding the entitlement of a multi-argument domain for the approximation of parameters under consideration. It is more flexible and reliable as it considers the further classification of parameters into their relevant parametric valued sets. In this paper, we proposed some trigonometric (cosine and cotangent) similarity measures and their weighted trigonometric similarity measures (SMs). Trigonometric Similarity measures (SMs) for intuitionistic fuzzy hypersoft sets (IFHSSs) are significantly implied to check the similarity measures and help to determine the similarity between different factors. Also, in order to evaluate the validity of the significant study and apply the results to a daily life problem. We use them to solve problems involving the selection of renewable energy sources. According to several technical contributing factors, the analysis identifies the ideal location for the implementation of the energy production units. Future case studies with many features and additional bifurcation along with multiple decision-makers can use the suggested methodologies. Also, several existing structures, such as fuzzy, Pythagorean fuzzy, Neutrosophic theories, etc., can be utilized with the suggested method.

Customized GRASP for rehabilitation therapy scheduling with appointment priorities and accounting for therapist satisfaction

Physical therapy in acute care hospitals plays an important role in the rehabilitation of patients. Nevertheless, the profession must deal with staff shortages caused by a lack of potential employees and absenteeism which are results of high physical and mental workloads. The therapist shortage negatively affects the total number of daily appointments the department can fulfill. For appointments that can be successfully scheduled, continuity of care with the same therapist cannot be guaranteed for individual patients. Lack of continuity of care negatively influences the therapist's satisfaction. Therapist preferences for individual appointments in general cannot always be guaranteed when designing schedules, which also hurts satisfaction. This paper develops a multi-criteria model for the daily therapy appointment-scheduling problem. The primary objective is to minimize the total sum of priority violations for unscheduled appointments. To improve therapist satisfaction, we consider therapist preferences including continuity of care as a secondary objective. Here, our integer programming formulation aims to minimize the total sum of preference violations for scheduled appointments. We are dealing with an operational planning problem with a daily planning horizon. The operational objective is to achieve therapist schedules in at most two hours. The therapists’ schedules together need to include several hundred appointments for a planning day. Due to intractability, the developed integer program cannot provide schedules for such problem sizes. Therefore, we develop a customized Greedy Randomized Adaptive Search Procedure (GRASP) with six innovative local search operations to improve an initially constructed solution. We test the heuristic algorithm on realistic data instances. The metaheuristic provides high-quality schedules for various problem sizes in short runtimes, i.e., within minutes. Comparisons with the optimal solutions for small problem instances show very good results of the GRASP with a similar number of scheduled appointments and good adherence to continuity of care and therapist preference requirements.

Nusantara Islam and Nusantara Christianity: Criticizing The Theology of Violence and Peace In ReligiousRelations In Indonesia

Violence has become a daily human problem, in every aspect of life. Even violence has become a culture, which is the result of human creation. Humans create violence to fulfill their desires. The debate about the relevance of religion and violence as well as peace has seen a rapid increase since the turn of the century. This is due to globalization which has brought interaction between various nations and civilizations, including Indonesia. The result of these changes is increased awareness of peace and security, and in turn, increased conflict and violence in almost all regions of Indonesia. For example, the rejection of the construction of places of worship and also the way of looking at religion by placing religion as a source of conflict, has given rise to various attempts to reinterpret religious teachings and then find common ground at a certain level in the hope that violence and conflict between religious communities will be reduced if the adherents of each religion tolerant. Elimination of blind fanaticism, religious education from an early age, having and cultivating an attitude of nationalism and dialogue between religious and cultural communities are also needed. Eliminate the defensive apologetic attitude and approach it through a non-apologetic attitude. As well as returning to local noble values that are rooted in Islam and Christianity from the beginning of the archipelago.

A hybrid computational approach to anticipate individuals in sequential problem solving.

Human-awareness is an ever more important requirement for AI systems that are designed to assist humans with daily physical interactions and problem solving. This is especially true for patients that need support to stay as independent as possible. To be human-aware, an AI should be able to anticipate the intentions of the individual humans it interacts with, in order to understand the difficulties and limitations they are facing and to adapt accordingly. While data-driven AI approaches have recently gained a lot of attention, more research is needed on assistive AI systems that can develop models of their partners' goals to offer proactive support without needing a lot of training trials for new problems. We propose an integrated AI system that can anticipate actions of individual humans to contribute to the foundations of trustworthy human-robot interaction. We test this in Tangram, which is an exemplary sequential problem solving task that requires dynamic decision making. In this task the sequences of steps to the goal might be variable and not known by the system. These are aspects that are also recognized as real world challenges for robotic systems. A hybrid approach based on the cognitive architecture ACT-R is presented that is not purely data-driven but includes cognitive principles, meaning heuristics that guide human decisions. Core of this Cognitive Tangram Solver (CTS) framework is an ACT-R cognitive model that simulates human problem solving behavior in action, recognizes possible dead ends and identifies ways forward. Based on this model, the CTS anticipates and adapts its predictions about the next action to take in any given situation. We executed an empirical study and collected data from 40 participants. The predictions made by CTS were evaluated with the participants' behavior, including comparative statistics as well as prediction accuracy. The model's anticipations compared to the human test data provide support for justifying further steps built upon our conceptual approach.

EMG-based hand gesture classifier robust to daily variation: Recursive domain adversarial neural network with data synthesis

ObjectiveIf a supervised classification model is used to predict hand gestures using electromyography (EMG), the EMG signals for training should be labeled every day due to their daily variations. However, annotating these signals every day is time-consuming. MethodsTo address this problem, this study proposes a new framework that updates the EMG classifier in a semi-supervised manner; the classifier was optimized to a target day by using the labeled past EMG signals and the unlabeled signals of the target day. Specifically, the following models were integrated to maximize the classification accuracy: first, a domain-adversarial neural network (DANN) was used to account for the domain shift between the EMG signals of the past and target days. Second, the past dataset was augmented by a data synthesis model incorporated with clustering, random selection, and correlation (CRC). Lastly, a recursive DANN structure was developed to augment the unlabeled EMG signals of the target day. ResultsThe performance of the proposed framework was validated with EMG data of four subjects and five different days. The classification accuracies are 58.22% (without DANN), 66.65% (with DANN), and 73.67% (with recursive DANN and CRC), respectively. ConclusionsThe recursive DANN with CRC enhances the domain adaptation effect, and thus it can address the daily variation problem of EMG. SignificanceThe proposed framework can also be extended to resolve a subject variation problem of EMG.

Socialization of Fermentation Technology on Eco Enzymes as Raw Materials for Agro-industry at Muhammadiyah 3 Jember Senior High School

Waste is a daily problem for human life in the world, this is because waste production occurs every day. The increase in the amount of waste per day causes various diseases that will have a more dangerous health impact on both humans and the environment for the next generation. A clean and healthy school environment will support a good teaching and learning process. Muhammadiyah 3 Jember Senior High School is one of the schools that is always developing to become a model school in Jember Regency. However, based on the results of preliminary observations, organic waste management has not been implemented. Eco enzymes are natural multi-purpose liquids, which are fermented from sugar, fruit/vegetable residues, and water. Eco enzymes can be liquids that provide many benefits for households, agriculture, and animal husbandry. Basically, Eco enzymes accelerate biochemical reactions in nature to produce useful enzymes using fruit or vegetable waste. This enzyme from "waste" is one way of waste management that utilizes kitchen scraps for something very useful. The purpose of this community service activity is to introduce fermentation technology to Eco enzymes through increased participation in waste management. This activity was carried out in the form of direct socialization and training to 46 students. The resource person explained the material using the lecture and discussion method, then the participants directly practiced making Eco enzymes. Based on the results of observations of community service activities, students have not implemented organic waste management.

Abstract 544: Evaluating The Diagnostic Ability Of Six Different Artificial Neural Networks From The Subcellular Microenvironment To The Clinical Manifestation

Peripheral arterial disease (PAD) is affecting a large number of patients in the USA and worldwide. The natural history of the disease leads to detrimental clinical implications including walking disability, pain and eventually limb loss. The clinical management of the disease is largely based on clinical criteria (e.g. pain, tissue loss). We have prepared muscle biopsy specimens from patients with PAD versus control subjects and utilizing electron-microscopy we captured in the subcellular level the mitochondrial architecture changes. Using the acquired data as input we tested the ability of six structurally different artificial neural networks (ANNs) to identify PAD patients. For the study we acquired human tissue biopsies from the gastrocnemius muscle. The specimens were processed and cut as ultrathin sections (70 nm) for analysis using a JEM1010 transmission electron microscope. The images were used to generate an image preprocessing database of 992 images and subsequently a custom python-based code was developed with Keras - TensorFlow platforms in order to perform the ANN analysis. We implementing six different deep artificial neural network architectures namely, VGG-19 , ResNet50 , Densenet201 , InceptionV3 , InceptionsResnetV2 and EfficientNetB7.The diagnostic accuracy was found to be respectively: VGG-19 0.81%, ResNet50 0.75%, Densenet201 0.81%, InceptionV3 0.79%, InceptionsResnetV2 0.78% and EfficientNetB7 0.87%.This study presents data for the ability of simple and more advanced ANNs to perform diagnostic predictions based on electron microscopy images of intracellular organelles. The ability to take advantage of applied artificial intelligence technologies and serve this information for clinical decision making could be a pathway for improved patient care. The presented work established insight in some of the currently available deep artificial neural networks, applied in a daily clinical problem and their ability to harness biological information. Our goal is to stimulate the development of higher computational capacity artificial intelligence systems in order to assist clinicians and support improved guidelines geared towards individualized precision medicine.

Humility and Laziness: The Two Faces of the Poor in Paraguay’s Early Modern Jesuit Missions?

Abstract Early modern European monarchies responded to structural poverty with a series of welfare programs while also implementing repressive measures against the poor. Perceived during the late Middle Ages as virtuous Christians accepting their fate with humility, the poor of the early modern period were accused of vagrancy and immoral laziness, thus posing a threat to the social order. In contrast, in the seventeenth- and eighteenth-century chronicles of the Jesuit missions of Paraguay, hardly any mention is made of the material deprivation of Indians. A catechism of work in Guaraní intended to moralize the Indians’ labor, however, reveals that poverty was at the core of the missionaries’ concerns. Through an analysis of the Guaraní lexicon in connection with poverty, this article demonstrates that destitution in the missions was a daily problem. The article also shows how the term poriahu, glossing the word “poor,” covered only part of the meaning of the same word in Europe, where it had positive connotations, having been passed down by medieval tradition. In parallel, stigmatizing representations of the “bad pauper” were set apart and constructed around particular glosses, such as that of “vagrant” or “lazy.” This separation allowed for the development of an assistance policy that responded to the needs of all missionary Indians and was indispensable for the sustainability of the missions.

Development and performance evaluation of a water lifting device

The uninterrupted availability and price of electric power is a great solicitude for a common man; thus, irregular power availability for irrigation is a daily problem faced by rural farmers. In most rural communities, hand-operated water pumps are very predominant due to lack of electricity to power borehole water systems. Conventional energy is also a great concern and now, core attention of the people is being diverted towards the use of an alternate form of energy derived from human and animals. The need for water as a basic necessity in life has led to various developmental and engineering innovations that are designed to meet this need optimally. The water pump is the most useful machine, and it is the second most commonly used equipment by the farmers for irrigation purposes. An appropriate alternative to diesel pump, which requires neither diesel nor electricity, yet meets irrigation requirements, may be beneficial. With this, a simple hand-operated water-lifting device has been developed at CSIR-Central Mechanical Engineering Research Institute at Durgapur, West Bengal. Three prototypes were developed and evaluated successfully. From the results, it has been observed that, input effort has the largest effect on efficiency. The highest efficiency was found as 80% in case of prototype-III.

MLP-Mixer-Autoencoder: A Lightweight Ensemble Architecture for Malware Classification

Malware is becoming an effective support tool not only for professional hackers but also for amateur ones. Due to the support of free malware generators, anyone can easily create various types of malicious code. The increasing amount of novel malware is a daily global problem. Current machine learning-based methods, especially image-based malware classification approaches, are attracting significant attention because of their accuracy and computational cost. Convolutional Neural Networks are widely applied in malware classification; however, CNN needs a deep architecture and GPUs for parallel processing to achieve high performance. By contrast, a simple model merely contained a Multilayer Perceptron called MLP-mixer with fewer hyperparameters that can run in various environments without GPUs and is not too far behind CNN in terms of performance. In this study, we try applying an Autoencoder (AE) to improve the performance of the MLP-mixer. AE is widely used in several applications as dimensionality reduction to filter out the noise and identify crucial elements of the input data. Taking this advantage from AE, we propose a lightweight ensemble architecture by combining a customizer MLP-mixer and Autoencoder to refine features extracted from the MLP-mixer with the encoder-decoder architecture of the autoencoder. We achieve overperformance through various experiments compared to other cutting-edge techniques using Malimg and Malheur datasets which contain 9939 (25 malware families) and 3133 variant samples (24 malware families).

The multi-trip container drayage problem with synchronization for efficient empty containers re-usage

We study a typical daily drayage problem concerning the last-mile logistics at seaports for inland container supply chains. A set of trucks available at an inland container terminal must fulfil shippers’ requests of transporting containers within time windows and, to do so, can perform multiple daily trips. A request may entail picking up or delivering containers either at the shippers’ premises, the inland terminal or the seaport. Demand for empty containers can be satisfied by either using the available limited stock at the inland terminal, by street-turning or, ultimately, by retrieving them at a local depot for empties resulting in extra mileage. Hence, the minimization of routing costs also entails synchronizing trucks’ trips that retrieve and add empty containers to the inland terminal stock to avoid unnecessary visits to the empty depot. After modelling the problem mathematically, we develop an exact column-and-row generation approach embedded in a branch-and-price framework. To accelerate the solving process of the pricing problem, we propose effective strategies by combining a set of tailored pricing algorithms. These strategies perform well on a set of adapted Solomon’s instances up to 100 nodes and against a standard branch-and-cut solver. Finally, experiments on real-world instances, inspired by a case study of an inland terminal at the Port of Rotterdam region, provide insights into current planning practices.

Responsive strategic oscillation for solving the disjunctively constrained knapsack problem

This paper presents a responsive strategic oscillation algorithm for the NP-hard disjunctively constrained knapsack problem, which has a variety of applications. The algorithm uses an effective feasible local search to find high-quality local optimal solutions and employs a strategic oscillation search with a responsive filtering strategy to seek still better solutions by searching along the boundary of feasible and infeasible regions. The algorithm additionally relies on a frequency-based perturbation to escape deep local optimal traps. Extensive evaluations on two sets of 6340 benchmark instances show that the algorithm is able to discover 39 new lower bounds and match all the remaining best-known results. Additional experiments are performed on 21 real-world instances of a daily photograph scheduling problem. The critical components of the algorithm are experimentally assessed.

Hybrid firefly genetic algorithm and integral fuzzy quadratic programming to an optimal Moroccan diet

In this paper, we solve the Moroccan daily diet problem based on 6 optimization programming (P) taking into account dietary guidelines of US department of health, human services, and department of agriculture. The objective function controls the fuzzy glycemic load, the favorable nutrients gap, and unfavorable nutrient excess. To transform the proposed program into a line equation, we use the integral fuzzy ranking function. To solve the obtained model, we use the Hybrid Firefly Genetic Algorithm (HFGA) that combines some advantages of the Firefly Algorithm (FA) and the Genetic Algorithm (GA). The proposed model produces the best and generic diets with reasonable glycemic loads and acceptable core nutrient deficiencies. In addition, the proposed model showed remarkable consistency with the uniform distribution of glycemic load of different foods.

An LSTM‐based mixed‐integer model predictive control for irrigation scheduling

AbstractThe development of well‐devised irrigation scheduling methods is desirable from the perspectives of plant quality and water conservation. Accordingly, in this article, a mixed‐integer model predictive control system is proposed to address the daily irrigation scheduling problem. In this framework, a long short‐term memory (LSTM) model of the soil–crop–atmosphere system is employed to evaluate the objective of ensuring optimal water uptake in crops while minimizing total water consumption and irrigation costs. To enhance the computational efficiency of the proposed method, a heuristic method involving the logistic sigmoid function is used to approximate the binary variable that arises in the mixed‐integer formulation. Through computer simulations, the proposed scheduler is applied to homogeneous and spatially variable fields. The results of these simulation experiments reveal that the proposed method can prescribe optimal/near‐optimal irrigation schedules that are typical of irrigation practice within practical computational budgets.

The effect of implementation of a creative thinking model on the development of creative thinking skills in high school students: A systematic review

AbstractCreative thinking plays a pivotal role in science, innovation and the arts, solidifying its significance in daily life problem solving as well as maintaining and fostering our well‐being. Creative problem solving (CPS) and critical reasoning enables students to be challenged and to grasp complex problems and is defined as one's ability to generate both original and useful ideas to be adopted in a wide array of situations. However, the literature concerning the integration and adoption of creative thinking models in educational systems and the implications this has on creative thinking skills is limited. To investigate whether the implementation of a creative thinking model leads to the development of creative thinking skills in high school students, and whether it affects any other abilities of high school students, Scopus, Web of Science and PubMed were systematically screened on 21 February 2022. Studies that adopted a CPS model in a high school setting and investigated its role in improving the creative thinking skills of students were included. Eight studies that spanned 542 high school students were included in this systematic review. Implementation of creative thinking models, including FPSPI, SCAMPER, quantum‐based CPS learning, and traditional CPS models, resulted in notable improvements in the development of creative thinking skills in high school students. Improvements were also evident in the participant's academic achievements, concept understanding and higher‐order thinking skills. A data extraction form was formulated in accordance with the PRISMA recommendations and (GRADE) criterion for risk of bias was incorporated. The implementation of a creative thinking model in educational institutions results in the development and improvement of creative thinking skills in high school students when used alongside traditional teaching methods. Future studies should focus on investigating the adoption of these models and others on a wider scale, spanning several geographical locations and education systems.

An efficient heuristic for the fixed-route vehicle-refueling problem

Trucking companies have to decide each day at which truck stops their trucks should be refueled. The underlying problem is called fixed-route vehicle-refueling problem (FRVRP). Since large trucking companies have more than 10000 trucks, small improvements in the solution quality or the required time lead to substantial cost reductions. This makes the FRVRP an important daily scheduling problem. This paper presents a fast and efficient heuristic solution approach. We introduce an exact algorithm to determine the optimal refueling policy given the selected truck stops. It has a worst case performance of O(n2) but requires linear time on average. Our computational study shows that the heuristic is able to solve instances with up to 2000 truck stops within a fraction of a second to (near) optimality. We propose two variants. The first found for 96.8% of our instances an optimal solution with an average solution gap of 0.001% while requiring 0.001 seconds of computation time on average. The second variant requires 0.1 seconds on average, but reaches an average gap of under 0.001%, and found optimal solutions in 99.0% of all instances.