Individual Demand Research Articles

Appeals against decisions ordering preventive measures

The Criminal Code highlights facts that constitute crimes and sanctions that can be applied to those who violate them in order to protect the interests of society, legal entities and citizens. The authorities are charged with enforcing the sanctions of the Criminal Code and other consequences, and the accountability of offenders must be done according to specific standards, corresponding to the needs of two major interests: the interests of society and individual interests. Carrying out normal work involving the rapid detection of crimes and the identification of offenders, as well as the implementation of legal consequences that serve as a means of combating and preventing crime, is in the interest of society. Individual interests demand that this activity be strictly regulated by law, with abuses and injustices outlawed, as well as provide citizens with effective ways to defend themselves against a false accusation or more serious than the real one. Official participants in criminal proceedings must do their job objectively and impartially to administer good repressive justice. The law has developed remedies to protect against unfounded or illegal judgments, as well as mistakes committed by courts or other entities with jurisdiction. These remedies may include checking illegal or unfounded decisions and correcting errors

Individual choice and objective demand in a Classical framework

AbstractTaking a cue from some passages of The Wealth of Nations, this paper defines notions of maximum willingness to pay and of individual surplus expressed in hours of labour for a two‐good pre‐capitalist economy. From the individual surplus, a consistent objective theory of individual choice is constructed, and a demand schedule free from subjective elements is derived.

Fostering Employability Skills for Engineers with Serious Games: A gamified GBL concept

The world is becoming increasingly complex, and as a result, the demand for individuals with strong employability skills is growing rapidly. It is essential for engineering education to adapt to this reality and provide opportunities for students to develop critical thinking, problem-solving, communication, teamwork, adaptability, and leadership skills. By doing so, engineering graduates will be well-equipped to navigate the complex challenges of the modern world and make meaningful contributions to society. Engineering education needs to adapt to this reality by incorporating more opportunities for students to develop their employability skills. This can be done through a variety of methods, such as experience- or project-based learning, internships, co-op programs, and industry partnerships. Game-based learning offers an engaging and immersive experience that can promote the development of these skills in a fun and interactive way. Within the Game-based learning approach implemented in our lecture the students play two subsequent game modules in which they first plan and build a factory building together and then put it into operation in the second part. The players are confronted with problems and situations that can only be overcome through communication and cooperation. This paper presents the self-developed online platform that uses gamification aspects such as micro-achievements for each student to represent the progress with the learning materials as well as within the gaming parts. The platform aims to provide students with an engaging and interactive way of learning, while also assessing their performance through collected data.

Rational Actors, Class Action Waivers, and the Emergence of Mass Individual Arbitration Demands

Rational Actors, Class Action Waivers, and the Emergence of Mass Individual Arbitration Demands

The role of peer influence in rooftop solar adoption inequity in the United States

Individual demand for emerging technologies can be influenced by the demand of other individuals within defined peer groups. These so-called peer effects have been demonstrated in emerging clean energy technologies such as rooftop solar. To date, peer effects have disproportionately driven solar adoption among relatively affluent households. Here, we use household-level income estimates of rooftop solar adopters to explore how peer effects drive adoption for low-income households. We find evidence of peer effects for both high- and low-income households and find that peer effects are generally stronger within than across income groups. Our results indicate that peer effects translate to adoption less frequently among low-income households. These results suggest that low-income peer effects are mitigated by barriers to low-income adoption. Heterogeneous peer influence is another demand shifter that explains the inequitable adoption of emerging technologies.

A Study on Retirement Preparedness and Confidence among Private Sector Employees in Malaysia

Retirement planning is particularly significant for an individual since it ensures that the individual's financial demands are met during the post-retirement period. A good plan prior to retirement is associated with significantly higher life satisfaction after retirement, given that many people spend a significant portion of their lives after a lengthy employment period. Personal retirement planning, on the other hand, is up to the individual. To ensure a comfortable retirement, employees must make a deliberate decision to set aside some of their earnings. However, past studies on retirement planning show that workers in Malaysia have made little or no preparation for their retirement. They only realize the need for retirement preparation when their savings are not enough to cover post-retirement expenses. Therefore, the main purpose of this study is to investigate the effects of attitude towards retirement, goal clarity, social influence, and financial literacy on the preparedness and confidence of private sector employees in Malaysia. Aside from that, the study intends to compare employees' retirement preparedness and confidence by gender as well as age group. The data for this study was collected through the use of a questionnaire. This study used ordered probit regression to see the effect of the determinants on retirement preparedness and confidence. The results of the study showed that attitude towards retirement, goal clarity, social influence, and financial literacy have a significant positive effect on retirement preparedness and confidence. Apart from that, male employees are found to be more prepared and confident to retire compared to female employees. Finally, employees between the ages of 31 and 40 are the most prepared and confident in their ability to retire. The study's findings have important implications for the retiring population in order for them to plan ahead of time so that they can survive throughout their retirement phase.

The cure effect: Individuals demand universal access for health treatments that claim to eliminate disease symptoms.

The present research documents a cure effect, whereby individuals are more likely to demand affordable prices when health treatments (e.g., drugs, medications, therapies) claim to eliminate (vs. reduce) disease symptoms. This preference for low-priced "cures" contradicts the fundamental premise of value-based pricing, which would expect individuals to tolerate higher prices for cures because they are putatively more effective and therefore more valuable. Five studies with over 2,500 participants provide robust evidence for the cure effect and show that it occurs because individuals judge a health treatment's acceptable price by focusing predominantly on its communal value rather than its market value. Given that cures are associated with maximal effectiveness, they are disproportionately endowed with communal value and more likely to yield price judgments that reflect concerns about universal access. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

On three shades of partial protection in elastic optical networks

Gone have been the days when the allocation of spectrum resources for a demand is over-provisioned due to worst-case based designs and the optical transmission technologies are based on the fixed spectral grid, modulation format and/or line-rate schemes. Elastic optical networks technologies featuring adaptive spectrum allocation mechanisms have been becoming the architecture and technology of choice for future core networks to meet explosive traffic growths in a cost and energy-efficient manner. Partial protection strategies based on the observation that in failure events, a service can tolerate a certain amount of degradation and therefore by reducing the protection traffic in the network, better spectrum utilization could be attained. Such concept has been widely studied in the traditional wavelength division multiplexing (WDM) context and yet has been somehow faded due to the fact that fixed transmission technologies provide limited room for spectral improvements. This paper aims to renew the interest in partial protection and re-adapt it to the context of elastic optical networks. The evolutionary perspective we lay out in this paper identifies a new route for achieving greater spectral efficiency in a pragmatic way by simply differentiating the protection services for each demand rather than the uniform treatment for all demands. As such, we present a new research problem entitled, routing, modulation level, spectrum and protection service assignment which is essentially an extension of the well-established one, that is, routing, modulation level, and spectrum assignment as the (partial) protection service for each demand is taken into account and optimized. Three variants of that problem reflecting shades of applying partial protection are covered in details. Specifically, the first one considers the intuitive case as the amount of protection traffic for each demand is provided as the input to the network planning process while the second one is dedicated to the special case of enforcing the same level of partial protection for all demands. More interesting case emerges in the third variant where given the service level agreement for the total protected network traffic, we provide the optimal solution that determines the protection service for each individual demand so as to minimize the spectral occupancy. Extensive numerical simulations have been conducted in the realistic COST239 network topology and it has been revealed that a remarkable spectral saving could be achieved thanks to introducing partial protection services. Moreover, our results have demonstrated that the demand-wise approach far outperforms the one-size-fits-all one and the difference could be up to 25% in our studied cases.

Content and language integrated learning: enhancing language acquisition and content understanding

Content and Language Integrated Learning (CLIL) is an educational approach that integrates the teaching of content subjects with the learning of a foreign or second language. This article explores the concept of CLIL, its benefits, and provides evidence-based citations to support its effectiveness in language acquisition and content understanding. The benefits of CLIL include enhanced language acquisition, improved content understanding, increased motivation and engagement, and the development of cultural and intercultural competence. Research has shown that CLIL students demonstrate improved language proficiency in all language skills, including listening, speaking, reading, and writing. CLIL also facilitates a deeper understanding of academic content by allowing students to explore complex topics using authentic language. Furthermore, CLIL programs promote active learning and intrinsic motivation, as students perceive the relevance and utility of the language being learned. The integration of language learning with content subjects creates opportunities for cultural and intercultural competence development, as students are exposed to diverse perspectives and collaborate with individuals from different linguistic backgrounds. Multiple studies have provided evidence supporting the effectiveness of CLIL. Research has consistently shown that CLIL programs result in significant improvements in language proficiency, both in receptive and productive skills. Additionally, CLIL students have demonstrated higher levels of motivation and engagement compared to those in traditional language learning settings. The findings from these studies highlight the positive impact of CLIL on language acquisition and content understanding. As the demand for multilingual individuals continues to grow in our interconnected world, CLIL stands as a valuable educational approach that equips learners with language skills and subject knowledge simultaneously. This article serves as a comprehensive overview of CLIL, providing educators, researchers, and policymakers with insights into the benefits and evidence supporting the implementation of CLIL programs in educational settings.

Neodymium upgrading of tin slag using acid leaching and multistage precipitation

Tin metal production in Indonesia increased in 2018 and 2019. As the production of tin metal increases, the production of slag from tin smelting also increases. Tin slag can be further utilized by extracting valuable metals from it, such as rare earth elements (REE). One of the rare earth elements observed in this study is neodymium (Nd). The use of Nd includes the manufacture of Nd-based permanent magnets. According to individual REE oxide demand and production data in the world, the supply of Nd2O3 in the world is experiencing a shortage of 2,025 tons per year. The purpose of this study is to determine the optimal concentration and ratio of solids to nitric acid in tin slag leaching after the alkaline fusion process and the pH of Nd precipitation. Alkaline fusion as a pre-treatment step to open the silica structure in the slag fusion was carried out for 2 h at 700 °Cwith a ratio of NaOH: slag (gram: gram) = 2:1 to produce frit, which will be leached using water. The residue from water leaching was followed by nitric acid leaching with a concentration variation of 1.75 M, 2.25 M, 2.75 M, 3.25 M, and 3.75 M, then continued with a solid percentage variation of 8%, 11%, 15%, 20%, and 26%. The nitric acid leaching filtrate was precipitated with ammonia at pH 6, 8, and 10. The results obtained were a percent of Nd extraction of 65.172% obtained at a concentration of 3.25 M, a solid percent ratio of 15% with a temperature of 60 °C, a grain size of −200 + 325 mesh, a stirring speed of 250 rpm, and a leaching time of 60 min. Nd deposition efficiency occurred at a pH 6 of 47.79%. Increased Nd content from 1.036% to 3.257%.

Impact of Channel Aging on Dual-Function Radar-Communication Systems: Performance Analysis and Resource Allocation

In conventional dual-function radar-communication (DFRC) systems, the radar and communication channels are routinely estimated at fixed time intervals based on their worst-case operation scenarios. Such situation-agnostic repeated estimations cause significant training overhead and dramatically degrade the system performance, especially for applications with dynamic sensing/communication demands and limited radio resources. In this paper, we leverage the channel aging characteristics to reduce training overhead and to design a situation-dependent channel re-estimation interval optimization-based resource allocation in a multi-target tracking DFRC system. Specifically, we exploit the channel temporal correlation to predict radar and communication channels for reducing the need for training preamble retransmission. Then, we characterize the channel aging effects on the Cramer-Rao lower bounds (CRLBs) for radar tracking performance analysis and achievable rates with maximum ratio transmission (MRT) and zero-forcing (ZF) transmit beamforming for communication performance analysis. In particular, the aged CRLBs and achievable rates are derived as closed-form expressions with respect to the channel aging time, bandwidth, and power. Based on the analyzed results, we optimize these factors to maximize the average total aged achievable rate subject to individual target tracking precision demand, communication rate requirement, and other practical constraints. Since the formulated problem belongs to a non-convex problem, we develop an efficient one-dimensional search based optimization algorithm to obtain its suboptimal solutions. Finally, simulation results are presented to validate the correctness of the derived theoretical results and the effectiveness of the proposed allocation scheme.

Pupil-linked arousal modulates network-level EEG signatures of attention reorienting during immersive multitasking

Objective. When multitasking, we must dynamically reorient our attention between different tasks. Attention reorienting is thought to arise through interactions of physiological arousal and brain-wide network dynamics. In this study, we investigated the relationship between pupil-linked arousal and electroencephalography (EEG) brain dynamics in a multitask driving paradigm conducted in virtual reality. We hypothesized that there would be an interaction between arousal and EEG dynamics and that this interaction would correlate with multitasking performance. Approach. We collected EEG and eye tracking data while subjects drove a motorcycle through a simulated city environment, with the instructions to count the number of target images they observed while avoiding crashing into a lead vehicle. The paradigm required the subjects to continuously reorient their attention between the two tasks. Subjects performed the paradigm under two conditions, one more difficult than the other. Main results. We found that task difficulty did not strongly correlate with pupil-linked arousal, and overall task performance increased as arousal level increased. A single-trial analysis revealed several interesting relationships between pupil-linked arousal and task-relevant EEG dynamics. Employing exact low-resolution electromagnetic tomography, we found that higher pupil-linked arousal led to greater EEG oscillatory activity, especially in regions associated with the dorsal attention network and ventral attention network (VAN). Consistent with our hypothesis, we found a relationship between EEG functional connectivity and pupil-linked arousal as a function of multitasking performance. Specifically, we found decreased functional connectivity between regions in the salience network (SN) and the VAN as pupil-linked arousal increased, suggesting that improved multitasking performance at high arousal levels may be due to a down-regulation in coupling between the VAN and the SN. Our results suggest that when multitasking, our brain rebalances arousal-based reorienting so that individual task demands can be met without prematurely reorienting to competing tasks.

A multilevel analysis of educational transition rates at secondary level in sub-Saharan Africa

Estimates show that, in 2019, only 41 per cent of students completed lower secondary and 28 per cent upper secondary education in the sub-Saharan Africa (SSA) region (UNESCO, 2021). One of the reasons for the low completion rates is the poor transition across secondary education due to the significant impact of factors at individual, household, and community levels shaping demand and supply constraints. This article employs a three-level logit to investigate the key determinants for transitions and their variability across countries and communities, and explores whether less wealth inequality is at odds with increasing countries’ and communities’ performances. It finds that variation on transition rates is 40–50 per cent larger between communities within countries than between countries themselves, and that heterogeneity is larger for upper secondary transition. Leading sources of inequality are wealth, gender, and communities’ prevalence of early marriage. Further, the article finds that the equity–performance trade-off does not hold across countries, but it does at the community level where communities with stronger rates of transitions are more unequal. The analysis suggests policies to improve SSA youth chances to move up to the next level of secondary education, starting for narrowing heterogeneity across communities, boosting chances for the poorest groups and female youth living in communities with weak social norms, and measures to diminish the impact of community wealth on their transition performance.

Mothers' job crafting and work-to-family enrichment: a self-concept perspective

PurposeBased on the self-concept perspective, the purpose of this paper is to explore the process of working mothers who conduct job crafting to build new role identities and self-efficacy, which ultimately affects work-to-family enrichment. The paper further explores the moderating role of inclusive leadership.Design/methodology/approachThe authors collected data at two time points. The sample for the study consisted of 216 professional working mothers in China who returned to work after the birth of their first child.FindingsResults show that working mothers' job crafting had a positive effect on work-to-family enrichment. In addition, working mothers' role identity and role self-efficacy played mediating roles between the links. Finally, inclusive leadership moderates the indirect effect of task crafting and relational crafting on work-to-family enrichment through role identity and role self-efficacy. The positive indirect relationships are stronger in high levels of inclusive leadership.Practical implicationsThe employers should provide opportunities, support, and freedom for working mothers to craft their jobs according to their individual demands for better self and home outcomes.Originality/valueThis study focuses on the job crafting of working mothers. The authors extend the consequences of job crafting to the nonwork domain. In addition, this study uncovers the influence of job crafting from the perspective of self-concept (i.e. role identity and role self-efficacy). Furthermore, the authors demonstrate the moderating role played by inclusive leadership in this process.

Review and Evaluation of Reinforcement Learning Frameworks on Smart Grid Applications

With the rise in electricity, gas and oil prices and the persistently high levels of carbon emissions, there is an increasing demand for effective energy management in energy systems, including electrical grids. Recent literature exhibits large potential for optimizing the behavior of such systems towards energy performance, reducing peak loads and exploiting environmentally friendly ways for energy production. However, the primary challenge relies on the optimization of such systems, which introduces significant complexities since they present quite dynamic behavior. Such cyberphysical frameworks usually integrate multiple interconnected components such as power plants, transmission lines, distribution networks and various types of energy-storage systems, while the behavior of these components is affected by various external factors such as user individual requirements, weather conditions, energy demand and market prices. Consequently, traditional optimal control approaches—such as Rule-Based Control (RBC)—prove inadequate to deal with the diverse dynamics which define the behavior of such complicated frameworks. Moreover, even sophisticated techniques—such as Model Predictive Control (MPC)—showcase model-related limitations that hinder the applicability of an optimal control scheme. To this end, AI model-free techniques such as Reinforcement Learning (RL) offer a fruitful potential for embedding efficient optimal control in cases of energy systems. Recent studies present promising results in various fields of engineering, indicating that RL frameworks may prove the key element for delivering efficient optimal control in smart buildings, electric vehicle charging and smart grid applications. The current paper provides a comprehensive review of RL implementations in energy systems frameworks—such as Renewable Energy Sources (RESs), Building Energy-Management Systems (BEMSs) and Electric Vehicle Charging Stations (EVCSs)—illustrating the benefits and the opportunities of such approaches. The work examines more than 80 highly cited papers focusing on recent RL research applications—between 2015 and 2023—and analyzes the model-free RL potential as regards the energy systems’ control optimization in the future.

Prioritizing decision-making of health and well-being response tactics: Incorporating organizational and individual shared demands.

As a major energy source in China, the occupational health and well-being (OHW) of miners is a priority. Various statistical techniques have been used to identify factors or assess OHW to provide valuable information for the implementation of health promotion activities. The main bottleneck is the limited focus on solutions that address the demands of both organizations and individuals, and scientific and effective decision-making is pending. Therefore, this study describes the OHW mechanism covering both antecedents and consequences through the driving force-pressure-state-impact-response model. A probabilistic model of management tradeoff analysis was established by using a Bayesian decision network. Causal relationships and dependencies between multiple factors are captured visually. The model was verified and applied with samples of miners (N=816). The results showed that the comprehensive strategy (R5) was the best tactic, and the management effect of stress (R2) and vulnerability (R3) was prominent. This study provides a valuable tool for managers to identify priority management factors. Prioritizing tactics formulated from dual demands of organizational and individual can ensure project feasibility, operability, and effectiveness. This study is a novel attempt to combine theory with practice, which is timely and necessary for management.

БАЛАНСИРОВКА ФАЗНЫХ НАГРУЗОК В НИЗКОВОЛЬТНЫХ ТРЕХФАЗНЫХ ЧЕТЫРЕХПРОВОДНЫХ СЕТЯХ

The imbalance of phase loads is a natural negative property of a low-voltage three-phase four-wire network due to uneven connection to the phases and the difference in individual consumer demand for electricity. The imbalance of phase loads in the network nodes leads to a decrease in the quality of electricity, overestimated technical losses in the network and to current overloads in one of the phases of power lines. In the paper, balancing of phase loads in the network is performed using a heuristic algorithm based on average hourly readings of loads and voltages readings of consumer smart meters. The effectiveness of the algorithm was evaluated by reducing losses, maximum currents in the phase wires and the neutral wire in a real feeder of the electrical network

Day-ahead forecasting of residential electric power consumption for energy management using Long Short-Term Memory encoder–decoder model

Energy management in smart buildings and energy communities needs short-term load demand forecasting for optimization-based scheduling, dispatch, and real-time operation. However, producing accurate forecasting for individual residential households is more challenging compared to the forecasting of load demand at the distribution level, which is smoother and benefits from statistical compensation of errors. This paper presents a day-ahead forecasting technique for individual residential load demand that is based on the Long Short-Term Memory encoder–decoder architecture, which is extended to consider possibly differing sets of past and future exogenous variables. A novel focus is posed on the validation of the proposed approach considering that it is tailored for use by energy management systems. A publicly available dataset was used for validation, and the approach was compared with three other methods, resulting in a reduction of the Mean Absolute Scaled Error by up to 8%.

The Unique Practice of the Clinical Nurse Specialist.

The Unique Practice of the Clinical Nurse Specialist.

물류 부동산의 입지결정요인에 관한 연구

As the frequency of face-to-face transactions decreased, offline commerce decreased, and as online commerce continued to increase, the e-commerce industry, such as Coupang, also grew in earnest. As the logistics demand of individuals and companies increases together, logistics industry officials are continuously striving to develop new logistics real estate and expand its scale in order to respond to customer demand. Accordingly, a survey was conducted on executives and staff members in the logistics industry to derive the main factors in determining the location of the logistics real estate, and to provide correct information to suppliers and consumers. In this study location factor data was collected through a questionnaire survey method targeting executives and employees in the logistics industry, and based on this, potential factors for logistics real estate location were analyzed and major location determinants were derived. Exploratory factor analysis was performed based on survey data on logistics and real estate locations. As a result of factor analysis on logistics and real estate locations, it was found that latent factors for measurement items were grouped into 7 latent factors based on the explanatory power of the total variance of about 60%.