Flexibility or Power? A Difference-in-Differences Test of the Remote Work Wage Discount
Remote work has expanded substantially, giving workers greater flexibility while raising questions about whether differences in remote and on-site wages reflect workers' willingness to trade income for flexibility and how long such wage discounts last. The research question investigates if compensating differentials persist or whether the wage discount for remote work shrinks as remote job options in an occupation expand. A difference-in-differences framework was used to compare occupations experiencing relatively high and low growth in remote job opportunities. The analysis combines occupation-level remote job-posting data from WFH Map with wage data from the American Community Survey analysed by Pabilonia and Vernon. Occupational wage information from the U.S. Bureau of Labor Statistics is also used to provide additional wage context. The high-expansion occupations experienced a 6.62 percentage-point increase in their remote wage differential compared with a 1.05 percentage-point increase in low-expansion occupations, producing a +5.57 percentage-point difference-in-differences estimate, indicating that the relative wage position of remote workers improved more strongly where remote employment opportunities expanded, providing partial support for the monopsony mechanism. The relatively limited period and data constraints mean that longer-term changes in the remote labour market may be needed to determine whether the wage discount predicted by the monopsony hypothesis becomes more evident over time.
Published by: Nevaan Vijayvergia
Author: Nevaan Vijayvergia
Paper ID: V12I5-1199
Paper Status: published
Published: October 16, 2026
Genetically Modified Crops: Benefits, Risks, and Future Prospects
Genetically modified (GM) crops are an important application of agricultural biotechnology, developed to introduce selected traits that may improve crop performance, quality, or resilience. This review examines the potential benefits, limitations, and future prospects of GM crops, with attention to their relevance for food security and sustainable agriculture. Desired traits can include resistance to particular insect pests, tolerance to specific herbicides, improved nutritional composition, and selected forms of tolerance to environmental stress. Depending on the crop, trait, and farming system, these technologies may help reduce losses and, in some cases, lower the use of certain insecticides. However, outcomes are not uniform: they depend on local ecology, crop management, resistance-management practices, and access to appropriate seeds and knowledge. Important issues include gene flow, the evolution of resistant pests or weeds, possible effects on non-target organisms, biodiversity, seed affordability, market access, and public confidence. Food and environmental safety therefore require case-by-case assessment based on the specific crop and trait, alongside transparent regulation and monitoring. Newer approaches, including CRISPR-based genome editing, may expand the range of traits available for crop improvement, although their benefits and risks also need careful evaluation. Overall, GM crops should be assessed using scientific evidence and local agricultural conditions rather than treated as universally beneficial or harmful. Their responsible use may contribute to resilient food systems when integrated with sound agronomy, biodiversity protection, and fair access for farmers.
Published by: Nirmala Choudhary
Author: Nirmala Choudhary
Paper ID: V12I5-1198
Paper Status: published
Published: October 15, 2026
Evaluating Machine Learning Signal Validation in Bollinger Band Mean-Reversion Trading: A Strategy-Dependent Experimental Study
Abstract Financial markets exhibit changing volatility and regime-dependent behavior, making the interaction between deterministic trading rules and machine-learning validation an important empirical question. This study evaluates a Bollinger Band Mean Reversion (BBMR) strategy and examines whether a generic machine-learning filtering layer improves the characteristics of its candidate signals. The supplied experimental registry contains 30 independent configurations: five unfiltered baseline configurations and 25 ML-assisted configurations formed by crossing five timeframes—1-minute, 3-minute, 5-minute, 15-minute and 1-hour—with five confidence settings of 0.50, 0.53, 0.55, 0.57 and 0.60. Across the registry, 99,807 recorded orders are reported. The five baseline configurations show a descriptive aggregate win percentage of 66.55% and a row-wise Net Realized sum of 316,612.56. The 25 ML-assisted configurations show a descriptive aggregate win percentage of 19.85% and a row-wise Net Realized sum of -135,285.70. Increasing the ML confidence setting reduces recorded activity from 15,278 orders at 0.50 to 2,373 at 0.60, but the reduction in activity is not accompanied by a corresponding improvement in win percentage or aggregate descriptive profitability. The ML-assisted win percentage remains below the baseline at every tested timeframe, and only the 1-hour ML group has a positive row-wise Net Realized sum. An implementation-level inspection further indicates that the validation feature layer is dominated by generic contextual variables such as moving-average slope, momentum, volatility and risk/reward measures rather than direct Bollinger mean-reversion descriptors. The findings therefore suggest a strategy-dependent limitation: the tested ML filter changes the trade population substantially, but does not improve the evaluated BBMR strategy and may be mismatched to the short-horizon mean-reversion mechanism. The result should not be generalized to all ML methods or all BBMR implementations; it is an empirical finding for the supplied configuration. Keywords: Bollinger Bands, Mean Reversion, Machine Learning, Signal Validation, Algorithmic Trading, Feature Engineering, Multi-Timeframe Analysis, Backtesting, Strategy-Dependent Validation.
Published by: Venkata Maheswara Rao Atchula, Dr. Pawan Kumar Pareek
Author: Venkata Maheswara Rao Atchula
Paper ID: V12I5-1189
Paper Status: published
Published: October 13, 2026
A Simplex based re-evaluation of Goal Programming in Product Mix Optimization Project Report
The most profitable use of a manufacturing company's scarce machine time occurs when the company is operating in the most efficient manner. The product mix problem of XYZ Company, a manufacturer of suits, jackets and trousers is used in this paper to illustrate the real-life application of the Simplex methodology of linear programming. The processing time, weekly station capacity, demand limits, selling prices and raw material costs are from a published case study. The goal is to determine the optimal level of production for each product per week in order to maximize profit. The model contains 3 decision variables, 11 constraints: 8 production stations and 3 demand limits. This was solved by using the Simplex tableau iterations and verified using the Microsoft Excel Solver. The best plan is to sell 476.23 suits, 753.75 jackets, and 600 trousers each week and the maximum weekly profit is ₺612,017.30. The demand limit for trousers is fully utilized and two stations are used: Model Preparation and Slaughterhouse. The shadow prices indicate that an additional minute at Slaughterhouse contributes approximately ₺236.31 to the weekly profit, whereas the other six stations have idle time. The results reveal that the Simplex method not only provides a production plan, but also provides a definite direction as to where additional capacity is worth purchasing.
Published by: Hridaay Chheda, Ishaan Chaskar, Ishan Agarwal, Jannat Sachdeva, George Augustine
Author: Hridaay Chheda
Paper ID: V12I5-1195
Paper Status: published
Published: October 13, 2026
Climate Change and Sustainable Economic Growth: Challenges, Opportunities, and Policy Propositions
Climate change has become one of the major challenges facing economic development across the world. Economic growth has improved living standards and reduced poverty, but activities such as industrialization, fossil fuel use, deforestation, and intensive agriculture have also increased pressure on natural resources and the environment. This paper examines the relationship between climate change and sustainable economic growth and explores how economies can continue to develop while protecting the environment and improving social well-being. The study is based on secondary data collected from books, academic journals, government publications, and reports of international organizations such as the IPCC, UNDP, World Bank, IEA, and UNEP. A descriptive and analytical approach is used to understand the economic effects of climate change and possible pathways towards sustainability. The study finds that climate change affects several important areas of economic activity, including agriculture, industrial production, infrastructure, public health, labor productivity, and financial stability. Climate-related risks can also slow progress towards several Sustainable Development Goals, particularly those related to poverty, food security, health, water, energy, employment, sustainable cities, and climate action. The paper highlights the importance of combining climate mitigation with adaptation measures to reduce these risks. It further discusses the green economy as a practical pathway towards sustainable growth through renewable energy, efficient use of resources, cleaner technologies, green innovation, and environmental protection. The study argues that economic growth and environmental protection should not be treated as competing objectives. Instead, sustainable policies can help economies achieve growth while building resilience, creating new opportunities, protecting natural resources, and improving the quality of life. A balanced approach that brings together economic progress, environmental responsibility, and social inclusion is therefore essential for achieving sustainable development for present and future generations.
Published by: Dr. Priyadarshini Hapse
Author: Dr. Priyadarshini Hapse
Paper ID: V12I5-1180
Paper Status: published
Published: October 13, 2026
Evaluation of Stress Management Behaviours and Psychological Well-being
Stress is a multidimensional psychological and physiological response that occurs when individuals perceive that environmental demands exceed their available coping resources. Although short-term stress may support motivation and adaptive functioning, persistent or poorly managed stress can negatively influence emotional health, concentration, sleep, interpersonal relationships, academic or occupational functioning, and overall quality of life. The present study evaluated stress-management behaviors and psychological well-being using responses obtained through a structured survey. The analysis included 210 consenting participants. Demographic characteristics, perceived stress experiences, coping behaviors, social support, lifestyle practices, and indicators of psychological well-being were examined using descriptive and inferential statistical methods. Mean, median, standard deviation, standard error, frequency, and percentage distributions were calculated. Composite stress-burden and perceived-support indicators were also examined. Pearson and Spearman correlation analyses were used to investigate relationships between continuous or ordinal variables, while chi-square tests examined associations between categorical variables. Exploratory analysis also included one-way analysis of variance and internal-consistency assessment where appropriate. The mean stress-burden score was 6.62, with a median of 7.00, a standard deviation of 2.68, and a standard error of approximately 0.19. Pearson correlation analysis demonstrated a small positive association between the examined variables (r = .138, p = .045). Chi-square analysis identified statistically significant associations between age group and high-stress classification (χ² = 11.831, p = .008) and between work/study duration and high-stress classification (χ² = 9.343, p = .025). These findings suggest that stress experiences are not distributed uniformly across demographic and lifestyle groups. The findings also support the importance of strengthening adaptive stress-management behaviors, social connectedness, healthy routines, physical activity, adequate sleep, and accessible psychological support. Because the study used a cross-sectional survey design, the findings should be interpreted as associations rather than evidence of causation.
Published by: Bhumi Gajendra Musale, Bindiya Sharma, Joana Johnson
Author: Bhumi Gajendra Musale
Paper ID: V12I5-1188
Paper Status: published
Published: October 9, 2026
