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without the requirement of a pricey instrument or highly-trained personnel.

Just five or six years ago, the term "green building" evoked visions of barefoot, tie-dyed, granola-munching denizens. There's been a large shift in perception. Of course, green buildings are still known for conserving natural resources by, for example, minimizing on-site grading, using alternative materials, and recycling construction waste. But people now see the financial advantages as well. Well-designed green buildings yield lower utility costs, greater employee productivity, less absenteeism, and stronger attraction and retention of workers than standard buildings do. Green materials, mechanical systems, and furnishings have become more widely available and considerably less expensive than they used to be-often cheaper than their standard counterparts. So building green is no longer a pricey experiment; just about any company can do it on a standard budget by following the ten rules outlined by the author. Reliable building-rating systems like the U.S. Green Building Council's rigorous Leadership in Energy and Environmental Design (LEED) program have done much to underscore the benefits of green construction. LEED evaluates buildings and awards points in several areas, such as water efficiency and indoor environmental quality. Other rating programs include the UK's BREEAM (Building Research Establishment's Environmental Assessment Method) and Australia's Green Star. Green construction is not simply getting more respect; it is rapidly becoming a necessity as corporations push it fully into the mainstream over the next five to ten years. In fact, the author says, the owners of standard buildings face massive obsolescence. To avoid this problem, they should carry out green renovations. Corporations no longer have an excuse for eschewing environmental and economic sustainability. They have at their disposal tools proven to lower overhead costs, improve productivity, and strengthen the bottom line.

Every company makes choices about the channels it will use to go to market. Traditionally, the decision to sell through a discount superstore or a pricey boutique, for instance, was guided by customer demographics. A company would identify a target segment of buyers and go with the channel that could deliver them. It was a fair assumption that certain customer types were held captive by certain channels--if not from cradle to grave, then at least from initial consideration to purchase. The problem, the authors say, is that today's customers have become unfettered. As their channel options have proliferated, they've come to recognize that different channels serve their needs better at different points in the buying process. The result is "value poaching." For example, certain channels hope to use higher margin sales to cover the cost of providing expensive high-touch services. Potential customers use these channels to do research, then leap to a cheaper channel when it's time to buy. Customers now hunt for bargains more aggressively; they've become more sophisticated about how companies market to them; and they are better equipped with information and technology to make advantageous decisions. What does this mean for your go-to-market strategy? The authors urge companies to make a fundamental shift in mind-set toward designing for buyer behaviors, not customer segments. A company should design pathways across channels to help its customers get what they need at each stage of the buying process--through one channel or another. Customers are not mindful of channel boundaries--and you shouldn't be either. Instead, they are mindful of the value of individual components in your channels--and you should be, too.

Practitioners often navigate LLM performance trade-offs by plotting Pareto frontiers of optimal accuracy-cost trade-offs. However, this approach offers no way to compare between LLMs with distinct strengths and weaknesses: for example, a cheap, error-prone model vs a pricey but accurate one. To address this gap, we propose economic evaluation of LLMs. Our framework quantifies the performance trade-off of an LLM as a single number based on the economic constraints of a concrete use case, all expressed in dollars: the cost of making a mistake, the cost of incremental latency, and the cost of abstaining from a query. We apply our economic evaluation framework to compare the performance of reasoning and non-reasoning models on difficult questions from the MATH benchmark, discovering that reasoning models offer better accuracy-cost tradeoffs as soon as the economic cost of a mistake exceeds \$0.01. In addition, we find that single large LLMs often outperform cascades when the cost of making a mistake is as low as \$0.1. Overall, our findings suggest that when automating meaningful human tasks with AI models, practitioners should typically use the most powerful available model, rather th

Large Language Models (LLMs) have revolutionized automated program repair (APR) but current benchmarks like SWE-Bench predominantly focus on userspace applications and overlook the complexities of kernel-space debugging and repair. The Linux kernel poses unique challenges due to its monolithic structure, concurrency, and low-level hardware interactions. Prior efforts such as KGym and CrashFixer have highlighted the difficulty of APR in this domain, reporting low success rates or relying on costly and complex pipelines and pricey cloud infrastructure. In this work, we introduce RGym, a lightweight, platform-agnostic APR evaluation framework for the Linux kernel designed to operate on local commodity hardware. Built on RGym, we propose a simple yet effective APR pipeline leveraging specialized localization techniques (e.g., call stacks and blamed commits) to overcome the unrealistic usage of oracles in KGym. We test on a filtered and verified dataset of 143 bugs. Our method achieves up to a 43.36% pass rate with GPT-5 Thinking while maintaining a cost of under $0.20 per bug. We further conduct an ablation study to analyze contributions from our proposed localization strategy, prompt

Diabetes mellitus is a chronic metabolic disorder that has emerged as one of the major health problems worldwide due to its high prevalence and serious complications, which are pricey to manage. Effective management requires good glycemic control and regular follow-up in the clinic; however, non-adherence to scheduled follow-ups is very common. This study uses the Diabetes 130-US Hospitals dataset for analysis and prediction of readmission patients by various traditional machine learning models, such as XGBoost, LightGBM, CatBoost, Decision Tree, and Random Forest, and also uses an in-house LSTM neural network for comparison. The quality of the data was assured by preprocessing it, and the performance evaluation for all these models was based on accuracy, precision, recall, and F1-score. LightGBM turned out to be the best traditional model, while XGBoost was the runner-up. The LSTM model suffered from overfitting despite high training accuracy. A major strength of LSTM is capturing temporal dependencies among the patient data. Further, SHAP values were used, which improved model interpretability, whereby key factors among them number of lab procedures and discharge disposition were

Sign language is a visual language that enhances communication between people and is frequently used as the primary form of communication by people with hearing loss. Even so, not many people with hearing loss use sign language, and they frequently experience social isolation. Therefore, it is necessary to create human-computer interface systems that can offer hearing-impaired people a social platform. Most commercial sign language translation systems now on the market are sensor-based, pricey, and challenging to use. Although vision-based systems are desperately needed, they must first overcome several challenges. Earlier continuous sign language recognition techniques used hidden Markov models, which have a limited ability to include temporal information. To get over these restrictions, several machine learning approaches are being applied to transform hand and sign language motions into spoken or written language. In this study, we compare various deep learning techniques for recognising sign language. Our survey aims to provide a comprehensive overview of the most recent approaches and challenges in this field.

Road damage is an inconvenience and a safety hazard, severely affecting vehicle condition, driving comfort, and traffic safety. The traditional manual visual road inspection process is pricey, dangerous, exhausting, and cumbersome. Also, manual road inspection results are qualitative and subjective, as they depend entirely on the inspector's personal experience. Therefore, there is an ever-increasing need for automated road inspection systems. This chapter first compares the five most common road damage types. Then, 2-D/3-D road imaging systems are discussed. Finally, state-of-the-art machine vision and intelligence-based road damage detection algorithms are introduced.

Vietnam requires a sustainable urbanization, for which city sensing is used in planning and de-cision-making. Large cities need portable, scalable, and inexpensive digital technology for this purpose. End-to-end air quality monitoring companies such as AirVisual and Plume Air have shown their reliability with portable devices outfitted with superior air sensors. They are pricey, yet homeowners use them to get local air data without evaluating the causal effect. Our air quality inspection system is scalable, reasonably priced, and flexible. Minicomputer of the sys-tem remotely monitors PMS7003 and BME280 sensor data through a microcontroller processor. The 5-megapixel camera module enables researchers to infer the causal relationship between traffic intensity and dust concentration. The design enables inexpensive, commercial-grade hardware, with Azure Blob storing air pollution data and surrounding-area imagery and pre-venting the system from physically expanding. In addition, by including an air channel that re-plenishes and distributes temperature, the design improves ventilation and safeguards electrical components. The gadget allows for the analysis of the correlation between tr

Technical debt is a metaphor used to convey the idea that doing things in a "quick and dirty" way when designing and constructing a software leads to a situation where one incurs more and more deferred future expenses. Similarly to financial debt, technical debt requires payment of interest in the form of the additional development effort that could have been avoided if the quick and dirty design choices have not been made. Technical debt applies to all the aspects of software development, spanning from initial requirements analysis to deployment, and software evolution. Technical debt is becoming very popular from scientific and industrial perspectives. In particular, there is an increase in the number of related papers over the years. There is also an increase in the number of related tools and of their adoption in the industry, especially since technical debt is very pricey and therefore needs to be managed. However, techniques to estimate technical debt are inadequate, insufficient since they mostly focus on requirements, code, and test, disregarding key artifacts such as the software architecture and the technologies used by the software at hand. Besides, despite its high rele

Researchers discovered that electricity can dramatically reshape how heat flows through certain ceramic materials, increasing heat conduction by almost threefold in a preferred direction。 The unexpected result could lead to much more efficient cooling technologies and energy-saving devices

T-Mobile to restore free lines lost during plan migration, but price hikes remain