Search Results (474)

The integration of photovoltaic generation, battery storage, electric vehicles, smart appliances, and dynamic electricity pricing has made residential energy management a challenging real-time optimization problem. Conventional demand-side management methods often depend on fixed rules and are less effective under uncertain solar generation, changing tariffs, and variable user demand. To address this issue, this paper proposes a Proximal Policy Optimization-based deep reinforcement learning framework for smart home energy management. The proposed PPO controller learns adaptive scheduling decisions using real-time PV output, electricity price, battery state of charge, EV charging status, and appliance operating conditions. The controller coordinates shiftable, controllable, and non-shiftable loads while reducing electricity cost and maintaining user comfort. The proposed method is compared with DDPG and TRPO. Simulation results show that PPO reduces the average daily energy cost by 4.7% compared with TRPO and 8.3% compared with DDPG. The results confirm that PPO is an effective and stable approach for real-time residential demand-side management. Full article

The Corporate Sustainability Reporting Directive (CSRD) extends mandatory, assured and standardised sustainability reporting to a European reporting population several times larger than that of its predecessor, on the premise that such disclosure is priced by capital markets. This paper examines whether equity prices responded to the five legislative and standard-setting milestones through which the mandate became public between April 2021 and July 2023. German DAX constituents falling within the scope of Article 19a are compared with matched S&P 500 firms, using an annual difference-in-differences design and a daily market model event study. The annual estimator yields a positive and significant coefficient of +0.204 that is robust to alternative specifications, standard error corrections and influence diagnostics. Four diagnostics nevertheless indicate that it does not identify a regulatory effect: the same design applied to year pairs containing no CSRD or ESRS event yields estimates of comparable magnitude and mixed sign; parallel pre-trends are rejected; the coefficient is concentrated among poorly matched firm pairs and falls to +0.046 once a caliper is imposed; and the design is underpowered for effects of the magnitude it reports. The event study, which measures each firm against its own home market and therefore does not rely on the cross-country comparison, detects no abnormal return at any milestone once multiple testing and cross-sectional dependence are taken into account: the cumulative 3-day reaction across all five events is +0.8 percentage points, with a 95% confidence interval of [−2.5, +4.0], which excludes a repricing of the magnitude the annual estimate implies. The paper contributes a set of design diagnostics that distinguish an identified estimate from one that is merely stable, and shows that the two-country annual comparisons common in this literature do not survive them. Full article

With the advancement of net-zero targets and the large-scale deployment of distributed photovoltaic (PV) systems, battery energy storage systems (BESS), and electric vehicles (EVs) in residential sectors, home energy management systems (HEMS) have become central to improving end-use energy efficiency. However, reliance on synthetic data, simplified and homogeneous load modeling, and lack of coordination between dynamic pricing mechanisms and multi-device scheduling, make them practically inefficient. Furthermore, most studies address only unilateral user-side optimization while neglecting the distribution network operational constraints and omitting rigorous anti-arbitrage mechanisms to preclude speculative user behavior. To address these gaps, this paper proposes a data-driven coordinated scheduling framework for residential PV-BESS and multi-device systems formulated within bi-level game-theoretic architecture. The upper-level employs particle swarm optimization (PSO) to determine dynamic additional price signals for peak shaving and distribution network security, with explicit constraints on distribution transformer capacity, node voltage deviation, and load ramp rate adapted to three-user scenarios. The lower-level formulates a mixed-integer quadratic programming (MIQP) model to achieve multi-objective optimization of user electricity cost, thermal comfort, device usage preference, battery cycle degradation, and end-of-cycle energy balance. Simulation results for representative summer and winter days indicate that the proposed framework reduces user-side electricity cost by around 30%, elevates PV self-consumption rate to over 70%, and achieves about 20% peak load reduction with around 15% peak-valley difference narrowing on the grid side. All distribution network security constraints and anti-arbitrage rules are strictly satisfied. The framework effectively reconciles the objectives of both residential users and the distribution grid. Full article

Background: The growth of e-commerce has intensified last-mile delivery challenges, including failed deliveries, delivery-time uncertainty, and pressure on urban logistics. Smart parcel lockers (SPLs) offer a technology-enabled out-of-home delivery solution, yet limited evidence explains consumer adoption in Saudi Arabia. This study examines which factors motivate Saudi consumers to adopt SPLs, how trust shapes adoption intention, and whether perceived risk affects intention, using an extended UTAUT2 framework. Methods: Data were collected through an online self-administered questionnaire from 415 residents in Saudi Arabia, and the model was analyzed using partial least squares structural equation modelling (PLS-SEM). Results: Performance expectancy was the strongest determinant of behavioral intention. Effort expectancy, social influence, trust, facilitating conditions, and hedonic motivation also had significant positive effects, whereas price value and perceived risk did not directly influence intention. Conclusions: The study contributes to SPL adoption literature by validating an extended UTAUT2 model in an underexamined Saudi context and highlighting the role of trust in technology-enabled LMD acceptance. Full article

With the promotion of public service infrastructure and the construction of intelligent communities, parcel lockers have become an effective alternative for last-mile delivery. But express companies still face strategic questions about whether to cooperate with third-party locker platforms, use advertising-supported platforms, or build their own platforms. This study examines how express companies and parcel locker operators should design pricing, advertising, and ownership strategies from a platform-economy perspective. Employing Stackelberg game models, four operational modes are analyzed: home delivery only, cooperation with a third-party platform, cooperation with an advertising-supported platform, and operating a self-built platform. We explore parcel locker strategies by considering customer-to-advertiser network externality and their impact on the pricing, advertising, and ownership decisions of parcel locker platforms. Comparative analyses show that introducing platform delivery increases express companies’ profitability and expands total delivery demand, while advertising can further reduce platform delivery fees and increase the profits of both the express company and the parcel locker platform. However, the self-built decision depends mainly on self-building and operating cost, customers’ perceived convenience, waiting cost, service substitutability, customer-to-advertiser network externality, service area, and locker capacity sufficiency. This study explains the economic and operational mechanisms through which parcel locker platforms may support more efficient and potentially more sustainable last-mile urban logistics. Full article

Social and economic satisfaction gaps are theoretical differences between a resident’s socially or monetarily most preferred affordable home attributes and the actual attributes of their current home. In this study, the satisfaction gaps of two samples of respondents are predicted with their social utility data for preferred attributes in 1987 and 2020, in conjunction with the asking or sale prices and comparable attributes of single detached or similar homes merged with small-area census data for periods up to those dates. Average social and economic gaps of less than 20% affirm respondents’ overall satisfaction with their current homes, most of whom were recent movers. However, wider social satisfaction gaps among the minority of residents may reflect dissatisfaction with new suburban locations in one sample and older inner city housing in the other. Four practical and theoretical contributions of the utility modelling of residential satisfaction gaps in objective and subjective home attributes, as opposed to direct surveying of residential satisfaction, are concluded. Full article

Polymers based on tricyclic decane skeleton in the role of high-performance polycarbon, polyester, polyacrylate, etc., are used in optical equipment, dental restoration, photoresist, and other fields because of their rigid ring structure and corresponding excellent heat/weather/impact/scratch resistance. The preparation process of monomer tricyclodidecane dimethanol is complex and has engineering safety problems. Also, it has been monopolized by a few enterprises for a long time, and the price is expensive. There is a lack of systematic reviews on the synthesis of tricyclodecane dimethanol. In this paper, focusing on the preparation process of tricyclic decane dimethanol, the preparation process of bicyclic decane dimethanol to be prepared by dicyclopentadiene is summarized, including the reaction path, catalytic system and separation method, and the homogeneous catalysis, aqueous/organic two-phase catalysis and heterogeneous catalysis in the hydroformylation of high-carbon olefins are discussed, as well as the difference between stripping, extraction, membrane separation and other methods in the separation methods of catalyst and product. Then, the current research status at home and abroad is summarized, and the advantages and disadvantages of the above reaction methods are analyzed according to the reaction system, catalyst used, solvent, reaction conditions, and final reaction level. Finally, the downstream application and market of tricyclic decane dimethanol are analyzed. It provides a reference for the design and optimization of the preparation process of tricyclodecane dimethanol. Full article

This paper evaluates residential energy management systems (EMSs) that combine on-site renewable generation and battery energy storage in an all-electric house. This work compares four levels of control complexity: baseline operation, deterministic rule-based control, an optimization-based benchmark, and adaptive control using machine learning, predictive control, and a transactive framework. A calibrated gray-box house model based on the Archetype Sustainable House in Vaughan, Ontario, was used to test each strategy under the same operating assumptions. The comparison shows a clear trade-off between simplicity and performance. Deterministic load-shifting strategies are easy to implement but deliver the lowest savings. The optimized controller provides a practical upper bound on achievable performance. The machine-learning controller, trained from optimized historical operation, produced the strongest annual savings and outperformed deterministic control by a range of about 15–22%. Predictive control showed promise, but its demonstration was limited by forecast-data quality; more than 40% of collected forecast files were unusable, leaving only a 10-day continuous case study. A transactive energy management system delivered moderate direct savings, but its main value was flexibility, agent-based coordination, and future applicability to community-scale control. Experimental work further showed that 98% of an air-source heat pump peak-hour load could be shifted using battery control hardware. Despite these technical benefits, this study finds that battery-supported residential EMSs remain financially unattractive under the electricity prices and battery costs considered here. The results suggest that the most realistic path forward is not a one-size-fits-all controller, but a staged transition from simple battery logic to adaptive and transactive control as hardware prices fall, data quality improves, and homes become more connected. Full article

Demand-side management (DSM) is a security-critical function in residential smart grids. The same communication and sensing infrastructure that enables fine-grained load flexibility also exposes schedulers to corrupted measurements, price manipulation, and delayed control signals. Conventional DSM formulations generally treat cyber and communication impairments as external disturbances, which are addressed only after the schedule has already been calculated. This study proposes and evaluates Cyber-Resilient and QoS-Aware Demand-Side Management (CQ-DSM) as a hierarchical optimization framework that embeds cyber-risk likelihood and communication quality-of-service (QoS) directly into the scheduling objective. Local home energy management systems (HEMSs) solve mixed-integer linear programs at the appliance level, and central aggregators broadcast compact coordination signals based on real-time prices, measured QoS, and a sliding-window GRU-feature MLP risk estimator. The key intuition is to convert uncertainty about trust and actuation reliability into scheduling prices: high cyber risk discourages exposed loads during vulnerable periods, whereas poor QoS increases the value of locally preserving thermal flexibility. Under the simulation conditions (NYISO August pricing, P = 50 prosumers, Seed 42), CQ-DSM reduces overall system costs by 5.75% and imbalance procurement costs relative to an attack-unaware baseline under normal operation, limits the FDI-induced cost increase to 0.46% versus 0.83% (44% reduction in cost overrun), and reduces thermal-violation penalties by 81% under degraded QoS. The ablation results are consistent with cyber-risk pricing and QoS-aware fallback being complementary rather than redundant under the scenarios tested. Full article

This paper develops a theory-first framework explaining how tariff policy reshapes participation and position in global value chains (GVCs). Building on the input–output price model and value-added trade accounting, we formalize the tariff cascade and introduce three portable diagnostics: the Tariff Propagation Multiplier (TPM), the Backward/Forward Tariff Elasticity Decomposition (BFTED), and the Stage-Shift Metric (SSM). We derive sign-robust propositions and provide aggregate-level empirical evidence consistent with the cascade mechanism using a dyadic panel of 260,473 observations spanning 1999–2018. Results show that home input tariffs significantly compress both backward and forward GVC participation, and that apparent GVC upgrading frequently reflects measurement composition rather than genuine technological relocation. A policy simulation calibrated to 25% tariff escalation scenario projects significant participation losses, with Canada’s high-exposure manufacturing sectors facing amplified cascade effects due to their dense cross-border input linkages. The framework offers actionable diagnostics for trade and industrial policy in an era of renewed protectionism. Full article

This paper presents a comprehensive spatiotemporal decomposition of equity returns for nine top-weighted constituents of the Dow Jones Industrial Average (DJIA) over a twenty-year period spanning January 2004 through December 2023, encompassing 5033 trading days and multiple market regimes, including the Global Financial Crisis (2008–2009), the COVID-19 crash and recovery (2020), and the Federal Reserve tightening cycle (2022–2023). Daily price movements are systematically partitioned into two orthogonal sessions: the open-to-close (OTC, or daytime) session, capturing within-session price discovery, and the close-to-open (CTO, or overnight) session, capturing the accumulated information arrival and liquidity dynamics between market closes and subsequent opens. Within this bipartite return framework, we construct and rigorously evaluate 24 distinct trading strategies, spanning directional (long/short), neutral (cash), momentum (inertia), and contrarian (reversal) approaches, applied independently to each session or in combinatorial cross-session configurations. Each strategy is evaluated under three transaction cost regimes (0, 1, and 2 basis points per trade) using an initial investment of $100, and assessed using annualized return, annualised volatility, Sharpe ratio, Sortino ratio, and maximum drawdown. The study universe—comprising UnitedHealth Group (UNH), Goldman Sachs (GS), Microsoft (MSFT), Home Depot (HD), Caterpillar (CAT), Amgen (AMGN), McDonald’s (MCD), Salesforce (CRM), and Honeywell (HON)—captures cross-sector heterogeneity across Healthcare, Financials, Technology, Consumer Discretionary, Industrials, Biotech, and Consumer Staples. The universe is selected from the top-weighted DJIA constituents as of early 2026; the paper is, therefore, best read as a focused, in-depth case study of index-representative large-cap names rather than a general cross-sectional statement about all U.S. equities. The principal findings are threefold. First, the overnight session consistently delivers superior risk-adjusted performance: seven of nine stocks record higher Sharpe ratios during the overnight period versus the daytime period, with the mean overnight Sharpe ratio (0.662) substantially exceeding the mean daytime Sharpe ratio (0.357), a statistically and economically significant overnight premium. Second, the hybrid Strategy #18—Long Overnight coupled with Daytime Reversal—emerges as the dominant cross-asset configuration, generating portfolio values as high as $8464 from a $100 initial investment (AMGN; Sharpe: 0.991) over the 20-year horizon. Third, Trajectory Change Analysis reveals (i) Lévy-stable tails with a mean stability index $\overline{\alpha }=1.667$ across all constituents, substantially below the Gaussian benchmark of $\alpha =2.0$; (ii) Hurst exponents clustering below 0.5 ($\overline{H}=0.417$), confirming dominant mean-reverting dynamics; and (iii) positive rolling CAPM alpha in 51–79% of rolling windows, indicating persistent risk-adjusted outperformance above the S&P 500 benchmark. These findings provide a rigorous empirical foundation for session-aware algorithmic trading system design and challenge the prevailing assumption of temporal homogeneity in equity return processes. Full article

Background/Objectives. Country-level evidence on the economic footprint of home care nursing is still scarce, particularly in systems where tariffs for community-based nursing are lacking. In Italy, recent laws have expanded home care; yet planning and funding remain constrained by the absence of robust micro-costing evidence. Objectives. To estimate the accounting cost of home care nursing visits in Italy using a bottom-up micro-costing approach and to identify the main cost drivers influencing expenditure. Methods. A multicentre, cross-sectional study was conducted. Data were collected in two phases: (1) a national survey of 3949 home care nurses from 70 Local Health Authorities (April–October 2023), describing workload, travel time, and the most frequently performed activities; and (2) a time-and-motion study of 527 consecutive home visits performed by 83 nurses in three Local Health Authorities (March 2024). Direct costs were estimated from the Italian National Health Service perspective and included nursing time, travel time and transportation, back-office activities, and materials. Personnel costs were derived from national collective labour agreements and inflation-adjusted. A base-case scenario estimated accounting costs directly measured in the study. An extended, illustrative scenario explored the economic value of nursing activities by applying existing outpatient tariffs. Deterministic and probabilistic sensitivity analyses (10,000-iteration Monte Carlo simulation) were performed. Results. The mean accounting cost of home care nursing was €27.78 per patient per day. At the provider level, the corresponding daily cost per nurse was €190.00, assuming a mean caseload of 6.84 patients per nurse per shift. In the extended scenario, the imputed economic value of nursing activities increased the estimated daily cost to €120.81 per patient and €826.32 per nurse. Sensitivity analyses identified organizational factors (particularly the number of patients per shift and the number of activities per visit) as the dominant cost drivers, while material and transportation costs had a comparatively limited impact. Conclusions. Home care nursing in Italy appears to be delivered at a relatively low accounting cost, with organizational factors playing a greater role than unit prices in determining expenditure. The absence of a dedicated reimbursement framework for nursing activities may result in a substantial under-recognition of the economic value of home-based nursing care. These findings provide preliminary evidence to support workforce planning, reimbursement policies, and the sustainable development of territorial care services. Full article

The increasing complexity of residential energy systems, driven by rising electricity demand, renewable energy integration, and dynamic pricing mechanisms, has intensified the need for intelligent load scheduling within Home Energy Management Systems (HEMSs). This paper presents a comprehensive literature review of optimization algorithms applied to residential load scheduling, based on an analysis of 78 peer-reviewed studies published between 2020 and 2025. The analysis reveals a clear shift from conventional deterministic optimization toward adaptive and data-driven approaches capable of operating in uncertain and dynamic environments. Metaheuristic methods are widely used for solving complex scheduling problems, while Machine Learning and Deep Learning (DL) techniques primarily support forecasting tasks related to energy demand and renewable generation. Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) approaches enable autonomous real-time decision-making, although challenges related to scalability, computational cost, and practical deployment remain unresolved. The review identifies hybrid architectures that combine forecasting, optimization, and control mechanisms as the most promising direction for future HEMS development. Finally, the paper highlights key research gaps, including limited real-world validation, insufficient consideration of physical infrastructure constraints, and the need for scalable distributed control frameworks for future smart grids and energy communities. Full article

Residential buildings are a major source of urban carbon emissions, yet the uptake of smart home retrofitting remains far below the level required to meet decarbonization and sustainability targets. While technical solutions for energy-efficient renovation are well established, less is known about how behavioral, psychological, and institutional factors jointly shape household retrofit decisions and their broader sustainability implications. This study develops an integrated analytical framework that combines UTAUT2 with perceived risk, trust, innovativeness, and regulatory pressure, interpreted through a socio-technical systems perspective, to examine smart home retrofitting in Thailand and its contribution to Sustainable Community Development Goals (SCDG). Survey data were collected from 448 households in Bangkok and Chonburi and analyzed using structural equation modeling. The results show that traditional UTAUT2 predictors such as performance expectancy, effort expectancy, and social influence do not significantly influence adoption intention in this high-cost retrofit context. Instead, innovativeness, trust, price value, perceived risk, and regulatory pressure emerge as key behavioral and institutional drivers, while facilitating conditions and habits shape actual use behavior. Actual retrofit behavior is found to generate significant economic, environmental, socio-cultural, technological, and public-policy sustainability outcomes aligned with SCDG. These findings demonstrate the limitations of conventional technology acceptance models in infrastructure-based contexts and provide a mechanism-based explanation of how retrofit adoption is driven in high-cost sustainability contexts. Full article

With heat pump adoption falling behind expectations, the UK faces a strategic challenge in meeting residential decarbonisation targets. This study investigates the barriers hindering adoption through an in-depth case analysis of a complete household heat pump adoption journey in a pre-Second World War family home in the UK. Combining an autoethnographic approach with quantitative analysis of 4.7 years of household data, it provides a rare, detailed examination of consumer experience in the UK. The analysis applies both rational and relational perspectives, showing that relational factors, such as trust and social networks, are particularly influential in early-stage markets. Key challenges are identified in retrofit system complexity, social engagement, installer capability and electricity pricing. Although the initial assessment found the heat pump was uneconomic, post-installation results showed an annual SPF of 3.22, a 59% reduction in energy demand and a 12% reduction in costs compared with a gas boiler. Behavioural adaptation contributed to demand reduction and lowered the effective electricity price by ~3 pence/kWh. However, a further 7 pence/kWh reduction is required to reach cost parity with a gas boiler, implying an electricity-to-gas price ratio of 2.3. The findings highlight important areas for policy intervention. Full article